With hundreds of AI-related companies increasingly crowding the market for enterprise dollars, IT leaders should prepare for the day when at least one of their AI vendors suddenly shutters or gets acquired.

While the AI market overall is showing few signs of slowing down, a shakeout will eventually happen — with potentially significant ramifications for CIOs. IT leaders only need to be reminded of Broadcom’s acquisition of virtualization giant VMware to visualize what could be at stake.

CIOs, of course, are not unfamiliar with the many issues that arise when an IT vendor shuts down or is acquired. However, suddenly losing an AI vendor creates some special challenges, according to industry experts, including data access and privacy concerns and the loss of AI models trained for specific customer needs.

“Assume your AI vendor will get bought or go under, and plan for it now,” says Dave Shanker, cofounder and managing partner at IT consulting firm STS Consulting Group. “Hundreds of these companies launched in three years, and most won’t last. The ones that do will get acquired, and then pricing, terms, and roadmaps can change overnight.”

Set your exit strategy up front

For now, the demand for AI products seems to be moving ahead at full steam. In May, Gartner projected that the worldwide AI market would grow by 47% in 2026, totaling nearly $2.6 trillion.

But many insiders, including Amazon founder and CEO Jeff Bezos and OpenAI CEO Sam Altman, have projected that the AI bubble will eventually burst, with many AI startups buried in the rubble.

One warning sign came in mid-October, when consumer-focused Character.AI filed for bankruptcy, with the startup agreeing to sell some of its assets to Disney for $20 million.

With many vendors likely to go extinct in a few years, it’s incumbent on CIOs to plan ahead for potential market disruptions, Shanker says.

He recommends that IT leaders ensure that their organizations own the data they’re putting into AI models. The organization’s prompts, outputs, fine-tuning data, and anything it has loaded into an AI tool should be exportable in a usable format.

In addition, IT leaders should write the potential exit from an AI vendor into the contract from day one, with notice periods, data return and deletion terms, and the right to leave without a penalty if the vendor is acquired built into the deal.

“Procurement should build this into every contract, AI vendor or not,” he adds. “If they aren’t, that’s a big risk they’re missing.”

IT leaders should also weigh portability against cost, he says. “Sometimes it makes sense to build tightly on one platform, because adding a layer that keeps you portable can double or triple what it costs to run,” he explains. “Redo the analysis at least once a year. Something that’s easy to move today can, after three years of heavy development, leave you stuck.”

AI-specific concerns complicate traditional vendor risk

Rodion Sorokin, CTO of deep-tech venture studio The Capital Index, agrees that CIOs need to prepare for an eventual market shakeup. Sorokin points to information from company shutdown assistance provider SimpleClosure, which noted that AI companies are currently a small and shrinking percentage of the types of closures they handle.

However, the median age at closure is lower for AI companies: 2.30 years versus 3.78 for other companies, according to SimpleClosure. 

“We hypothesize that the 2023-2025 AI cohort is still in its early days, and we’re only beginning to see how it plays out,” SimpleClosure CEO Dori Yona wrote in a blog post,

Even if the early warning signs are few and far between, CIOs should be prepared to quickly switch vendors, Sorokin says. Not only should IT leaders be able to export prompts, outputs, logs, fine-tuning data, and other information in usable formats as STS Consulting’s Shanker advises, but they should also test those processes before they sign a contract with any AI vendor, he recommends.

“Assume every AI vendor will be acquired or shut down within your contract term, and negotiate for that day,” he says.

CIOs should also be aware of specific concerns that pertain to the sudden loss of an AI vendor, Sorokin says. For example, their organization’s data and customizations may live within the vendor’s product, and it’s possible all that gets deleted if the vendor goes under or is acquired, he notes.

In addition, switching AI tools can render AI-aided workflows suddenly inoperable, he says.

“Switching isn’t a migration; it’s a revalidation,” Sorokin says. “A new model behaves differently on the same prompts, so every workflow has to be retested. Without your own evaluation suite, you can’t switch safely.”

CIOs should also know that notice can be measured in days, he adds. In June 2025, Anthropic cut Windsurf’s direct access to its Claude models with less than five days of notice, amid reports of an OpenAI acquisition of the smaller company.

While some observers say there may be safety in going with the big players in the AI space, others suggest startups may offer services that aren’t available from the big boys. In addition, large AI vendors have also cancelled services on short notice, Sorokin notes, with OpenAI shutting down its Sora app in April and the Sora API in September.

“Size helps, but it isn’t continuity,” he says. “Choose portability over logos: an open-weight model you can run yourself as a fallback, and contracts that guarantee export.”

IT leaders exploring options

While signs of a busting bubble are hard to find, many IT leaders are preparing for the future, says Colin Woods, CEO and co-founder of high-performance infrastructure provider FreedomTechSolutions.

A growing number of IT leaders are beginning to reassess the level of dependency they are willing to place on AI platforms and cloud-hosted services, he notes.

“From our perspective, concerns around AI vendor consolidation are already influencing customer decision-making,” Woods explains. “While the rapid growth of the AI market has driven innovation, it has also created a crowded ecosystem of providers, many of whom face uncertain long-term viability.”

In some cases, IT leaders are moving away from hyperscalers for certain AI workloads, with a renewed focus on infrastructure ownership, data sovereignty, and operational control, he says.

“Organizations are increasingly concerned about where their data resides, how it is processed, and what happens if a vendor is acquired, changes its commercial model, or discontinues a service altogether,” he adds. “For many businesses, AI is becoming too strategic a capability to be entirely dependent on an external provider whose roadmap they do not control.”

