Every year, thousands of enterprise organizations walk into technology negotiations trapped by the same fundamental mistake: they negotiate a product instead of solving a problem.

Having advised over 2,000 enterprise customers annually on technology acquisition over two decades, I see this pattern play out daily. Purchasing committees meticulously list the specific features, software modules and server specs they think they need, converting their organizational requirements into a static shopping list. On paper, this strategy feels disciplined. It creates a standardized, predictable item that procurement can use to negotiate a line-item discount with providers.

When you buy a fixed product, you get the immediate, gratifying perception of a lower price. But that upfront discount can come at a hidden cost: you risk cutting yourself off from the disruptive innovation of emerging technologies like artificial intelligence (AI).

By prescribing the solution before the negotiation even begins, enterprises force vendors into the narrow role of commodity suppliers rather than strategic partners. You ask them, “How cheaply can you build what our internal committee specified six months ago?” instead of asking, “What is the most efficient way to achieve this outcome today using capabilities we might not even know exist?”

In an era where AI can reshape entire operational workflows, anchoring your procurement strategy to fixed product specifications means you may pay less for yesterday’s way of doing business while missing the value of new capabilities.

When procurement treats technology as fixed products, it tries to bundle mature products, such as software-as-a-service (SaaS) licenses, with rapidly evolving capabilities, such as AI services billed by token consumption, into one grand contract. You end up applying three-year static product licensing to AI capabilities that change on a monthly basis, locking your company into rigid pricing models and outdated technology.

A request for proposal (RFP) is the ultimate manifestation of product-first buying. It forces vendors to answer 200 static questions about prescribed features rather than offering alternative solutions. Procurement celebrates negotiating a 15% discount on a legacy customer service product, completely unaware that an outcomes-based AI approach could have eliminated 70% of the support tickets altogether. You win the unit-price battle, but lose the total cost of ownership (TCO) war.

Procurement departments use RFPs with the goal of driving down unit costs and reducing risk. But as behavioral expert Rory Sutherland has argued, corporate purchasing can prioritize blame avoidance over value creation. RFPs require the buying organization to specify the technical solution long before it enters a room with the experts who could help solve the original problem.

The RFP backfires in two distinct ways:

You force vendors to answer the wrong question

An RFP asks vendors: “How much will you charge to build what our internal committee decided we need six months ago?” It denies the seller the ability to say: “The problem you are trying to solve can now be eliminated using a new approach you haven’t considered.”

Shifting costs rather than reducing them

RFPs do not necessarily lower TCO; they can shift costs around while inflating administrative overhead. Software goes unused, becoming shelfware, because the focus was placed on winning the RFP pricing battle rather than user adoption and operational enablement. The company then has to spend more on adoption, while employees may turn to unsanctioned tools, known as shadow IT, duplicating costs and eroding the negotiated savings.

When enterprise buyers attempt to simplify procurement by bundling SaaS, infrastructure-as-a-service (IaaS) and AI into a single, multiyear contract with a hyperscaler, they can make switching providers harder. Research published by Harvard Business School on platform strategy explains how bundling can reinforce customer retention even when one component disappoints. Buyers need to evaluate the flexibility of each component, not just the combined discount.

A good example is what happened to one of my customers: an unmonitored script loop written by a developer unexpectedly burned through 18% of their annual infrastructure commitment in AI token consumption over a single weekend. By bundling emerging, unpredictable technology with stable infrastructure services, they exposed an otherwise predictable IT budget to financial volatility.

Buying technology correctly means matching the contract model to the technology’s maturity stage. Treating an emerging, fast-evolving space like AI with the same three-year static lock-in used for corporate email is a governance failure.

Another of my customers, a regional insurance company, issued a 200-question RFP to replace its customer service center software, seeking an AI-driven system to handle support tickets 20% faster.

Consider the limitations of that RFP: procurement issued a spreadsheet demanding details on call-center headcount, staff rotation rates and employee training certifications. An AI startup whose autonomous agent framework proactively fixes customer problems before a support ticket is even logged refused to participate because the questionnaire assumed a human workforce. The deal went to a legacy vendor that answered all 200 questions and offered a 15% discount. Procurement celebrated a price drop on seat licenses, even though it had just committed millions to perpetuating the existing operational model.

The buyer saved 15% on seat licenses, but spent $3 million over two years maintaining the call-center workflow. Had the company abandoned the rigid RFP, it could have eliminated 70% of its call volume entirely. Procurement successfully minimized the unit cost of the contract while missing the potential value of a different capability.

If we want to stop buying technology wrong, CIOs and purchasing committees must adopt three fundamental shifts:

1. Decouple contracts by maturity stage: Keep mature SaaS, optimizable IaaS and emerging AI in independent contractual structures. This preserves the flexibility to swap out emerging tools as better models hit the market without triggering financial penalties on core systems.
2. Replace rigid RFPs with proof-of-value (PoV) sandboxes: For disruptive technologies and services, replace prescriptive questionnaires with small, rapid experiments that allow vendors to prove outcomes. Define the business problem, success criteria, budget and security requirements before testing. Pay to solve the problem, not just to acquire the product.
3. Prioritize local enablement and FinOps over direct discounts: A 10% direct manufacturer discount means little if 30% of your cloud infrastructure is overprovisioned or if your employees never adopt the software. FinOps aligns technology spending with business value through collaboration among engineering, finance and business teams. Evaluate what your internal team or a multicloud partner can deliver in user adoption, local-currency billing, support for local tax requirements and ongoing cost management. Compare the full cost and return on investment (ROI), rather than the discount alone.

It’s time for enterprise leaders to stop managing technology procurement like a commodity auction. The goal of buying technology isn’t to get the lowest price on yesterday’s specs; it is to secure the greatest speed, flexibility and business value.

There is a fundamental problem hiding behind today’s technology forecasts: the technology industry is projecting spending growth at rates that many of its customers cannot economically sustain.

Gartner’s July 2026 forecast calls for worldwide IT spending to increase 14.2% in 2026 to $6.37 trillion. Within that number, software is projected to rise 15.5%, Infrastructure-as-a-Service 29.3%, and data-center systems an extraordinary 62.5%. Gartner itself acknowledges that technology budgets are being strained by inflation, supply shortages, hardware and memory costs, and AI investment.

The AI projections are even more dramatic. Gartner’s September forecast puts 2026 worldwide AI spending at $2.7 trillion—up 49.5% in a single year. McKinsey has separately estimated that generative AI alone could drive $175–$250 billion of software spending by 2027, adding two to six percentage points to software-sector growth.

These are extraordinary numbers. But they expose a fundamental flaw in the way the technology market is being viewed: The forecasts measure what the technology industry expects to sell—not necessarily what its customers can afford to buy.

That distinction is critical.

The forecasts may describe what technology suppliers want to sell, what hyperscalers intend to build, and what analysts believe enterprises will demand. They do not demonstrate that the typical enterprise has the economic capacity to increase its technology budget at anything approaching those rates.

Dr. Howard A. Rubin

That is the collision illustrated in this cartoon. Most companies cannot indefinitely grow technology spending at 10%, 15%, 20%, 30% or even 50% when their revenues, operating margins and the economies in which they operate are growing at only a fraction of those rates.

And the affordability problem is actually worse than the headline spending forecasts suggest.

Enterprises simultaneously face the economic headwinds: IT inflation; AI premiums; rising compute, server and memory costs; cybersecurity; energy and datacenter requirements; technical debt; scarce technology talent; software and cloud price escalation; regulatory and control costs; and the continuing expense of maintaining yesterday’s technology while financing tomorrow’s.

The result is what I call the Technology Affordability Gap: Technology demand + technology inflation + AI investment + infrastructure requirements are growing faster than the economic capacity of many enterprises to pay for them.

This produces an extraordinary paradox. Every technology forecast can appear individually plausible from the supplier’s side while their collective implications become economically implausible from the customer’s side.

A hyperscaler can forecast enormous cloud growth. An AI company can forecast enormous AI adoption. A software company can forecast higher subscription revenue. A semiconductor company can forecast explosive demand for AI infrastructure. An analyst can add all of those expectations together.

But ultimately there is only one customer balance sheet paying the bills.

Something, therefore, has to give.

Enterprises will have to slow spending, eliminate legacy costs, consolidate vendors, renegotiate contracts, defer lower-value projects, substitute AI and automation for labor, radically improve technology productivity, and demand demonstrable economic returns from AI and other technology investments. Otherwise, technology spending will increasingly have to be financed by taking money away from other parts of the enterprise— or by sacrificing margins.

That is the impending Technology Economic Crisis.

It is not a crisis caused by a shortage of technology. It is potentially a crisis caused by more technology ambition, more technology supply and more technology spending expectations than the underlying economy can afford.

The question for 2027 and beyond, therefore, isn’t simply: “How much technology will companies want?”

It is: “How much technology can they actually afford?”

CIOs have always felt pressure to deploy emerging technologies rapidly. Implementation brings challenges, including calculating and measuring upfront investment costs. Deploy too quickly, and when the hype fades, many IT departments are left with a costly architecture to support, security gaps, and operational issues.

The same trend is happening with AI. Many CIOs procured enterprise-wide AI model licenses and empowered employees to experiment with language models and develop AI agents. Others asked DevOps teams to accelerate AI code generation and vibe coding. The shift from rapid experimentation to deploying more AI capabilities to production can leave CIOs with mounting AI debt and AI FinOps gaps.

CIOs’ near-term concern is managing budgets amid consumption-based AI pricing models. Even as AI token costs decrease, more employees are using AI, and agents are given more challenging, expensive assignments. Here are seven common mistakes IT leaders make that result in blown AI budgets.

Defining an AI strategy without measurable business outcomes

Most CIOs and CAIOs recognize the need to establish AI strategies focused on areas where AI can provide lasting business value. But many fail to quantify targeted business outcomes, leaving employees to use adoption metrics as the primary objective.

“Every token spent on a pilot that never proves its value is money leaders can’t get back and lost opportunity cost, which is a governance failure more than a technology one,” says Rob Scudiere, CTO at Verint. “Every agent and workflow should have to earn its budget by showing results that achieve the desired business outcome, or lose it.”

Kris Beevers, CEO and cofounder of Netbox Labs, says to use existing business metrics to see whether the areas adopting AI are improving them faster. “If adoption is climbing but the outcomes aren’t moving with it, that’s a pretty good signal to rethink where you’re spending,” Beevers says.

Recommendation: Financial impacts may be hard to target during experimental phases, but employees should set financial objectives before moving AI agents into development and deployment phases.

Experimenting without AI governance

CIOs may find they have a real mess on their hands when a few pilot AI agents quickly scale to thousands running in production. Much of AI governance focuses on identifying owners, setting data-access rules, defining target business outcomes, and establishing accuracy guidelines. CIOs must also educate employees on underlying cost factors, including model selection and platform standardization.

“Delayed AI governance gets framed as a compliance risk, but it’s just as much a budgetary concern,” says Guy Bourgault, head of agentic systems at Concentrix. “The longer organizations wait to define ownership, standards, and controls, the more they end up funding inefficiency, rather than progress.”

Varun Goswami, global head of product and AI at Newgen, says AI budgets are wasted on scattered pilots, duplicated integrations, and agents that run quietly without ever proving value. “Build on one governed platform rather than a dozen parallel stacks, reuse templates and connectors instead of custom-building each use case, and match the model to the job rather than routing everything to the priciest foundation model,” Goswami recommends.

Data quality is a third concern, according to Collibra and The Harris Poll, with 96% of IT leaders saying project failure almost always traces back to poor data foundations, and 51% reporting significant staff hours spent manually correcting autonomous AI agent outputs. CIOs should set data quality metrics and establish data products to build a trustworthy AI context layer.

Recommendation: CIOs should update and communicate their AI governance policies monthly, as AI cost, capabilities, compliance, and security considerations evolve rapidly.

Focusing tokenomics on model costs and not business value

Much of the discussion around AI token economics, or tokenomics, focuses on the costs of using different AI models. But the FinOps Foundation defines tokenomics as “the study of how the production, distribution, and consumption of tokens generate business outcomes and AI value within an organization.”

Because many organizations use multiple models, SaaS with AI agents, and internally developed AI agents, CIOs should partner with the CFO to measure total impact, including AI’s direct token costs, employee efforts, ongoing inference costs, and the value delivered.

“Companies can quickly overspend on AI when they focus too narrowly on token consumption, and the starting point should always be the business outcome,” says Prakash Kota, CIO at UKG. “I use the Three T’s: does the work require talent for judgment or expertise, can existing tools solve it efficiently, or do tokens create enough value to justify the investment?”

CIOs should then set AI budgets at the employee, department, and workflow levels. Each group’s budget should be reviewed and adjusted regularly based on outcomes. This discipline promotes fiscally smart decisions about when and how to use AI capabilities and can drive a top-down culture change.

“The same tokens can go to a skilled engineer shipping a product faster, or to a non-expert generating 10 versions of the same slide deck,” says Tom Shea, CEO and cofounder at OneStream. “The line item in the budget looks identical either way, and true token efficacy requires governance to ensure those tokens are empowering experts to accelerate output, rather than fueling a massive cost explosion.”

Recommendation: Setting AI budgets too soon may curtail employees’ enthusiasm in learning and experimenting with AI. Establish financial targets based on a maturity curve to encourage experimentation first, then fiscal discipline when AI agents are deployed to production.

Developing AI agents without standards

CIOs should communicate these two cost considerations to DevOps teams using AI code generators.

First, according to Stack Overflow’s 2026 Developer Survey, 73% of developers use AI coding assistants and agents daily, but developers care more about quality (75%) over cost (38%) when shaping prompts and picking models. CIOs should set guidelines for balancing cost and quality tradeoffs, establish DevOps non-negotiables, and require code analysis in deployment pipelines.

“Coding is no longer a constraint, and the bottleneck has moved to how we ensure the correctness of code,” says Anurag Dhingra, SVP and GM of networking and collaboration at Cisco. “There’s just no humanly possible way to review every piece of code that is written by AI.”

The problem is that 96% of developers don’t fully trust AI-generated code and 48% always check their AI-assisted code before committing, according to Sonar’s State of Code Developer Survey. As large organizations may use several AI code-generating tools, they need to benchmark quality, cost, and developer satisfaction.

“Teams need clear guardrails and engineering standards, especially as AI agents accelerate development and can introduce costly rework,” says Andrea Malagodi, CTO at Sonar. “Having live data aggregated from your one or many providers, to enable you to see the cost per PR [pull request], is a good governance mechanism.”

Recommendation: While AI code generation is a remarkable breakthrough, it also brings new costs and quality considerations CIOs should track.

Using frontier models for all AI needs

Another cost consideration comes as organizations deploy hundreds of AI agents and provide developers several AI models to choose from. Developers may choose models based on preferences rather than performance and cost considerations.

Craig Wiley, VP of AI at Databricks, says 77% of companies now juggle multiple model families because the best model for one task is rarely the best for another. “The flexibility comes at a cost of managing shifting prices, scattered vendors, and zero visibility into where your budget’s actually going. The fix is real-time budgets and smart routing that automatically sends queries to the most cost-effective model,” Wiley says.

Recommendation: Josh Fecteau, chief data and AI officer and CIO at Teradata, says that frontier models are expensive and not necessary for every task. “Diversify your model strategy with cheaper small language models, custom models, and industry-specific models,” Fecteau recommends.

Deploying AI agents without monitoring cost spikes and inefficiencies

A key challenge with AI agents is their variable costs, especially when they are given tasks of varying complexities. While some AI providers are moving to outcome-based pricing models, many still use a license model with a fixed number of tokens, plus consumption-based pricing for overages. This means CIOs must monitor costs, set AI observability standards, and define rules for handling performance variances in AI agents.

“I spent 25 years in telephony where every minute had a price, and agentic AI is the first software I’ve seen that bills like a phone call and gets budgeted like a license,” says CJ Combs, AI strategy executive at Columbus. “AI costs stop being predictable the moment an agent can loop, because it keeps pulling in context, retrying, and calling tools in ways nobody budgeted for.”

Experts suggest several implementation cost inefficiencies to review. Babak Pahlavan, founder, CEO, and CPO at NinjaTech AI, says that AI agent processing duration is an easy indicator. “A single autonomous agent running a multi-hour task can burn a month’s allocation in an afternoon because every tool call, every retry, every piece of context it re-reads gets billed,” Pahlavan says.

Other cost factors require experts to review an AI agent’s decision logs to determine optimization. Adi Elimelech, CTO and cofounder of Jedify, says, “If every query drags an entire database schema back to the LLM, token costs scale with the size of your environment rather than the complexity of the question. The cost compounds, since the model often doesn’t get it right on the first pass and loops back for another round trip before landing on the correct answer.”

