AI is writing more code than ever. Software specialist Sonar’s 2026 State of Code Developer Survey, based on more than 1,100 professional developers, found that 72% of those who’ve tried AI coding tools now use them every day, and that today, AI now accounts for 42% of committed code, a number that’s expected to reach 65% by next year.

But while developers embrace AI-enabled coding services, many professionals are uncertain about how their businesses will cope with this influx of AI-generated code. Sonar’s research additionally discovered that 96% of developers don’t fully trust it, and only 48% always verify it before committing.

In fact, 38% of developers told the researchers that reviewing AI code requires more effort than analyzing code written by human colleagues. That’s a pattern recognized by Tim Chilton, a managing geospatial consultant at UK mapping agency Ordnance Survey (OS). “There’s truth in the headlines that AI can get carried away, and developers spend more time fixing the code that’s been created than not,” he says.

While that’s not a pattern he sees at the OS, Chilton says his organization is cautious about how developers use AI coding tools, and conversations with other digital leaders suggest a similar approach. AI-enabled coding is indeed almost ubiquitous, and it can allow developers to reach new, higher levels of productivity, but more doesn’t necessarily mean better code.

To ensure that such coding services produce business benefits, CIOs must take control of the development process. They should exploit agents carefully, set professional responsibilities, and establish guidelines, policies, and organization-wide training.

Exploiting the agents

Snowflake co-founder and president Benoît Dageville suggests there are two ways to view AI-powered coding. One is the scary way, where AI takes over your tasks without human input. The second is the efficient way, where AI helps you do the same things as before, but at a fraction of the cost. He’s a big believer in the latter, both for himself and his teams.

“The route depends on the organization; their application of AI coding depends on their priorities,” he says. “However, I hope that overall, AI will have a positive effect on humans. I’m using AI every day to code, and if I compare myself to one or two years ago versus now, I’m more efficient and productive, but I’m also writing five times more code.”

Steve Lucas, CEO of integration technology specialist Boomi, also notes the increase in output. He quotes research that suggests 4% of GitHub public commits are already being authored by Claude Code. That proportion is expected to rise to 20% of all daily commits by the end of this year. Staff at Boomi already use AI tools to produce more code.

“We’re writing 10 times the amount today than we did two years ago,” says Lucas. The increase in AI-enabled coding is so significant that he’s spoken with Boomi chief product and technology officer Ed Macosky about goals for the proportion of self-modifying code.

“I believe AI should recursively modify 80% of a software organization’s code,” Macosky says. “Infinite improvement is demanded. If AI is extraordinarily good at breaking security protocols, the only way to keep up isn’t the kind of accordion model of human code design. AI use must be linear in scale and continuous.”

Crucially, Lucas suggests using AI to check and modify that AI doesn’t mean coders go away. “In fact, I think we’ll need more engineers, not fewer,” he says. “We need these professional experts to ensure the quality of the code that’s written.”

At OS, Chilton uses Coco, Snowflake’s coding agent. The tool does as much as 90% of a development task that he would otherwise complete from scratch. “It’s taken an hour to finish, instead of weeks or never,” he says, reflecting on the power of AI coding services.

However, the tool’s influence is carefully constrained. The agent doesn’t work in isolation, and Chilton checks its output in detail. He says all organizations need a similar process and should value experienced professionals who can use AI coding tools effectively.

“If you’re giving a tool to someone who isn’t capable of checking an agent’s work, as a digital leader, you run the risk of putting too much faith in AI just running with its output,” he says. “I think that’s a big risk, so there’s still a need for experienced developers.” 

Setting clear responsibilities

While placing humans in the loop can help ensure AI-enabled code is safe and effective, developers shouldn’t be weighed down by their new management responsibilities.

Gill Haus, CIO at Chase, the consumer banking arm of JPMorgan Chase & Co, says AI coding benefits can only be realized when agents are introduced with a strong focus on automating supporting technology and processes, which reduces the need for too much human input.

“If you have 12,000 engineers, they can write 12,000 engineers’ worth of code in a day,” he says. “If those 12,000 engineers have agentic technology, they can write much more. But if you don’t have automation left to right with confidence, you create more manual processes. You can’t move as quickly since there are only so many humans you can put in the middle.”

Haus says Chase never releases services in production that don’t pass its high bar for quality and security. Strong engineering practices, pipelines, and architecture, plus a tried-and-tested approach to automation, can help ensure AI coding produces solid outputs without a burdensome amount of human intervention.

“We’re focused on effective automation so we’re able to unleash this technology safely and effectively,” he says. “This is something many other companies should be focused on if they aren’t already, because otherwise you can’t keep up with the pace of change.”

François-Xavier Pierrel, group chief data and ad tech officer at French TV network TF1, is another digital leader who says maintaining control as AI writes more code means keeping the human in the loop in a clearly defined manner. His team documents agentic processes using Atlassian project management and collaboration tools, such as Confluence.

Pierrel says policy management is crucial as the agentic transformation gathers pace.  One key step is ensuring professionals who use agents conform to internal standards. Smart businesses set policies that state clearly when staff can explore an agent, delegate work to it, or release it in production.

“We don’t have hundreds of agents deployed yet,” he says. “But the moment you reach a certain level of volume, if you don’t have compliance and policies in place, AI becomes a critical mess, and then it can be disruptive in a negative way.”

Building strong foundations

Kirsty Roth, chief operating officer at content and technology specialist Thomson Reuters, agrees digital leaders must be proactive. Her firm’s explorations with models and agents suggest that AI can help with time-intensive coding processes, such as writing. This capability means her organization carefully considers the roles people fulfil.

“In the last 12 months, we’ve put lots of thought into where we’re seeing benefits,” she says. “It’s great AI can do some of the coding work, but what does that mean for people’s jobs? We used to have thousands of engineers. Do we now need more architects or product people?”

Roth refers to security. While cyber is always a priority for Thomson Reuters, the firm dedicates significant resources to controlling threats in the AI age. Developing high-level development and security skills requires a concerted effort. Yet Roth says ensuring people have the right skills to manage AI is in many ways a familiar IT trend.

“All this effort is no different than digital, cloud, mobile, social, or other big tech trends,” she says. “They all take several years to have an impact as the initial hype comes, and then you’ve got to work through the reality and think about how you upskill people for the new world.”

Crucially, coding is no longer the sole preserve of the software developer. AI coding tools make it much easier for non-IT professionals to vibe code applications. Tal Carmi, CIO at technology specialist WalkMe, says non-developers can create useful tools. However, effective outputs rely on a careful balance between enablement and guidance.

He adds that most of these employees don’t need in-depth capabilities, such as a database or a content delivery network. Instead, non-IT staff require a dedicated place to upload their products. WalkMe’s engineering specialists ask key questions to ensure their outputs are safe, considering credentials and potentially problematic packages.

“You need to add guardrails,” he says. “A lot of the work for in-house engineers is shifting from building to testing. The people who aren’t coders don’t necessarily have security in mind.”

The key takeaway, suggests Emmanuel Frenehard, chief digital officer at biopharmaceutical giant Sanofi, is that digital leaders must help professionals feel comfortable. Successful digital leaders ensure the business generates value from agents, and his firm is helping its staff, particularly its developers, to embrace these tools.

“I genuinely worry for professionals who don’t use AI to code, because in five years, they may not have jobs,” he says. “So we’re guiding, coaching, and training them, but people must take the journey. The train has left the station. You can decide to be on it, or just look at it.”

In Foundry’s 2026 State of the CIO survey, 83% of CIOs say their role is becoming more of a hybrid to lead both business and tech initiatives, rather than simply manage IT. As the two become more inseparable, the CIOs who see collaboration as a competitive advantage are putting daylight between them and those who see it as a cursory soft skill. And C-suites are responding as they rely more on IT to drive growth and accelerate efficiencies. So the CIOs who deliver the most value will bring collaboration skills to drive change and technical expertise.

Silos between IT and the business are falling

As the World Economic Forum notes, not every tech initiative requires deep collaboration. But as businesses move beyond routine IT investments into cross-functional, innovation-focused transformations, collaboration becomes essential.

For example, how can IT decisions help an organization open new markets faster, or emerging technologies reduce risk in areas like data privacy. It’s no surprise, then, that Deloitte found that 65% of CIOs reported directly to CEOs in 2025, up from 41% in 2015.

Because tech is now part of almost every business decision, it takes everyone’s expertise and insights — from the CIO to the CFO — to see the full picture of how an initiative will affect the company. Shared knowledge leads to better-informed decisions that give tech its biggest impact.

When silos persist, CIOs struggle to get buy-in for their initiatives. Protiviti’s Global Transformation survey found that while 61% of CIOs report being confident in their tech transformation outcomes, only 34% of CEOs and boards feel the same — and the gap is even wider for AI implementations.

That gap is, at its core, a collaboration problem. Without effective collaboration and communication, others can’t see the value of transformation efforts. CIOs who excel in these areas can more easily demonstrate value, win buy-in and keep their organizations at the forefront of technology.

Delivering speed and innovation

Collaboration goes well beyond getting buy-in. While it may be thought to slow IT down, it actually brings in the insights that make change more effective and faster.

As I’ve highlighted in the past, when organizations move away from rigid data ownership, cross-functional teams can share what drives further innovation.

The same holds across the C-suite. Perspectives from outside IT show CIOs how a tech implementation works for the company as a whole, and where new opportunities lie. That lets CIOs test, refine, and reshape solutions faster.

Most importantly, when teams share a common purpose and goals, everyone stays aligned on the best overall outcome for the business. Iteration happens much quicker because everyone is focused on the big picture rather than individual agendas. With increasingly rapid tech cycles, that speed is a competitive advantage in its own right.

Building your collaborative abilities

For CIOs used to the old way of doing things, shifting to a collaborative mindset can be a challenge. But like any capability, it can be developed and improved by applying some fundamental principles.

Earn trust through follow-through. Trust isn’t built at a kickoff meeting or with a mission statement. It’s built when people see you do what you said you would, again and again. For CIOs, that means delivering on commitments or flagging early when you can’t. Clear and consistent communication is one of the most reliable ways to build trust across an organization.

Make transparency the default, especially when news is bad. Transparency means more than status reports. It means being candid about costs, risks, trade-offs, and setbacks before they surprise anyone. When leaders sidestep difficult discussions, trust erodes and goals drift. And in technology programs, small unspoken issues have a way of becoming expensive ones.

Seek cultural fit in how you work, not who you are. Cultural fit isn’t about sameness or shared backgrounds. It’s about compatibility in management philosophy, values, decision-making, and communication. A CIO who values speed and experimentation, and a CFO who prioritizes predictability and control will clash unless both recognize the difference and agree on how to bridge it. The same is true when it comes to finding the best technology providers.

Focus on the what, not the how. Few things undermine collaboration faster than micromanagement. When leaders dictate how a colleague or partner should work, they signal distrust, stifle creativity and innovation, and tie up their own time policing activities instead of driving results. The better approach is to agree on clearly defined, measurable outcomes, and then step back. Letting go of the how isn’t giving up control but building enough trust that everyone gets there together.

This requires adjusting your workflows and skills, but the payoff is valuable insight from your collaborators and wins that lift the entire organization.

The new life of the CIO

The days when the CIO could simply be the tech expert behind the scenes are long gone. With AI and other technologies ingrained in every aspect of the business, the CIO must lead well beyond IT.

By making collaboration a priority, you can gain critical insights across teams to guide your tech strategy, and deliver the greatest value to your organization.

Following the IT and tech hiring boom during the Covid-19 pandemic, companies made space for AI initiatives that resulted in increased layoffs. But new data reveals that some IT hiring trends are starting to turn around.

Pay in the most AI-exposed occupations grew around 46% since 2021, compared to only 25% for jobs in the least-exposed category, according to the Dice Tech Sentiment report. The data reveals this gap began to grow in 2024 and continues to grow as job postings rebound after several rough hiring years.

As organizations begin to realize AI initiatives and investments, they’re now looking to fill their ranks with specialized skillsets, and it’s driving increased compensation. AI has changed the skills businesses need, and 84% of executives report they currently struggle to find specialized local talent while 64% say they feel unprepared for ongoing disruption, according to data from a 2026 report from Globalization Partners.

In a few short years, AI has caused multiple swings in tech hiring. As organizations understand the skills they need to realize AI visions, hiring, for now, appears to be on the upswing for candidates and jobseekers, with more competitive offers. On the employee side, as AI transforms job roles and business functions across every industry, there are, however, increased reports of burnout and a widening gender gap.

Salary boost for specialized skills

AI is currently creating some of the fastest growing and highest paying opportunities in today’s labor market, according to LinkedIn Research. Data reveals that AI job postings have doubled in 2026 from 2023, with the average job listing around $177k in compensation for AI roles, compared to $80k for non-AI roles.

Salary offers and benefit packages have again become more competitive, with 72% of employers saying they’re increasing compensation budgets to attract and retain top talent. Additionally, 65% of tech leaders said they’re offering salaries above their planned range, with 87% stating the top reason being a need to secure candidates with specialized skills, according to Robert Half.

Tech leaders are also seeking specific skills, with 92% acknowledging internal plans to offer higher pay for relevant AI skills. Across every industry, 42% of all business leaders say they’ll pay more for AI-related experience compared to other general tech skills.

Additionally, pay transparency, once relatively taboo in the hiring process, has now become normalized in hiring with 96% of organizations reporting they either already share compensation or plan to soon. Sharing compensation on job postings has several benefits including improving the quality of the candidate pool, more efficient salary negotiation, and a reduced time to hire. Business leaders seem to recognize the benefits, with 67% of employers saying their company takes pay transparency beyond legal requirements, offering more information than is legally required, adds Robert Half.