Some organizations are deploying smaller, dedicated AI environments in regional or edge data centers, Woods says. “These AI nodes allow organizations to run inference and specialized workloads closer to their data, while retaining full governance over models, datasets, and infrastructure,” he 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.”

The first wave of enterprise AI deployments was straightforward: a chat interface calling a model, wrapped around a few clear use cases. The next wave is driven by autonomous agents that maintain state, revisit context, call tools and act under real permissions. This means enterprise AI teams must design inference, compute and deployment so those agents stay affordable, governable and compliant.

CIOs are used to making hard calls whenever the computing paradigm shifts. The move to client/server, web-based services and the cloud all required bets on platforms and architectures that shaped IT for years. The shift to AI feels similar, but the choices are more complicated, harder to see and harder to come back from. And the decision most tech leaders focus on first — which model to use — matters less in the long run than a set of less obvious but critical choices.

More tokens, more authority, more concerns

In the chatbot era, most organizations were token consumers. A user sent a prompt, the system generated a response and the interaction ended. Cost per interaction was bounded and often small enough to vanish into a cloud bill.

Agents do not stop at a single request‑response. They break work into steps, call tools as needed and keep going until a goal is reached. That behavior can drive significantly more inference traffic than a traditional chatbot, especially as the organization moves AI workloads from pilot to production. Over time, that shift turns enterprises from occasional token consumers into regular token producers, making inference cost a first-class strategic concern rather than a background line item.

At the same time, the risk surface is growing, to the point that some are warning of an existential threat and are calling for a slowdown in AI development. Right now, agents have ready access to your data and can act, not just read. They can call internal tools, move money, change records or trigger workflows at machine speed. They can also make mistakes at the same speed, especially when guardrails are not clear (or don’t exist). That combination of more tokens and more authority makes organizations understandably reluctant to stream corporate data into third‑party tools when they are uncertain where that data is processed, how long it is retained or how it might be reused.

Two years ago, those concerns were easy to dismiss as premature or even insignificant. Today, ignoring them creates deployment friction: security reviews that stall, compliance questions that are hard to answer and stakeholders who are not comfortable handing their most sensitive data and systems to black‑box services. AI also accelerates adversarial threats by dramatically shrinking the window between vulnerability discovery and exploitation.

All of this makes inference design critical — and challenging, since organizations may not have paid attention to it before. Inference is the operational phase of AI — the point where a trained model is actually run on real prompts and data to produce answers. It’s also where tokens are consumed and billed and where access controls and logging have to work. The way you build your inference layer determines whether you can see, control and cap token usage. It also determines whether identity and policy are enforced consistently or whether each application bakes its own security into custom code.

Once you have an inference layer that you can observe and control, the next structural question is the model layer itself — or, better yet, the portfolio of models the organization will rely on. The choice of model is the one that many teams started with, assuming the model was the whole story. Now it becomes one layer in a stack you can change rather than a decision you are stuck with.

In fact, many organizations thought — and often still think — that it is best to pick a single “frontier” model and treat it as the standard. However, AI’s future is a mixture of proprietary, open source and domain‑specific models. To maximize cost, performance and control, AI models must map to the right use case (and not vice versa). Just as there is not one single environment powering enterprise IT, there is not one single model that fits every workload. For example, smaller, open-source models are ideal for solving smaller or niche problems, where a frontier model’s power and scale would be wasted.

Maintaining in‑house models “as a service” enables fine‑tuning on proprietary data while minimizing the risk of leaks. A consistent abstraction layer or platform helps shift workloads among models as business needs dictate, without forcing every application team to rewrite prompts and pipelines. Otherwise, a choice made for one proof of concept can lock an entire organization into a single provider’s roadmap.

AI compute needs democratization and customization

For agents, flexibility only pays off if the underlying compute can flex as well; the hardware you choose determines both how far you can scale and what your token economics look like.

GPUs and other AI accelerators are the most visible line item in many AI budgets. It is easy to assume more is always better, but 10 percent utilization means burning money while 100 percent means no room to grow.  Flexibility in compute provides resilience against hardware shortages and supply‑chain concerns, without being fully shackled to a single architecture. Put another way, you do not need a wall of top‑shelf GPUs to run a customer chatbot, but you do need serious horsepower for something like drug discovery. The right answer depends on knowing what workloads you are actually running and sizing to that, not buying everything in sight and hoping it fits.

AI must run anywhere

Once you have a handle on accelerators and utilization, the next structural choice is location: which environments you trust to run AI workloads and under what constraints.

“Sovereign AI” can sound like someone else’s problem — European regulators, highly regulated industries and so on. Strip away the labels and it comes down to three very familiar concerns:

• Control over where and how data is processed
• Transparency through audit trails and data lineage, even when models are black boxes
• Flexibility to adapt when, for example, a regulation changes or a provider goes out of business

Successful IT strategies, including AI, need a single, consistent operational experience across the hybrid cloud, from the edge to hyperscaler environments. AI workloads need to reside closest to where the data is born, whether a factory floor, a sovereign data center, or a public cloud instance. Sovereign AI strategies demand regional and country‑specific boundaries to data ingestion and training, meaning location is as important as any other factor in AI.

Some organizations are already building AI “factories” or fabrics: repeatable patterns, shared tooling and governance that let teams create AI applications in a consistent, auditable way, often with sovereign deployment as a first‑class requirement. Without that kind of fabric, you are just turning up the money dial on a single service when you need more scale or new features.