Recommendation: CIOs will need to mature several AgenticOps and site reliability engineering practices to monitor AI agents and respond to cost, security, quality, performance, and other incident types.

Building agents without a change management plan

One of the biggest gaps in deploying AI agents is that deployment is just one step in a change management process. AI governance requires identifying agent owners who are accountable for decisions and responsible for outcomes. These owners should also sponsor the underlying change management process.

“The biggest AI cost overruns rarely come from technology,” says Abhijit Kakhandiki, SVP and GM of digital business automation at BMC Software. “They come from organizations deploying AI without redesigning processes, ownership models, and governance.”

Part of the change process requires identifying human-in-the-middle responsibilities to review the agent’s recommendations. The review process builds trust and lets owners switch to autonomous decision-making for low-risk, high-volume, trusted recommendations.

“It’s not going to be a binary switch where, from day one, you’re going to trust AI agents to run tasks autonomously,” says Joel Hellermark, chief AI officer at Workday. “Progressively, they’re going to earn the right to run those workflows autonomously. If it’s your fiftieth time approving an expense below $50 for one of your direct reports that looks exactly on policy, you can trust the agent to run it for you.”

Recommendation: Growing adoption isn’t the end game, but it’s a key step toward delivering business value. Metrics to track adoption include AI response quality, trust scores, repeat usage, and value delivered to adopters.

A simple rule is that you can’t manage what you don’t measure. CIOs should set budgets, track costs, and measure outcomes, especially as more agents move into production.

With 2027 quickly looming, IT leaders should soon be finalizing their objectives for the new year, one certain to be rife with many unknowns if 2026 is any indication.

This year began with the call for a pivot from AI pilots to AI ROI, but questions around AI governance and accountability quickly took center stage as agentic AI made further inroads in the enterprise and high-profile incidents showed the risks of AI gone awry and the value of security-first approaches to AI agents.

Now, CIOs must anticipate how AI will evolve in 2027, how they’ll get their enterprises ready for all the risks and opportunities of that evolution, whether their teams will have the skills to seize on technology advances, and more.

To get a glimpse of how CIOs are approaching next year’s IT agenda, we asked IT leaders to share the key questions they are asking to shape their strategies for 2027. Here’s what they shared.

How do we ensure our AI investments provide tangible value as the cost calculus changes?

The big question as CIOs plot their IT agendas for 2027 still centers on providing verifiable business value, as 98% of CIOs report that board pressure to demonstrate measurable AI ROI has increased since 2024, according to a 2026 report based on a Dataiku/Harris Poll survey of 600 enterprise CIOs worldwide.

Doug Gilbert, CIO and chief digital officer at Sutherland, a digital transformation services company, is among those IT execs pondering this question as he executes his fiscal 2027 budget, which started in July.

“When I’m out speaking to people and they say, ‘It would be great if AI could do that,’ I say, ‘Yes, but would it provide value? Are we building value or are we just burning tokens?’” Gilbert says. He notes that he and his CIO peers are being increasingly judged on the outcomes they deliver for their organizations, meaning they have more incentive than ever to ensure the technology initiatives they undertake deliver ROI.

But the calculus on that is getting trickier than it was at the beginning of 2026 — in part because AI total cost of ownership (TCO) is challenging to calculate, but also because an AI pricing reset is on the horizon. So, to that end, CIOs such as Gilbert are now thinking about what level of ROI is high enough to justify the investment.

That’s a key question to ask now and into 2027, Gilbert says, particularly as the cost of tokens continues to rise. An AI use case that provides marginal ROI could stop producing any value in the near future if tokens increase even slightly, he notes.

As a result, Gilbert is working to factor in those dynamics when he and his team work with the business to identify which AI use cases to pursue — and he expects to do so even more moving forward. “I plan to pay a tremendous amount of time thinking about whether [a proposed project] is the right one to pursue,” he says, to ensure “when you put math on paper there is value with the project.”

How do we ensure observability and auditability of our AI systems?

According to a 2026 survey from OneStream, 47% of senior finance and IT executives say they have made a material business decision based on inaccurate, incomplete, or outdated financial data in the past 12 months.

Citing that statistic, Gilbert says he’s also asking critical questions about the observability and auditability of all of Sutherland’s AI uses cases. He wants to be sure he, other executives, and users are confident the company’s AI systems always have high-quality data — and thus accurate outputs. And he wants IT to achieve that observability and auditability so that the company can accurately track, measure, and improve its AI deployments over time.

“We will not deploy any AI that we can’t observe,” he says, a policy that he put in place about a year ago and will continue in 2027.

A wide range of tools under the moniker “AgentOps” are emerging to help IT leaders monitor AI activity, tools, and costs, starting with observability.

How do we move to more autonomous agentic processes?

“The challenge most technology executives are grappling with at the moment is how you move AI from human-assisted agentic to much more autonomous agentic processes with minimal human intervention or supervision,” says Jody Fullman, CIO of Reconomy and recently recognized as one of the UK’s top 100 CIOs in the CIO 100 UK Awards 2026.

Moreover, how do you achieve that securely and in a way that’s economically viable? he asks.

Fullman is implementing strategies to address this challenge. He is strengthening IT’s agentic controls by aligning Reconomy’s governance program to an industry-recognized framework. He is also implementing tools to monitor and manage tokens. And he’s moving some AI workloads to lower-costs models.

All that, he says, will set up the organization to develop and deliver more autonomous agentic capabilities ahead, noting that he and his team have been transitioning successful proof-of-concept agentic initiatives into MVPs to put the company on the path to more autonomous agentic processes.

How should OpenAI’s Hugging Face incident shape my security strategy?

Security has been a central factor in IT agendas for years. But Jeff Stovall, CIO for Dallas, Texas, says the 2026 Hugging Face incident, in which autonomous AI agents built by OpenAI escaped their sandbox testing environment and hacked into the infrastructure of AI open platform company Hugging Face, has him adjusting his plans for 2027.

“Hugging Face demonstrated that there was a lot more peril in the world of AI as it relates to cybersecurity than what we were ramping up for,” he says. “There’s now this notion that AI may have more downside for us than upside for us in the near future,” he adds, explaining that AI-fueled threats to the organization are more significant than the positives its use brings.

“In the upcoming six months to a year, what I see is that we have to take a hard turn in thinking about how AI poses a significant malevolent threat to our organization when wielded by actors seeking to do harm,” Stovall says. He points specifically to hackers’ ability to use AI to create swarms of bots that can autonomously collaborate and exploit all vulnerabilities nearly instantaneously and all at once. “That’s a dramatic acceleration and amplification to the threats we face.”

In response, Stovall says he’s shifting more resources to maturing his security program in 2027 than he initially budgeted — drawing funds away from internal AI deployments to “shore up the walls even further” by adding more AI automation and agents to have “much more machine speed in security response.”

How do we use AI to transform?

Marco Bill, SVP and CIO at Red Hat, believes using AI for productivity gains is now table stakes. So as he develops his plans for IT for 2027, he’s looking to push that notion even further, asking how the company can use AI to transform processes as a whole.

“This question helps me to keep reminding ourselves that we’re not just here to do IT. It’s more of a guiding principle,” he says.

And that in turn is underscoring the importance of transforming the IT organization into a more collaborative and business-centric one.

As an example of what’s brewing at Red Hat, Bill points to the work his IT team is doing with the company’s sales team to devise ways to use AI to enable account managers to have better conversations with customers to close deals faster. IT is also working with the marketing department to make go-to-market process more powerful, and his teams are adding agentic AI to their own IT operations to create fully autonomous workflows that cost less to run and manage.

All told, Bill and the IT team at Red Hat are building further on the work many CIOs began in 2026 by helping to reimagine business processes to reap AI benefits and bringing AI transformation home to IT workflows.

How do we empower users to transform their work with AI?

“Citizen AI” is a rising trend in the enterprise, and this is something Reconomy’s Fullman will continue to address in 2027, explaining that his company has found that much of its “success with AI was not by being a tech project but by empowering our users and business colleagues to transform their work and processes with AI.”

Fuller believes that his company will see more AI-enabled transformation, and see that transformation faster, by continuing to put more AI in the hands of users throughout the organization instead of expecting IT workers to become experts in all the processes that could be transformed by AI.

As such, Fuller says the upcoming year’s IT plans will continue to put AI tools in the hands of the users, and do so with governance, security, and transparency in place. He’ll also leverage the skills of workers in process transformation roles, which were recently created and installed in Reconomy’s business units but still work closely with IT architecture and data teams.

“They’re plugged into tech and the business to make sure we’re prioritizing the right things and doing things the right way as we drive transformation,” he explains.

How do we prevent our AI governance council from becoming a bottleneck?

Company leaders at intelligent automation platform provider Celigo want every team to build with AI, something CIO Amy Farrow knows makes security and controls critical. But Farrow also wants to make sure she enables speed and doesn’t unnecessarily slow down innovation, noting that barriers could lead to shadow AI.

“If [teams] all have to wait too long, they’ll find a way around the process,” she says.

To ensure that governance isn’t a bottleneck, Farrow explains that her “governance council doesn’t approve projects. It sets the rules: risk tiers, design criteria, a checklist every agent has to pass.”

She adds: “The teams approve their own work against those rules, and we audit. My team owns the integrations, access controls, and observability. My job is making it faster to build the right way.”

What controls do we need to put in place for AI agents?

As AI agents increasingly enter enterprise workflows, IT leaders will need to the level of permissions agents should be granted relative to the people on whose behalf those agents will be operating.

Farrow, who previously served as global CIO at Infoblox and Lyft, believes agents should resolutely have fewer permissions than the people whose tasks they will be performing.

“Someone in finance can issue a credit or update our ERP because we trust their judgment. An agent doesn’t get that by default,” she explains. “We start every agent with the least autonomy required to achieve the outcome. It can interpret a request or recommend an action. Changes to records go through checks the model can’t override, and bigger changes need a person to sign off.”

This approach also has a fiscal benefit, she says, noting, “Less autonomy means fewer model calls, which keeps costs in check.”

As AI increasingly goes agentic, CIOs will increasingly need to plan for ways to address this issue in the year ahead.

What happens when vendor agents start acting inside our systems?

“Many of our SaaS vendors are shipping agents, and some can make changes to our data by default,” Farrow says of another key security- and governance-related question many CIOs are facing. “For 2027, I want vendor agents held to the same standard as any other integration.”

Farrow has other questions related to the rapid rise in vendor agents, asking, “What can they touch? Can we see what they did?”

IT leaders need to ensure they can answer those types of questions, noting that California’s and Colorado’s new rules on automated decisions start in January. She wants to be able to give strong answers to those questions, stressing that “‘the vendor’s agent did it’ is not a defensible answer.”

How do we meet immediate business needs while building a foundation for growth?

Mieko Shibata, CIO at R&T Deposit Solutions, sees this as “the overarching question” for him as CIO.

“That question guides our investment decisions and how we allocate our team’s time,” she explains. “Our strategic direction is consistent, but each year’s agenda must balance immediate business priorities with progress toward our longer-term technology foundation. We consider both the value each investment delivers and how it contributes to the environment we are building.”

Shibata is taking a multiprong approach in her response to that overarching question.

First, she’s ensuring technology modernization turns into measurable business value and that modernization initiatives are prioritized based on that value.

She is also considering how IT responds to growth opportunities while keeping its longer-term transformation on track. “Our response is to consider the immediate commercial value alongside the longer-term technology implications. Can we reuse the capability? Will it scale? What will it take to support? Does it help simplify our environment?” she says.

And she’s thinking about how the company prepares for AI’s evolution while maintaining clear accountability for its use. “For 2027, my focus is on building a repeatable approach to evaluating and adopting AI as it evolves, with clear business benefits, appropriate controls, and accountability for the results,” she says.

Is our AI vision ambitious enough?

For Lauren Wilkinson, CIO for financial advisor services at Vanguard, AI ambition is top of mind as she develops her IT strategy for 2027.

AI strategy should accelerate the business strategy and “push leaders to reconsider what outcomes with AI are possible, rather than simply finding ways to make today’s processes faster or more efficient,” she says.

“It helps to start with an aspirational business vision, including what a few big outcomes could look like. Then you can work backward to determine where AI and technology can fundamentally change what is possible,” she explains.

In her work with financial advisors, she’s found that they’re primarily using AI to support administrative tasks such as drafting emails, conducting research, and taking meeting notes. But she sees an opportunity to build on those use cases in the upcoming year to deliver on that objective for a more ambitious AI program.

Like many CIOs, Wilkinson wants to take AI to the next level at her organization and ensuring that ambition is high enough is paramount to achieving that goal.

Do we have the talent to win in an AI-native world?

Wilkinson believes how CIOs address the AI talent question will be critical for organizational success.

“As AI continues to evolve, the skills that differentiate great technologists will also evolve, placing an even greater premium on AI expertise and deep business acumen,” she says. “CIOs should be building teams that understand the business, can identify the highest-value problems, and know how to orchestrate AI-native workflows to solve them at dramatically greater speed. The differentiator won’t simply be who has access to the best AI, but who has the people who know where and how to apply it.”

Most CIOs know this, so it is not surprising that this year’s State of the CIO survey found that AI tops the lists for both most sought-after skills and the hardest to find. As a result, CIOs will have to get creative to build up their teams’ AI know-how in the year ahead.

How should IT be reshaped to maximize value creation?

CIOs, including Shelia Anderson, EVP and chief information and digital officer at Unum Group, are also asking how they should shape IT as an organization to ensure they can deliver value to the business.

“That has been a big focus for us this year and will continue to move down that path in 2027,” she says.

The question has shaped Anderson’s work at Unum Group since she started in her role in May 2025. She has organized her IT department around end-to-end value streams and says that approach has helped her IT staff work more closely with the business and more readily identify where improvements can be made. She also has her IT organization working to better understand business processes, and has been working on building more capabilities to measure and manage AI costs as Unum seeks to transform using AI. And she’s looking at what investments and improvements are needed in IT’s foundation.

All these moves will enable the IT department to get better at value creation, Anderson says.

She plans to augment this work in the coming year by providing AI training and education for IT workers and employees throughout the company “so they know how they can drive value and find opportunities in their everyday use of AI.”

Ensuring she has the right mix of talent — meaning she has the right skills in the right seats to deliver value — will also shape the actions she takes as CIO in 2027, Anderson says.

“In 2025 and 2026 there was a lot of testing and learning, but 2027 will be a year of maturing. You’ll see more formal processes and settling in around how you’re going to leverage the skills in your organization to drive value,” she adds.

There’s no point building your own agentic AI stack for a non-core function when a vendor already has a product ready to go that meets your needs. For non-tech companies in particular, building from scratch can take time, money, and expertise they don’t have.

Take for example, Fertitta Entertainment, with more than 600 Landry’s restaurants and the Golden Nugget hotel and casino. After the pandemic, the company had problems adequately staffing its call centers, says its director of contact center operations Brian Jeppesen. “The agents didn’t want to come back,” he says.

The virtual assistants didn’t work because the user experience was bad, and with the incoming call volume, 40 to 50% of them were abandoned.

“Everybody wants AI, but nobody wants BS,” Jeppesen says. “I talked to my CFO. This is what we’re abandoning. This is the potential revenue we’re losing. I can’t throw bodies at it anymore.”

Agentic AI has upped their game for the virtual assistants. Instead of only answering simple questions like check-in hours, it allows the company to connect APIs and carry out more complicated tasks, like making reservations.

“My abandonments fell to under 10%,” says Jeppesen. “From waiting 45 minutes to an hour, they can get their questions immediately answered, or immediately transferred and taken care of.”

And turnover went down. Call centers typically see 100% turnover, says Jeppesen. Not because everyone quits, but because the bottom 25% quit and need to be replaced over and over again.

Now that bottom tier doesn’t have to be rehired, while the people who stay are the most knowledgeable. “They give a better experience for my customers,” he says. “And it reduced my attrition by 50%.”

Forrester did a total economic impact study for the specific vendor that Fertitta used, PolyAI, and the ROI came back at 391% over the course of three years, with a payback period of less than six months.

Jeppesen says he reviewed his own numbers and found that the automated reservations system alone brings in 3,000 reservations a month. “That’s $600,000 in revenue a month for us without the need to speak to an agent,” he says.

Next, Fertitta plans to expand it to handle to-go orders and web chats. There are also opportunities for agentic AI on the technical help desk, Jeppesen says.