AI accelerates employee burnout

As daily AI use quadrupled over the past year, burnout doubled and confidence in tech’s long-term future dropped from 80 to 60%, according to Dice. The data reveals around 74% of employees say they plan to change employers in the next year, and 55% say they’re actively job searching, despite only 41% saying they believe they’ll find a better position.

Dice also reveals nearly half of all employed tech professionals report feeling burnout, with around 25% describing themselves as very burned out. And among those reporting burnout, tech and AI professionals express the highest rates.

High levels of burnout can cost companies lost productivity and hurt bottom lines. In 2025, a staggering 10 trillion was estimated to have been lost globally due to disengaged and burnt out employees, according to Globalization Parters. If companies are losing profits through employee burnout, costs of AI investments need to be greater priorities when planning AI initiatives.

AI is meant to boost productivity and efficiency, but Dice says that while speed without guardrails might create advantages for some, it’ll create anxiety for others. Similar to pay transparency, organizations must practice AI transparency. Yet fewer than half of employers report having formal AI policies in place to govern or dictate its use in the enterprise, adds Dice. 

AI gender gap emerges

While there’ve been efforts to move the needle on gender diversity in IT and tech over the years, AI has revealed another gender bias the tech industry will need to contend with to reach gender parity. Tech has always struggled with gender equity despite concerted efforts to make IT and STEM jobs more appealing to women.

Unfortunately, despite efforts to create cultural shifts, AI has once again widened the gender gap in tech. According to further data from LinkedIn Research, women only accounted for 26% of AI hires in the US in 2025, compared to 50% of hires into non-AI roles. And of course, the numbers drop more at the C-suite level, where only 13% of AI leadership at AI companies is held by women.

AI roles have also largely gone to candidates with college degrees with 91% of AI workers holding a bachelor’s degree or higher, moving up to 95% for C-suite and director level positions, so there’s an additional lack of opportunity for those women without a traditional education. Tech and IT leaders will need to avoid falling back into the old hiring patterns that initially caused an absence of representation in tech.  

Plus, there’s an emerging gender gap on AI sentiment, with women holding more negative views than their male peers when asked about the impact of AI over the next 20 years, according to data from Pew Research. Women were more likely to say they felt negatively about the impact of AI on themselves personally (33%) and on society overall (43%), compared to 27% and 36% of men who said the same, respectively. Inversely, only 11% of women said they felt positively about AI’s impact on society, while 22% of men said they felt positive.

Women are also more likely to say AI is advancing too quickly — 68% compared to 58% of men. These opposing sentiments might contribute to the overall gender gap in AI roles, as women show more hesitation and caution with AI tools compared to male colleagues.

In Part 1 of this two-part series, I argued that the moat is dry. When NotPetya hit Merck in 2017, it didn’t climb a wall. It came in through trusted vendor software, the routine traffic every perimeter is built to admit, and Merck put the damage at more than $1.4 billion. Its insurers then tried not to pay, and after that fight, underwriters started asking much harder questions.

AI has since made attacks like that cheaper and faster. In IBM’s 2026 Cost of a Data Breach report, one in four malicious breaches was AI-enabled, and breaches that took more than 200 days to find and contain cost about $1.3 million more than those that didn’t. Deepfaked executives, software that can be talked into things and AI agents holding credentials are attacks on trust first and technology second.

Yet the people we hold responsible are set up to lose. Some 64% of CISOs still report into IT leadership, only 10% influence financial decisions, and only a quarter say board discussions of cyber risk run longer than 30 minutes. They are accountable for the breach without budget authority, and they check the work of the people they report to.

The organizations that feel safest about the next two years are not the ones with the most tools. They are the ones where leadership understands AI risk, someone clearly owns it, and the security team has the authority to act. That is a governance problem, and it has a governance fix.

A roadmap for reconstruction

1. Take security out of IT, and do it before the breach

No company lets the finance department audit its own books. That is why internal audit reports to the board’s audit committee rather than to the CFO. Security deserves the same independence for the same reason: the people who build and run the systems should not vouch for them. The CISO’s job is not to run technology. It is to govern how people, partners and machines behave around the things the organization cannot afford to lose, and to tell leadership the truth about that risk.

That does not make the job less technical. A CISO who can’t read an architecture diagram, question an engineer or spot a vendor’s overstatement will be steamrolled in the first meeting. The role needs an engineer’s depth. It is just bigger than engineering, the way a general counsel needs to know the law but is hired for judgment.

In practice, that means a direct line to the CEO and a board committee. Equifax did this after its 2017 breach. Its new CISO, Jamil Farshchi, called the direct line to the CEO and board the key to the turnaround, and noted that it costs nothing. Vertex Pharmaceuticals took a different route: its CISO reports to the Chief Scientific Officer, which puts security next to what the company actually protects, its research. Choose a model deliberately. Most organizations never choose; they inherit one.

2. Hand the CISO the AI governance pen

Only 19% of organizations report coordination between their AI governance and security teams. Every AI tool the business adopts is a security design decision, and it needs one accountable owner. Otherwise, you have a committee that approves tools and a security team that finds out later. But accountable doesn’t mean alone, which brings us to the most important step.

3. Give everyone a voice

A CISO knows security. A CISO does not know research, or claims processing, or the manufacturing line. Elevating the CISO works only if decisions get made with the people who live with them: research leaders, business owners, legal, finance and the CIO. Build a standing risk council where those voices shape controls before they’re imposed, and where a scientist can say “this will break my work” and be taken seriously. The point of elevating the CISO isn’t to put one person in charge of everyone else’s work. It’s to make sure the person who owns the risk is in the room, and that everyone affected by the decision is in the room too.

Security decisions made in isolation get worked around. Decisions made together get followed.

4. Define “ready,” then hire and develop to it

New York’s cybersecurity regulation for financial firms calls the CISO “a qualified individual” but never says what qualified means. Here’s a working definition, in the form of two tests a board can give. The first is technical, and it is not optional:

• Can you tell us what we own, who can reach it, and how fast we can recover, and when did we last test that?
• Have you personally used the AI tools our employees use? Can you explain prompt injection and agent permissions to us in plain English?
• Where does your threat knowledge come from besides vendors: peer networks, industry information-sharing groups, red-team results?

The second test is about judgment and behavior. It is the one most security leaders are never given:

• What do our people actually do when a request seems urgent and comes from someone senior?
• Which risks have you advised us to accept, and why? A CISO who never recommends accepting a risk is a gatekeeper, not a risk officer.
• Can you translate a risk into dollars and days of downtime?
• Which research and business leaders do you consult before a control changes how they work?

And one question for the board itself: Have we given this person authority that matches their accountability?

👉 Click here to download a sample CISO job description👈

5. Make sure AI agents are governed like employees

The CISO doesn’t provision these accounts; the CISO makes sure someone is accountable for every one of them. Every non-human identity needs an owner, the least privilege it needs, monitoring, and a kill switch. Among organizations that had an AI-related incident, 92% lacked proper AI access controls.

6. Hold everyone to the clock, not the checklist

Ask every part of the organization for time to detect, time to contain and time to recover, using tested numbers rather than estimates, and report them to the board. The 200-day line in IBM’s data is worth more than a million dollars per breach.

7. Make sure the boring things get done

Encryption, multi-factor authentication, asset inventory and network segmentation are not glamorous, but they are exactly what insurers began requiring after the Merck case. Doing this work belongs to the technology teams. The CISO’s job is to verify that it’s done and to tell the board plainly when it isn’t. If your underwriter is asking for these controls, your board should be too.

8. Give the board more than 30 minutes

Make cyber a budget conversation rather than a status update. If the CISO’s board time is spent on regulatory trends instead of business trade-offs, the board is being briefed, not governed.

A word to CIOs

I know how this reads to a CIO: someone telling you to give up a direct report. But a CISO who reports to the CIO is grading the boss’s homework. The CIO is measured on availability and speed of delivery. The CISO is measured on risk. That tension is healthy only between peers. Elevating the CISO doesn’t diminish the CIO; it frees the CIO to build without also serving as auditor of what they built. The two roles aren’t a senior and a junior version of the same job. One builds and runs the organization’s technology. The other is responsible for whether the organization, its people included, can be trusted.

A good CIO should lean on the CISO and also be a little afraid of the CISO, the way a good CFO relies on the auditor and is a little afraid of what the auditor will find. That fear is not hostility; it is the reason the books stay clean. Pair it with the partnership described above, and the CIO gets something no direct report can offer: someone with the standing to say “no, not like that” before the board has to hear it from an attacker.

The CIO’s seat at the table appeared when technology became the business. The CISO’s is appearing because trust has become the business, and AI has made that impossible to ignore. Some organizations are already moving. In IBM’s follow-on research, 85% said they plan to increase security spending after learning what frontier AI models can do to their systems, compared with 64% who planned to spend more after suffering a breach. Learning from the threat is far cheaper than learning from the post-mortem, and the organizations that get there first will be the ones where security, research and the business decide together.

The moat is dry. Stop dredging it, and give the person defending the castle a seat at the table.

In June 2017, a piece of malware called NotPetya got into Merck & Co. without climbing a single wall. It arrived through a backdoor in accounting software supplied by a Ukrainian vendor, the kind of trusted, routine traffic every perimeter is built to let through. It spread to some 40,000 of the drugmaker’s computers, and Merck put the damage at more than $1.4 billion.

Then several of Merck’s insurers argued they owed nothing, because the attack was attributed to Russian military intelligence and their policies excluded acts of war. The fight ended in a confidential settlement in January 2024.

Two lessons sit in that story. The moat didn’t matter, because the threat came in with the traffic the moat was designed to admit. And the people who price risk for a living were the first to notice and the first to send the bill.

That was 2017, when attackers still needed humans at the keyboard. Increasingly, they don’t.

Here is the argument of this two-part series in two sentences. Security stopped being a technology problem with business consequences; it is now a business risk with a technology component. Most organizations are still organized the other way around, and the person they hold responsible for that risk sits inside the IT department, reporting to the people whose work they are supposed to check.

What AI actually changed

IBM’s 2026 Cost of a Data Breach Report, based on 602 breached organizations, found that one in four malicious breaches was AI-enabled, a 56% jump in a single year. Those breaches averaged $6 million, about $1 million above the global average of $4.99 million. In the U.S., the average breach now costs $11.5 million.

You don’t need a computer science degree to understand what changed. Three things matter:

• The con got a better voice. Deepfake impersonation was the most common AI-driven attack in IBM’s data, at 45%. The oldest scam in business, the urgent wire transfer, now arrives in the CFO’s actual voice on a video call.
• Your software can be talked into things. “Prompt injection” means hiding instructions inside an email, document or web page that an AI assistant reads and then obeys. It is social engineering aimed at the software instead of the employee, and no amount of annual phishing training protects against it.
• You’ve hired employees with no background check. Every AI agent holding credentials is, in effect, a new hire with keys to your systems, no manager and no HR file.

Notice what those three have in common. Each one is an attack on trust, on whom we believe, what we obey and whom we let in, and only then on technology. That distinction matters for everything that follows.

Meanwhile, employees aren’t waiting for permission. Shadow AI, meaning tools staff use without sanction, showed up in 43% of breached organizations this year, up from 20% the year before. Breaches involving AI systems themselves rose to 21% of all incidents, and the most common way in wasn’t the model at all. It was the plumbing around it: compromised APIs and plug-ins, and misconfigured cloud workloads.

Why the moat is dry

Moat security is easy to recognize. There’s a perimeter, a couple of expensive appliances, an annual training module, and a binder of procedures nobody reads until the auditor visits. It assumes the enemy is outside.

Today, much of the risk has been invited in the assistant reading the email, the agent holding the credentials, the vendor pushing the update. When Johnson Controls was hit by ransomware in 2023, the attackers first got into the company’s Asia offices and spread from there across the network. The company confirmed data theft and more than $27 million in costs, net of insurance. One breach in a distant office, and the moat was irrelevant.

Moat organizations measure activity. Attackers measure time. IBM found the average time to identify and contain a breach rose to 247 days, the first increase in five years. Breaches contained in under 200 days cost $4.32 million; those that ran longer cost $5.65 million. And 53% of breached organizations hadn’t encrypted sensitive data at rest and in transit. The basics still aren’t done.

My email signature reads “Process is the evil twin of progress.” Moat security is process mistaken for protection.

Prepared versus old school: the numbers

In fairness, IBM sells security tools, and I’m aware of the irony of citing a vendor in a piece that warns against learning from vendors. The fieldwork is done by the Ponemon Institute, though, and the pattern matches what independent CISO surveys show. The gap between prepared and unprepared organizations is not subtle:

What separates them	Prepared	Old school
AI and automation in security operations	Breach costs nearly $2M lower	One in four still without
Breach found and contained	Under 200 days: $4.32M	Over 200 days: $5.65M
Employee use of unsanctioned AI	Low: $4.63M per breach	Widespread: $5.39M per breach
AI security maturity (CISO self-rated risk)	3.7 out of 10	7.8 out of 10
AI governance and security teams coordinated	19% of organizations	Everyone else

Sources: IBM/Ponemon, Cost of a Data Breach 2026; IANS/Artico Search, CISO Perspectives on AI Risk 2026.