Platforms built on choice

To make choice structural rather than aspirational, it helps to think in terms of two pillars that sit across these decisions: one for inference and one for agents.

• Inference pillar. The inference layer should be the place where you measure and manage token usage. That means visibility into tokens consumed per application and per agent, the ability to forecast and cap spend and the power to route requests across different models without changing how applications talk to the platform. Identity and policy enforcement live here, as well, so that switching models does not mean rebuilding access control and governance from scratch.
• Agent pillar. Agents should be treated like extremely powerful users. Decide up front what agents are allowed to do, what tools and systems they can touch and when they can step out of the sandbox. A controlled runtime defines how long they can run, what they can modify and how their actions are logged. Permissions, time limits and audit trails are not afterthoughts; they are the mechanism that turns “autonomous” from a risk into a feature.

Together, these pillars turn choice into something you can enforce. You choose the models. You choose the environments in which they run. You choose the guardrails and policies that govern how agents behave. And you retain the freedom to run open source and commercial models on infrastructure you control.

Choice, sovereignty and security

In the end, this is about making choice structural: inference you can observe and control, a model portfolio you can change, compute you can rebalance and deployments you can move without rewriting everything. Sovereignty keeps those choices under your control, while security makes sure agents and inference follow the same governance as the rest of your stack, including the freedom to run open-source models on infrastructure you own alongside commercial models where they make sense. Open source strengthens both, because what can be inspected can be secured.

There are a few concrete steps tech leaders can take to surface the AI choices that really matter and then make them in ways that avoid tomorrow’s lock‑in:

• Treat PoCs as architecture, not just experiments. As soon as something works in a demo, assume it might go to production and capture what that implies: how data flows, which inference server you are using, which ports and services are exposed and what pipelines will have to change if this becomes real.
• Be deliberate about accelerators and utilization. Match GPUs and other accelerators to actual workloads so you are not burning money or leaving no room to scale.
• Pay attention to inference and model flexibility. Avoid locking yourself into an inference setup that forces you to rewrite prompts and datasets every time you change models. Understand whether the model weights you rely on are open or closed, because that determines how much control and flexibility you really have over model behavior and where it can run.
• Think in terms of transparency, control and an AI fabric. Design for where data is processed, how workloads are audited and how easily you can adapt to new rules by building repeatable patterns. Think “AI factory” or “AI fabric” rather than one‑off stacks tied to a single service.

The model(s) you start with will change. Agents will evolve. Regulations will tighten and change. What will matter is whether the environment you build around them makes those changes feel like routine upgrades — or full‑scale extractions. The winners in this next phase won’t be the fastest agent adopters; they’ll be the ones who make choices that enable deployment on infrastructure you control, as well as the freedom to pick models, environments and guardrails as conditions change.

At Oktane a couple of weeks ago, Okta and a group of enterprise heavyweights, including AWS, CrowdStrike, Databricks, Google Cloud, Salesforce, ServiceNow, Wiz and Zscaler, launched the Blueprint Alliance. Their goal is a shared plan for securing and governing AI agents across the enterprise.

The biggest names in tech are forming bigger alliances, signing bigger deals and making bigger promises. Everybody wants to rule the world.

Bigger doesn’t always mean better for the people writing the checks. More than half of CEOs told PwC this year that AI had delivered neither higher revenue nor lower costs over the prior 12 months. Only one in eight saw both.

You will work with alliances like this one. The real question is whether you’ll still hold the keys when you do.

What the Blueprint gets right

Credit where it’s due. The Blueprint Alliance is a multi-vendor coalition that co-authored an open reference architecture for governing AI agents at enterprise scale. It’s built around four practical questions: Where are my agents? What can they do? What are they doing? How do I respond?

Those are the right questions, and the timing is right. Okta cites a Gartner prediction that the average global Fortune 500 company will run more than 150,000 agents by 2028, up from fewer than 15 in 2025. No single product will govern that.

Ganesh Kirti, founder and CTO of TrustLogix, wrote in a LinkedIn post that the blueprint “treats agent security as an enterprise architecture problem, not a single-product problem.” He also landed on a principle every CIO should tape to the wall: “agents need identities, but identity alone does not make an action trustworthy.”

At their best, industry alliances do good. They create shared language, speed up interoperability and give buyers a baseline to hold vendors to. Coalitions gave us SAML, OAuth and FIDO. Those made my job easier for two decades.

Notice what’s missing, though. None of the four questions asks what an agent is worth. That one stays with you.

So, I’m a fan. I have also been in the Buyer’s seat. Buyers read the fine print.

The one-way door

Jeff Bezos famously split decisions into two types. Two-way doors you can walk back through. One-way doors you can’t. Alliances often look like the first and behave like the second.

Here’s why. When the companies that already run your cloud, identity, endpoints and data agree on how AI should be governed, the easiest path is to govern AI with the tools you already own. That’s convenient. It’s also how a governance decision quietly becomes a procurement decision.

Incumbents don’t need to win a new category. They need the status quo to hold. Their established product lines fund the roadmap, and new AI capabilities get folded into renewals you were going to sign anyway. An alliance makes that path feel safe, because everyone you already trust is standing on it.

That’s how successful businesses behave. Your job is to notice when their interests and yours start to drift.