Agentic AI on the menu

Fogo de Chão, a Brazilian restaurant chain with around 120 locations, is also deploying agentic AI in small, focused use cases, and sees ROI with some of the projects.

For instance, it deployed virtual call center agents from PolyAI, and saw positive ROI from day one since there were no significant up-front costs or usage-based pricing. After several months of testing and small-scale pilots in 2025, the system has been rolled out across the US, with 100% of calls now being answered instead of up to 25% being dropped, and a 10% increase in total bookings that wouldn’t have happened otherwise, says Michael Breed, the chain’s chief marketing officer.

“If we compared it to a media channel, it’d be twice the ROI from other media channels,” he says. “It’s almost like another customer acquisition vehicle.”

The company is also building agentic workflows for its team members.

“We’ve identified jobs that are very repeatable and time-consuming, and we’re training agents to take those tasks,” he says. These are low-risk jobs, like proofing menus or other text, or replacing manual processes, such as pulling data from one system into another that had no automatic integrations.

“The agent does a great job bringing in the data, then the human in the loop can inspect it,” says Breed. “Is it getting classified right? Is it getting to the right places? And when guests are dissatisfied, can we make it better?”

Built for SMBs

For smaller companies in particular, off-the-shelf agents make the most sense. AltaClaro, for example, builds training simulations for lawyers, and counts more than 100 of the top 200 US law firms as customers, all with just 10 employees.

One product is a deposition simulator, says CEO Abdi Shayesteh, a completely agentic, voice-based platform.

“It’s a fully simulated environment that creates the experience of doing the deposition,” he says. “Different agents play the role of the witness, opposing counsel, and the court reporter.”

He says it’s kind of like a Zoom call. The product is built on top of technology from Verbit, a legal tech company with a custom agentic platform.

“They’re taking care of the technical infrastructure and we have the material to ground the AI.”

A different vendor, Credal, provides a secure AI platform that allows AltaClaro to switch between different models. It can obfuscate names and keep other sensitive data from leaking out, Shayesteh says. “It ensures everything is anonymized and does secure calls,” he says. “We standardized on Credal because it’s highly flexible and secure. We serve law firms and we’re as invested in security as they are.”

But security isn’t the only issue that enterprises face when deploying agentic AI.

The hidden costs of buying AI

The temptation to jump into agentic AI is high, says Dana Simberkoff, chief risk, privacy, and information security officer at AvePoint Inc., a data security company. And off-the-shelf agentic AI can be so easy to adopt that companies deploy it before they’re prepared for it.

Enterprise vendors will often add the technology at no extra cost, just to get people using it, and companies turn it on for their employees before truly understanding what it does, and before systems and workflows are ready. “Then it immediately exposes problems that were already there,” she says.

Say, for example, an agent helps with research, normally considered a low-risk task.

“AI will follow sharing links across documents,” she says. “So if you haven’t taught your users how to properly use sharing links, the AI agent is just going to follow that path, and it could expose information that might have been obscured in the past. It’ll gobble up everything it can.”

The result is it could expose HR data, personal information, or proprietary information to people who shouldn’t have access to it. One company she knows had to shut down their AI project immediately and go back, classify, and label all their information, put governance controls in place, and teach people how to apply metadata.

According to Gartner, by next year, 40% of enterprises will demote or decommission autonomous AI agents due to governance problems that only show up after they’ve gone into production.

Token management

Another hidden cost of off-the-shelf AI? Token use. When agentic AI is embedded into a system that a company is already using, then the cost of switching it on can be as low as zero. But someone has to pay for all that token use.

When agentic AI comes out of pilots and goes into production, usage can change dramatically, and token costs can quickly spiral out of control. It doesn’t help that agentic AI use typically requires much more tokens than AI chatbots.

“A lot of the embedded AI should be utilized,” says Doug Vargo, head of the national AI and alliances team at consulting firm CGI. “If big parts of your business processes are living and breathing in ServiceNow, Salesforce, or another application, you should leverage that, but you need to understand the cost.”

This is playing out in the marketplace right now, he adds, as underlying model providers change their token costs.

“That changes the economics of the decisions,” he says. “And six months from now, it’s a different discussion, because they’re going to continue to move the needle.”

The employer’s prerogative

Another hidden cost that companies might miss is they need to put training and support systems in place for employees. Many off-the-shelf agentic AI tools might seem easy to use, but actual adoption still requires change management. CGI did offer training to employees when it rolled out GitHub Copilot, a popular coding platform with agentic functionality. “And we expected them to adopt and leverage it on day one,” says Vargo. But people quickly went back to their old ways of working, he says, and CGI had to pivot its strategy.

“It’s going from training programs to habit formation or transformation programs,” he adds. “And we’ve seen adoption skyrocket.”

The change isn’t in just how individuals adopt AI to improve their own work, though. “You’re now a manager of 10 to 20 specialized agents that are driving that work for you,” he says.

The error is when companies put the technology first. “They fall in love with the tech or a vendor, but they’re not really thinking about how they’re truly integrating these tools and technologies into the business.”

Embracing change

Change management is a particular challenge with agentic AI because the tools are changing at such a fast pace.

“It’s technology that’s constantly changed almost every month, or maybe every week,” says Scott Likens, chief AI engineer at PricewaterhouseCoopers. “The challenge I see is the timescale of change is much faster than what we’re used to.”

We’re still in the early stages of agentic AI, he adds, and waiting isn’t a good strategy. “You have to start, but keep flexibility over longevity in mind,” he says.

For Luis Huerta, group head of gen AI and agentic automation at International Airlines Group (IAG), change management is one of the most complex parts of agentic AI adoption, as it uses agentic AI to improve customer experience, operations, and efficiency.

“The technology is powerful, but the bigger shift is organizational,” he says. So the company isn’t just focusing on building agents, but also on business ownership, adoption, and governance, and helping colleagues understand how these capabilities change the way work gets done and how it can support them, rather than replace them.

Huerta says IAG also uses agentic capabilities embedded in enterprise platforms, but also builds its own agents, too.

They’re not alone. According to Anthropic’s 2026 State of AI Agents report, 47% of organizations take a hybrid approach to AI agents, while 21% rely entirely on pre-built agents, and 20% build their own.

“We take a technology-agnostic approach,” says Huerta. “Our priority is to select the right solution for each use case.”

According to a quartet of Accenture intellectual heavyweights, what CIOs need now to account for and control AI costs is tokenomics, sweet tokenomics.

The idea of linking AI spend to business outcomes may be a noble one, but “Tokenomics” only pretends to do so, unless you think tallying costs and measuring business outcomes are one and the same thing.

But before we get to Tokenomics we need to set the stage.

Start with Function Point Analysis (FPA), a venerable and mostly forgotten discipline, which should say something about its efficacy.

The folly of cost metrics you can’t understand

Back in the day, IT leaders worried that boards of directors worried that business executives worried about the cost of information technology and how to manage it.

IBM introduced Function Point Analysis in 1979 to solve this issue by breaking a software application into chunks — function points. Analysts could then sum these function points to compare the application’s overall complexity — and therefore its cost and risks — with that of any other piece of software.

When presented with the number of function points a proposed software project would require, your average, hopelessly naïve business leader asked IT to point to a few of the project’s function points, expecting mocked-up screenshots, sample reports, and so on.

Sadly, IT had to decline because, the design analysts would explain, a function point is (as Google’s AI Overview explains) “an abstract, synthetic unit of measurement rather than a physical object or a single piece of code.”

The Function Point Analysis value proposition, then, was that business leaders could manage IT costs and risks better by relying on cost metrics they couldn’t understand.

Function points for the AI era

Fast-forward to today, and the equivalent function-point cost metrics for AI are tokens — the chunks of text that a large language model reads, processes, and generates. Which, in case you missed the briefing, supposedly makes tokenomics a way to manage the costs and risks of AI.

As the aforementioned Accenture gurus “explain” (scare quotes intended): “Enterprise AI is creating a new cost of doing business: tokens. As consumption scales, token economics is becoming a material risk and opportunity, drawing increasing attention across the C-suite and the board.”

Which raises the question: Where are our tokenomics pundits finding C-suite and board members who give even an infinitesimal damn about tokens? They’re probably the ones who still pay attention to function points, too.

A short lesson in Brickonomics

But before we delve deeper into Tokenomics, capital T, we need to understand building maintenance and the critical role Brickonomics plays in it.

It’s like this: Brick walls are made of bricks. So if a brick wall needs attention, a building management project estimator should need to understand the costs and risks associated with managing a standard brick in the crumbling wall they’re supposed to fix. Multiply the standard management cost of a standard brick (a brickon?) by the number of bricks in the wall, and voilà! Building management knows …

Well, it knows less than it would have if had it instead invested in a conventional tuckpointing analysis. Among the missing pieces: In tuckpointing analysis, what usually needs and gets attention is the mortar between the bricks, not the bricks themselves.

Putting it all together — because you will have to

Now we’re ready for Tokenomics, and its conjecture that dividing an LLM into tokens and assigning a cost to each of those tokens will prove more useful than dividing software into function points or a wall into individually managed bricks.

The next questionable aspect of Tokenomics is that it depends on the proposition that all AI implementations are built on large language models or similar architectures.

To be fair, Tokenomics doesn’t have to be limited to analyzing LLMs. It can be adapted to any collection of thingies that’s composed of chunks that can be parameterized.

But that really isn’t good enough, is it? Because given the current pace of AI innovation, assuming that anything about it is stable, especially that its future core architecture will be tokenizable, is a shaky assumption at best.

It’s a mortar versus bricks challenge.

Then there are those pesky function points. Just because AI is involved in an implementation, that doesn’t mean software isn’t also involved. Which would seem to suggest that Tokenomics will have to integrate Function Point Analysis into its methods and practices as well.

Imagine you’re the one who has to explain this mess to those boards and business leaders who are, supposedly, clamoring for it. Because, as CIO, you will be.

Chargebacks and culture

We haven’t even gotten to the other big risk of Tokenomics: that it focuses decision-makers on AI’s costs, with only token attention — no pun intended — paid to its benefits.

Deep down inside, Tokenomics’ value depends on its use for chargebacks and showbacks. The logic for imposing these is superficially appealing — it makes business owners take responsibility for the AI spend driven by their consumption.

Those CIOs who dove into the chargeback rabbit hole discovered just how futile chargebacks can be. That’s because of a time-honored practice updated to the digital age: arguing about the bill.

Because that’s all a chargeback is: a bill. It’s a statement of how much money will change hands so that someone who has provided a good or service is reimbursed for the value they’ve created.

By the numbers: (1) a chargeback is a bill; (2) the point of Tokenomics is to support chargebacks; and (3) no business leader will be able to understand the bill they’re supposed to pay given Tokenomics impenetrable vocabulary and construction.

But what about showbacks, then? Letting business leaders understand the costs they’re driving would seem harmless and potentially useful.

And in fact showbacks can be harmless and useful, so long as they’re applied in the right business culture. That’s a culture in which managers consider themselves to be leaders of the whole company, basing their AI utilization decisions just as they base their other decisions — on the extent to which they benefit the whole company.

With a culture like this, IT can limit its application of tokenomics, little t, to its technical architecture practices. This way, the CIO can inform business managers of AI’s costs just as the CIO informs business managers of the other costs of running IT.

Where, the business manager might ask, did these cost numbers come from?

“You have two choices,” the CIO might reply. “You can trust me, or I can explain it to you.”

Now there’s a threat worth paying attention to.

CIOs experiencing sticker shock from recent software bills should be questioning how they got there.

That’s because IT leaders at large enterprises are paying a huge so-called software tax for vendor-driven upgrades, migrations, and licensing changes they would not have chosen for their organizations.

About 28% of software budgets at large organizations — an average of nearly $12.9 million per year — are being eaten up by vendor-pushed initiatives, according to a survey commissioned by independent software maintenance partner Origina.

Ninety-five percent of IT leaders surveyed said their organizations have paid for a vendor-driven upgrade in the past three years that they believed would actually reduce performance, according to the survey, which covered US and UK organizations with annual revenues higher than $1 billion or with more than 10,000 employees.

The sheer size of this “software tax” surprised Tomás O’Leary, Origina’s CEO, even though the company expected to see substantial costs for vendor-driven upgrades.

“Software was supposed to make businesses better, and it does,” he says. “But it’s also imposing a hidden cost on companies, quite a significant one. I’m quite taken aback because we know this exists, but we never quantified it before.”

When vendors control your IT spend

Big vendors have several major advantages over software customers, with the cost of switching or refusing to upgrade topping the list, O’Leary notes. In many cases, CIOs are faced with a loss of support if they turn down vendor-driven upgrades, leaving them with little room to refuse, he says. CIOs dependent on VMware are familiar with this squeeze play, as are those facing SAP deadlines.

“CIOs are left in a situation where the alternative may be worse, so upgrading is the lesser of two evils,” O’Leary adds. “When people buy technology, they don’t realize how long this stuff is going to hang around. Nobody realized this thing was going to be embedded, and the dependency was going to be greater and greater with time.”

In addition, software vendors have an experience advantage when negotiating contracts, O’Leary says. CIOs and procurement officers may try to negotiate favorable terms for upgrades, but big software vendors are expert at writing contracts that give them an edge, he notes, adding that the survey results suggest IT leaders’ current approach to negotiating better contracts is a failed tactic.

“If you keep trying the same thing over and over again, and it doesn’t work, you’ve got to try something different,” O’Leary says. “There’s a relief on the buyer side when the negotiations are over, and on the seller side for sure. But the seller side has to get back up again and do it again the next morning with somebody else, and the buyers don’t.”

Omar Amassine, founder and technical director at TripleW Digital, says between 20% and 30% of the IT services firm’s software and maintenance budget goes to vendor-driven migrations he didn’t request.

The biggest culprits? CRM, marketing automation suites, and cloud infrastructure, he says.

“Vendors frequently bundle mandatory AI features or restructure seat-based pricing into usage tiers, driving effective 20% to 35% price jumps with zero incremental utility for the core workflow,” he says.

IT leaders can fight back, however, by using modular, open standards and headless architecture, he adds. “The less proprietary logic locked inside a vendor’s walled garden, the higher your leverage when they mandate expensive upgrades,” Amassine says.

TripleW Digital also runs migration feasibility studies six months before software contract renewals. Having a migration roadmap can bring the vendor back to the negotiating table offering multi-year price freezes, he notes.

Emphasize partnership — and a credible threat of defection

Zachary Lewis, CIO of University of Health Sciences and Pharmacy in St. Louis, hasn’t seen the same software spending mandates as the organizations reflected in the survey.

The university has negotiated many of its software contracts to ensure upgrades and security patches are handled by the vendor, either with Lewis’ approval or during an agreed-on maintenance window, he says.

Even without costly upgrades, there’s still a time tax, he adds.

“With the sheer volume of new vulnerabilities being discovered, we’re spending more time following up with vendors to make sure they’re applying patches in a timely manner,” Lewis says. “My team simply isn’t large enough to manually manage patching across every application and software component we use.”

Here, establishing a vendor management strategy that emphasizes partnership over provisioning can help. And if vendors still drive costly upgrades, IT leaders can always switch providers.

But Lewis cautions that, as tempting as that is, it’s often a complicated process.

“Switching vendors is always an option, but it’s rarely simple,” he says. “Depending on the application, you may have years of integrations, processes, and institutional knowledge built around it. Sometimes the cost of moving away from a vendor is significantly higher than the cost you’re trying to avoid.”

Customers of large ERP solutions in particular are facing a significant reckoning on decades of customizations.

Collective bargaining power comes with communication

To regain their purchasing power, Origina’s O’Leary advises CIOs and procurement officers to negotiate shorter contracts, which would enable them to renegotiate terms more often and exit more quickly if their vendor jacks up prices.

“You have to get to a point where you can have a more frequent engagement with the vendor,” he says. “Negotiate your exit, and negotiate more frequently.”

While short-term contracts may impose more up-front costs on customers, they can pay off with the additional flexibility they provide, he says.

CIOs can also turn to third-party software management firms or consider internal support of their software packages if they have the resources, O’Leary advises.

CIOs should also share their software purchasing success stories and nightmares with each other more than they do now, he says.

“We all benefit from having a little bit more collective bargaining power,” he adds. “CIOs just need to take that leap of faith that others have the same problems, and maybe we can have some sort of collective pushback on this.”

Last year, my company decided to rebuild our enterprise AI platform, which our customers had been using for over a decade. But instead of simply updating it, we started over with a different ambition: to build an AI-native platform using AI.