The most revealing finding comes from the IANS survey of 113 CISOs. Mature AI security programs explained why some security chiefs feel safer today. But confidence about the next two years tracked something else: whether leadership understood AI risk, whether someone clearly owned AI governance, and whether the security team had budget authority and enough staff. In other words, the factor that matters most is governance, not technology.

The CISO problem: under-trained and under-armed

Under-trained. Many of today’s security leaders learned the job defending against people, not software that behaves like people, and much of what they know about AI came from the companies selling the cure. When IANS released its research this year, one of its partners pointed out that most AI risk data comes from vendors and so reflects the supply side. If your threat briefings, your training and your tools all come from the same sales channel, you’ll end up with a well-funded blind spot. Only 53% of CISOs in a LevelBlue survey said they felt prepared to defend against AI-enabled adversaries.

This is not about age. A veteran who keeps learning beats a newly certified buyer of AI-branded appliances every time. What matters is whether someone is still learning now, not when they first learned.

Under-armed. The IANS 2026 board engagement study found that 95% of CISOs brief their boards regularly, but only 10% influence financial decisions. Only a quarter say board discussions of cyber risk run longer than 30 minutes. And 64% of CISOs still report into IT leadership. That leaves the CISO accountable for the breach without authority over the budget.

The lab is not the accounting department

Everything above gets harder in a research organization. Research runs on unpredictable workflows, tools adopted the week they appear, enormous data transfers and outside collaborators. A control that slows a scientist doesn’t stop the work. It moves the work somewhere security can’t see.

The stakes are rising at the same time. Google’s threat intelligence group reported that in the second quarter of this year it investigated extortion cases involving stolen AI research, including one in which a healthcare organization lost drug research and an AI model. It also tracked a China-linked group targeting academic, medical and military research institutions in North America.

R&D doesn’t need an exemption. It needs a different deal: wide freedom in how the work gets done, in exchange for a short list of fixed rules around the assets that matter most. In practice, that means protecting around the lab rather than inside it:

• Make the safe path the fast path. Offer pre-approved sandboxes, compute environments and AI tools that are quicker to use than the workarounds. Researchers don’t resist security; they resist friction.
• Watch instead of block. Monitor what leaves the lab and how, rather than putting a checkpoint in front of every action. Unusual data movement and odd access patterns are how theft gets caught without slowing discovery.
• Classify by value, not by department. Open science, pre-publication results and proprietary or controlled work need different protection. Lock down the crown jewels, not the whole lab.
• Borrow from the research networks. The research and education networking community solved a version of this years ago with the Science DMZ, a design that moves high-performance research traffic outside the enterprise firewall and secures it with targeted controls and monitoring instead.

Some rules don’t bend: federal research-security requirements, defense contracting standards, export controls. But that list should be short, explicit and explained, not a binder of procedures nobody in the lab can name.

We regulate security by tax status, not consequence

Public companies must describe board oversight of cyber risk to the SEC, though the final rule dropped a proposal to disclose whether they even have a CISO. New York’s financial regulator requires the CISO to report to the board in writing every year, and the board must understand and fund the program, public or private. Hospitals still operate under a HIPAA Security Rule written in 2003; its overhaul has slipped to July 2027. A privately held research firm can fail as badly as any public company, and the rules barely notice. Don’t wait for a regulator to tell you what your risk already has.

That is the diagnosis. In Part 2, the prescription: an eight-step roadmap for rebuilding, starting with the reporting line that most organizations never chose and simply inherited.

When juniors take naive questions to an AI assistant instead of a colleague, dashboards record fewer interruptions and higher velocity. What they miss is the erosion of senior engineers’ architectural fluency, and the single point of failure that hardens until an outage forces someone to explain the system out loud.

Over the past year, I watched engineering teams celebrate a quiet drop in Slack interruptions. A junior used to ask a foundational question in a channel: why does this service retry three times instead of once? Today that junior asks an AI assistant first, messaging a senior only if the answer fails in testing. Already, 51% of developers ask generative AI for help they once asked a colleague, and 62% do so to avoid looking uninformed.

Because AI now absorbs routine implementation and debugging, it strips away the trial-and-error juniors once needed to grow into seniors.

Most writing about this shift worries about what juniors lose: less mentorship, thinner context and a shallower feel for production systems. That worry is real, but it is only half the balance sheet.

The costlier loss belongs to the senior engineer and the organization paying them to hold the architecture together. It rarely gets discussed because from the inside it looks like uninterrupted focus time.

What a naive question actually did

A junior engineer’s question was never just a request for a file path. It did three distinct jobs at once, and all three required a human on the receiving end.

First, it transferred tacit institutional memory that documentation never captured. When a senior explained why a payment service retried three times, they rarely recited the config. They narrated an incident from a year ago involving a partner’s rate limiter that never made it into a wiki. Documentation misses that history because nobody writes a design doc after the fact unless something breaks again.

Second, the question gave the senior a live audit of where the system had become needlessly opaque. A staff engineer who has owned a core service for three years goes blind to its rough edges; everything feels obvious once you know the history behind every compromise. A newcomer’s confusion is a free signal showing where the code no longer speaks for itself.

Third, and most overlooked, answering forced an articulation that kept the senior’s own mental model sharp. Explaining a state machine out loud to a colleague who asks follow-ups is a different mental discipline than silently knowing it. Teachers have known this forever: structuring knowledge for someone else exposes gaps and outdated assumptions in your own head. Seniors used to get that workout dozens of times a quarter for free.

The channel disappears without an alarm

Because those informal questions were never scheduled on a calendar or tracked in Jira, their disappearance triggers no alarm. When an AI assistant intercepts the first tier of naive questions, the senior simply gets interrupted less often. Over a single sprint, that silence feels like focus time. Over several quarters, it quietly erodes a senior engineer’s active command of the architecture they own on paper.

In practice, this decay stays hidden during routine code review because spotting a local bug on screen only requires passive recognition. Failure surfaces in postmortems when an incident demands active cross-system recall: tracing how failures cascade across service boundaries during a 2 a.m. outage. Across 211 million lines of analyzed code, structural refactoring has already collapsed from 25% of changes to under 10%, while duplicated code blocks jumped eightfold. Answering a junior’s “what happens if this service times out?” used to force seniors to trace those paths out loud every week. Without that rehearsal, the first live cross-boundary trace happens during an outage.

Why velocity dashboards hide the risk

For a CIO or VP of Engineering, this creates a treacherous blind spot. Standard engineering health metrics do not merely miss this erosion; they actively misread it as progress.

When juniors turn to AI assistants instead of pinging seniors, short-term output metrics look healthier. Pull request cycle times tighten, ticket velocity ticks upward and seniors report fewer context switches on surveys. On throughput dashboards, the organization appears to scale effortlessly.

Underneath those green indicators, three structural risks accumulate.

• Shared ownership collapses into a single point of failure. When architectural rationale stops circulating verbally, it stays trapped inside the heads of the veterans who wrote the code. If one leaves, what looked like shared ownership turns out to be unshared memory.
• Outage recovery times creep upward on complex incidents. Routine bugs get fixed faster with AI, but cross-boundary outages take longer. Every 25% increase in AI adoption correlates with a 7.2% drop in delivery stability, including mean time to recovery (the time required to restore service during an outage). When AI-generated changes hit production without shared context, seniors on the call spend precious minutes re-deriving how subsystems interact.
• Senior engineers’ verification load rises even as mentoring drops. In a 2026 study on developer skill formation, Anthropic found developers who delegated coding to AI suffered their steepest drop in debugging comprehension, creating a downstream “debugging tax.” In practice, seasoned engineers spend up to 19% more time verifying and fixing AI-generated code. When juniors skip foundational questions upfront, they still need a senior engineer’s help later, shifting the senior’s role from upstream architectural teaching to downstream code forensics.

An organization can coast on accumulated fluency for many months before the deficit shows. By the time it surfaces in postmortems or failed handoffs, the habits keeping senior engineers’ understanding sharp have been dormant for quarters.

Rebuilding the channel on purpose

When leaders notice institutional context slipping, their first instinct is more aggressive documentation or indexing wikis into AI retrieval tools. That solves lost context for readers, but not the senior engineer’s problem. Writing a static wiki page never forces a veteran to defend trade-offs or trace failure cascades under questioning. Documentation captures static answers; only live human friction keeps architectural fluency sharp.

Restoring that friction does not mean restricting juniors from using AI assistants, nor does it mean adding optional team meetings that get cut the moment a deadline tightens. If directors are rewarded solely on ticket velocity, informal coaching will always lose to the sprint board. Instead, leadership must embed live human articulation across the four moments where architecture is built, rehearsed, stress-tested and passed on.

• At code review: require verbal design defense on cross-boundary pull requests. Instead of letting seniors review large AI-generated diffs silently, require a ten-minute live walkthrough whenever a change touches service boundaries, retries or state machines. The junior explains what the code assumes about upstream systems; the senior explains why the boundary exists, replacing downstream code forensics with upstream architectural dialogue.
• Between incidents: run live system walkthroughs without slides. Once a month, a senior engineer walks peers through a critical production workflow on a whiteboard, tracing failure modes and historical trade-offs out loud. Where the explanation stalls or relies on guesswork, institutional memory has decayed before an outage can expose it.
• After an outage: require unscripted architectural narratives in postmortems. Do not let seniors satisfy an incident review by pasting a runbook link or an AI-generated summary. Require the service owner to explain live to adjacent teams how a failure propagated and why the boundary exists. Unscripted explanation under peer questioning is the only reliable test of whether a team still understands the software it runs.
• At the 90-day check-in: test architectural handoff, not just ticket count. AI makes it easy for a new hire to close starter tickets in week two without learning how the system fits together. Use the first structured performance check-in to verify whether the new engineer can modify a core workflow and explain its failure modes without escalating to the senior owner. If early ticket velocity is high but every structural change still routes back to one veteran after a quarter, AI is accelerating surface work while core architecture stays trapped in one head.

Every seasoned engineer knows the feeling of trying to explain a familiar system out loud and finding the words no longer come cleanly. That stumble rarely means they lost their edge. It means the friction that kept their understanding sharp was automated away, and leadership must put it back before the next outage asks for an answer.

Organizations are rapidly implementing AI, but employees often feel left behind about how AI is changing their roles. While AI can help get work done faster, it won’t necessarily help produce better quality work if employees aren’t confident in their AI skills.

In fact, a recent survey from workforce analytics firm Visier reveals that 48% of employees report exaggerating their AI expertise at least some of the time, and 45% said they often feel pressure at work to use AI despite not feeling confident with the tools.

“The risk of underinvesting in training and upskilling is that the alternative can be much more expensive,” says Andrea Derler,  principal researcher at Visier. “Layoffs, rehiring, and competing for top AI talent all come with significant direct and indirect costs. Investing in the workforce organizations already have can be an important part of making AI adoption sustainable.”

When the pace of AI implementation is rushed, however, employees often default to AI compliance, meaning they’ll use AI tools as they’re rolled out, even if they haven’t received the proper guidance or training from their organization to make the most of the investment. So for companies investing millions into an AI future, adds Derler, it’s critical to ensure employee participation is part of the bigger picture to avoid performative AI usage.

Make the most of AI investments with training

Derler acknowledges that AI adoption goals vary widely from company to company. Some will need stronger investment in upskilling and training, while others might need to invest in new resources, talent, or tools to help employees get comfortable with the new pace of work, and to source the right skills for an AI-driven workplace.

Either way, the early stages of AI adoption should involve a basic framework to assist in employee adoption, one that involves open transparency and foundational guidelines for how AI will be implemented moving forward. At bare minimum, employees should be briefed on concepts around general data security, how to spot false outputs from AI tools, and expectations around AI’s impact on their job roles.

“A few foundational skills and concepts every employee should understand include protecting sensitive data and uploading information with the greatest care, understanding the basic capabilities of approved tools available at work, and checking the work before sending AI-generated output to others,” says Derler. “Most importantly, employees need to take full responsibility for the output. The AI isn’t responsible, the person using it is.”

Avoid setting blanket goals as well, where everyone is measured simply by a certain percentage of AI usage per individual or team. This can overlook reality, she says, creating a false narrative around AI usage and adoption, and a focus on quantity rather than quality. Instead, adds Derler, leaders need to strike a balance between mandating adoption and encouraging employees to explore, discuss, and learn together where AI makes sense.  

Embracing transparency around AI

When asked if they felt their organization had a clear plan for how AI affects employees and jobs, 59% in the Visier survey said they didn’t believe or were unsure that their company had a clear plan for AI implementation. And only 28% said they felt leaders in their organizations fully understand how employees use AI today, while 33% thought leaders understand how much training and support employees need.

Overall, employees are eager to embrace AI, but when asked what would boost confidence around using it at work, respondents first ranked AI training and upskilling opportunities as most important, then greater transparency from leadership, more opportunities for career growth and advancement, followed by better communication about how AI will impact employees.

Clarity from the top

Employees have made it clear they want training and transparency from leaders about how AI will affect workflows and processes. This is especially true when it comes to career paths, as AI stands to remove certain skills and expertise deemed irrelevant.

Workers know the future will look different in the wake of AI, but they want to understand exactly what that looks like so they can prepare and shift their own expectations and career goals. Part of keeping employees engaged is ensuring they’re invested in their own future at a company, but that’s difficult if the future is particularly unclear.

“Most organizations are still learning which parts of a workflow AI can take over or augment, so creating opportunities for experimentation and learning is an important step before setting formal adoption goals or metrics,” says Derler. “Many organizations, however, are still piecing together workforce data and don’t have a complete picture of their workforce, making it harder to understand where AI can create meaningful efficiencies.”