Watch for these signs that the door is closing behind you:

• AI governance arrives as a “free” add-on to a renewal, with pricing that changes in year two.
• The architecture assumes one vendor’s control plane for identity, policy and logging.
• Your agent activity data lives in a format only one platform can read.
• Exit costs, data portability and migration support are missing from the contract.
• ROI slides count seats and adoption instead of business outcomes.

I’ve walked through this door before

I spent 2007 to 2015 at VMware as an Oracle consultant, and then as the head of IAM. We deployed Oracle’s identity and middleware stack to get the company through its IPO. It was the right call, and it did exactly what was needed at the time.

Then the costs and complexity grew. New environments took weeks to build. Every change needed engineering and admin time.  That eventually led to an informal effort that we called Get Off Oracle, or GOO.

The surprise was where GOO led. To regain control, we virtualized the entire Oracle stack, including Fusion Middleware and Oracle RAC, using VMware software and automation we built ourselves. Build and delivery times dropped from weeks to hours. That speed helped fuel VMware’s explosive growth and accelerated time to market. Even Okta came by to study how we did it.

The lesson stuck with me. Our leverage came from owning the architecture. Once we controlled how the stack was built, deployed and moved, we negotiated from strength. The vendor stayed. The dependence didn’t.

Price is not risk

Every time an AI system acts on data you can’t fully trace, bound or prove, you take on Verification Debt. It compounds quietly and comes due at the worst moment: a release, an audit, a regulator’s letter.

That’s why the sticker price misleads. A $100,000 AI project with a clear economic mechanism can carry less risk than a $20,000 project nobody can explain. The price tells you what you’ll spend. It tells you nothing about what the system can reach, what it can decide or what it would cost to unwind.

Two companies show the difference.

Klarna rolled out an AI assistant for customer service and cut its cost per transaction from $0.32 to $0.19 over two years. But cost became the main scorecard. Its CEO later admitted that the focus on cost produced lower quality, and Klarna started hiring human agents again. He now describes human support as something close to a VIP service. The savings were real. The scorecard was lopsided.

Morgan Stanley took a slower path. The firm built an evaluation framework to test every AI use case before deployment. Today more than 98% of its advisor teams use its AI assistant.

Both companies worked with the same AI partner. Different discipline produced different results. PwC found the same pattern at scale: CEOs with strong AI foundations were three times more likely to report meaningful financial returns.

A strategy for sovereignty

Here’s the economics most renewal decks leave out. Year one is the honeymoon. Pricing is generous, services are bundled and the executive sponsor is thrilled. By year two or three, costs rise, complexity grows and switching gets harder. Value stays flat while leverage moves to the vendor.

Sovereignty is how you keep that leverage. It means you can join any alliance and still walk away from any vendor. Five moves get you there:

• Own your data layer. Keep governance metadata, lineage and agent activity logs in formats you control.
• Build your own AI harness. Put models, agents and identity providers behind a layer you control.
• Price year three before you sign year one. Cap increases, define exit rights and require data portability in writing.
• Measure value in business terms. Tie every AI commitment to revenue, cost, risk or customer outcomes, with a named owner.
• Keep the program in house. Vendors advise. Your leaders decide.

The harness is where sovereignty gets real. An AI harness is the layer you own between your people, your data and whatever models or agents you run. It carries the context, policies, evaluations and logs, so any model can plug in, and any model can be swapped out.

Pair it with an intelligence layer: a governed view of what your enterprise knows, drawn from every system instead of trapped inside one.

Together, they change the economics. Data silos stop dictating what AI can see. Per-seat SaaS pricing loses its grip, because the value lives in a layer you own. And every vendor becomes a position in a portfolio you can rebalance. That’s leverage at the negotiating table and at renewal time.

It’s the GOO lesson all over again. Own how the stack is built, and every vendor has to keep earning its place.

The Blueprint Alliance will produce real success stories. It will produce regrets too. Just don’t let them be yours.

The organizations that come out ahead will have active leadership, clear governance and the discipline to keep every vendor honest and accountable, including their favorites.

Everybody wants to rule the world. Build your own harness, and you decide who rules yours.

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.

From an enterprise perspective, AI sovereignty means complete control over the entire AI stack. For businesses with the human, technical, and monetary resources, this may be possible. For the vast majority, however, external help is required. The danger, as with any vendor relationship, is being locked in to a supplier or technology. With a mature technology that performs discrete tasks, that may not be a problem. But with an emerging and rapidly evolving one like AI and its ability to impact multiple processes across an enterprise, difficulties could arise. Gartner recently predicted that AI platform lock-in will increase from 5% now to 35% next year. So how do you avoid being in that high percentile?

1. Be clear of the trade-offs

As might be expected, there’s an inverse relationship between lock-in risks and operational overheads, including upfront costs, longer-term licensing fees, and internal talent requirements. Relying on open-source models, applications, and self-hosting is the lowest risk in terms of lock-in, but it requires a significant investment in AI engineering skills and infrastructure. In the middle, a multi-cloud or hybrid API approach provides high flexibility to swap underlying models, but relies on managing the abstraction layer. The fastest way to AI deployment is contracting a proprietary all-in-one platform requiring less operational overhead, but risks longer-term vendor dependency.

2. Break down the stack

A key priority to reduce AI lock-in is decoupling that application layer from the model layer. Deploying orchestration frameworks like LangChain or LiteLLM creates a controllable layer between the software applications an enterprise already uses, and the underlying AI models. The backend LLMs can then be swapped around as needed with minimal code rewrites. Model-agnostic architecture at this level ensures new models can be brought in as older ones become obsolete. The one certainty in AI at the moment is rapid change.