Using AI to rebuild a platform of that size gave us first-hand experience with AI economics. We employed AI throughout the entire engineering process to write code, review changes, generate tests and accelerate development. It helped us reduce years of engineering to just a few months but also forced us to keep close watch over every single token we consumed and truly consider why we were consuming it.

This experience challenged one of our key assumptions: It turns out that token consumption is influenced as much by architectural decisions as by choice of model.

What token sprawl actually looks like

Indeed, we found that most of the token consumption waste wasn’t due to complex engineering tasks, but the architecture around them; this is something that is referred to as the architecture tax in enterprise AI.

Recursive agents kept spawning subtasks and subagents, and although they technically completed every task, each handoff introduced new prompts and context. Furthermore, frontier reasoning models were being wasted on routine classification tasks that deterministic code could have completed at a fraction of the cost. And, because we hadn’t clearly defined ‘success,’ retry loops continued firing and burning tokens to chase marginal gains, instead of improving business outcomes.  Finally, we experienced context inflation, with workflows carrying information from earlier steps long after they had stopped contributing to the decision being made.

None of these decisions seemed expensive in isolation. Things like a few extra prompts, an unnecessary reasoning step or a larger context window barely impacted the cost of a single workflow. Across millions of executions, however, they grew into a level of token consumption that no model upgrade alone could offset.

Designing for token efficiency

Once we figured out where all our tokens were going, we had to determine how to spend them more deliberately.

First, we accepted that not every step in an AI workflow needs the same amount of intelligence. Some tasks legitimately require reasoning, but others are structural, deterministic or repeatable, and are better handled through software, business logic or tools that are reused rather than reasoned through every execution.

Even when a model is required, not all tasks need the same amount of reasoning. This concept is better known as Reasoning Effort Optimization. If you’re designing systems, exploring alternatives or making difficult decisions, high reasoning effort has value. However, once you’ve established the approach, you can usually produce the same outcome with far less reasoning.

It’s comparable to organizations that structure work as a pyramid of expertise. The most experienced engineers tackle the most difficult problems, make important architectural decisions and handle any ambiguity that arises. Then, once their work is complete, they split it into small, well-defined tasks that other engineers and machines can execute consistently.

Allocation is only part of why this works, though; persistence is equally important. A senior engineer’s judgment gets built into interfaces, schemas and conventions, allowing everyone downstream to build on those decisions without having to derive them again. The same applies to AI systems. If you fail to capture their reasoning as a durable artifact like a graph, specification or compiled skill, the system repays for it every single time it runs.

We apply the same principle to AI systems. Advanced reasoning should only be used when judgment is required. Smaller models can handle well-defined implementation work, and deterministic software and reusable tools can execute tasks where the outcome is already known. The goal is to apply the right level of intelligence to each stage of the workflow.

Architect once, inherit everywhere

So, how much of an AI system should teams rebuild for every new workflow?

In our experience, very little. Architectural capabilities like evaluation, orchestration, guardrails, context management and auditability aren’t unique to a single AI application; every enterprise AI system eventually needs them. Rebuilding them for each workflow is a waste of time and money that forces every AI system to solve the same infrastructure problems independently.

It’s much more efficient to move these capabilities into a common harness. Orchestration, governance and evaluation can then be developed, tested and improved at the platform level rather than separately inside every workflow. Each new AI system can reuse this foundation while teams focus on the business logic that makes the application distinct.

This architectural discipline extends to the execution layer. The amount of computation an AI system performs before producing an outcome depends heavily on the context provided, how its instructions are designed and how its agents are structured.

A 2026 study found that consolidating three or four agents into a single call reduced token consumption by 53.7% and latency by 49.5%, with comparable accuracy. Most of these savings came from not repeatedly passing the same context between agents.

The study also demonstrated that, as the number of tasks assigned to each agent increased, the agent became less reliable at choosing the best way to approach each task. However, increased routing helped recover much of that performance. In short, AI efficiency depends on how the work is structured and routed, not just how many agents are involved.

Optimize throughout the lifecycle

Building the right architecture creates the foundation for continuous optimization.

Model selection, for example, shouldn’t be a one-time decision. As models improve and workloads evolve, it’s worth re-evaluating every AI workflow to determine whether the same outcome can be achieved with less reasoning and lower cost. Demonstrating that system design can outweigh raw model capability, Andrew Ng, a computer scientist, AI entrepreneur and Stanford University adjunct professor, highlighted that while zero-shot GPT-3.5 scored 48.1% on the HumanEval coding benchmark and GPT-4 scored 67.0%, wrapping GPT-3.5 in an iterative agentic loop pushed its score to 95.1%.

The same applies to cost visibility. Token economics should be visible across every workflow, model and task so teams can identify ways to reduce costs before they accumulate.

AI is much less expensive than it used to be, but lower costs encourage more use. Organizations can run more workloads and take on more ambitious problems, so it’s even more important to know where the AI is actually creating value.

Allocate intelligence by the value it creates

Most organizations start with fixed token budgets, because they are easy to administer. However, not every AI task has the same value.

Think about AI as an engineering resource, rather than a shared utility. The teams that consistently choose the right model for the right task and avoid unnecessary reasoning should get more compute because their tokens are producing better outcomes.

Over time, I expect enterprises to manage AI the same way they manage any scarce engineering resource — by using it where it has the greatest impact.

CIOs entered the year with ambitions to bring more AI into their companies and start using AI to transform how work gets done. At the same time they also wanted to leverage tech to cut costs and boost productivity throughout the business. And they hoped to use their post to help drive the strategic vision for their organizations.

Now into the fourth quarter, CIOs say they’re still pursuing those objectives and plenty more. They say they still have much work to do, and they’re aiming to close the year with wins in myriad areas.

Here, IT leaders share advice on what their peers should focus on based on what they are racing to finish before year’s end.

1. Be good to your word

One of the top to-do items for many CIOs as they near year’s end is delivering on their promises.

As Robert Field, global CIO at tech advisory firm Adler Advisors, says, people may not remember all the work IT did in any given year, “but they’ll always remember the things you didn’t do that you said you would.”

Field also cites fiscal reasons for his drive to finish up all IT work planned for 2026, noting that the company, which runs on a calendar year, has already allocated the money for the work in its annual budget and has pegged some of it to anticipated tax credits.

Moreover, employees’ performance reviews are done on the calendar year, he says, which means planned projects need to be completed so leaders can accurately assess employees’ work and contributions.

Jason Brucker, managing director for CIO solutions at consulting firm Protiviti, says every year around this time he hears from many CIOs who are looking to finish what they started. But it can be a hard-fought win, he says. End-of-year tasks are piling up on workers, teams are starting to take time to plan for the upcoming year, and the C-suite is stressing the need for IT to deliver measurable value to the business.

Still, despite the distractions, closing the books on promises made can help prove the value of IT’s ability to not only deliver solutions but make good on allotted investments — an important win for securing funding for the next slate of projects.

2. Prove this year’s solutions deliver when it matters most

The last months of the year are always the busiest for Kenco Group, which as a large third-party logistics provider experiences a spike in demand for its services. Pal Narayanan, its chief digital and information officer, wants to use that peak volume to make clear that his IT department has no problem keeping operations up and running smoothly even at full tilt.

Although demonstrating IT’s ability to keep pace with the business is always critical, Narayanan says he wants a win this upcoming busy season because IT has added more automation and robotics within company processes “and we want to make sure those work flawlessly.”

But his IT team needs to be prepared to work quickly if there are any issues that need to be addressed, as Narayanan notes that this is the first peak season the company will go through with the new technology in place. “Every minute matters during peak season, so I’m making sure everyone is in place, and everything is built for scale, and if any additional capability needs to be turned on, our vendors are ready,” he adds.

3. Fortify business alignment for the year ahead

For Adler Advisors’ Field, building relationships with colleagues is an ongoing task. But year-end provides a ripe opportunity to check in with his peers, tie IT work to hoped-for business outcomes, and discuss goals for the upcoming year, he says.

“I use this time to talk to people and to meet up and align IT with their strategic plans for next year, so my team is aligned to achieve that with them,” he says.

Field is disciplined in his outreach, as he keeps track of meetings and his colleagues’ preferred communication modes. He has had workers on his own IT team also work on building relationships with their business counterparts to ensure those team members understood the business and how IT could provide “exponential value” to it.

And while Field meets with leaders in the business all year, he uses the fourth-quarter meetings to reflect on IT’s performance and solicit feedback on how it could do better and be more impactful. “It’s an opportunity to hear where they need me or my department to lean in on more, so we can deliver what they need,” he adds. “Inevitably there’s something I wasn’t thinking about.”

4. Advance the AI agenda

Many CIOs are looking for AI wins to close out the year, says Brucker of his client work for Protiviti. Today that means not just deploying AI capabilities but “rolling out AI to gain value.”

“They’re looking to go from the experimentation phase to the business value realization phase,” he says, noting that how they plan to do that varies from one organization to the next.

Kenco Group’s Narayanan hopes to accelerate his company’s AI strategy and plans to use the rest of this year to position his IT department to deliver on that. He’s currently identifying what is working and what is not so that IT can focus on what’s bringing success and shed what isn’t. To deliver on his goal before year-end, Narayanan is also establishing partnerships with new vendors — both nimble startups and mature, established providers — to bring a combination of agility, innovation, and experience. He’s implementing AI training and education to build competency and excitement, and he’s putting in financial metrics that can be used to measure the ROI on new AI initiatives. He believes doing this work now will allow for faster selection and adoption of AI in 2027 as well as faster achievement of benefits.

Meanwhile, Zach LaValley, CTO of RapidSOS, an intelligent safety platform and public safety AI company, is creating what he calls an “internal intelligence plane that everybody — humans and AI — can tap into.”

LaValley, who oversees internal IT at the company, envisions this replacing the data silos that limit AI use in organizations, with the plane having the needed security protections, access controls, and integration points that would allow AI applications to quickly and easily connect with it. LaValley says by year’s end he wants to connect all the data sources to this new plane. For additional 2026 wins, he wants to port over all the AI apps built by employees and start making those discoverable so that employees won’t have to reinvent apps that already exist.

Nina Tatsiy, CIO of Quadient, a global automation platform, wants to spend the next two months convincing everyone in the company “to embrace agentic AI.” She’ll consider it a win if she can accomplish that.

Along with that, Tatsiy wants to technically ready the organization for agentic AI transformation. “My biggest focus is how to set up an operating model for agents, governance for agents, and building a strategy around what that means for the rest of the technology stack,” she says.

5. Rein in AI costs

Like many IT execs, Sebastian Arriada, CIO of Globant, a global technology and IT services company, is seeing the cost of using AI spike as the number of “tokens we consume at the company grow almost exponentially.”

So, with an eye on reducing those bills, Arriada’s infrastructure team implemented Nvidia boxes so that Globant can deploy open-source models on them. Arriada explains that the move will let Globant balance the use of tokens between commercial and (less costly) open-source AI models.

There’s more work to be done before the boxes are operational, Arriada says. His goal is to have them up and running as well as expand the use of the open-source models by 5X this year. He expects to hit that goal by the end of November, noting that he wants to deliver another 5X in increased capacity in 2027.

Not all CIOs are ready to make the leap to hosting their own open weight models on expensive Nvidia hardware, but eyes on AI costs is common theme. Finishing out the year with a battle-tested plan for reining in AI spend is a worthy win regardless of how it’s executed.

6. Devise a 2027 budget for maximum impact and future-proof potential

Research firm Gartner projects that IT budgets will grow by an average of 3.7% in 2027 but cautions that, accounting for inflation, that increase will be marginal for most CIOs.

Actual budget figures for next year vary (and are not publicly disclosed), but CIOs of every stripe say one of the last wins they want for 2026 is to lock in an IT budget for 2027 that can maximize impact while keeping the price tag down.

It’s a task that “comes with a lot of pressure,” Adler Advisors’ Field admits. He expects to draft multiple iterations of his 2027 budget this fall, working with his IT team and consulting with business counterparts to come up with figures capable of supporting all of IT’s necessary run, grow, and transform costs — without breaking the bank. He’ll consider it a win when the budget is not just done but accepted by the board.

Quadient’s Tatsiy has a similar objective as she looks ahead for IT, saying she’s working “to transform all the functions I’m managing, not just for cost savings but to future-proof them.”

She says she’s looking at what she can rationalize and where she can deploy agentic AI to “change my function to be less focused on run and more focused on investing in the future.” Like Field, she says devising a 2027 budget that moves her further along that path would be a laudable way to close out the current year.

Per-token pricing has become the default commercial language of generative AI, and practically every budget conversation now happens in those terms (tokens consumed, tokens generated, cost per million tokens, etc., etc.).

The problem is that this model treats every AI interaction as a discrete transaction, separate from the infrastructure required to process it. Yes, tokens are a billing unit, but the work they represent also has a physical footprint in memory and storage, and as agentic AI drives context windows into the hundreds of thousands of tokens, that footprint has outgrown the GPU.

So why does this matter? Well, according to recent analysis published in Harvard Business Review (HBR), corporate AI is about to experience a massive “cost shock”, as vendors move away from unmetered or bundled AI features and begin passing on the cost of the infrastructure required to deliver them.

As HBR points out, organizations are currently being paid, in effect, to adopt AI, with major software vendors quietly absorbing the steep costs of GPUs, inference and tokens on their customers’ behalf. That subsidy is not built to last.

Take memory and storage costs, for example. DRAM prices have risen sharply as manufacturers focus on High Bandwidth Memory (HBM) production for AI accelerators, tightening supply for conventional server memory. Meanwhile, demand for AI is exerting sustained pressure on the availability and pricing of high-capacity SSDs.

This is totally unsustainable, and there was always going to come a point at which vendors could no longer continue covering these costs themselves and would need to pass more of them on to customers. That point is now rapidly approaching.

The scale of the shift is hard to overstate. Google was processing 9.7 trillion tokens a month in 2024; by mid-2025 that had risen to 480 trillion, and at this year’s I/O, Sundar Pichai put the figure at 3.2 quadrillion tokens a month. Goldman Sachs projects industry-wide token consumption will multiply another 24 times by 2030. Whatever an organization budgeted for inference last year, the volume assumption behind it is already out of date, and a growing share of that volume is input, not output: agentic workflows that plan, call tools and re-read context on every step are pushing input tokens, not generated ones, to the top of the cost stack.

Your AI budget has a serious NAND problem

The clearest sign of where this is heading came at CES 2026, when Nvidia unveiled what it first called its Inference Context Memory Storage Platform, built around its coming BlueField-4 DPU, and has since formalized as the NVIDIA CMX context memory storage platform. CMX standardizes the offload of the model’s working memory, the KV cache, onto NVMe SSDs, extending GPU memory into a shared, pod-level context tier and making it persistent across inference runs. Nvidia says the approach can deliver up to five times greater power efficiency and up to five times higher tokens-per-second throughput, and it already has more than a dozen storage partners lined up to support it, including Dell, IBM, NetApp, VAST and WEKA.

That is a significant validation point: several storage vendors were already extending GPU memory onto flash before ICMSP existed. Nvidia standardizing the approach across its ecosystem confirms this is now an architectural direction for the industry, not a niche workaround. But it also means the economics of inference are no longer determined solely by GPU pricing; they are now directly exposed to storage media pricing.

That exposure matters because flash pricing is anything but stable. According to VDURA’s own Flash Volatility Index, 30TB TLC SSD pricing rose 472% between Q2 2025 and Q1 2026, and the price multiple between high-capacity QLC flash and hard drives widened from roughly 4.9x to more than 22x over the same period, while HDD pricing moved about 35%. If cost-per-token now depends on flash-resident context tiers, then token costs inherit flash volatility, and organizations running all-flash inference infrastructure are carrying unhedged exposure to the NAND market on every query.

Instead, flash should be reserved for hot context data where its performance genuinely affects inference. This is because not all types of data generated or retained by AI workloads have the same latency requirements. For instance, data retained for future use, such as checkpoints, does not necessarily need flash performance. The hyperscalers worked this out long before the current price cycle: Google, Meta and Microsoft do not run all-flash storage; they run mixed fleets, with just enough flash to saturate compute and intelligent tiering draining everything else to high-density disk. A mixed-fleet architecture separates performance capacity from bulk capacity, giving organizations a structural hedge against volatile flash prices.

Tread carefully

If they aren’t doing so already, infrastructure leaders should model token-cost sensitivity to media pricing before committing capital. Do not forget that agentic workloads can disproportionately increase token consumption on the input and context sides. As a result, cost models must account for how much context requires high-performance flash capacity and how much can be stored on lower-cost tiers. Teams should regularly test how projected AI costs change as CMX-style architectures push more context onto NVMe, and as SSD pricing rises further while context requirements grow.