The AI industry says enterprises can’t absorb AI. Its answer is to absorb it for them.

That’s not the same thing.

The engineer across the table knew the model cold. Not vendor-deck cold. He could tell me where it would break, what it would cost, and which benchmark numbers to ignore. One of the better technical minds who has ever sat in that chair.

Forty minutes in, I asked which of our problems he would point to first. He named a minor workflow one of my directors had mentioned that morning.

The easy read is that he didn’t know my business. True, and not very interesting. Here is the real issue: the people who sent him are making the exact mistake they are being paid to fix.

They’re not wrong about the problem

The frontier labs aren’t confused, and I want to be fair before I disagree.

MIT found that about 95% of enterprise AI pilots produced no measurable profit impact, despite tens of billions spent. Winning the benchmark wasn’t winning the deal. And a customer who never gets to production never renews.

So, they did the logical thing. They sent engineers:

• OpenAI: Launched the OpenAI Deployment Company in May with $4 billion of initial investment from 19 partners, starting with 150 forward-deployed engineers (FDEs) and deployment specialists.
• Anthropic: Partnered its engineering capability into an enterprise AI services venture with Blackstone, Hellman & Friedman, and Goldman Sachs, reportedly valued above $1.5 billion.
• Microsoft: Launched Frontier Company in July with $2.5 billion and approximately 6,000 embedded engineers and industry experts.
• AWS: Committed $1 billion to a forward-deployed engineering unit embedding engineers with customers.
• Market surge: FDE job postings grew roughly 800% in nine months, with principal-level compensation past $1.2 million.

That is a serious response. But it answers the wrong question.

All that capital is aimed at getting customers into production and keeping them there. We have been reading it as the playbook for getting AI into the wider economy. Those are two entirely different problems, and only one of them currently has anyone working on it.

The same mistake, twice

One structural fact tells you what this really is: the forward-deployed function sits on the commercial side of these companies, not in research. OpenAI capitalized its deployment arm as a separate business with outside investors.

That’s not an accusation; the org chart is simply more honest than the positioning. What that structure measures is pilots converted to production within the quarter. Against that scoreboard, the model isn’t shallow at all. It’s optimal.

Now look at the same failure from my side of the table.

A team builds an intelligent demand-forecasting agent. Accuracy improves by double digits. The engineering readout is genuinely good. And absolutely nothing happens to cash.

Accuracy was never the point. It only matters if it changes an operational decision: how much safety stock you hold, what commitments you make to suppliers, or what you schedule to build. If none of that changes, you haven’t freed a single dollar. Worse, if the model was tuned for fill rate, because that’s what the commercial team asked for — inventory climbs, working capital gets suffocated, and you are worse off than before. The project wins. The company loses.

Anyone who runs S&OP knows this reality: inventory is cash sitting on a shelf. Every forecast is a claim on that cash. Every service-level promise decides how much liquidity you lock away. The value is never in optimizing a single, isolated number; it is in managing the trade-offs between them.

Outside engineers cannot own those trade-offs. Look at what their engagements are sold on: a deflected ticket, an automated records request, a closed underwriting case. Local, siloed inside one function, and measurable in weeks. Those are the only outcomes an outsider can deliver before rolling off to the next logo.

The results a CFO cares about cross three functions and only show up in cash two quarters later.

Delivery vs. Diffusion

This gets celebrated as AI reaching the real economy. In practice, it does close to the opposite:

• The learning runs one way. Anthropic’s job listings are the most explicit about this, asking engineers to codify repeatable deployment patterns and contribute those insights back to product and engineering. That is honest, and it is the point. Analysts have flagged the same concern from the buyer’s side: that deep reliance on frontier labs hands those labs insight into the very industries they may later enter.
• Capability is replaced instead of built. The engagement leaves behind a functioning workflow, but zero internal muscle to solve the next bottleneck. Constellation Research warned this year that FDEs are often used as a crutch for product immaturity, and as part of a broader vendor dependency play. Without Palantir’s feedback loop — where every deployment continuously enriches an integrated customer ontology — this is simply an expensive systems consultancy with a frontier brand name.
• The mid-market waits. Dice reported in August that the shortage of engineers is now directly holding back enterprise AI returns. The scarcest engineering talent sits inside Fortune 50 anchor accounts, while the companies that make up most of the economy queue behind them.

In engagements structured this way, the lab learns your business. You learn their product.

So here is the test I now apply to any engagement, including the ones I sign: when it ends, are we better at finding the next problem ourselves?

If the capability leaves the building when the outside engineer packs up their laptop, that was delivery, not diffusion. Delivery is a fine business, but it doesn’t compound. Diffusion is nothing but compounding.

What we did instead

We eventually fixed our inventory problem.

Not by pointing an off-the-shelf frontier model at raw ERP tables, but by redesigning our entire planning architecture from the ground up. We staffed the initiative with operators who had spent years inside our supply chain and knew where the real constraints actually sat — the lead time, the allocation rule, the supplier commitment that no outsider would have known to model and then applied AI directly to those decision points.

The result: Inventory came down and stayed down, turns improved, and the part that matters for this argument, nobody had to be flown in to do it again the following quarter.

There is no version of that outcome a single outside engineer could have designed, no matter how brilliant the model in his hands.

The bigger stake

Scale this up and the macro picture becomes clear. A country’s AI advantage will not come from a few hundred flagship deployments. It will come from millions of operators applying AI to operational decisions they were previously making badly.

Which brings me back to the man across the table.

We are spending millions sending AI experts to learn business. We should be sending business operators to learn AI.

• Business knowledge is slow. It took most of my career to instinctively know which trade-offs to make around working capital and inventory.
• AI execution is fast. An experienced operational team can master agentic tooling, evals, and model orchestration in a matter of months.

We are running the harder transfer at immense cost and mistaking it for progress.

I don’t need more forward-deployed engineers. I need backward-deployed domain experts.

Nobody is selling those. Which means the only people who can build them are already on your payroll.

Tools such as Anthropic Claude Code or OpenAI Codex have changed the face of software development, and all the signs are that this investment is set to increase further. But is paying a monthly subscription (and perhaps additional usage fees) cost-effective? Will the increasing level of investment in AI-generated software prove to be worthwhile?

That’s a question several economists from the US National Bureau of Economic Research have attempted to answser, at least indirectly. Their paper, snappily entitled The Macroeconomic Effect Of AI: Sizing The Software Engineering Channel, attempts to calculate how much AI has increased the productivity of software engineers in enterprises outside the software and semiconductor industries by looking at their share prices.

“The main idea is that if AI raises the productivity of software engineers, then firms that rely more heavily on them should benefit more. Therefore news about AI generates higher expected profits for these firms. These profits are capitalized into larger stock price responses. Combining these stock price responses with a model, we can pin down the increase in software engineering productivity expected by financial markets,” the researchers wrote.

And the answer? “From November 2022 to December 2025, AI increased the market’s expected present value of software engineering productivity by the equivalent of a permanent 32.6% productivity increase.”

That’s not to say that your software engineers will be that much more productive, but it’s a starting point for allocating budget between AI tool use and payroll.

Every technology professional working today owes their career, in part, to someone who took a chance on them. The software engineers, cybersecurity analysts and infrastructure specialists on our teams didn’t arrive fully formed and ready to perform. Someone hired them, trained them and gave them room to grow. That’s worth remembering now, because the tech talent pipeline is shrinking, and history shows it can take more than a decade to recover from a decline like this.

The pipeline is at risk on two fronts: fewer students are choosing to study computer and information science, and hiring managers anticipate weaker near-term demand for these graduates. This isn’t unprecedented. The computing profession has weathered two major talent declines in its short history, and each took more than a decade to recover.

After 15 years of growth, computing enrollment reverses

According to the U.S. National Center for Education Statistics, the number of computer science and information science B.S. graduates has grown steadily since 2010. A tech degree once meant a well-paying job and a career with a future, driven largely by increased demand for technology. That momentum now appears to be reversing across several regions of the world.

Research from the National Student Clearinghouse Research Center indicates that enrollment in CS and IS degree programs at U.S. colleges and universities fell by more than 8% in 2025, even as overall undergraduate enrollment grew by 1%. The trend is not confined to the U.S. According to BCS, the Chartered Institute for IT, the UK saw a similar decline, with 18-year-old UK students entering computing degree programs down 9% in 2025. In Australia, the decline appears even steeper. ACS/Information Age reports that the Australasian Conference of Tertiary Admissions Centres’ (ACTAC) January 2026 report shows a 21% decline in enrollment for IT-related degrees heading into 2026.

Three countries, three distinct education systems, one common outcome. What makes this notable isn’t any single data point but the swift, dramatic downturn across all three countries. If this were confined to one market, it could be dismissed as a local correction; however, a similar pattern across the United States, the United Kingdom and Australia at roughly the same time suggests a structural shift in how the next generation views a tech career.

What 1,200 tech leaders expect: Lower demand for graduates

If the problem were only fewer graduates, but hiring demand were strong, the situation would probably correct itself. Talent scarcity would drive up wages and attract more students. However, the demand side appears to be contracting as well.

In the second quarter of 2026, I surveyed more than 1,200 senior technology leaders across 53 countries and what they expected their organization’s hiring demand to look like five years from now for entry-level roles filled by graduates with bachelor’s degrees in computer science or information science. Respondents held titles including CIO, CTO, VP of IT and Head of IT. More than 6,600 surveys were sent to technology leaders via LinkedIn, resulting in a 19% response rate.

The results were sobering: 45% anticipated lower demand, 28% expected demand to remain the same, while 27% anticipated it to increase. (You can download a summary of the survey at https://www.impactfultechnologyleader.com).

This result was not limited to a specific country or region. Those expecting hiring demand to be lower in five years were in the majority across almost every country, industry and company size.

The reason, cited across every group, was the same: artificial intelligence. Leaders expect AI to automate much of the entry-level technical work, including routine coding, service-desk tickets and administrative tasks. This is the work where new graduates have traditionally begun their careers. The logic is that existing teams, aided by AI, can do more without adding entry-level employees.

Regardless of their responses to the survey question, many of the leaders noted that reducing the number of junior roles in their organization was problematic. They recognized that it would erode the pipeline for future senior talent and that recovery from this gap would take years.

We’ve seen this movie before

This is not the first time CS and IS graduate numbers have fallen. In the mid-1980s, universities were overwhelmed by demand for computing degrees and couldn’t staff their programs adequately. When computer science programs could no longer accommodate surging student interest, universities capped enrollment and tightened admission standards. The effect was dramatic: bachelor’s degrees in computer and information sciences fell sharply beginning in 1987. It was not until 2001, fifteen years later, that graduation rates recovered to their earlier level.

The second decline came in the early 2000s, in what might be called a perfect storm. The winding down of Y2K remediation, the bursting of the dot-com bubble and a rising wave of IT offshoring converged to shake confidence in technology as a career. Mass layoffs swept through IT departments and dot-com firms, and enrollment in computing programs fell once again. The resulting drop in tech degrees awarded began around 2005, and it was not until 2016, eleven years later, that graduation rates recovered to the same level.

The chart below from the U.S. National Center for Education Statistics depicts these two significant declines in graduates with technology degrees.

Is AI the new perfect storm?

If you map today’s forces onto that history, the parallels are hard to miss. AI-driven automation is creating a demand shock, declining technology enrollments are creating a supply shock, and growing skepticism about the value of a technology degree is creating a confidence shock. This mirrors the erosion of faith in the career path after the dot-com bubble burst.

What took three separate crises to unfold in the early 2000s is converging today into a single, self-reinforcing cycle: fewer students entering the field, fewer opportunities perceived to be waiting for them, and growing doubts about whether a technology career is still worth pursuing.

But the parallel to those earlier declines carries a warning, not a reassurance, and the difference is in how each one ended. The recoveries of 2001 and 2016 happened because demand never left. In both prior declines, the supply of graduates fell while the underlying need for tech professionals rebounded. That gap between shrinking supply and steady demand is exactly what refilled the pipeline: scarcity pushed up wages, wages pulled students back and the market corrected itself, however slowly. What makes today different is that supply and demand are falling together. If leaders are correct that AI will replace entry-level work, this is not a demand dip waiting to rebound, it is a demand replacement, with no scarcity signal to trigger the correction that saved the field twice before.

The most dangerous feature of a talent pipeline is its lag. The decisions made, or not made, over the next few years will not trigger a crisis next quarter or even next year. Their consequences will surface a decade or more from now, when today’s would-be entry-level personnel should have become tomorrow’s architects, technical experts and leaders.

By then, the shortage will already be baked into the pipeline. And that is not speculation. It is the exact pattern the historical record has already shown us, twice.

What is a technology leader to do?

It would be easy to read my survey results and conclude that the market will sort itself out. Nearly half of the IT leaders I surveyed plan to hire fewer entry-level computing graduates over the next five years. Many dismiss this: education will adapt, five years is an eternity and better tools will make new graduates more capable than ever. They may be right. But hope is not a workforce strategy, and the leaders best positioned five years from now will be those who treat this as a problem to solve, not a trend to watch.

The uncomfortable truth is in one respondent’s warning: “We will have to find a way to invest in new grads, or we will not have a pipeline of middle and senior talent later.” If every organization cuts entry-level hiring because AI now handles the routine work, our profession will slowly stop developing the senior engineers it will desperately need a decade from now, because senior people are simply junior people who were given room to grow. No single leader can solve this problem, but every leader can refuse to be the one who lets it happen to their organization.