3. Keep an open mindset

Frontier models from OpenAI and Anthropic may still lead the pack in terms of performance, but open models (open weight and open source) aren’t far behind. The 2026 Stanford HAI Index shows the performance gap as 3.3%, making open models a serious option for many enterprise deployments. The overhead here is fine-tuning those models, which will usually require third-party help. Preventing this becoming a pathway to lock-in requires ensuring the weights, parameters, and training configurations remain exportable and your intellectual property.

4. Protect your crown jewels

With varying degrees of friction, all technology is replaceable but your data isn’t. Enforcing strict data portability and pipeline autonomy are the core foundations to building a sustainable AI strategy. “The only truly sovereign AI is the one that doesn’t present a new form of lock-in,” says Chris O’Brien, CTO of AI systems integrator, Advania UK. “The assets a CIO can’t substitute are their data and intelligence of how their business actually works. At that level, models are largely interchangeable unless you built your architecture around a single one.”

This requires keeping data preparation, vector databases such as Pinecone, Milvus, and Qdrant, as well as storage layers outside any single vendor’s proprietary AI cloud. Organizational knowledge stores, embeddings, and training data should be kept in open, standardized formats that can be easily re-indexed and, if necessary, migrated to a different platform.

5. Plan your exit

The market for AI products and solutions will look very different in a year’s time. Models are heading to commoditization as performance improvements shrink between generations, open-source alternatives improve, and competition among frontier model providers will likely see falling token costs. Avoiding long-term volume and/or spend commitments will help enterprises more safely navigate this rapidly evolving scene. Negotiating pay-as-you-go or short-term contracts can reduce risk as well as ensure contracts explicitly state your ownership of all prompts, inputs, outputs, and derived contextual metadata.

6. Build your internal skill base

Building core engineering expertise internally around core AI engineering standards such as Python, containerization, and API management will provide a solid foundation for future growth. Knowledge of vendor tools is important, but to build capability for an uncertain future requires foundational and adaptable skills.

Every business is unique

Eventually, AI technologies and platforms will mature into a more stable environment for businesses. Deployment decisions made now need to build in sufficient flexibility to adapt to this uncertain future. For most enterprises, SMBs in particular, there’s a massive imbalance in knowledge, resources, and skills compared to AI vendors and consultants at the leading edge of this transformation. Protecting your unique data assets and knowledge of your workflows, and the sector you operate in to build a replaceable AI layer on top of that, is the way to a more sovereign future.

Ask IT leaders what sovereign AI is, and you’ll get a wide range of answers. Some will even struggle to define the term.

Sovereign AI is an emerging concept focusing on giving organizations — or countries —control over how they develop, deploy, and govern the technology, often using in-house talent, data, and infrastructure.

But only 13% of respondents in a survey from AI platform provider Cohere and IDC say sovereign AI is widely understood across their organizations, and one in three IT leaders had difficulty describing sovereign AI in their own words.

It’s important for IT leaders to understand the concept, because it can help them control costs, keep internal data private, and avoid vendor lock-in, advocates say.

A solid sovereign AI plan can help organizations avoid disruptions caused by forces outside their control, says Joelle Pineau, chief AI officer at Cohere, which offers an AI platform that enables customers to host AI models on premises.

“Over the past year, enterprises and governments have confronted a hard truth: AI systems that rely on external infrastructure can be disrupted without warning by decisions and actions outside their control,” Cohere says in a recent report. “Recent model access restrictions and several high-profile cybersecurity incidents have become a global wake-up call, exposing how fragile technological dependencies can be.”

Sovereign AI is about giving organizations as much autonomy, choice, and control as possible as they deploy and run AI systems, Pineau says.

“The notion of sovereignty really is about giving users control over their tech stack, the ability to choose how it’s deployed, how it works, what data is fed into the system, and how employees are exposed to the technology,” she adds.

Pineau wasn’t particularly surprised about the lack of understanding about sovereign AI reflected in the survey. Cohere’s accompanying report is an attempt to bring more clarity to the issue, she says.

Many goals under one umbrella

Confusion about sovereign AI in part reflects practitioners’ varying goals. Some users want to maximize their AI model options, some want better control over data ingested into AI systems, some want data to reside within country borders, some want to control costs, and others may want to run AI models optimized to their native language or culture.

For Berk Yilmaz, co-founder and CTO at AI integrated development environment provider Noah Labs, sovereign AI encompasses five characteristics: data sovereignty, legal jurisdiction, model provenance, operational control, and supply chain independence.

“Fulfilling one of those does not mean fulfilling all the others, so two executives can agree with sovereign AI and have little in common,” he says.

Freedom of choice doesn’t always mean a company has to host an AI model on premises or data must reside within a certain country, advocates suggest. Sovereign AI is more about preserving options when something unexpected happens.

“The sovereignty model performs well even in a situation where the vendor breaks off the contract, your model is added to the list of models that are banned for exports, and the connection is off,” Yilmaz says. “Each of these three scenarios has already played out somewhere in the last year.”

Others have different definitions. Confusion over sovereign AI isn’t surprising because it is four separate concepts that were collapsed into one, says Jeet Pattanaik, founder and CTO of AI solutions provider Glokal AI. Those four concepts: where a company’s data physically sits, what country’s law can compel access to it, who controls the AI model, and whether a company could still operate if the relationship with the model provider ends.