That all being said, HBR’s concluding analysis is extremely apposite: as long as executives are being asked to redesign their organizations around a technology with unpredictable pricing, they should tread carefully.

I have audited enough celebrated technology rollouts to notice a quiet omission across the industry: nobody talks much about what happens to the unit economics once the pilot ends and the software enters daily production.

I have anonymized the company and changed identifying details for confidentiality, but the operating economics and workflow behaviors below are taken directly from the post-implementation audit.

About six months ago, I was brought into a mid-sized enterprise to review a document workflow that had recently received an industry innovation award. The platform vendor published a case study highlighting the deployment. The trade press ran a short feature on it. Internally, the business unit sponsor pointed to a ninety percent reduction in turnaround time for vendor onboarding agreements, using that headline metric to secure a larger operational mandate for the coming fiscal cycle.

A couple quarters later, the finance team reached out with an uncomfortable question. Document volume had stayed flat, yet the monthly operating expenses attached to processing those agreements were climbing steadily every single billing cycle.

They asked me to trace the spending back through their systems and reconcile the operational data with the general ledger.

Under the previous manual workflow, an operations clerk opened an incoming contract, checked five or six key clauses against internal guidelines, verified the vendor details and filed the document away. The entire review took about four to five minutes. Using the department’s fully loaded labor rate, including benefits and allocated overhead, that came out to roughly eighty cents per document.

The automated pipeline was running between $12 and $14 for the exact same file.

The system was technically stable. It processed thousands of files a month without dropping connections or crashing servers, and the operations dashboard stayed green. But the business was spending $12–$14 to complete work that had previously cost about 80 cents.

Every enterprise award celebrates speed and throughput. Few of them audit the cost to finish a single unit of work.

Nobody caught the discrepancy during the original three-month pilot because the computing bills, data lookups and third-party model access fees had been charged to a central innovation fund. The departmental budget never saw an invoice. As far as operations was concerned, the tool was essentially free software that dramatically reduced turnaround times on incoming requests.

The moment the pilot concluded and the system moved into full daily production, the accounting changed. Corporate finance closed the innovation project code and began allocating the actual infrastructure invoices directly back to the business line. That was when the operational reality surfaced. Within sixty days of running full transaction volume on the department’s budget, the economics of the unit turned negative.

Traditional enterprise software often has a predictable cost curve. Once the baseline platform is running, processing another five hundred records usually does not require the same kind of fresh computation and external model consumption as an automated pipeline.

In this kind of workflow, each customer interaction, incoming contract or automated data extraction can trigger additional computational steps. The system had to interpret the document, check it against internal rules, compare the result with reference data and validate the answer before completing the transaction.

If an automated pipeline requires multiple model queries and several background checks to finish a task that an experienced employee used to resolve in five minutes, you have not eliminated an operational cost. You have swapped a predictable human payroll line item for a consumption-based cost that scales directly with the work.

Falling model prices don’t mean falling workflow costs

The standard counterargument is that computing power is getting cheaper. That part is true. Research published in the Stanford HAI AI Index Report documented that the cost of querying benchmark foundation models dropped by more than 280-fold between late 2022 and late 2024.

It is tempting to assume that automated business processes will experience the same cost reductions.

In practice, a dramatic drop in per-token rates does not guarantee a lower operating bill if the production workflow performs significantly more retrieval steps, document parsing passes and validation checks to complete a single business task.

Gartner’s latest forecast points to the same tension, predicting that inference costs per agentic workflow will increase more than fivefold through 2028 as increasingly complex workflows offset falling model prices.

Both things can be true at the same time: base model prices can fall while the total cost of completing a business transaction rises.

Cheap models do not guarantee cheap business processes.

The economics of an automated system depend on the cost of the entire workflow, not just the base model.

Production complexity is where the bill gets bigger

During our audit, the operational problem showed up the moment real-world documents stopped looking like the clean samples used during vendor sales demonstrations.

The demonstration files were digitally generated PDFs with uniform fonts, consistent layouts and standard contractual language. The live production queue was full of low-resolution scans, photocopied forms rotated sideways, documents with handwritten marginal notes, and agreements containing conflicting payment terms.

A human operations specialist can often resolve that kind of ambiguity with a quick judgment call based on institutional context. The automated workflow struggled when the document fell outside the assumptions built into the process.

When the system encountered messy formatting or contradictory clauses, it triggered extra processing routines. It ran multiple extraction passes, queried internal reference databases and executed secondary verification checks to resolve the ambiguity. Each extra step added to the cloud invoice, yet the system still struggled to reach an acceptable confidence score.

When confidence fell below the required threshold (which occurred in roughly forty percent of daily transactions), the pipeline halted and routed the file into an exception queue.

That created a secondary labor expense the original business case never budgeted for.

When an agreement landed in the exception queue, an operations specialist had to log in, read the system’s partial extraction notes, diagnose why the software stalled and manually enter the correct data into the core database. Because the specialist had to review both the original document and the system’s confused output, resolving an exception took ten minutes (twice as long as the original manual baseline).

The company found itself paying twice. It was paying the full monthly cloud invoice for the automated software, while simultaneously retaining the payroll cost of the operations staff required to supervise the exceptions.

Goldman Sachs has raised the broader macroeconomic question of whether the massive corporate spending on AI infrastructure will produce productivity returns sufficient to justify the investment. Our audit showed what that question looks like inside a single department: replacing an eighty-cent clerical task with a fourteen-dollar automated pipeline that still requires human labor for four out of every ten transactions.

The problem rarely comes from the user-seat price on the purchase order. It comes from what the system consumes to complete the work.

The vendor can price the platform around usage, but the customer owns the business case. If an automated pipeline requires multiple processing passes to interpret a crooked document scan, the customer still has to absorb that consumption whether the final output is usable or not.

When you scale a human operations team, the additional cost is relatively predictable. You know what another specialist costs in wages, benefits, equipment and management time. For this kind of workflow, automated costs can rise with the ambiguity of the inputs.

When the fiscal quarter wraps up, this creates an organizational disconnect where every department claims victory while the business loses margin:

• The engineering team reports ninety-nine percent system uptime and clean infrastructure performance.
• Operations leadership reports that turnaround times on clean files dropped from forty-eight hours to under a minute.
• The software vendor references the client in marketing collateral as evidence of enterprise transformation.
• Finance is left trying to figure out why the cost of goods sold increased while revenue stayed flat.

When leadership asks why an automation milestone fails to improve operating income, engineering points to platform uptime, product leads point to system adoption and software vendors point to successful transaction counts. Every department hit its individual performance goals. Yet the business process itself became strictly more expensive to run than it was before the project started.

In this deployment, the team tried to fix the issue by adding secondary filtering scripts and tuning prompt instructions. Those additions increased system complexity and cloud consumption without solving the core unit cost problem.

An automated workflow can execute with zero technical errors while still undermining departmental margins.

Three questions before you call an AI project a success

Before approving a production, rollout or expanding an automation pilot, finance and technology leaders should put three direct questions to their teams:

• What does one completed transaction actually cost compared with the manual baseline, accounting for all infrastructure compute, database lookups and third-party model charges?
• How often does a human still have to intervene, and what is the loaded payroll cost of that review time?
• Do the economics still work at production volume, or is the organization simply moving an operational expense from payroll into an unpredictable consumption meter?

If the economics only work during the pilot, the system is not creating operational leverage. It is creating an operating expense the pilot never measured.

I recently met with a client’s CEO and CIO together. The CIO asked, “How much should we spend on AI next year?” My response was, “If you do your job right, your CEO will ask why you aren’t spending more.”

I spend a lot of time with CIOs who are planning their AI investments. One pattern keeps repeating itself. Our discussion starts with token costs, model pricing and consumption forecasts. It rarely ends there.

Somewhere along the way, we start talking about which AI investments drive business performance (revenue, margin, customer satisfaction, churn, time to market, scale).  This turns AI budgeting from an IT cost to a business ROI.  I find this interesting because it reflects a fundamental shift for the CIO. That changes the CIO’s job from controlling AI spend to figuring out where the business should invest more and benefit more.

Consumption cost concerns are valid

For decades, technology leaders have worked with relatively predictable costs: buying servers and depreciating them or purchasing X software seats for Y dollars and renewing annually.

Consumption-based AI changes that equation. It’s more like paying a utility bill than owning an asset, and traditional IT budgeting wasn’t built for that variability.

Few CIOs I talk with have faced cost becoming a problem because adoption worked too well. We see some organizations considering constraining deployments or renegotiating contracts as costs increase. That’s the wrong strategic path. They’re managing costs when they should be more discriminating about which AI initiatives deserve investment.

Think like a capital allocator, and focus on value realization

Organizations realizing real value from AI are leveraging disciplines long used in finance and corporate strategy to optimize their capital investment portfolios. They are rigorous about evaluating the impacts that investments have on the business. There may be hundreds of projects underway, but only a handful matter most to the business right now. That’s where the resources go.

I’ve also seen this firsthand at West Monroe. Our adoption curve accelerated as people moved from experimentation to transforming work with AI-enabled workflows. We found that our top two percent of users accounted for roughly half of AI usage. They also had outsized impact across sales, delivery and other operations. That visibility shifted the conversation from, “Why are we consuming so much AI?” to “Where should we invest even more?” 

A recent IBM study found organizations that manage AI as a portfolio rather than a series of individual investments deployed 2.4 times as many agents without a higher AI/IT budget.

The economics of AI require a new management approach that connects AI activity to business economics and, subsequently, to investment decisions. Measurement, funding and governance must all change together, along with the CIO’s focus.

Measurement should prove what AI is producing

Few organizations can answer the question, “What did we get for what we paid?” Most measure tokens or similar usage rates. These are meaningful leading indicators because they reflect the foundation for value capture. But they aren’t measures of business value.

When evaluating value, many executives mistakenly equate correlation with causation: “We have a lot of AI use in a particular group. Revenue is up. That must be because of AI.” What they need to do is link AI activities to the KPIs that drive the business and ultimately impact revenue or margin. This is much harder because it requires a deep understanding of business processes. If you use AI to remove 10 steps from customer onboarding and, as a result, customers complete the process 70% of the time instead of 50%, you’ve achieved a 20-point increase in retention. You can then calculate how that translates to the P&L. 

Measuring value requires a system to model business processes before and after AI, and to understand how they drive KPIs and ultimately the P&L. The good news is that with AI, this type of modeling is possible. And new ways to measure value are fast emerging, such as return on tokens.

Modeling isn’t easy, so start small. Identify and measure one or two business metrics that AI could directly influence, then build the measurement framework before building anything else. Here’s an example: My company introduced training on using AI in the sales process and measured sales performance before and after. People who completed the training had a 47% larger pipeline and 57% more wins than those who didn’t. This insight helped us decide whether to accelerate our AI investment.

Funding should follow the value

In the early stages, it makes sense for IT to fund AI. Over time, though, keeping AI costs within IT limits flexibility because it competes for a finite budget, while other parts of the business reap the benefits.

We found that AI usage on delivery work for a key project cost us $18,000 per month but generated $500,000 in productivity gains, a 20X ROI. The team that does this work should obviously pay for the AI. If we left that cost in the IT function, our CIO would say, “You’re spending too much on AI; try to limit it.” That would reduce ROI.

By creating visibility into how AI activity drives business outcomes, CIOs can begin to shift costs to the respective function, as with labor or office space. In fact, if you treat AI as part of workforce costs rather than an IT cost, it becomes part of enterprise-wide operating expenses, rather than sitting entirely within the IT budget.

This isn’t easy or quick. There are always organizational politics, but this isn’t any different. Many CFOs don’t feel they have control over AI activity and its impact on the business. A CFO can’t reasonably fund what the organization can’t model. This goes back to the point above. Our organization built the modeling capability first. When other parts of the organization can measure it, price it into deals and plan for it, we move the cost out of IT. This insight also gives our CFO and business leaders more confidence in future investment decisions.

Governance must facilitate rapid adaptation

Traditionally, governance exists to reduce risk in a relatively stable environment. With AI, we’re governing through change. And, in this case, the change is continuous.  Everything — technology, cost structures and best practices — is changing rapidly. Organizations need an adaptive governance approach. Governance teams should be multidisciplinary, including business stakeholders, finance, risk and IT. They should meet frequently to understand and respond to a changing reality. Every month, they should ask: Are the things we planned to do in AI still the right ones? Will they have enough impact? Are we going fast enough? Are we being too risk-averse or taking too much risk?

A good analogy is gasoline consumption amid rising prices. If gas were free, we’d floor it all the time. But we’ve learned to manage consumption through various mechanisms, such as dashboard metrics and features that automatically stop and start the engine at traffic lights. Annoying as these are, we’ve become more efficient without driving less. That’s what’s happening with AI.

You can manage costs by suppressing consumption, or you can manage the economics by improving the return on consumption. The latter is what a portfolio manager does. That brings me to the CIO’s role.

IT must lead an intelligence transformation

I think of CIOs in eras. In the data center era, those who excelled at building data centers became IT leaders. Next came the SaaS application era. Now, we’re in the intelligence era. AI is most effective when it has access to data, which forces CIOs to consider how to make data more useful, effective and accessible, and what agents can automate. That’s new and, frankly, terrifying for many. It is easier to say “no” than to unleash agents.

I believe the CIO’s job has never been more challenging. AI is harder to budget for and forecast. There’s more pressure than ever to align technology spending with business outcomes and demonstrate ROI. But there’s also more opportunity than ever to do so.

Today’s most effective CIOs are closely aligning with the business and leading an intelligence transformation. Those perceived as gatekeepers will be replaced by more progressive individuals.

The CIO’s opportunity

The best AI budget conversations still begin with technology, as they should. But they quickly shift to where the organization should spend more, as in my recent conversation with the CIO and CEO. The CIO’s influence grows as AI spending becomes inseparable from business performance. Ultimately, this moves the role toward what it has long sought to be: a trusted partner to the business.

Most CIOs can easily describe their AI initiatives, but struggle to demonstrate and report on AI value and ROI. This is evidenced by Gartner’s AI ROI Vendor Sentiment survey, which found that 27% of respondents strongly agree that ROI from AI initiatives is difficult to quantify. In addition, only one in four had a clear process to evaluate success of AI initiatives.

In the digital transformation era, compute costs were relatively cheap, and the focus was less on FinOps and more on user experience, and the classic software attributes of availability, reliability, scalability, performance, and security.

AI-native and AI-enabled applications still bring these considerations, along with a host of new ones, such as accuracy, validity, explainability, safety, and all the other trust attributes like those in the NIST AI RMF. But as AI scales, it’s quicky becoming a FinOps exercise, too.

I’ve discussed techniques CIOs can apply to improve AI value and ROI such as architectural decisions related to controlling efficiency at the data layer, fine-tuning the application layer, and closing the gap between AI strategy and governance.

Here, we’ll look at three best practices for CIOs looking to formalize their process to evaluate the effectiveness of their AI initiatives through approaches, frameworks, and techniques that support measurement and ROI reporting across the entire AI portfolio.

1. Take a portfolio approach in line with AI ambition

The first step to AI value is to take a portfolio approach and look across your entire AI estate and how it supports your AI strategy and broader organizational strategy. This means thinking about your AI innovation ambition and how you’d like to move the needle year-over-year by adjusting your percentage of spend across quick wins versus must haves, internal (employee-facing) versus external (customer-facing) applications, near-term versus long-term initiatives, and cost savings and productivity versus revenue generation.

This AI innovation ambition approach is a best practice in innovation management as organizations work to gain quick wins early on to prove value, and then get more ambitious in subsequent years and allocate a larger percentage of their spend on more strategic, longer-term, and externally focused initiatives for revenue generation.

For example, an early maturity organization might focus 80% of its AI investment on internal-facing initiatives and 20% on external-facing ones in the first year. Then, it moves to a 70/30 split in year two, and so on. While many organizations may not track these spend allocations, it’s a valuable discipline and one which often differentiates AI leaders from the rest of the pack. In PwC’s 2026 AI Performance Study, the top 20% of companies capture 74% of all AI-driven economic value, delivering a 7.2 performance multiplier over peers.

Deploying focused pilots alongside quick wins is a great way to capture early insights into AI value and ROI before scaling applications. According to Joe Vaccaro, SVP and GM of Network Platform and Assurance at Cisco, networks have become distributed, continuously changing systems that produce far more signal than any human-led operating model was designed to interpret.