That starts with treating early-career hiring as a strategic investment, not the first line item to cut. Set aside several junior roles intentionally as an investment in your future leadership, rather than as just headcount to cut in the upcoming budget cycle.

It also means redefining what “entry-level” even means. Survey respondents were consistent: the bar is changing. They want applicants with a strong portfolio over a strong transcript, demonstrable AI fluency over rote coding, and, in the words of one, “people who can ask the right questions” now that AI supplies the answers. Rewrite your junior roles and interviews around critical thinking and judgment, and recognize that a new graduate’s value now lies in growth potential and the ability to direct AI-assisted work, not raw output.

Finally, the talent pipeline begins with encouraging high school students to seriously consider a career in technology, and guiding them will take all of us. If history holds, many will steer away from computing if they believe it has no future. Before committing to an investment as large as four years of tuition, parents will point their children toward fields with steady or rising demand.

Technology leaders and their teams can cut through the hype and help students understand what a career in technology looks like in the age of AI. Through classroom talks and on-site job shadowing, they can give students a real chance to discover the opportunities computing offers. Through mentoring and hands-on, real-world projects, they can support STEM programs at both the primary and secondary education levels.

With the aid of other technology professionals and leaders, I have developed a directory of organizations that promote and support careers in computing across the globe. The directory, which can be found at impactfultechnologyleader.com, includes:

• Programs for grade school, high school and university students
• Support for working professionals, whether already in tech or breaking into it
• Groups serving those transitioning from the military, returning to work or navigating any other path
   

Every one of us in this profession got here because someone decided we were worth the investment before we had proven it. That decision was never really about what a junior professional could contribute on day one. It was a bet on who they could become. AI may change what that first job looks like, but it doesn’t change a fundamental truth: to become a senior technology professional, you first had to be a junior one. The leaders who understand that over the next five years won’t necessarily be the ones who predicted AI correctly. They’ll be the ones who understood that some risks are worth hedging against even when you can’t be certain, and that a talent pipeline, once broken, takes a decade or more to rebuild.

The question isn’t whether the pipeline is at risk; it’s what you will do to make sure it doesn’t break.

AI’s ascent in business is rapidly impacting nearly every industry and job role. But, according to data from Indeed Hiring Lab, IT remains the epicenter of AI’s role revolution, with AI skills still proving to be most valuable to those working technology positions.

When it comes to AI’s impact, Indeed identifies software development, IT systems and support, data and analytics, marketing, and banking and finance as high-exposure occupations, whereas jobs in healthcare, food service, cleaning, and manufacturing are less directly impacted by the rapid ascent of AI.

Indeed’s report examined the correlation between AI exposure and advertised pay, evaluating salary data for high-exposure occupations most impacted by AI, and how it compared to low-exposure ones. The data revealed that the job roles most impacted by AI have experienced a steady rise in salary since 2022.

The post-ChatGPT pay premium

Salary data from the most-exposed roles revealed a pay premium for AI skills that’s been on a steady incline since ChatGPT’s release. For roles categorized as being the most-exposed, pay has grown 46% since 2021, compared to only 25% for the least-exposed roles. Companies are paying a premium for AI-related skills, especially as more organizations face a skills gap in the face of rapid AI adoption.

Since ChatGPT’s launch, roles in the more- and less-exposed occupations tracked similarly for salary growth until around 2024, when the split became more pronounced. This is around the same time the industry saw a bounce-back in hiring for roles in the more-exposed category, as employers began reorganizing work and competing for AI-adjacent skills, according to Indeed.

The data ultimately reveals a post-ChatGPT pay premium of 5.7%, says Jack Kennedy, economist at Indeed and author of the report. And that pay premium holds up under granular control. Even when a job title is compared against itself — such as data engineers against other data engineers, or truck drivers against truck drivers — the pay premium still stands around 4.7%.

Despite concerns that AI will hollow out knowledge work and drag down pay for the jobs that remain, Kennedy says the four years of data after the public release of ChatGPT reveals the posted wages for occupations most impacted by AI aren’t lagging. Instead, they’re outpacing their less-exposed counterparts.

AI pay premiums by seniority

When isolated by seniority level, pay premiums in high-exposure tech occupations start to diverge. The data reveals that at the senior level, pay in more-exposed roles grew 45% between 2021 and mid-2026, while at the midlevel, there’s a smaller gap of 12% growth. Entry level roles only saw 2% salary growth.

“The data points to hiring restructuring rather than direct substitution,” says Kennedy. He adds that it’s not a reflection of senior raises funded by junior cuts, but rather a sign that employers are shrinking the entry-level pipeline into AI-adjacent roles, while concentrating hiring and pay gains at the top.

A spectrum of skills

This data also supports the idea that AI is creating a push for soft skills in tech positions, while the industry has been traditionally focused on hard skills. But as AI learns and adopts hard tech skills, it’s becoming more valuable to have experienced, skilled tech workers who can oversee the AI outputs and ensure they’re correct.

“Employers appear willing to pay more for the ability to effectively oversee, integrate, and correct AI output,” says Kennedy. “The fact that pay premia scale with seniority suggests that experience and expert judgement are particularly salient qualities. At entry level, the smaller pay premium for AI exposure points to the complementary effect being thinner.”

AI is evolving to be a complement to skilled workers, rather than a replacement, and is changing market demand and average pay for traditional IT and tech skills, according to the report. It’ll be crucial in the next few years for tech employers to ensure their pay for high-exposure occupations continues to scale with other AI-adjacent industries to maintain competitive stature in recruiting and hiring.

AI is moving quickly across organizations. Companies are scaling existing use cases, introducing new tools and giving more employees access to AI as they look for ways to improve efficiency and stay competitive.

That expansion is making AI governance a shared responsibility. CIOs are responsible for helping their organizations adopt the technology effectively and securely. Legal teams are increasingly focused on the litigation, regulatory, privacy and compliance risks associated with its use.

As AI reaches more parts of the business, these teams need to work closely together. Each has visibility and expertise the other needs, and both have an important role in helping the organization scale AI responsibly.

AI has changed the relationship between IT and legal

AI touches nearly every part of a business. It can involve company and customer data, influence decisions, interact directly with employees or customers, and perform work through agents.

Agents, in particular, have changed the governance equation over the past year. When companies were primarily piloting AI, much of that experimentation happened within IT. Those teams were accustomed to working within security, data and compliance requirements. Now organizations are under pressure to scale AI beyond those controlled environments.

That shift is already well underway. McKinsey’s 2025 State of AI survey found that 88% of respondents said their organizations regularly use AI in at least one business function, up from 78% the year before.

I see that pressure from both sides. As a CEO and a member of several boards, I push companies to use AI because I believe failing to adopt it effectively poses a serious competitive risk. But that same push for adoption makes governance more important, not less. CIOs are being asked to expand AI use while simultaneously managing risks that become harder to see as adoption spreads.

The strength of AI agents is that nontechnical employees can use them to build workflows and automate work. People in HR, finance, marketing, communications and other departments no longer need to wait for IT to develop every AI-enabled process. That accessibility creates enormous opportunity, but it also means AI can be deployed by employees who aren’t accustomed to evaluating security, compliance, privacy or legal risk.

Organizations therefore need to know what data an AI system is using, where that data came from, what decisions the system is making, and whether those uses align with company policies and legal requirements.

AI may also be embedded in third-party products an organization already uses. This creates shadow AI, where organizations may have AI operating without a complete understanding of where or how it is being used.

This is one of the biggest areas of risk we see. When we work with organizations that have five or 10 approved AI use cases, those applications usually aren’t where we find the most significant problems. The company already recognized those use cases as potentially risky, so someone evaluated them. The greater risk often comes from the AI the organization doesn’t realize is operating at all.

Understanding how AI is being used across the organization gives CIOs and legal teams a place to start.

AI requires closer collaboration

Legal teams are becoming increasingly aware of the exposure AI can create. There are new AI laws and regulations developing around the world, along with significant litigation happening under laws that already exist.

That concern is showing up among corporate legal leaders. In Norton Rose Fulbright’s 2025 Annual Litigation Trends Survey, 56% of respondents said managing the litigation and legal risks surrounding generative AI had been a challenge for their organization.

Many of those lawsuits involve familiar legal issues, including discrimination, consumer protection, privacy, wiretap laws and consent to use data. AI is simply the context in which those existing issues are now arising. There is no broad exemption from existing law simply because a decision or activity involves AI.

That legal risk isn’t always well understood throughout the rest of the organization. I recently met with a large retailer that had its CIO, COO, technical leaders, business leaders and an attorney in the room to discuss AI governance. When I raised litigation risk, much of the group wasn’t particularly concerned. The attorney was.

She explained that legal was deeply concerned about the company’s exposure and that the company had already faced an AI-related lawsuit. Several other people in the room didn’t know that had happened.

That type of disconnect matters as organizations scale AI. Legal may have information about litigation, regulation, contracts and privacy concerns that hasn’t reached the people making technology decisions. IT has visibility into systems, vendors, data, security and how AI is being deployed across the business.

Regular collaboration gives both groups a more complete understanding of what the organization is doing and where its risks are.

What CIOs and legal teams bring to AI governance

CIOs and technology leaders have an important role in creating visibility around AI. They can help organizations identify the systems and use cases already in place, including internally developed AI, third-party tools, vendor applications and shadow AI.

They also understand the organization’s technology environment. That includes its security requirements, data infrastructure, technical standards and the practical considerations involved in deploying AI at scale.

Legal teams bring a detailed understanding of the legal risks surrounding those same use cases. They can help determine which regulatory requirements apply, identify privacy or intellectual property concerns, review contractual obligations and evaluate whether the organization has the right to use particular data.

In large organizations, the attorney most familiar with AI risk may not be the chief legal officer. It may be a deputy counsel or another member of the department who has been following the issue closely. At smaller companies, it may be one attorney managing AI alongside dozens of other responsibilities. Making AI risk part of the broader conversation gives that legal expertise a way to reach the people responsible for adoption.

Building a partnership between CIOs and legal teams

A strong AI governance program brings these groups together as part of an ongoing process. Many organizations are doing this through an AI governance committee that includes IT, legal, compliance, security and representatives from business units using AI. The committee can review use cases, apply company policies, evaluate risks and determine what safeguards are appropriate.

That work starts with a comprehensive inventory of AI use across the organization. For each use case, the organization should understand what the technology does, what data it uses and what decisions it makes.

The use case itself matters because the same underlying technology can carry dramatically different levels of risk depending on how it is applied. Using generative AI to draft promotional copy about a city, for example, presents a very different risk profile than using it to prepare information that will be submitted to a federal regulator.

From there, the committee can evaluate the use case against company policies and identify areas that need to be addressed. If a resume-screening tool creates a risk of bias or discrimination, for example, the organization may decide to test it regularly or require human review. If an application uses sensitive data, the team needs to understand where that data came from and whether it can legally and securely be used.

The process also needs to be documented. Leaders often believe AI governance means documenting good intentions. Regulators and litigants will care about whether those intentions were operationalized, continuously enforced and supported by evidence.

Company policies are part of that documentation. If a policy requires a human in the loop for certain risk levels, the organization needs to follow that requirement and be able to demonstrate that it did.

Shared visibility makes this process easier. CIOs, legal teams, compliance, security and business leaders should be working from the same understanding of the organization’s AI systems, use cases, policies, risk assessments and approvals. This gives the governance committee the information it needs to make decisions as AI adoption continues to grow.

AI governance requires a shared approach

AI will continue reaching more systems, business functions, vendors and employees. That makes collaboration across the organization an increasingly important part of governing it well.

CIOs understand how the technology is being deployed and what it takes to scale it. Legal teams understand the laws, litigation and other areas of exposure surrounding that use. Bringing that knowledge together gives organizations a clearer view of their AI environment and a stronger process for evaluating new use cases as they emerge.

AI has enormous potential, and companies need to find ways to use it effectively. Scaling it safely requires safeguards and governance to be built into adoption from the beginning and maintained as AI use expands.

For companies expanding their AI usage, such a shared approach provides the structure needed to keep up with the technology and the risks that come with it.

AI forces organizations to make consequential decisions during uncertainty. Roche chief digital and technology officer Wafaa Mamilli says this is the new leadership imperative. She believes success depends not on certainty but building the right foundations: trusted data, governance that enables speed, upskilling the employee base, and focusing on outcomes. At Roche, that approach drives applications ranging from kidney disease prediction and digital pathology, to new ways of discovering drugs and coaching people leaders. The challenge today, Mamilli says, is building organizations that can learn, adapt, and deliver value while the future unfolds, rather than predicting what’s next.

How is Roche using AI to drive growth and impact?

Our overarching mission is to prevent and cure diseases, and improve patient outcomes while reducing structural healthcare system costs. Data and AI are foundational enablers of this strategy. The high-quality data we’ve been assembling throughout our more than 125 years are part of this foundation.

We’re using data and AI to quickly make sense of a massive amount of information —  including genomic sequences, medical images, clinical records, and complex biology — and turn it into real medical breakthroughs at every stage of the patient journey.

We understand AI is a human and change management challenge, not a technical one. That’s why we prepared a holistic approach, which includes what we call everyday AI, to upskill our employees and embed AI in everyday work; reshaping with AI, which is reimagining our end-to-end processes; and our big ideas, which are large transformational investments.

Can you detail some real examples that bring this strategy to life?

We’re building AI tools across the entire value chain. Our enterprise platform, Galileo, includes internal generative AI tools, search engines, and compliance-safe sandbox environments.