“Vendors usually sell you the first and call it sovereignty, because data residency is easy to demonstrate and makes a good diagram,” says Pattanaik, author of the book Sovereign AI: The Enterprise Guide to AI That Is Private by Design, Compliant by Default, and Yours Forever. “The hard one is the second, and it’s a legal question rather than a technical one. A server in Frankfurt owned by a US company is still reachable under US law.”

While the concept is largely about control, few companies want full control of their AI stack, he notes.

“Building your own models is expensive and usually worse,” Pattanaik says. “What CIOs actually want is bounded dependency: knowing exactly what you depend on, what happens if it changes, and having an exit that doesn’t take three years.”

Future impact

But the benefits aren’t always immediate, Pattanaik notes.

“The value arrives in specific moments, not continuously — when a regulator asks who processes this data and under whose jurisdiction, or when a vendor changes terms at renewal,” he says. “Organizations that thought about sovereignty already have an answer. Everyone else discovers the question and the crisis at the same time.”

Still, Pattanaik sees momentum building for the concept, with regulated industries such as banking, healthcare, and the public sector paving the way, treating it as a requirement.

“Most others are still at the stage of asking during vendor selection and accepting whatever answer comes back,” he says. “From where I sit it’s moved from philosophy to a procurement line item over roughly the last 18 months, but unevenly.”

David Wang, COO at enterprise AI gateway provider Tetrate, sees similar adoption trends with regulated industries and defense contractors leading the charge.

Other organizations should focus on a handful of questions to decide whether to investigate sovereign AI, he recommends. Companies that can most benefit include those with more than one regulator or legal entity, including recent acquisitions; those with a huge developer population running coding agents; and those with one AI model vendor that commands more than half of their AI spending.

“At that size, a supplier price change becomes a budget event,” Wang says.

Like Pattanaik, Wang suggests that sovereign AI is part of a long-game strategy rather than immediate gains. A good plan enables organizations to quickly shift to open AI models when a frontier model gets too expensive, he says.

“This work mostly prevents a loss rather than creating a gain, which is why it rarely wins the budget on its own,” he adds.

Cohere’s Pineau sees benefits for a broad range of organizations. With token costs a major concern for many companies, a sovereign AI plan can explore alternatives to current AI vendors, she notes.

“A lot of companies care about it, but they care about different aspects,” she says. “In regulated sectors, they care about the compliance aspects, and in some sectors with tight profit margins, they care a lot about the cost control. The manufacturing, the telecoms, and the energy sectors care about the ability to control their costs.”

Brands like Snickers, M&M’s, and Twix are familiar to most consumers, but Mars Inc. doesn’t just produce snacks. The family-owned company, with a revenue of approximately $65 billion, is also one of the largest manufacturers of pet food and ready meals, and its more than 100 production facilities operate around the clock. Of course, this places considerable demands on its IT.

“Our team must ensure that every system, including production lines, runs at maximum performance so we can continuously deliver the products and services our customers value,” says Luciano Batista, the company’s VP of enterprise services delivery.

However, Batista and his team realized that the existing data infrastructure could no longer reliably support operations, especially during peak periods such as Halloween and the pre-Christmas shopping season. So with the support of hybrid, multi-cloud data storage service Everpure, Mars is rebuilding its data and IT infrastructure.

“The Everpure platform met all our requirements,” says Batista. “It’s a scalable platform that futureproofs our operations and integrates seamlessly with our hybrid cloud infrastructure.”

Unified storage environment 

Mars initially consolidated its complex network of storage systems for business-critical databases like Oracle and applications like SAP onto a single Everpure Flash Array system. These software-defined, all-flash storage arrays are available in versions for different workloads, and typical use cases include databases, virtualized environments, SAP applications, and AI and analytics applications. 

Mars has since expanded its flash array infrastructure and now supports mixed workloads, including VMware, Windows, and Linux in areas of production, development, and quality assurance. It also uses Everpure Flash Blade as the basis for the global SAP file system. And while Flash Array is optimized for structured data, the scale-out systems of the Flash Blade series are designed for unstructured information.

“At peak times, Everpure supports up to 300,000 IOPS without any performance degradation,” says Lincoln Silva, product owner for Linux and on-prem storage at Mars. From his perspective, another point speaks favorably of the new platform in that he estimates his team saves approximately three months of planning time thanks to the Evergreen subscription model. This is because the vendor provides regular updates for the storage platform’s hardware and software. As a result, Mars’ IT professionals can focus on more critical tasks. 

Basis for hybrid cloud strategy

Mars also works with choice vendors to implement its approach to cloud. Dedicated local storage capabilities, for instance, are being integrated into Microsoft Azure cloud workloads, which simplifies restore processes and increases resilience.

Snapshots from the local environment can be replicated to the cloud, too. Recovery point objectives (RPEs) of four to 24 hours are available, depending on system priority. “Our success is also the success of our partners,” Batista says. “We embrace a spirit of reciprocity to get the most out of our collaboration.”

The hybrid cloud allows Mars to run VMware workloads and extend its IT infrastructure to the cloud as needed. And the company aims to expand its use of cloud-native applications via Microsoft Azure at a lower cost.

“We’re seeing a data reduction ratio of 18 to one. That’s nine times the expected compression rate,” Batista adds. “This puts us on track to save up to 50% on cloud storage costs. We can now work more efficiently and make better decisions thanks to intelligent solutions and automation.”

Fewer racks and lower power consumption

By consolidating on the flash platform, Mars has also reduced the space requirements and power consumption of its data centers so they only use one sixth of the power, and the number of racks has decreased significantly.