“Our survey of 1,000 IT and NetOps leaders found more than half believe a failure to automate will open critical security gaps and elevate enterprise risk,” he says. “For CIOs seeking to demonstrate ROI from AI investments, a credible path is to start with targeted, measurable, and reversible workflows. Focused pilots can validate outcomes and strengthen governance before broader deployment.”

At this level where organizations look at their holistic AI spend, it’s also important to have a well-crafted set of AI value metrics, which can be used to assess the current health and productivity of the portfolio. Borrowing from best practices in innovation management, AI value metrics, such as those that look at input and mix, health and efficiency, and outcomes, help to look across the entire innovation pipeline, from idea to value.

• AI innovation input and mix — What’s going into your pipeline in terms of the types of innovations in the queue.
• AI innovation health and efficiency — The flowrate through your pipeline and the amount of funding being applied.
• AI innovation outcomes — The measure of return on innovation in terms of commercialized innovations that have captured revenue or other objectives.

2. Develop an assessment framework focused on full cost benefit

While many organizations are laser-focused on the cost side of their AI initiatives, your assessment framework needs to incorporate a full cost benefit analysis. “While the current focus for many organizations is squarely on AI tokenomics, it’s important to look at both sides of the coin when it comes to AI value, and look at the business value that’s being delivered by each initiative to give a clear picture of actual return,” says David Evans, AI and growth strategist at thought leader networking platform Thinkers360. 

Ben Schein, VP of AI and analytics at AI infrastructure software company Progress Software, adds that CIOs should stop measuring AI activity and start measuring outcomes where possible. “Usage tells you the meter is spinning, not that value came out,” he says. “Instrument the cost side completely, since every AI action can carry its workload, model, and cost. Then pair costs with benefits where the work allows, so cost per workflow run against value per workflow run, never enterprise-wide spend against anecdotal wins.”

However, Dan Priest, PwC US CAIO, recommends getting back to basics, starting with the investment side but adding a few modern updates. “AI ROI has been illusive for many, in part because organizations are still learning how to achieve it, and because AI sometimes gets a pass on the typical disciplines required to measure it,” he says. “For major AI initiatives, starting with investment might include new infrastructure, labor for the build, models for the intelligence layer, and variable expenses like token costs. All of these should be contemplated in ROI calculation, some of which will require new tracking and controls.”

His benchmarking also points to a threshold effect: ROI stays insignificant until AI investment crosses roughly 1.6% of revenue, and above that line, EBITDA is up 9.5%, revenue is up 3.5%, and total shareholder return is up 20.2%.

3. Include benefits and measure what matters

Another best practice from innovation management is to look at both the quantitative and qualitative benefits of AI initiatives, not solely the direct financials.

“It sounds obvious, but CIOs should measure the business value created like productivity, growth, and other outcomes rather than activity completed, such as building an app, creating a dashboard, or collecting customer feedback,” says Priest. “We recommend looking across five dimensions of value: financial, operating, functional, trust, and workforce. That gives leaders a more consistent way to measure and compare value across an AI portfolio, even when individual investments are designed to produce different outcomes.”

Calculating projected as well as actual ROI can also give CIOs insight into how their AI portfolio is living up to expectations. This is where it’s important to factor in governance costs early in the lifecycle before initiatives get funded, particularly for high-risk applications where downstream governance requirements are high. 

“Ultimately, measure what matters, keep an eye on variable costs, and monitor actuals against expected costs and benefits,” adds Priest. “That gives CIOs the information they need to determine where investments are delivering, where changes are required, and where to lead, lag, or exit.”

Quantifying AI value and ROI doesn’t require a completely new methodology or new approach. It simply takes the same financial rigor applied to many other previous investments. The variable cost dynamics may be more complex, but these are solvable if you align with your AI innovation ambition, apply your assessment frameworks consistently, define your AI value metrics, and measure what matters.

The global memory shortage that has driven up the cost of servers, storage and PCs through 2026 will get worse in 2027 and 2028, according to memory maker Micron Technology.

“We expect memory and storage supply-demand conditions to be much tighter in calendar 2027 and 2028 than they were in 2026,” CEO Sanjay Mehrotra said in prepared remarks for the company’s fiscal fourth-quarter earnings call.

Even with new cleanroom space planned across the industry, “We do not have line of sight to when supply and demand will return to balance,” he said. Earlier industry forecasts had expected the two to return to balance in 2028.

Mehrotra added that new plants would not bring quick relief. “Production from new DRAM and NAND fabrication facilities takes time to ramp and gradually becomes more meaningful starting a few quarters after initial output,” he said.

Neil Shah, vice president of research at Counterpoint Research, said the outlook means CIOs “will have to be prudent about which equipment to upgrade and which to stretch to maintain cost efficiencies.”

Higher prices, less memory

On the same call, CFO Mark Murphy said Micron’s “inventory levels and supply remain extremely tight.” Its DRAM prices rose by a percentage in the high teens in the fiscal fourth quarter, while NAND prices climbed about 30%, he said.

Taiwan-based market research firm TrendForce expects prices across the industry to keep rising. In a Sept. 30 report, it forecast that conventional DRAM contract prices will increase another 10% to 15% in the fourth quarter from the third. It expects NAND flash prices to climb 15% to 20%. The firm said increases are slowing but the market remains undersupplied.

IDC expects PC buyers to pay more as well. The research firm forecast in June that average PC selling prices will rise 17% in 2026.

TrendForce has also tracked a shift toward less memory per server. Cloud providers and OEMs have moved some servers from 96GB and 128GB memory modules to 32GB and 64GB modules since the first half of 2026, the firm said in a July report. Analysts had warned in January of higher prices and lower memory specifications for enterprise PCs.

Supply committed years ahead

Micron has already committed more than 75% of its 2027 output, and most of its customer discussions now concern 2028, Mehrotra told analysts on the call.

Much of that supply is locked into multiyear, take-or-pay contracts that Micron calls strategic customer agreements (SCAs). “Any new discussions on SCAs where pricing is involved are negotiated with higher pricing based on prevailing market conditions and outlook,” Mehrotra said.

Some cloud providers have signed similar long-term agreements with memory makers, TrendForce said in its July report. That has left buyers without such deals as the main source of server DRAM price increases, it said.

Shah said enterprises should lock in pricing too. “Companies should secure multiyear pricing for the computing capacity they know they will need,” he said. Moving workloads to the cloud will not avoid rising hardware and energy costs “because providers will pass them on,” he added.

Which refreshes to delay

When it comes to replacing existing equipment, Shah said, the right call depends on the workload. “For general back-office PCs and routine file servers, stretching lifecycles from three years to five is harmless,” he said. “But for core infrastructure and engineering seats, delaying refreshes can backfire.” Aging equipment can drag on productivity, lose software support and fail more often, he added.

Shah also cautioned against turning to older memory to save money. Memory makers have been converting production lines to high-bandwidth memory and DDR5, so DDR4 is no longer cheap or plentiful, he said. “If you buy legacy platforms today to shave 10% off upfront server costs, you’re buying into systems which won’t have serviceable parts two years from now.”

He recommended starting with the hardware already in place. “Enterprises often waste 30% to 50% of memory by provisioning for peaks that rarely occur,” he said. Right-sizing virtual machines, quantizing AI models and batching workloads more efficiently can cut memory use significantly, according to Shah.

Before buying more hardware, he said, “CIOs should think about optimizing on the silicon already in place.”

What hardware vendors say

Hardware vendors had no helpful advice to offer budget-constrained buyers.

Lenovo did not answer questions directly but pointed to remarks executives made on its Aug. 13 earnings call.

Chairman and CEO Yuanqing Yang said then that he expects memory demand to keep rising and supply to remain constrained at least through the end of 2027. He said Lenovo can respond quickly to rising component costs: “When the material costs rise, we can adjust the pricing at the front end in a timely manner.”

Luca Rossi, president of Lenovo’s Intelligent Devices Group, said he expects the PC market to shrink about 15% in units in the six months to March, with business demand holding up better than consumer demand.

On the server side, Ashley Gorakhpurwalla, president of Lenovo’s Infrastructure Solutions Group, said a “strong server refresh cycle is underway.”

Dell, HPE, HP, Cisco and Supermicro did not respond to requests for comment by publication time.

Many IT leaders still struggle to find ROI when deploying AI agents at scale, but a few IT sweet spots are emerging as use cases that can provide CIOs tangible cost relief.

Rhonda Baldwin, CIO at LaunchDarkly, says coding agents and service desk agents are helping the SaaS provider cut IT costs.

“Coding agents increase engineering capacity without requiring a proportional increase in headcount,” she says. “They also help us optimize cloud costs and reduce workflow consumption costs.”

In addition to coding and cloud cost optimization, IT leaders see tier 1 and 2 IT support as another promising area for agents to free up IT budget, although many have just begun to track savings from AI agents, and those that do use several different metrics.

For Baldwin that includes cost avoidance, as coding agents have helped the company build internal tools rather than buying additional software, she notes.

Here, the savings have been substantial, she says, with the company foregoing about $1 million in spending on two optimization projects and another $120,000 by building its own enterprise asset-management solution with AI coding tools.

In addition, LaunchDarkly has saved about $50,000 in annualized savings by using agentic AI for tier 1 IT support. “This not only boosts employee productivity but also helps us avoid costs related to software, implementation, asset management, and having remote service technicians,” she says.

IT support and self-service resolution

IT support is a strong use case for IT leaders looking for agent cost savings, says Gil Pekelman, CEO of AI agent provider Atera, whose company recently released a joint study with Forrester claiming significant ROI for its IT incident support agent.

“The calculation is very straightforward,” he notes. “We have a company that has 10,000 employees, and all IT support is offshored. If they’re going to close that down now, they can immediately measure the savings there.”

Kevin Rooney, CIO at West Monroe Partners, reports significant savings for the digital consultancy through just such a deployment.

Now, employees can an internal IT and HR support agent in Teams or Slack to get answers to common questions, request software access, reset passwords, unlock accounts, submit and update tickets, or connect with a live support professional when needed, he says.

“By handling high-volume, repeatable requests and improving ticket intake and routing, it helps our IT team spend more time on complex issues and higher-value work,” Rooney adds.

West Monroe is evaluating the impact of the agent in terms of self-service resolution, ticket volume, response time, and capacity savings, but it has driven a 40% reduction in yearly managed service provider costs, he says. The bot has also led to an estimated operational time savings of 2,700 hours per year, he adds.

Cost savings in the cloud

Matthew Wallace, co-founder and CTO of KamiwazaAI, has also found major IT savings from agentic AI. The Ai orchestration platform provider uses agents in several areas, including software engineering, project management, data science, and systems engineering.

Cloud optimization is another area where the company is saving money by using agents, he adds.

“We have a set of agents that monitor new systems in the cloud, coordinate with those turning them up, and coordinate budget approvals, with an easy way to shut down unauthorized spend and log it,” he says.

That agentic system has replaced a function that required a high amount of human coordination and attention, he notes

“You have to check and understand who set it up, when, what the cost is, whether it has internal approval, and various details about resource sizing, placement, etc.,” he explains. “Later, you have to check whether someone has responded with information and whether anyone in management has offered an approval or rejection.”

The first round of cloud optimization agents that Wallace’s team deployed cut cloud spending by 70% immediately, he says.

Check the numbers

However, Jeet Pattanaik, founder and CTO of AI-base solutions provider Glokal AI, says some organizations are inflating their IT savings from agentic AI because they’re not measuring all the costs.

While he believes some IT leaders are finding real savings, they’re often smaller and slower than advertised, he says, noting that in reality most organizations are still hunting for ROI.

“The pilot always looks great because it runs on the easy tickets,” Pattanaik says. “Then, production brings in all the odd cases, and somebody has to check what the agent actually did. That checking is what quietly eats the savings, and it almost never shows up in the business case.”

He sees tier 1 support — password resets, access requests, answering questions already in the documentation — as the place where organizations are saving IT budget using agents.

“The sweet spot is work that’s high volume, well documented, and cheap to undo if the agent gets it wrong,” he says. “Tier 1 fits that nicely. Tier 2 only partly. Anything that changes someone’s permissions or touches production systems isn’t ready yet, because one bad action there can cost more than a thousand good ones save.”

IT leaders deploying agents for IT support should keep an eye on how often tickets get reopened, Pattanaik adds. “A ticket the agent closed isn’t always a problem solved,” he says. “If people keep coming back with the same issue, you didn’t save anything, you just pushed it down the road.”

He gives this example: A service desk handles 10,000 tier-1 tickets a month at about €15 each. The agent closes 40% of them, which, on paper, saves €60,000 a month. But if 15% of those come back reopened, that’s €9,000 of work returning to humans. Add around €15,000 for people checking what the agent did, and the real cost reduction is closer to €36,000, about 60% of what the pilot promised.

There’s also a cost to eliminating workers focused on tier 1 support, he adds. “One cost nobody seems to count: Tier 1 is where people learn the job,” he says. “Automate all of it, and you lose the path that turns juniors into your future senior engineers.”

Alternative clouds have emerged amid major cloud providers such as AWS, Google Cloud, and Microsoft Azure, and often specialize in niche areas like AI-native computing, and sovereign and edge deployments, while professing to provide cost reductions and best-of-breed performance.

“Many organizations are rebalancing away from all-in hyperscaler strategies to avoid lock-in and concentration risk,” says Kyle Campos, CTPO at CloudBolt, a cloud management platform. “At the same time, alternative clouds are often purpose-built for specific advantages.” To him, benefits include lower-cost object storage at scale, better edge performance for latency-sensitive apps, and faster access to dedicated GPUs for AI workloads. Data sovereignty is also pushing adoption of localized providers.

But as alternative clouds, also called neoclouds, make headlines and amass considerable funding rounds, their use in enterprise environments remains relatively nascent. While enterprise executives see the potential in using alternative clouds, especially for GPU workloads, many are skeptical, taking a balanced approach to assessing maturity, costs, and whether it’s worth sacrificing continuity with existing clouds.

Smartsheet CIO and CISO Ravi Soin is one of these executives taking a measured approach. “We’ll grow our use of alternative clouds, but within a clearly defined perimeter,” he says. “That discipline is what separates considered adoption from sprawl.” For Soin, many neoclouds still fail early in the evaluation process, especially for regulated workloads where a mature compliance posture is non-negotiable.

In order to fully evaluate alternative clouds, the platforms must be treated with the same vigilance as any other IT acquisition. “This remains consistent across any tech stack: fit, form, and function,” says Nigel Gibbons, director and senior advisor at the NCC Group, a cybersecurity consulting firm. “This means alignment of the cloud to workload performance, security, and sovereignty needs.”

Overall, CIOs aren’t rushing into alternative clouds. It’ll take a serious proof spanning regulatory impact, auditability, resilience, and total cost planning in order to convince large organizations to migrate away from well-established cloud infrastructure. More realistically, such clouds will be evaluated on a per-workload basis.

Compliance and transparency

When evaluating neoclouds, compliance is top of mind for enterprise executives. “Compliance posture such as SOC 2, ISO 27001, and sovereign-specific certifications like FedRAMP or IRAP is the first filter and, for most neoclouds, an early exit,” says Soin.

Smartsheet runs AI on enterprise customer data and therefore requires strict compliance and sovereignty frameworks. “That defines a hard boundary where regulated workloads stay with certified providers,” he adds. “Workload identity doesn’t federate natively across clouds, so every trust relationship must be hand-built and audited. For a platform where data sensitivity varies enormously, that governance gap isn’t theoretical.”

Gibbons adds that trust validation is becoming a pressing requirement from regulators and the overall market. “The uncomfortable truth is that most organizations have operated on deferred risk captured in registers, softened in board narratives, and tolerated due to limited visibility,” he says. The pattern of tumultuous changes in the AI market, he adds, is encouraging more of a resilience mindset among technical executives assessing new technologies.

Operational transparency is another pressing area to evaluate. For Soin, the infrastructure that supports AI workloads requires granular auditability. “Can the provider give you end-to-end observability across the inference layer: tamper-evident logs, model versioning, the ability to trace what happened to customer data from input to output?” he asks.

Smartsheet’s enterprise customers also ask how AI is applied to their data and what controls exist, and that answer has to be defensible. “A neocloud that can’t support it isn’t just an operations risk, it’s a risk to the customer trust our business is built on,” he says.

Per-workload evaluation

Although neoclouds are often framed as a replacement to hyperscalers, they’re more often complementary infrastructure, purpose-built for individual use cases. “A neocloud isn’t a type of cloud, but more a design principle,” says Gibbons. “We can think of it as infrastructure optimized for a specific workload, trust model or control requirement, rather than general-purpose scale.”