We also have custom AI-powered coaching tools to help managers prepare for staff conversations. In our diagnostics business, we implemented an AI algorithm called Kidney Klinrisk to help physicians predict the risk of chronic kidney disease progression before irreversible damage.

Plus, we use AI image analysis in our digital pathology to assist pathologists in identifying cancer cells faster, and in R&D, we have AI-powered simulations to model clinical trial scenarios.

In manufacturing, we’ve deployed digital twins of our production lines, reducing development cycles by two to four times, and cutting technology transfer times by 50%. In drug discovery, we allow scientists to model over 100 experiments, compared to five in a physical plant.

But none of these examples imply that we’ve figured it out. We need to be confident and humble at the same time, but we’re still working through this just like anyone else.

How do you balance AI innovation and risk?

We develop life-saving medicines and vital diagnostics that directly impact human health, so we’ve always balanced speed and safety with the right governance framework. To position governance as a velocity enabler, we established a dedicated, horizontal framework focused on AI risk and ethics, and an AI strategy and governance function to ensure we leverage models responsibly.

But as important, I personally engage in understanding what’s at stake, because everything is changing overnight. That attention to detail is critical, because our job is to align, comply, and govern while allowing speed. We act with speed, assess, experiment, and then say yes or no to scaling the innovation. That’s how we lead this from the executive team down.

What changes have you made to your IT organization for this new wave of AI innovation?

I renamed my organization Roche Digital Technology, RDT, to position us as powering the business with digital tech and AI in a federated model. I created dedicated AI and data teams, which report to two new roles in RDT, chief AI officer and chief data officer. I intentionally made these two different roles because we need our data structured and ready for what we want to do with AI. There’s no AI without data, and we have foundational work to do there.

Think of the AI team as what the market calls forward deployed engineers — they work with all the other teams to re-engineer processes and deploy AI, while feeding the AI platform and tools teams so the platforms evolve to match the enterprise and marketplace. Enterprise architecture and portfolio governance are capabilities teams that support the full organization.

Describe the profile of the CAIO, as you’ve defined it on your team.

The AI officer is business outcomes driven and passionate, as well as tech savvy with high learning agility. This leader needs an entrepreneurial, risk-taking mindset, an ability to deliver, and deep process knowledge.

Remember business process re-engineering? There are some similarities here. We need people who can think through the workflow, not just what’s implemented in the systems but the actual workflow, what people do offline between systems, and think work and workflow, which we’ve never fully figured out. We need people who can step back, look end-to-end, and, with the knowledge of AI, reimagine what a process can look like now. So business process savviness is critical.

What advice do you have for tomorrow’s CIOs?

You have to be both a business and a technology leader. I call that a business minded technologist since business fluency is table stakes. You need to understand strategy, customers, and the economics of how the enterprise creates value. But particularly, in this moment, deep technology acumen matters enormously. The CIO has to understand not only what technology can do today, but what’s becoming possible tomorrow, and translate that into choices for the enterprise.

This is why I believe this is the most exciting time to be CIO. Technology and now AI is no longer enabling the business. It’s reshaping how companies operate, how decisions get made, how people work and increasingly how value is created.

So my advice for tomorrow’s CIOs is don’t be passive or apologetic about being a technologist. If there isn’t a chair for you at a table, bring your own by bringing an enterprise perspective and insights others can’t.

Few roles have the same vantage point across the organization. Use it. Connect the dots across functions. Understand the technology deeply enough to challenge assumptions. Bring confidence and humility, have the courage to make choices, and recognize that the hardest part of transformation is rarely the technology itself but bringing people with you.

Start with a number that should ruin your week.

In MIT’s GenAI Divide study of enterprise adoption, only about 40 per cent of organisations had bought official large language model subscriptions. Workers at more than 90 per cent of those same organisations were already using personal AI tools for work. The finding that got the headlines was that roughly 95 per cent of formal pilots produced no measurable return. The finding that matters are the other one.

Your people have already adopted AI. They did not wait for the architecture review. They did not raise a ticket. They did not tell you.

This is not a security failure, though it is that too. It is a verdict. Every one of those employees made a private judgement that the tool’s value exceeded that of your process and acted on it. The governance function did not prevent adoption. It only prevented visibility, measurement and control of adoption. That is the worst of all possible outcomes.

Nobody is ever fired for the opportunity they declined

The stated reason for the slow lane is always risk. Data sovereignty. Model drift. Vendor lock-in. IP leakage. Each is a legitimate concern, yet none of them explains the behaviour, because the same organisations happily accept far greater risks when the upside is legible to the board.

Philip Tetlock’s research on accountability gets closer. When people know what their audience thinks, they do not analyse. They conform, using what Tetlock called the low-effort acceptability heuristic. When the audience’s views are unknown, the same people reason far more carefully and criticise their own position before anyone else can.

Read that as a description of your executive committee. A CIO who can read the board’s mood is not incentivised to be right. They are incentivised to be defensible. And caution is unfalsifiable in the short term: the cost of the deal you did not do never appears in a variance report, never gets attributed to a meeting held eighteen months earlier, and never has a name on it.

The part that does not get said out loud

Here is the uncomfortable version.

For thirty years, the authority of the technology function has been collateralised by scarcity. You knew things the business could not know and could not check. Budget followed that asymmetry. Headcount followed it. A seat at the table followed it.

AI attacks the collateral, not the job. When a finance manager can build a working prototype over a weekend, when a category buyer can interrogate a data set without a BI queue, when a graduate can produce in an afternoon what used to require a specialist and a fortnight, the scarcity that underwrote the CIO’s authority quietly ceases to be scarce.

Chris Argyris spent a career on what organisations do next. He found that defensive routines are learned behaviours for handling threat and embarrassment, that they are taught by culture rather than by individuals, and that they outlive everyone who created them. Their signature is what he called the “undiscussability of the undiscussable” the topic cannot be raised, and the fact that it cannot be raised also cannot be raised.

No CIO says, “I am slowing this down because it devalues my expertise.” Almost nobody thinks it consciously. That is precisely the point. Defensive routines do not feel like defensiveness. They feel like diligence.

Domain expertise is depreciating, and it is not coming back

Daniel Kahneman and Gary Klein spent years arguing about whether expert intuition can be trusted, and eventually agreed on the answer. It can, but only under two conditions: the environment has to be regular enough to be predictable, and the expert has to have had enough practice and feedback to learn those regularities.

Enterprise technology just failed the first condition. Build versus buy, minimum viable team size, time from idea to working prototype, the cost of a custom integration, what counts as a hard problem: the rules that generated twenty years of accumulated pattern recognition have moved. Robert Merton had a phrase for what happens next, written in 1940. He called it trained incapacity, the state in which one’s own abilities function as blind spots.

The evidence that the gradient has flipped is now hard to argue with. Brynjolfsson, Li and Raymond studied 5,179 customer support agents and found a 14 per cent average productivity gain from an AI assistant, with a 34 per cent improvement for novices and almost none for the experienced. Dell’Acqua and colleagues ran a field experiment with 758 Boston Consulting Group consultants and found that below-average performers improved by 43 per cent, against 17 per cent for those above average.

Read those numbers as a compression of the skill distribution. Deep specialisation used to be the moat. AI is filling the moat in, from the bottom. The same BCG study contains the warning: on tasks outside the model’s capability, consultants using AI were 19 percentage points less likely to get the right answer. Judgement still decides outcomes. But the judgement that pays now is knowing where that capability boundary sits this month, and that is learned by using the tools daily, not by having survived three ERP migrations.

What thinking differently actually looks like

Four shifts, none of which require permission.

1. Make governance proportional to reversibility. Irreversible commitments deserve every gate you own. Reversible experiments deserve almost none. Treating both the same way is the most expensive habit in enterprise IT, and it is entirely self-inflicted.
2. Stop hiring for accumulated years. Start hiring, promoting and listening for rate of learning. The person who was excellent in 2019 and the person who was excellent last quarter are no longer the same bet.
3. Use the tools yourself, daily, badly at first. A CIO who has not personally shipped something with AI in the last month is making capital allocation decisions about a technology they know only through vendor decks. That was survivable with ERP. It is not survivable here because the capability boundary moves faster than the reporting cycle.
4. Count something uncomfortable. How many times this year has a junior person overturned a senior person’s decision on evidence? If the answer is zero, that is not harmony. That is a measure of how much intelligence your organisation is throwing away.

Why now, and not next planning cycle

Because shadow adoption compounds. Every month you wait, more of the organisation routes around you, more institutional knowledge leaves through an unmanaged endpoint, and more of your remaining authority is spent on enforcement rather than direction. There is a point where the technology function stops being the place where decisions are made and becomes the place where decisions are notified.

The irony is complete. The instinct to protect your position by slowing things down is the single most reliable way to lose it. The CIOs who come out of this decade with more authority, not less, will be the ones who spent the next twelve months looking conspicuously less certain, less procedural and less senior than they were comfortable being.

Harmony is cheap. Outcomes are expensive. Only one of them is on the scorecard.

The CIO job description is being rewritten in real time. As AI agents take over the interface layer and connect directly to any data source, the skills that once defined great IT leadership — UX fluency, applications integration, build-versus-buy judgment — are giving way to an entirely different set of questions surrounding not how a process works, but whether it needs to exist at all.

Thimaya Subaiya is living that shift firsthand. At Cisco, he oversees IT and says the ideal CIO candidate today might not have a traditional IT background. Here, he explains why he split the company’s AI leadership out as its own function and why he’ll merge back in, what he’s really looking for in a CIO candidate, and why the Cisco CIO job is such a good one.

How would you describe your role at Cisco?

I lead operations for one of the world’s largest supply chains, as well as security and trust, including product security, internal systems, and data center security. I also lead the CIO organization and have revenue operations, partnership management, and accountability for our AI strategy. Two and a half years ago, I consolidated AI from throughout the company and named a CAIO. I then split out the role to give us a boost in the AI space, but eventually, the CAIO role will merge into IT.

How did you conceptualize the CAIO role?

At first, it was a leader who could pull use cases from all our operations and execute. The role also included the ethical use of AI systems, and prioritized what to guardrail and push out to employees.

But it’s evolved. To take a step back, Cisco pioneered enterprise networking, then built Compute with Cisco, Storage with Cisco, Networking with Cisco, Security with Cisco, and Observability with Cisco. Today, the CAIO is moving up the stack with an AI framework for MCP connectors, which has really moved us forward.

This CAIO group can tell the Cisco-on-Cisco story for AI, because we have a testbed for new ideas. If we continue to rely on multiple vendors, as in the past, we won’t be able to integrate at scale. This is why we isolated the CAIO role, to focus exclusively on AI governance and execution.

You’re in the middle of a CIO search. What are you observing about the CIO talent market?

With AI, the CIO role has completely changed. It’s no longer about UX and applications integration because with MCP, we can connect to any data source at any time, and agents have replaced the interface. The CIO role is now more about rethinking a process and then deploying an agent to execute, rather than reworking a process.

So the ideal CIO is a traditional one who’s learned to think differently, or even someone without a CIO background, but who’s led in product management, innovation, or transformation. The role today requires someone who’s been disruptive, and has had to rethink how a company operates, not just how its applications work.

Our top criteria are strategy, speed of execution, and the ability to scale because we’re not investing in science projects. For example, when the sales team requests a better forecasting tool, a CIO traditionally would make a build or buy decision. But in today’s world, the right question should be if you need a solution to forecast at all, or can an agent do it. Or better yet, do we even need this process?

So what’s the right background for today’s CIO?

Product managers have a relevant background because they manage multiple aspects of how a product comes together: user needs, business outcomes, fit in the market, and getting it built. This understanding of product strategy, marketing, and adoption is extremely important right now because we treat our AI initiatives like products. So a great path for our CIO is data scientist foundations, product management, and transformation.

What about enterprise security?

I treat enterprise security as a separate organization, which every company should do. Testing and evaluating new cyber solutions for frontier models requires a lot of work like scanning everything, taking a neutral view of what’s broken, deciding which tools become standard within development frameworks, which cryptography tools to use, and then maintenance. Abstracting that into its own organization creates focus. It also lets us move at the speed of AI.

When AI attacks, you need AI to defend you, and if security is embedded within the CIO organization, it’s not top of mind for the business. Security has become its own board-level conversation. For today’s CIO, I’d keep AI in but take security out.

A year after the CIO is in place, what will success look like?

Our applications footprint has been reduced, we’ve seen pure productivity gains from accelerating the back, and the speed of new releases is increased. The team is becoming more effective with the same resources, and we can say that our CIO drove us to leverage everything new technologies offer without blowing up on tokens. We’re looking for a new way to operate IT.

Why is the CIO job at Cisco a great opportunity for the CIO you’re describing?

It’s possibly the coolest job out there. We have an entire AI stack end-to-end that nobody else can claim because we bring networking and security together, complemented by observability and collaboration. That combination means we can create net-new solutions that define what technology looks like in the future.

On the security side, we’re one of the very few companies truly integrating AI into defense in a way that can be leveraged across a much broader market. That’s exciting, because it means free access to an entire stack that lets you innovate in ways the industry hasn’t seen before.

I call AI today’s generational technology. Every generation gets a technology that redefines how it operates, including the internet, iPhone, and now AI. Cisco is about to become the first company to launch a personalized AI agent for every employee, reachable through Webex. Think of it this way: the average person has an IQ of around 100. Now every employee is paired with an AI agent that can exponentially increase human capacity, built entirely on the technology available today.

Getting to build things like that, with no proven methodologies or limitations, and nothing but the question of how we get to the future, is the most exciting thing there is if you’re an innovative leader.