“We’re shaping a sustainable future by changing the way we work,” says Batista. “The decisions we make today will impact the world we leave behind, and Everpure aligns with our commitment to thinking in generations, not just business quarters.”

Vendor consolidation is sold as discipline. Fewer vendors, simpler architecture, better pricing through volume, one throat to choke when something breaks. Every one of those benefits is real on paper. The problem is that the biggest cost of consolidation rarely appears on the slide the procurement team uses to sell it internally, and it does not show up on the savings tracker until the first renewal cycle after the ink is dry.

Within CIO Mastermind’s topic-specific cohorts, which I sometimes facilitate, I hear a version of the same story often enough to recognize the pattern early. A consolidation program gets pitched against a strong multi-year savings target. The first year or two look good. Then a renewal arrives, the remaining vendor prices to the switching cost the company just built for itself, and a meaningful share of the projected savings quietly erodes. The company still ends up with fewer vendors. It does not always end up with the leverage the original business case promised.

What consolidation actually removes

What consolidation actually removes is competitive pressure on the vendor you keep.

That is the part most business cases leave out. Going from a dozen vendors in a category down to three or four feels like simplification, and it is. It is also a message to the vendors you kept about how expensive it would be for you to leave. The fewer live alternatives you maintain, the more accurately a vendor can price to your captivity rather than to the open market. A consolidation deck typically shows how many vendors are being reduced. It rarely shows how many of the remaining vendors could credibly be replaced inside a reasonable switching window. That second slide is the one worth building before the program starts. That capability is often missing.

Procurement teams are not being dishonest when they leave that slide out. Their incentive is to close the program and book the savings target, and the pain of a diminished market shows up two or three years later, on someone else’s dashboard. By the time the first hard renewal arrives, the people who built the original business case have often moved to a different project entirely, and the CIO who is still in the seat is the one negotiating from the position the program created.

The condition that decides the outcome

Consolidation programs succeed or fail on one question, and it has to be answered honestly before the program starts.

Can you walk from this vendor at renewal?

Not in theory. Not with twelve months of migration work. At renewal, inside the window the contract gives you, with a credible alternative that has been exercised recently enough to be real. If the answer is yes, the vendor will price to keep you. If the answer is no, the vendor will price to what you can absorb. Consolidation that leaves you unable to walk is a long-dated price increase with a celebratory kickoff meeting, dressed up as a savings program.

Contract language deserves particular scrutiny here, because it is where a lot of the false confidence comes from. Multi-year agreements often include price increase caps that look protective at signing. Those caps are usually written around the product as it exists at signing. Vendors may repackage functionality into new or higher-priced tiers, leaving the contractual cap covering less of what the company actually needs. A cap that looked airtight in the negotiation can end up covering a shrinking share of what the company actually pays for at renewal.

CIO.com has covered the leverage problem for years, including a piece on how to increase your renegotiation leverage with vendors that frames the handcuff problem directly. The advice in articles like that one is sound. The hard part is applying it in the middle of a consolidation program, when the procurement team is telling you that keeping alternatives warm is wasteful and the CFO is asking why the savings number is dropping.

The CIOs who hold their leverage tend to do one thing differently. They keep one credible alternative warm in every major category they consolidate, even after the primary vendor is chosen. Warm means more than a name on a shortlist. It means a live relationship with the alternative’s account team, some recent proof of concept, and at least one internal team that has actually touched the alternative’s platform. That readiness carries a real cost. Maintaining it may cost far less than an uncontested renewal can quietly take away.

The number that actually matters to the CFO

Most consolidation programs get measured against a single number: the savings projected in year one of the business case. That number rewards aggressive consolidation and quietly punishes the CIO who keeps an alternative warm, because the carrying cost of that alternative shows up immediately while the protection it buys only shows up at the next renewal, two or three years later. Judged against a one-year number, the cautious approach always looks worse.

The number worth tracking instead is the savings figure three years out, measured against what the business case originally promised. That is the number an aggressive consolidation program tends to miss once a full renewal cycle has run its course, and it is a fairer test of whether the program actually worked. It also reframes the conversation with the CFO. A carrying cost presented as insurance against a specific, quantifiable renewal risk is a different ask than a carrying cost presented as overhead, and it tends to get a different answer.

What to do if you inherited the problem

Most of the CIOs I talk to are not starting a consolidation program. They inherited one. They sit down in a seat where the leverage is already gone and the next renewal cliff is six or nine months out.

If that is where you are, the fastest way back to a real negotiating position is not to rebuild leverage everywhere at once. That approach takes years and asks the CFO to fund carrying costs across the entire portfolio before there is any evidence it will pay off. Pick one category instead, ideally not the largest one but the one where a credible alternative can be stood up fastest, and rebuild it inside twelve months. Speed matters more than scale here. A live proof of concept in a smaller category, exercised recently enough to be real, does more for your negotiating position than a partially built case in a larger one.

One proof that you can still move part of the portfolio changes the conversation at every other renewal table. A vendor who knows you have already done it once treats the next renewal differently than a vendor who has only heard you claim you could.

The second you cannot walk, the price stops being yours to negotiate. Most consolidation programs remove your ability to walk as their first move, and most CIOs do not realize they have given it up until the next renewal arrives and reminds them.

Petco Park consistently ranks among MLB’s top ballparks for fan experience. That doesn’t happen by accident, and it didn’t wait for a star-studded roster or a deep postseason run.