Their usage hinges on the project at hand, which typically involves AI/ML projects. “We work with a mix of alternative providers depending on workload fit,” says CloudBolt’s Campos. “We’ve used alternative cloud providers for VMware as part of a cloud migration strategy.” Their customers use alternative clouds for object storage, edge and low-latency needs, AI/ML GPU capacity, and legacy virtualization workloads that don’t map cleanly to hyperscaler-native services.

Executives must determine which environment makes sense for the workload at hand, and act accordingly. “CIOs should begin by clarifying the specific workload opportunity an alternative cloud is meant to accelerate, then evaluate providers against developer and operator experience inclusive of ability to scale with business need,” adds Campos. “From there, the priorities are whether the platform can be governed and operated alongside existing clouds, with strong visibility into usage and unit economics. Security, compliance posture, and credible SLAs for reliability and performance should be evaluated at the same bar as hyperscalers. The goal is to add flexibility without creating a new operational silo.”

Not all workloads are a fit for neoclouds or alternative providers, however. While these platforms benefit numerous workload-specific options, they’re not viewed as wholesale replacements for hyperscalers. For general-purpose enterprise functions, hyperscalers may still retain advantages around integrated services, discounts, operational maturity, and the availability of relevant job skills.

For this reason, many technology leaders are still in an exploratory phase. “Our infrastructure today runs on hyperscalers, and that’s been the right choice for where we’ve been,” says Smit Shanker, global CIO of Xebia, a global IT consulting and software engineering company. Still, he encourages CIOs to begin actively evaluating alternatives. “We’re now in that evaluation phase, looking at where specialized providers, sovereign options, or hybrid approaches make more sense for specific workloads.”

Shared responsibility model

CIOs require a high-degree of enterprise-grade trust that must be codified beyond empty promises and proven under real-world stress tests. As such, before diving headfirst into a newfangled cloud, Smartsheet’s Soin advocates verifying its shared responsibility model, which stipulates commitments the cloud provider has to uphold, and where responsibility lies with consumers.

For Soin, it’s especially critical to understand how a cloud provider acts when incidents occur. “Request post-incident reviews before signing anything, not templated RCAs but the real communication timeline,” he says. “Shared responsibility models are well-documented until they’re tested. Find out what happened the last time they were tested.”

Ultimately, alternative clouds need to meet the same enterprise maturity bar as established cloud providers. “Assurances must include auditability, compliance, resilience, transparent SLAs, interoperability, portability, and independently validated trust, not vendor assertions,” says NCC Group’s Gibbons.

Total cost analysis

Understanding the economic impact is necessary to determine whether an alternative cloud is worth it for the business. This is an area where alternative clouds shouldshine, with reports estimating savings of up to 70% compared with hyperscaler GPU instances. Traditional cloud computing pricing models are often opaque, and charges related to add-ons, upsells, and hidden fees around egress and ingress are notoriously known to surprise cloud computing consumers.

For certain workload categories, the economics can favor alternative clouds. “Traditional cloud providers often impose substantial data egress fees that inflate the total cost of ownership for data-heavy architectures,” says Dave McCarthy, VP of cloud and infrastructure services at global market intelligence and advisory firm IDC. “Alternative providers frequently minimize or eliminate these bandwidth penalties entirely, offering a more transparent cost structure,” he says.

Still, a full cost audit is necessary before going all-in on an alternative cloud. “What I’d recommend to anyone is do the full-cost model before you commit,” says Shanker. He suggests conducting a comprehensive analysis of cloud compute, inference, API volumes, and maintenance costs for things like infrastructure upgrades projected against the actual demand curve. When viewed together, he says, they create compounding costs that most C-suites haven’t modeled.

“Most enterprises are still modeling cloud economics on implementation costs alone, and that’s incomplete,” he adds. “The organizations getting AI right are the ones treating infrastructure economics as a first-class design decision, not a footnote.”

As cloud infrastructure choices can produce vendor lock-in, CIOs should use all the criteria at their disposal to plan and inform a neutral decision-making process. “Whatever path you choose — hyperscaler, alternative, sovereign, or some combination — the discipline that matters is vendor-agnostic, framework-led design,” adds Shanker. “Don’t let infrastructure choices constrain what’s possible.”

Where the market is heading

Neoclouds have made a mark for themselves by offering direct access to latest-generation hardware, which is important for AI-focused workloads requiring high performance. There’s also the argument that opting for alternative clouds aids a distributed multi-cloud strategy, which can benefit resilience and meet certain regional sovereign cloud mandates.

Nearly 90% of IT leaders say an organization should never commit to one cloud provider for their entire architecture, according to a HostingAdvice survey from last year. The same study found 73% of leaders use two or more alternative cloud providers, underscoring a clear interest in alternative clouds.

Still, while some industry reports are favorable, enterprise executives express more of a cautious reality of selective evaluation, not wholesale migration. For Soin, a top concern is the compliance posture and observability capabilities for alternative clouds, especially when hosting workloads that run AI inference. “The question isn’t just where inference happens but whether we can see what happened, explain it, and stand behind it,” he says. “That obligation shapes which providers are even in the conversation.”

While state-of-the-art cheap GPU compute is alluring, CIOs should keep an eye on the full governance picture when evaluating alternative clouds. “The neoclouds that earn more of our workload won’t just be the ones with the best GPUs,” says Soin. “They’ll be the ones that have seriously invested in model governance infrastructure and can demonstrate it. That’s a higher bar than most currently clear, but it’s where the market needs to go.”

A focus on inference

IDC forecasts that by next year, the computational demands of AI will force 80% of organizations to modernize legacy cloud environments by switching to platforms designed for AI workloads, with half of this being processed locally on endpoints or edge nodes by 2030.

“Enterprises are increasingly looking beyond major hyperscalers to avoid vendor lock-in and optimize specialized workloads,” says IDC’s McCarthy. “Rather than relying on a single, centralized infrastructure, organizations are adopting these alternatives into a broader portfolio.”

Much of this new computing paradigm will center on inference, or the post-training execution of AI models in production, using live customer or enterprise data. As inference moves into more business-critical workflows, decisions about where those workloads run, and what criteria guide those decisions, will have long-term implications.

Alt and neo clouds will likely have their place in inference, as well as in future sovereign and decentralized cloud models, and it’s not a decision CIOs should take lightly or without an informed evaluation framework. “Where workloads run becomes one of the most consequential decisions a leader makes,” says Shanker.

Over the last four years, artificial intelligence has captured the imagination of technology and business leaders worldwide, holding out the promise of revamped business models, new growth avenues and efficiency gains. In the past 12 months, however, enterprise conversations have undergone a sharp strategic pivot. As the initial AI hype cycle cools, corporate boards and CFOs are asking harder questions regarding implementation costs and bottom-line return on investment (ROI).

Given the scale of capital involved — with Gartner projecting global AI spending by enterprise and vendors to hit $2.59 trillion — the C-suite must take a rigorous look at how AI initiatives are scoped, budgeted and governed.

A recent survey of 500 senior US and UK finance leaders showed that 79% (4 out of 5) of large enterprises missed their AI budgets in the past 12 months. In another survey^, 85% of enterprises said that they missed AI forecast by greater than 10% and a quarter of them said that they missed it by 50% or more. These numbers are staggeringly high. The silver lining, however, is that 6% of large enterprises have reported an EBIT impact of greater than 5% driven by an enterprise-wide implementation of AI. This indicates that there is a clear path to ROI, provided one runs a disciplined, prudent and efficient governance model for all AI initiatives.

Simply put, an enterprise can increase the ROI on AI investments by addressing the two components of the equation appropriately:

1. Defining and amplifying the business value (numerator)
2. Defining and optimising the cost of AI implementation (denominator). 

Defining and amplifying business value

An enterprise must focus on chasing value, not chasing AI. The board has an important role to play here — it needs to reconsider whether it is asking the right questions. By aggressively pushing for an “AI everywhere” strategy, the board unwittingly shifts the focus to proliferation of AI tools and use cases rather than tangible business value. Pressure from the board incentivizes “tokenmaxxing” by the teams and legitimizes rampant use of AI tokens by those who suffer from AI FOMO. This severely compromises the returns on AI investments, which is later used to question the value of the technology itself.

A few steps an organization could take:

• Allocate costs appropriately: It would be prudent for organizations to keep AI training and access costs under a separate training budget while tracking ROI only for business-approved use cases. Track ROI only on costs linked to the use case.
• Avoid force-fitting: Enterprises should resist the temptation to force-fit AI into every solution, and instead apply it only when the business problem requires it. The board and CEO can take a significant step towards ROI simply by resisting FOMO, distinguishing signals from the noise and pushing back on hype while cautiously embracing AI as required.
• Define ‘kill’ criteria: If AI is deemed fit to solve or reimagine a business problem, it is critical to have clear termination criteria for AI pilots, with an alignment of the metrics to be tracked, their frequency, success or failure criteria and the termination process.
• Capture true value: It is imperative to ensure that business value generated by AI is correctly captured. For example, If AI leads to a 10% time saving to the sales team, that is equivalent to having an additional salesperson for every team of 10 salespeople. This should result in additional sales and this resultant value must be included as part of the value captured in the numerator of the ROI equation.

Defining and optimizing the cost of AI implementation

The reason most organizations exceed AI budgets is that budgets are still being calculated on a ‘per user’ basis. AI licensing cost per user does not give any indication of the tokens that the user will consume, which is where costs scale exponentially. Since token consumption is linked to the task the agent performs, linking AI budgets to the number and complexity of the tasks in a workflow would give a much clearer view of expected costs.

Cost estimates must also account for realities of production environments which are typically missed out in sanitized test environments, including poor-quality long prompts, hallucinations, retry loops, network latency, employee ‘bad’ behaviour trying to ‘break’ the AI tool, etc. Cost estimates must make reasonable assumptions about a combination of these elements happening all the time, which will make token costs more realistic and higher than in more sanitized, non-production Test environments.

Costs can be further optimized in three more layers:

Optimizing at the prompt layer

It makes little sense to upload five voluminous PDF documents for three queries that a user may have and then get the LLM to process all of it when the relevant information might be in 20% of the data uploaded. Instead, chunking the data, vectorizing it with model embeddings and storing it in a vector database will make model usage a lot more effective, with reduced latency, increased retrieval accuracy and reduced cost. This kind of prompt pruning can be very useful. Prompt caching can also be considered if, via extensive user training, users can be trained to give a standardized prompt.

Alternatively, the enterprise could keep a middleware layer between the user and the LLM, which will append every prompt from every user into a standardized format for the LLM to process. By caching the response, the user can access a stored response rather than pinging the LLM for a re-computation on a prompt that has been asked earlier. Large language model providers provide up to an 80% discount on cached responses. Semantic caching, again achieved through vectorization, will enable the LLM to understand the intent of a prompt rather than look for an exact match, which can again reduce token costs significantly.

Optimizing model usage

Likely, a very large percentage of prompts in an organization do not need a frontier LLM to process them. A mid-tier model which comes at a significantly lower cost can deliver the desired output. This is where model cascading can play a critical role in reducing costs. Proposed by three Stanford researchers, it demonstrates an AI framework with model cascading at its core. By classifying prompts based on complexity, understanding model appropriateness for each prompt, caching responses and by assigning each prompt to the most appropriate model optimized for the trade-off between cost, accuracy and latency, they demonstrated significant savings in AI implementation costs.

Optimizing at the architecture layer

To avoid runaway AI bills, it is important for the CIO to embed cost controls within the architecture. As an example, the technology team can embed hard token caps for workloads, which are activated if token consumption is found to be much higher than anticipated for a given workflow or velocity circuit breakers if there are sudden spikes in token consumption, respectively. In addition, it is possible to embed a lot of the above-mentioned strategies (at the prompt and model usage layer) into the AI architecture, thereby paving the way for architectural FinOps, as distinct from the legacy FinOps that most enterprises have today. As an example, a middleware layer can help in prompt pruning, response caching and model cascading.

Many enterprises also change the architecture by moving the inferencing to an ‘on-premises’ environment to avoid incurring API and cloud costs for every prompt execution.

By incorporating all of it as part of the architecture, the CIO has greater visibility and ability to control costs in near real time.

In summary, technology leaders can drive ROI through a mix of sound business judgment, user awareness and training, and tighter control on processes and architecture.

Last month I made the case for an ROI exchange rate: the formula you negotiate with finance before deployment that converts KPI movement into dollars. One point of first-call resolution equals this many dollars. One hour of engineering time recovered equals that many.

A common objection was a version of the same question. Fine, we agreed on what the benefit is worth. What did it cost?

That column is empty at most companies. Not “roughly known” or “we’re working on it.” Empty. And an empty denominator makes the numerator useless. You can prove the KPI moved and still lose the argument, because the CFO is not funding improvements; she is funding improvements that cost less than they return.

Which brings me to the most-discussed AI budget story of the year, and why I think almost everyone drew the wrong lesson from it.

What Uber actually ran out of

We owe Uber some thanks for being this transparent. They rolled out Claude Code in December 2025. By February, 32% of engineers were on agentic coding tools; by March, 84%. Somewhere in there, the company burned through its entire 2026 AI budget in four months, a number CTO Praveen Neppalli Naga confirmed to The Information in April. In June, Bloomberg reported the response: a hard cap of $1,500 per employee per month, per tool.

Simon Willison called the cap rational, and he is right. Given a budget set before agentic coding existed, a ceiling was the correct emergency move. I would have done the same thing.

But look at what Uber’s COO said when asked whether the spending was working. Andrew Macdonald, on the Rapid Response podcast: “It’s very hard to draw a line between one of those stats and ‘OK, now we’re actually producing like 25% more useful consumer features.'”

That is not a cost complaint. That is a measurement complaint. Uber did not cap spending because tokens are expensive. It capped spending because it could not price what the tokens were buying, and you cannot defend a number you cannot connect to anything. The cap is what you reach for when the exchange rate column is empty.

Here is the part that should worry you more than the budget overrun. Software development is the best-instrumented workflow in the enterprise. Pull requests, cycle time, deploy frequency, escaped defects. It is the one function that already has the scoreboard I spent last month telling everyone to build. Uber had all of it. And cost per task still was not on the board.

If it is missing there, it is missing everywhere.

Two levers, one of them untouched

Look at how the industry has responded to token maxing: quotas, per-seat caps, model downgrades, approval gates, dashboards. Every one of those controls how many passes you make.

Not one of them touches what a pass costs.

That is the whole argument. There are two levers, and the industry has been yanking on one of them for six months while treating the other as if it does not exist.

The compounding nobody budgets for

In a naive agent loop, the full conversation history gets re-serialized and re-injected at every step. Message history grows linearly. Billed input tokens grow quadratically.

Run a modest 20-step loop that adds 1,000 tokens of history per step. Multiply 20 by 1,000 and you get 20,000, which is the number most people carry in their heads. The actual billed input is 210,000, because step 19 pays for everything steps 1 through 18 said.

Longtime readers will recognize the shape. In June, I argued that AI cloud strategy is a physics problem, because 50 milliseconds of cross-region latency does not cost you 50 milliseconds; it costs you 50 milliseconds times every hop in the loop. Same structure, different axis. Trim 800K of junk off one call and you save 80% of one call. Trim it off every hop and you save 80% of a quadratic.

“But we have prompt caching”

Every major provider now discounts re-sent prefix tokens. Anthropic bills cache reads at 0.1x standard input, with a 1.25x premium on the write. OpenAI’s newer models landed on the same 0.1x multiplier. Google’s implicit caching runs about 75% off.

So, does caching kill the argument? Run it and see. Same loop, reads at 0.1x, writes at 1.25x:

Caching takes 79% off the uncached bill. It is the single largest cost lever available and every team reading this should be using it. It also leaves you at 2.2 times the number you had in your head. Caching flattens the quadratic; it does not remove it entirely and it comes with a caveat.

Caches are keyed on the exact prefix, so any change at the front invalidates everything behind it. If your retrieval pipeline injects freshly selected chunks near the top of the context each turn, you are not just paying for tokens that did not earn their place; you are breaking the cache for every stable token that follows. Model a 25% prefix-break rate on that same loop and 44,000 effective tokens become 90,000.

Order the prompt stable to variable: system prompt, tool definitions, long-lived context, then the current turn. Then measure the hit rate. That is an afternoon of work, and it protects your best cost lever.

The part that makes this more than a FinOps memo

Usually cost reduction costs you quality. You buy the cheaper thing, and you get the cheaper thing.