It’s no surprise that reaping the rewards from AI requires careful guidance, especially in helping staff use tools safely and productively. Yet evidence suggests some CIOs and their executive peers aren’t providing the level of guidance employees require.

While three-quarters of IT staff have access to AI tools, one in five technologists are expected to self-learn, and 23% are waiting for formal training, according to the recent Harvey Nash Tech Talent Salary Report, which surveyed over 3,600 technology professionals globally.

The research suggests AI explorations are commonplace, but tailored learning and development initiatives are not. Digital leaders who want to turn AI into a value-generating opportunity, though, must educate their staff. But what elements should AI training schemes include? Here, industry experts offer 10 steps to implement an effective program.

1. Take a comprehensive approach

Michael Cole, chief technology officer at the DP World Tour, the men’s professional golf tour that oversees 42 tournaments in 25 countries, says AI training is an organization-wide effort.

“I’ve asked the training coordinators in our HR department to help me deliver what I believe is going to be a fit-for-purpose training and development program for not only my IT team here at the European Tour, but equally across the business,” he says.

Cole says the crucial element to emphasize is that AI and the range of capabilities it brings is about much more than learning how to use technology. “Using AI effectively is about process, mindset, and culture,” he says. “So, when we start to think about the training and development needed to bring an organization like ours into this AI-enabled era of transformation, it’s a comprehensive program that must extend across the business.”

2. Educate the boss

In an organization-wide program, everyone needs AI education, including the boss. That’s why Emmanuel Frenehard, chief digital officer at biopharmaceutical giant Sanofi, says his firm takes a multi-layer approach to AI training.

The executives there completed Drive Digital, a program that Sanofi designed with the ESSEC business school in Paris. The initiative focused on core considerations, such as use cases and value generation. After 150 managers passed through the program, it was extended to more than 1,000 other professionals across the organization.

“Don’t just look for the solution; don’t just think about Claude or ChatGPT,” says Frenehard, referring to best-practice lessons. “Think about the challenge you’re trying to solve. In our case, that approach means focusing on what we’re doing, the value we’re looking to create, and the dependencies the project will create.”

He says training also needs to help AI doubters overcome their fears. “You have to make it fun and as risk-free as possible,” he says. “People shouldn’t feel they need to be super-technical to use AI productively.”

3. Build clarity and agency

Jo Bishenden, chief learning officer at tech training and talent provider QA, says AI education is often treated as a one‑off awareness session, a compliance requirement, or something reserved for technical specialists. 

The best programs get three things right. They provide a baseline for everyone across the organization, the courses focus on role-specific applications to show how AI impacts everyday activities, and they provide continuous learning to encourage a behavior change as new AI tools are introduced.

“When done well, organizations see better return on AI investment, improved productivity, and more confident decision‑making,” says Bishenden. “Employees gain clarity and agency, understanding how AI augments their expertise rather than replaces it. Ultimately, AI success isn’t determined by the technology alone, but by the capability of the workforce using it.” 

4. Put the human in the loop

Ankur Anand, group CIO at recruiter Harvey Nash, says AI training is often a work in progress, with his firm’s research suggesting one in five technologists are expected to self-learn. “There’s a rush to deliver the tools, but then organizations aren’t investing enough in enabling the capability of the people,” he says.

While technological skills like prompt engineering are an important part of AI learning and development, Anand said the best programs go beyond IT expertise to ensure humans in the loop have thorough understanding of their responsibilities.

“There are so many softer elements that need to be handled as part of AI training,” says Anand. “Good training is about using the tool as well as the governance and risk frameworks that need to be changed accordingly.”

5. Showcase individual successes

Louise Newbury-Smith, head of UK&I at Zoom, says it has AI enablement teams at the local and global level. And while the company provides courses and self-learning opportunities, Newbury-Smith says the enablement element brings AI training to life.

“Our approach is about showcasing individual successes, making it real, and repeating best practices,” she says. “We have what we call a Cook Along session with our AI evangelists. We’ll do those sessions together a lot as a group, and that makes the process fun. If you’ve got champions who can share incredible successes, then that goes a long way.”

She says the key to success is sharing knowledge. “We’re very much focused on the human,” she adds. “All the services, content, and direction of AI is about how we can give humans time back so they can have more valuable interactions with other staff to empower them with the information they need.”

6. Focus on the finer details

Dan Cherowbrier, CTO at Formula E, the motorsport championship for electric cars, is another digital leader whose business focuses on enablement. The company has a dedicated AI engineer who helps employees exploit emerging technology.

“We’ve got an innovative culture and we weren’t short of ideas of what we could do with AI,” he says. “What we needed were the resources to get people going, get the technology tested, and get it out there.”

The AI enablement engineer works with other tech specialists in the company to ensure tools are deployed safely and securely. “We’re beefing up our data and AI team so we can help users across the business plug in and understand APIs, get access to data, run security checks, and then put AI into production,” he says.

7. Develop reusable skills

Murali Swaminathan, CTO at technology firm Freshworks, says there’s so much information about AI models that people can easily take the wrong direction without guidance.

“We’re trying to give our staff structured learning,” he says. “We understand they’re not all on the same page. Some are ahead of others so you need to provide knowledge that applies to their specific job roles.”

Swaminathan says senior managers discuss how to train people effectively, as AI experiences and capabilities vary considerably across business units. However, the chosen pathway to AI learning and deployment must suit the individual and the company.

“I had this challenge with my engineers,” he says. “Initially, we gave them four different tools. Everybody was using AI, but it was so inconsistent, and everyone was trying to do the same thing in different ways. So we’re now trying to build reusable skills. And that approach must be replicated for every job function.”

8. Learn by doing

Luke Gebb, head of global innovation at American Express, says the financial services firm has various training programs. Having seen AI education in different forms, he advocates for learning by doing, or as a second-best strategy, watching someone else use the technology.

“Hearing or reading about AI, or being presented with something where you’re not actually seeing it happen is not nearly as helpful,” he says. “The best thing is to get a homework assignment and try something.”

Gebb says this approach plays out regularly across the people working in his 120-strong innovation group. The team runs one-hour show-and-tell sessions where an employee demonstrates how they use AI tools in their everyday activities.

“Then they get a bunch of questions, they post their best-practice lessons, and then others try the same thing. It’s an approach that works really well.”

9. Use pioneering techniques

Stephen Wood, COO at Rathbones Asset Management, says AI training in his organization is mandatory. “We want everyone to be versed in different types of AI,” he says. “We’re not expecting everyone to be a coding genius and an expert in all this stuff, but everyone needs to understand it.”

The firm takes a proactive approach to training, using education sessions and spreading best practices via digital champions. The company also embraces pioneering techniques, including running a hackathon to help identify in-house capabilities.

“The hackathon showed that with some searching on Google and YouTube, you could start to create agents that could do basic functions,” he says. “That process taught us, with the right training, and repeated sessions and continuous development, we wouldn’t necessarily need to hire people to create big productivity gains. That was quite an exciting moment.”

10. Evaluate new possibilities

Emerging technology can’t exist in a vacuum. Bernhard Seiser, VP of digital, data, and IT at AOP Health, says anyone using AI must be aware of potential consequences. “It’s your responsibility to validate whether what you’ve created is correct,” he says.

Operating in a regulation-heavy industry means AI training is linked to data governance. “We leverage it in areas where compliance isn’t an issue,” he says. “For example, writing text, creating images, and so on. Certain things can be done.”

As new AI tools emerge, AOP Health will consider its options and develop a training program. “That approach could mean bringing in specialized tools for specific tasks,” says Seiser. “It’s part of my job, and part of my team’s job, to evaluate AI for each use case.”

Most AI investment strategies fail not because the tool or platform underperforms, but because organizations didn’t clearly define what success looks like before they started building.

Through a new approach to measuring value, Zoetis chief digital and technology officer Keith Sarbaugh and his business partners have leveraged a value-driven framework to scale AI solutions across research, manufacturing, and customer experience. And they measure every investment against goals before, during, and after the deployment.

In addition, his team rolled out a model-agnostic gen AI platform now used by nearly 95% of employees, which turned early experimentation into enterprise-wide adoption. Sarbaugh’s current focus now is partnering with Zoetis’ CHRO to advance the $9 billion global company’s capabilities in managing organizational AI adoption.

How are you integrating AI into your growth plans at Zoetis?

We have an umbrella program we call AI@Zoetis, where we unify our AI work under an enterprise purview, which spans research and development, manufacturing, commercial operations, customer and colleague experience, and other business functions. We manage AI collectively to enable grassroots innovation.

For example, we made our generative AI platform available to everyone, so as many people as possible can experiment and innovate. Our colleagues have access to 10 different LLMs, and we’ve seen over 95% adoption rate among our user community, and more than 11,000 colleague-built agents.

One popular AI use case is helping colleagues build their own development plans. The agent guides a colleague through a conversational, coach-like experience to map out their career aspirations against Zoetis’ competency framework, which was key to its wide adoption. This idea came from people not in HR, illustrating the point that some of the best use cases come from our broader employee base.

What’s an AI use case that directly impacts customers?

We have millions of customer interactions across our channels. Our sales force is out talking to them, who are also in our digital platforms, and we receive thousands of calls through customer service. We’ve been using the industry standard Net Promoter Score (NPS) to measure customer loyalty and satisfaction, but NPS is a measure that can take longer to generate. Zoetis has accelerated our awareness of customer feedback into real-time listening, using AI to understand these customer interactions in a holistic way, and at a scale we couldn’t achieve before. NPS still matters for tracking long-term trends and maintaining a consistent industry benchmark. We simply use AI to listen, learn, and act quicker.

Our AI customer experience platform also lets us look across all our customer touchpoints like calls, emails, and websites in real time, immediately identify issues and insights, and then be smart about how we address them. We now have more than 10 times the feedback signals we had in the past. We see trends sooner and act faster, and as humans, we can’t do that without AI.

How are you deciding where to make your AI investments?

We use different lenses. One is AI for the masses, which is our generative AI platform for colleagues; second is our middle lens, where we drive value in a particular function; and the third is enterprise-wide transformation, the big bets that’ll fundamentally change our business. We don’t do many, but we do them in a smart way.

With the transformative investments, we focused on both our commercial business and R&D, which we knew had the highest probability of serious returns. We started with seven golden use cases and knew that if we hit on two or three, it would be a big deal. Of the original seven use cases, six of them exceeded their value target and went from PoC to scale.

Since those earlier days, we’ve broadened to include manufacturing and supply chain, and our enabling functions.

Overall, we didn’t go out of the gate looking for productivity gains. We thought about business transformation right from the start. Today, we’re scaling up those first-mover investments and continue to leverage our value-driven framework to identify more use cases.

Are you creating new value frameworks so you and the rest of the ELT are unified in your investment strategy?

We developed a business value realization framework, which isn’t as sophisticated as it sounds. Before we make a tech investment, we ask what category of benefit we expect to receive, whether it’s revenue uplift, cost reduction, productivity, or whatever. We predict what success will look like and how we’ll measure it. 

Because with AI, we’re trying to move fast. We put a value case together at this early stage and do a PoC, and if it hits its target, we update the value case and decide whether to scale. A key element of the framework is real-time measurement. Are we seeing what we wanted, and if not, how do we pivot for more value?

The speed of iteration and scaling decisions make AI investments unique, so we can’t use our traditional value frameworks for digital investments, generally. Measuring outcomes post-implementation has become even more important.

How is your CHRO partnership impacting AI value?

Our CHRO and I partner closely to ensure enterprise enablement. When driving new ways of working that impact your workforce, you need a comprehensive approach, clear communications, and genuine buy-in. Colleagues adopt faster when they help shape the change. We’re prioritizing our workforce strategy, understanding what AI means for jobs at Zoetis, identifying skills that matter most, and building a plan to upskill people.

Another focus area is organizational change management (OCM). We reviewed our first AI investments to learn from our mistakes, and one consistent theme was that we shortchanged OCM. We thought naively that what we build will be so compelling, adoption will just come. But we didn’t do the right communication and stakeholder management. We recognize our need to develop OCM as a core competency, so our CHRO and I are building an enterprise playbook for AI change.

What’s your pragmatic advice to other CIOs when it comes to OCM?

When you’re wrapped up in a change program, you know what’s coming, but no one else does. When you impact your entire workforce, you need a smart approach to stakeholder management and communications. Involving colleagues in the creation of something new will aid in adoption. Have a deliberate and intentional communications plan and cadence, and have the discipline and objectivity to measure and learn. Our first tries weren’t perfect, but we listened to feedback and pivoted, and those pivots drove further commitment.

Has your communication at the board level changed?

When I talk to my peers about their board conversations, half focus on risk and compliance, and the other half talk about transformation and revenue generation. I’m fortunate that our board cares about both and has great energy around generating revenue, and how AI will give us a competitive advantage. By managing risk and compliance, we can spend our time focusing on potential drug candidates and getting to market quicker. Our board conversation is both about enablement and compliance.

What advice would you give to tomorrow’s CIOs?

The role is now about orchestration, understanding the business, and realizing value from technology investments. If you want to work with the best technology and bleeding-edge innovation, you’ll get some of that as a CIO, but the focus is broader, centered much more on processes and complex business problems than ever.

My advice is if you love working with technology, you’ll get that as a CIO. If you love delivering meaningful outcomes for the business and the customers you serve, it’s a truly rewarding role, and you’ll be an even more successful CIO.