According to Padres CIO Ray Chan, the club made a deliberate choice more than a decade ago to run its tech organization as if every seat were full and the team was playing in October every year. The philosophy was simple — build a World Series-level digital foundation so when the on-field product caught up, the elite fan experience would already be there.

More than a ballpark

Most people know Petco Park as the home of the San Diego Padres, which it is, but the venue was designed to be more than that. In a typical year, it hosts 81 regular-season home games, plus potential postseason contests, and then adds concerts and private events in renovated premium spaces.

By Chan’s count, that totals to nearly 400 annual events, often with more than one on the property in a single day. Different parts of the venue may host different audiences simultaneously, with IT expected to turn spaces quickly and support the unique digital requirements of each event.

The multi-use model puts a premium on flexibility and speed. Spaces are designed to be reconfigured quickly, and the underlying technology stack must adapt just as quick.

An always‑on network

When Chan arrived 15 seasons ago, Petco Park looked very different from a connectivity standpoint. On sellout nights, fans often couldn’t place a call or send a text once they were inside the building. There was no real concept of a digital fan journey.

The first major shift came with deploying a full-venue managed distributed antenna system (DAS) from Verizon, and an Extreme Networks Wi-Fi solution, providing fans, staff, and baseball operations with reliable connectivity throughout the ballpark. That network has since become the converged backbone for almost everything that happens at Petco, including digital ticket entry via the MLB Ballpark app, security and operations, tech like instant replay and dugout tablets used by coaches and players, and in‑venue IPTV and signage, all riding on the same IP infrastructure.

For fans, the network is invisible. For Chan’s team, it’s non‑negotiable. “None of this stuff works without the infrastructure in place,” he says.

Consolidated convenience

The Padres have leaned heavily into the league-standard MLB Ballpark app, which provides a consistent digital experience across all 30 venues, while allowing clubs to customize the local section. At Petco specifically, that app becomes the fan’s control center for digital ticketing, ballpark navigation, and a built-in payments and discount wallets tied to offers like Padres Pay and contactless options.

The result is a highly digitized journey, and for many fans, their first and last interaction with the ballpark occurs on their mobile device, and that’s by design.

Toward frictionlessness

Chan and his team are already looking beyond digital barcodes to facial-authentication-based entry, leveraging MLB’s Go Ahead Entry program rolling out at several parks. In that model, fans enroll once in the app with a selfie, then simply walk through a designated lane while overhead cameras verify identity and automatically scan tickets.

The promise is a hands-free, eyes-up experience where fans no longer need to take out their phones at the gate. Chan says this is the most frictionless way to enter a ballpark, and it even enables personalized greetings by name at the turnstile, another small but memorable touch to create a World Series-caliber experience.

Concessions are another example of how Petco’s IT modernization seamlessly enhances the fan journey. Petco is now a fully cashless venue, so fans pay with credit cards, mobile wallets, or the dedicated Padres Pay capability integrated into the Ballpark app. This reduces transaction friction, speeds lines, and improves security by minimizing cash handling.

IPTV everywhere

The expanding IP television footprint is another hallmark of Petco’s fan experience strategy. New screens throughout the venue serve multiple roles to ensure game coverage is never lost, even when fans leave their seats.

They also show real-time updates and wayfinding, an L-bar format that combines live video with adjacent ad inventory and informational content, and full-screen takeovers during concerts or special events, letting the venue transform its look and feel to match what’s happening on the field or stage.

Because it’s all IP-based, game-day operations and marketing teams can reskin the park on the fly, turning screens into a flexible engagement and monetization channel rather than relying on fixed signage.

Constant modernization

Despite opening in 2004, Petco Park doesn’t feel like a 22-year-old venue. Chan says that’s intentional and points to a continuous program of infrastructure upgrades and capital projects to redo suites, unify premium spaces such as the Western Metal rooftop and loft, and find areas to transform into new experiences.

Beneath those visible changes lies ongoing modernization of the network and systems in terms of upgrading switches, faster Wi-Fi, and backend platforms to support the latest apps and services. As Chan puts it, the goal is to make the park look and feel no older than a couple of years, which requires consistent ownership commitment and alignment between IT and operations.

Perhaps the most important part of Chan’s playbook, however, is cultural rather than technical. He describes the Padres as a listening organization that actively solicits and incorporates fan feedback to refine the experience across seasons.

That mindset is shaping the club’s approach to AI, so instead of chasing it for its own sake, Chan is focused on use cases that improve customer service by using chatbots or AI-assisted voice lines to free staff for higher-value interactions, and solve specific operational problems such as using AI to match lost-and-found queries with a database of found items.

The bigger IT picture

The way Petco Park manages its technology operations offers patterns that can apply beyond sports venues, starting with establishing a converged, resilient backbone. Connectivity is a shared utility layer that everything else depends on, rather than a series of isolated projects. That makes it easier to add new capabilities later without rearchitecting every time.

Chan’s philosophy of building as if every seat were filled also applies across all e-commerce peaks, clinical surges, and manufacturing seasonality. Capacity planning, observability, and failover should be set at Black Friday, not an average Tuesday. And treat your environment as a multiuse and continuously modernizing platform. Petco’s “more than a ballpark” mindset reflects the shift toward mixed-use destinations or campuses that blend learning and events, and offices that evolve into collaboration hubs that chip away at legacy infrastructure. IT leaders across sectors can apply the same rolling-renovation model to networks, identity, observability, and edge infrastructure, keeping technical debt manageable.