Not here. The low-relevance context you are paying to send is the same context degrading the answer. This is the load-bearing claim in the piece and there is a lot of vendor handwaving in this space, so here are two peer-reviewed sources, none of them selling retrieval infrastructure.

The canonical result is Liu et al., “Lost in the Middle” (TACL, 2024): accuracy follows a U-shaped curve across the window, strong at the edges and weak in the middle, and it drops as input grows even on models built for long context.

The one that surprised me is Du et al., “Context Length Alone Hurts LLM Performance Despite Perfect Retrieval”(Findings of EMNLP, 2025). The authors held retrieval at perfect and grew the input anyway. Accuracy still fell, 13.9% to 85% depending on model and task, well inside advertised limits. It held when the filler was whitespace. It held when the irrelevant tokens were masked out entirely. Most of the damage landed inside the first 7K.

The tokens you did not need are not free even when they are cached. They are diluting the reasoning you are paying for. Cheaper and more accurate, same move. That is not a trade-off; it is a mispricing.

The question becomes: of the million tokens I could send, which 200K earn their place?

That is not a procurement question or a policy question. It is a retrieval question, answered out of your RAG pipeline or your agentic memory. And unlike a spending cap, it has an engineering answer.

Six ways to pick the right 200K

Cheapest to hardest, with the business consequence next to each, because a CIO who will never touch a chunking strategy still needs to know what skipping it costs.

1. Filter before you search. Metadata and scope narrowing costs nothing and kills most irrelevant candidates before semantic search runs. You are paying a similarity search to rediscover what you already knew.
2. Rerank, don’t just retrieve. Vector search optimizes recall; at the point of injection, you need precision. Without it, precision is whatever your embedding model happened to give you.
3. Chunk on meaning, not character count. Fixed-size chunking splits reasoning that needed to stay together. You pay 3x for one idea and the model sees it in pieces.
4. Compact, don’t accumulate. Summarize prior turns instead of re-sending raw transcript. This is the only item that changes the shape of the curve.
5. Dedupe across turns. Agent loops re-retrieve the same chunks repeatedly and almost nobody measures it. You are paying several times per session for identical text.
6. Know when to stop. At what point does the next chunk stop paying for itself, in dollars and in dilution? No answer means you do not have a retrieval strategy; you have a default.

One, two and five are checkable this week without a budget cycle.

The number you take back to finance

Number six deserves pulling out of the list, because it is the only one that produces a figure rather than an improvement.

Cost per task. Not cost per token, which measures your vendor’s pricing. Not cost per seat, which measures your headcount. Cost per resolved ticket, per merged pull request, per closed claim. The denominator under the exchange rate. The other five are how you improve it; this one is how you report it, and it is what lets you argue the bill up when it is earning. No company under a blanket cap can have that conversation.

Objections, and one honest caveat

“Context windows keep growing and models keep getting better at using them.” Both true, and neither makes paying for irrelevant tokens rational. The Du result suggests length itself carries a cost that capability gains have not erased, and the compounding is structural: it gets worse as agents get more autonomous, not better.

“We already do RAG.” A pipeline with fixed-size chunks, no reranker and no eval is tokenmaxxing with extra steps.

“Caps worked for Uber.” They controlled the budget, which was the immediate problem. Ask what they did to cost per shipped feature, and whether your best engineers are now the ones rationing hardest.

The caveat, because I would rather say it than have it said to me: some workloads genuinely want the whole document in the window. Long-form legal review, whole-codebase refactors, anything where relationships between distant sections are the point.

The ledger

A spending cap controls the bill. It is the right emergency move and a permanent admission that you never built the measurement.

Cost per task is the other column. It tells you the difference between an expensive workload and a wasteful one, and those are not the same thing.

Context length is the lever you do not control. Cost per pass is the one you do.

Artificial intelligence may prove to be one of the most transformative technologies in human history. But amid the excitement over smarter models, autonomous agents, enormous data centers and seemingly unlimited computational power, we may be overlooking a much simpler question:

Does AI create more value than it costs?

My position is that the ultimate constraint on artificial intelligence may not be chips, algorithms, data or even electricity. It may be economics.

We are becoming extraordinarily good at producing machine intelligence. We are far less capable of measuring what that intelligence is actually worth.

And that gap could become one of the defining economic problems of the AI era.

We are building factories for intelligence

AI is usually described as software. Increasingly, that description is misleading.

Behind every AI prompt is an enormous physical industrial system: semiconductors, electrical generation, transmission networks, data centers, cooling systems, storage, telecommunications, software and people.

AI mega-data centers are, in effect, the factories of the Intelligence Economy. Instead of turning steel into automobiles, they turn electricity and computation into predictions, recommendations, decisions, software, images, knowledge and other forms of machine-generated intelligence.

This changes the economics of computing.

Intelligence now has a cost of production.

And unlike the Internet services we became accustomed to thinking of as almost weightless, AI consumes very tangible resources: capital, land, electricity, water, chips, networks and increasingly scarce technical talent.

That is why the enormous investment in AI infrastructure matters. We aren’t simply financing another generation of software. We are financing the industrialization of intelligence.

The AI infrastructure paradox

This leads to what I call the AI infrastructure paradox.

The world is committing extraordinary amounts of capital and physical resources to producing intelligence before we have developed equally sophisticated ways of measuring the economic value of the intelligence being produced.

We can measure GPU utilization.

We can count tokens.

We can measure power consumption.

We can count AI users, agents, models and applications.

We can calculate cloud costs almost to the penny.

But ask a corporation a different question — exactly how much incremental economic value did your AI produce? — and the answer frequently becomes much less precise.

That is a serious problem.

Activity is not value.

A company processing billions of AI prompts is not necessarily creating more economic value than one processing a few million. Deploying thousands of copilots or agents tells us that AI is being used. It doesn’t tell us whether the company has become proportionately more productive, profitable, innovative or competitive.

We are becoming extraordinarily sophisticated at measuring the consumption of intelligence while remaining surprisingly primitive at measuring its economic productivity.

Someone ultimately has to pay

There is another side of the AI boom that receives far less attention: affordability.

Data centers cost money. Chips cost money. Electricity costs money. Water, networks, cybersecurity, governance, data engineering and specialized talent all cost money.

Those costs cannot simply disappear.

Ultimately, they must be absorbed somewhere — by consumers through higher prices, enterprises through higher technology spending, shareholders through lower margins, or governments and taxpayers through subsidies and infrastructure investment.

This is where AI encounters an economic constraint that no increase in model intelligence can eliminate.

If the cost of intelligence grows faster than the value of intelligence, eventually something has to give.

This also challenges one of the most persistent assumptions of the technology industry: that technology inevitably gets cheaper.

Individual units of computing may indeed become cheaper. But organizations can still spend more because they consume vastly more computing, cloud capacity, cybersecurity, software, data and AI.

I call this phenomenon IT Inflation.

Technology can simultaneously become more efficient and more expensive.

The important economic question, therefore, isn’t whether the cost of a token, GPU operation or gigabyte falls. It is whether the economic value produced by technology grows faster than the total cost of consuming it.

The community eventually gets a vote

The physical scale of AI creates another economic problem.

The costs and benefits of AI infrastructure frequently occur in different places.

A community hosting a massive data center sees the land use, transmission lines, water consumption, electricity requirements and public incentives directly.

But much of the economic value may flow elsewhere — to technology companies, cloud providers, software developers, users and shareholders around the world.

That creates a reasonable local question:

If we absorb the costs, where is our share of the value?

As AI infrastructure expands, communities will increasingly demand answers. Data centers will require more than construction permits and electrical connections. They will increasingly require what might be called a social license to operate — evidence that the economic and societal benefits justify the resources being consumed.

The great measurement failure

This brings us to what may be AI’s biggest weakness.

It isn’t intelligence.

It is measurement.

Traditional accounting was developed for an economy built primarily around physical assets, labor and financial capital. It tells us remarkably well what organizations spend and what they earn.

But technology has evolved from a supporting function into something closer to a factor of production.

Like labor, technology performs work.

Like capital, it requires investment.

Like infrastructure, it enables economic activity.

AI takes this one step further because organizations can increasingly acquire and consume machine intelligence almost as previous generations consumed electricity.

Yet our accounting and economic measurement systems have not caught up.

That is why I believe we need a discipline of technology economics.

From technology management to technology economics

Technology economics asks a different set of questions from traditional IT management.

Not simply: Does the system work?

But: What economic value does it create?

Not simply: How much AI are we using?

But: How productive is that AI?

Not simply: How much are we investing?

But: What return are we receiving relative to the capital consumed?

The framework I propose includes measures such as IT intensity, IT inflation, technology cost of capital, AI expense per share, AI value per share, margin per unit of IT intensity, the technology leadership index and gross domestic intelligence.

Consider just two of these.

AI expense per share asks shareholders to view AI investment in familiar economic terms: how much AI expenditure is effectively being borne per share?

AI value per share asks the other half of the equation: how much measurable value is AI generating for those shareholders?

Similarly, margin per unit of IT intensity asks whether an organization is efficiently converting its technology dependency into economic performance.

Two companies can spend similar amounts on AI and produce dramatically different results. The winner shouldn’t be the company that consumes the most intelligence. It should be the company that converts intelligence into the most value.

AI needs its own measurement revolution

Every major economic transformation eventually produced new ways of measuring itself.

Industrialization gave us modern accounting.

Mass production drove the development of productivity measurement.

Global finance produced increasingly sophisticated theories of capital, risk and return.

The Intelligence Economy will need its own measurement revolution.

Companies may eventually need something resembling a Technology Balance Sheet showing their productive technology assets and obligations, and a Technology Income Statement connecting technology expenditures with the economic value those expenditures generate.

Investors will increasingly expect companies to disclose not merely how much they are spending on AI, but what they are receiving in return.

Technology executives, consequently, will need to become economic leaders as well as technical leaders. Boards will increasingly expect CIOs and other technology leaders to explain affordability, productivity and value — not simply reliability, security, modernization and innovation.

The same problem exists at the national level.

GDP measures economic activity remarkably well, but it was never designed to measure intelligence as a productive resource.

That suggests the need for complementary concepts such as gross domestic intelligence: an attempt to understand the intelligence capacity that increasingly enables economic activity.

Whether that particular measure ultimately becomes standard is less important than the underlying idea.

If intelligence has become an economic resource, eventually we will have to measure it as one.

The question that comes after the hype

None of this argues that AI will fail.

Quite the opposite.

AI may transform productivity, science, medicine, education, finance, manufacturing and almost every other significant area of human activity.

But technological capability does not repeal economics.

The railroad was transformative. So was electricity. So were telecommunications and the Internet. Each also experienced periods when infrastructure investment ran ahead of sustainable economic returns.

AI may follow a similar pattern.

The critical divide may therefore not be between companies that adopt AI and companies that don’t.

It may be between those that understand the economics of intelligence and those that simply consume more of it.

We already know how to make machines increasingly intelligent.

The next challenge is learning how to make that intelligence economically productive, affordable and sustainable.

Communities will want evidence of societal value. Investors will want evidence of shareholder value. Boards will want evidence of business value. Governments will want evidence of national value.

All of those demands eventually converge on one question:

What is intelligence worth?

The industrial revolution created modern accounting. The information age created digital technology management. The Intelligence Economy may require technology economics.

And the most important innovation of the AI era may ultimately turn out not to be another model, another chip or another trillion-dollar generation of data centers.

It may be the measurement system that finally allows us to answer the question behind all of them:

Does the value created by intelligence exceed the cost required to produce it?

Everything else follows from the answer.

IT modernization remains a key pillar of CIOs’ strategic agendas, and even CEOs are coming around to the value that addressing legacy systems has in fueling transformation and innovation.

Yet a solid majority of application and infrastructure modernization projects are failing to meet expectations, with many IT leaders having to cut IT corners to divert money for AI, in addition to all the traditional problems that plague them.

Sixty-one percent of organizations have had to pause, delay, scale back, or fully abandon IT modernization initiatives in the past 24 months, according to a recent survey from managed services provider Ensono.

Major IT projects fail for a variety of reasons, including poor project management and a lack of clear metrics, but tight budgets and cost overruns create tangible impediments. More than seven in 10 respondents to Ensono’s survey say their modernization initiatives have exceeded the original budgets, with 27% exceeding budgets by 51% or more.

The failure rate is often due to a combination of factors, but IT budget diversion to AI projects is a major challenge for IT leaders, says Brian Klingbeil, chief strategy officer at Ensono.

“People are redeploying spend toward AI projects,” he says. “There’s a lot of pressure to keep up with everyone else.”

Reallocated budgets

Many organizations are stuck in long-term IT modernization traps, such as scope creep, while facing declining budgets for traditional IT upkeep due to AI.

“In what I see, scope creep contributes the most, and it is a discovery problem more than a governance one, because the organization learns what the legacy estate actually does at the moment it starts to move it,” says Michele Grant, executive global director at Cognizant, of IT modernization delays and failures.

“But AI budgets are a real competitor for the same money, and over the past two years I have watched modernization funding reallocated toward AI initiatives that promise a faster visible return,” she says.A recent report from digital adoption platform provider WalkMe found that, while digital transformation budgets grew from an average of $39.4 million to $54.2 million in a single year, most of that spending went to AI-related projects, says KJ Kusch, global field CTO there.

“I wouldn’t call it scope creep,” she adds. “It was a deliberate move.”

Kusch also sees adoption problems contributing to poor IT modernization outcomes when newer IT systems are too complicated for employees to use. “Users experience too much friction, so they go back to the ways they worked before,” she says.

The irony, Ensono’s Klingbeil says, is that AI can help accelerate IT modernization projects by performing tasks such as code discovery and cleaning up legacy code. Most organizations haven’t tried it yet, but using AI tools can significantly cut the costs and timelines of IT modernization, he adds.

The irony, Ensono’s Klingbeil says, is that AI can help accelerate IT modernization projects by performing tasks such as code discovery and cleaning up legacy code. Most organizations haven’t tried it yet, but using AI tools can significantly cut the costs and timelines for IT modernization, he adds.

“Two years ago, if you had a legacy platform you weren’t happy with, for whatever reason, choice A was to invest $150 million and five years of your life and maybe get lucky and pull it off,” he says. “Choice B was suck it up and live with it, and there was no choice C.”

The price of misalignment

Another factor that can sink an IT modernization initiative relates to organizational outcome and cost misalignment, Cognizant’s Grant says.

“Modernization is approved as a technology program and then asked to carry a business outcome that requires process and organizational change, and the cost of that change was never inside the original number,” she says.

It doesn’t surprise Grant that 61% of organizations in the survey had to pause, scale back, or scrap IT modernization programs. A pause is sometimes the right choice, she says.

“When the business case was written against an operating model that has since changed, continuing to spend against that case is the more expensive choice,” she explains. “What I would look at in that 61% is not the pausing. It is that the organizations I see pause without naming the condition that would let them resume, and a program that stops with no restart condition turns into spend that nobody owns.”

A good way to control IT modernization costs is to fund the program in phases tied to named outcomes and give one executive the authority to stop the work, Grant recommends.

“A program that can only be slowed by consensus is never slowed; it is extended,” she adds.

AI needs modernization

Even as AI initiatives take spending away from modernization efforts, the two transformation priorities are closely linked.

Stu Bradley, senior VP of risk, fraud, and compliance solutions at analytics and data management vendor SAS, emphasizes that IT modernization leads to better AI outcomes.

“Seeing AI and modernization as competing budget lines is a bit of a trap,” he says. “In reality, AI and modernization are increasingly two sides of the same coin. Many organizations are discovering that the AI capabilities they want depend on investments in data, integration, infrastructure, and governance that they’ve deferred for years.”

Recent SAS research found that only 17.5% of organizations have data infrastructure fully optimized to support agentic AI readiness, he notes.

“Rather than argue over competing budgets, leaders need to understand which modernization investments are prerequisites for the AI outcomes they’re aiming to achieve and fund them as part of the same business case,” he adds.

It also doesn’t surprise Bradley that most IT modernization pause or fail. “When you consider the pace of technology and how quickly priorities are shifting around AI, I’d frankly be more surprised if organizations weren’t regularly reevaluating their transformation projects,” he says.

But pauses or delays in project aren’t always failures, he notes. The priority during a pause should be identifying and addressing the issues that stalled the project, he recommends.

“CIOs should be willing to reevaluate initiatives rather than let momentum blindly drive continued investment,” he says. “Where organizations get into trouble is simply bumping the project into the next budget cycle hoping that somehow conditions will be different.”

See also:

• 6 strategies for accelerating IT modernization
• 8 IT modernization traps CIOs must avoid
• Massive modernization: Tips for overhauling IT at scale