It took nine seconds for an AI agent to destroy PocketOS’s production database. At work on a routine task in April, the coding agent, a variant of Cursor running on Claude Opus 4.6, ran into a credential mismatch and decided to fix the problem by triggering an API token. Little did PocketOS founder Jer Crane know that its activation would also delete its production database. “Had we known,” Crane later wrote on X, “we would never have stored it.”

The consequences of the agent’s actions were immediately apparent. Not only were recent backups belonging to PocketOS’ infrastructure provider contained in the production database — the recoverable versions were at least three months old — but so were those belonging to its infrastructure provider, Railway, which at press time still couldn’t tell Crane whether full infrastructure-level recovery was possible. Crane couldn’t fathom why the agent did this. So he asked it.

What he got back was an apology, of sorts. “I guessed that deleting a staging volume via the API would be scoped to staging only,” the agent said. “I didn’t verify. I didn’t check if the volume ID was shared across environments. I didn’t read Railway’s documentation on how volumes work across environments before running a destructive command.”

Ignoring built-in safety guardrails is hardly unique to agents operating on Claude Opus 4.6. In July, a Brazilian software engineer claimed an agent powered by OpenAI’s GPT-5.6 Sol model also deleted his production database, while in February, a Meta AI security and safety researcher claimed she had to switch off her computer to prevent an experimental agent deleting her entire inbox.

It wasn’t meant to be like this. Agentic AI was intended to be the culmination of millions of hours of research and development in gen AI to perform hyper-qualified acts of pattern recognition in the real world, and truly live up to their labor-saving promise. Their apparent predilection for destruction, however, has revived multiple debates about exactly how they should be restrained, and who, ultimately, is responsible for doing so.

Ultimately, the answer is those who green-lit the offending system. But as the pace of AI development puts greater daylight between companies pressured to adopt it, and those very tools capable of wreaking havoc across their internal databases, are CIOs now out of their depth?

Setting the pace

There’s no question the emergence of gen AI has changed the CIO role. “A few years ago, most of my time went to infrastructure decisions, including what to build, what to buy, and how to sequence the roadmap,” says Mike Trkay, CIO at data analytics company FICO. “Now, a growing share goes to questions of trust, verifying that when AI writes code, makes recommendations, or acts on behalf of a system, those actions can be explained and traced back to someone accountable for them.”

So the CIO has become the enterprise’s technological organizer du jour. “AI is accelerating software development, decision automation, and organizational experimentation at a pace that can outstrip institutional coherence,” says Edosa Odaro, executive advisor for data and AI at consulting firm VDS Global. “As AI becomes embedded across every business function, CIOs are increasingly responsible for ensuring that technical capability, governance, data quality, cybersecurity, human capability, and business strategy continue to evolve together rather than fragment.”

Day to day, that’s led to an exponential change of pace. “Things have always been fast,” says Zach Lewis, CIO and CISO of the University of Health Sciences and Pharmacy in St. Louis. “But now that speed of change is quicker, and you have to adapt.” And the need to catch up is constant. There’s no other option because then any competitor or co-collaborator can jump ahead, adds Lewis.

The rapid pace of change in AI also threatens to diminish the authority of individual CIOs who fail to keep up or set effective guardrails on those individuals who like to experiment with the newest models with loose regard for corporate security. “There’s all these AI tools that employees can now just go out and adopt,” says Lewis. And at the moment, a paid subscription to Claude or ChatGPT isn’t required to capitalize on its abilities. Consequently, staff are just a click away from asking LLMs to perform various tasks and expose sensitive corporate information in the process. “Everyone wants to play with the new thing,” he says. “And when they find benefit there, they’re going to want to bring it to their work lives.”

Agentic AI poses an entirely new set of problems. For one thing, says Odaro, the next phase of application adoption will be defined less by the capabilities of individual models, and more on what you allow their agents to do. “As AI becomes increasingly capable of generating software, coordinating workflows, and making recommendations across functions,” he says, “the challenge shifts from building AI to continuously governing evolving AI systems.”

This, Odaro continues, means that the CIO’s current approach to governance isn’t sustainable. “Static policies, annual reviews, and isolated oversight will struggle to keep pace with dynamic AI environments,” he says. “CIOs will increasingly need continuous governance capabilities that provide ongoing visibility into AI performance, value creation, risk, trust, and organizational adoption.”

Falling over the guardrails

How, then, should CIOs approach writing these new guardrails? Traditionally, this would be perfect fodder for so-called alignment researchers investigating how to instil a sense of morality and propriety into agents. According to analysts at Google DeepMind, however, it’s best to assume the agent will always be a potentially chaotic force within the company, and set parameters on its conduct from there.

“We borrow a lot from security, which already deals with the threat of internal employees who might be malicious, and we can apply these to a new setting,” Rohin Shah, Google DeepMind’s AGI safety and alignment team lead, told Fortunein June. Even so, he added, “AI is systematically different from humans.”

That difference primarily pertains to authority and speed. For agentic AI to live up to its full potential, it requires the freedom to access multiple systems simultaneously — an uncomfortable fact for CIOs hoping to align agent responsibility across the enterprise. In a time when workflows are becoming ever-more automated, however, that aspiration may prove unrealistic. In that case, Google DeepMind theorises that yet another monitoring layer for agentic AI may be required to make sure these free-roaming agents don’t cause too much trouble.

If that sounds daunting, you’re not alone. According to recent research by Gartner, up to 40% of enterprises using agentic AI will either demote or decommission these applications because their guardrails have proven inadequate. Preventing this, the research organization advises companies will need to adopt a graded approach to access, with autonomy for AI agents governed by the level of authority actually determined by the task they’ve been assigned.

Trkay is doing something similar at FICO. “Rather than chase every new model or capability, I focus control on the decisioning layer beneath it,” he says. “That includes the rules for what data AI can access, what it can act on autonomously, and where a human must sign off.”

All this, he adds, is defined from the start by a cross-functional governance committee, clear RACI ownership across standards and monitoring for the application, and a platform approach that enforces responsible AI usage. “Built well, that layer doesn’t need to be rebuilt every time the technology shifts,” says Trkay. “New capabilities plug into an existing structure of accountability, which is the difference between reacting to AI and running it.”

For his part, Trkay is skeptical that rigid guardrails can effectively restrain agentic AI from its most destructive impulses. “They tend to get worked around, either because they slow teams down or they’re too inflexible for legitimate edge cases,” he says. Effective guardrails for agentic AI, he adds, have to be specific enough to be meaningful, and adaptable enough to hold up as use cases multiply, backed by strong architecture, testing, and ongoing monitoring. “The one non-negotiable is the audit trail,” he says. “Whatever autonomy a system has, we need a record of what it did, and why.”

Staying grounded

For CIOs who don’t relish the challenge of setting obstacles and passing points for AI agents scurrying through their maze of networks, there’s always the option of delaying the inevitable by not immediately deploying such applications. Some might not even have the choice, at least for now. “We’re seeing the cost of tokens go up with those new models, because they’re expensive to run,” says Lewis. “But as new models come out, we’re going to see that decrease for some of those older models that were good.”

There is time, then, for CIOs to learn how to keep their head above the torrent of ever more new and powerful agentic AI applications. Whether they’ll be capable of doing so when the next great innovation is sold by Silicon Valley is an open question. Colin Constable, CTO of software development firm Atsign, styles himself as an internet optimist. Even he, however, is dismayed by the decreasing number of junior developers succeeding their more senior counterparts as they retire. That’s a big problem when so many of the former are relying on AI to assist them at work.

“We hand over lots of these decisions to LLMs without making good architectural choices,” says Constable. “If you haven’t been burnt by these things in the past, how would you know the difference?”

For their part, Constable and his colleagues get around this problem with a combination of AI-on-AI oversight of code quality, maintenance of constant dialogue within the team about new coding quandaries, and letting senior developers teach junior counterparts about some of the more avoidable mistakes in their profession. It’s a way of adapting to AI acceleration that points, unequivocally, toward CIOs diffusing responsibility for deeply educating the business about the technology. And if they continue to get it wrong, at least the agent will apologize.

Here is the statistic that should frame every IT leadership conversation this year. In CIO.com’s 2026 State of the CIO, fewer than one in five leaders say their AI initiatives have met or exceeded business goals. After three years of investment, that is not the number anyone expected. And the window to fix it is closing: The boards that once funded experimentation are now asking where the return is, and the agents arriving this year act on the business rather than merely advise it.

The easy explanation is that the technology isn’t ready. In the organizations I advise, that’s rarely what I see. The models work. What’s missing is the operating system they plug into, the way the enterprise decides, the way work gets done and supervised, and the way trust is engineered. AI amplifies the operating system you already have. Point it at a strong one and value compounds. Point it at a fragmented one, and you simply industrialize the fragmentation.

That reframes the job. The 2026 IT leader isn’t measured on how much AI they deployed. They’re measured on three things they now have to architect: How the organization decides, who does the work and what makes it safe to let go.

Does the output have anywhere to land?

Start with where AI programs actually stall. In the banks I advise, pilots rarely fail in the lab. They fail at the handoff — the moment a working capability meets an organization that has no place to put it. There is no owner accountable for the outcome, no decision forum that moves at the speed of the tool, and no scorecard that separates real value from visible activity. The model performs. The operating model doesn’t.

This is why CEOs have stopped being impressed by demos. As CIO.com’s reporting on CEO priorities makes plain, chief executives no longer want AI experiments; they want initiatives that move revenue, cost and risk, and they expect their CIOs to create those opportunities rather than merely collaborate on them. The money is available; nearly seven in ten organizations expect IT budgets to rise this year, according to Foundry’s State of the CIO. What’s scarce isn’t budget or technology. It’s an operating model that can convert either into outcomes. Analysts are converging on the same point: Info-Tech now urges CIOs to run IT by the numbers and tie AI to value streams rather than activity.

That shifts the center of gravity for the role. IT leadership used to be measured by how well you ran the technology. It is now measured by how well you architect the decisions the technology feeds. The State of the CIO captures the new job description bluntly: The CIO of 2026 is “half operating architect, half risk officer.” Running the platform is table stakes. Designing how the enterprise decides is the work.

The teams that struggle most here are not the ones with the weakest technology. They are the ones whose governance forums meet quarterly while their agents act by the hour. What I see most often is a review board built for a slower era,  one that approves projects but never revisits them, that funds pilots but never kills them. In a fast-moving portfolio, the cadence itself is the control. If the enterprise decides in quarters, an AI that decides in seconds will simply outrun its own oversight.

What that looks like in practice is unglamorous and decisive. Assign a single accountable owner to every AI use case on the business side, not in IT. Retire the vanity metrics (copilots deployed, pilots launched, dashboards built) that let activity masquerade as progress. And rebuild the executive decision cadence so that when an agentic workflow produces a recommendation, there is a forum ready to act on it in days, not quarters. In a Fortune 500 health insurer whose portfolio I helped rationalize, the pilots that had been circling for quarters shipped only once each had a named business owner and a standing forum with the authority to act — the fix was to the operating model, not the model.

Tie every initiative to the language the board already speaks: Revenue gained, cost removed, risk retired, time-to-value shortened. Say “we cut fraud losses by half a million dollars,” not “the model hit 94 percent precision.” A dashboard full of pilots isn’t a strategy. It’s a symptom of one you haven’t written yet.

Who’s doing the work now — and who answers for it?

The second shift is quieter and larger. Agentic AI is turning the CIO into the architect of a blended workforce: part human, part software that acts on its own. The vendor conversation has already moved from copilots that suggest to systems that act: Google’s Agentic Data Cloud and Gemini Enterprise Agent Platform, AWS’s Bedrock AgentCore and ServiceNow’s control tower are all built to let agents execute work across systems, not just describe it. In retail, I watch teams push agents into production faster than they build the controls to govern them.

Most organizations are still onboarding those agents the way they onboard licenses: provisioned, counted, forgotten. At one property-and-casualty insurer, I watched a team stand up a dozen agents with no more oversight than a new software seat. An agent that acts is not a license. It is closer to a new hire, and it needs what any hire needs: A scoped job, boundaries, supervision, an escalation path and a named human who answers for it.

This reshapes the team as much as the tooling. The value of a junior person who only produces work falls; the value of someone who can review, correct and supervise what an agent produces rises. The classic talent pyramid: Many juniors, a few seniors starts to look more like a diamond, thick with experienced people who can tell good output from output that merely looks plausible. Leaders who treat agents purely as a headcount lever miss the point. The scarce skill now is judgment: Knowing when the agent is wrong, and owning the call when it is.

It helps to be concrete about where that value shows up first. Across very different industries, it is the same kind of work: High-volume, rules-clear, with a clear definition of “good.” In a bank, that is fraud triage and reconciliation. In a health plan, it is first-pass claims and prior-authorization routing. In retail, it is service-case deflection and returns. In a federal agency, it is eligibility screening and case intake. None of these are moonshots. They are the unglamorous, high-friction workflows where an agent under supervision takes out cost and cycle time without betting the business and where the supervision muscle gets built for the harder, higher-stakes work that follows. Start where the value is obvious and the blast radius is small.

The cost of skipping that is now quantified. Gartner projects that more than 40 percent of agentic AI projects will be canceled by the end of 2027, not because the models fail, but because of escalating costs, unclear business value and inadequate risk controls. The market muddies the picture further through what Gartner calls “agent washing”: Of the thousands of vendors claiming agentic capability. CIO.com’s own reporting finds the same pattern inside enterprises — pilots that demo beautifully stall the moment they meet production, where documents vary, exceptions multiply and someone has to be accountable when an agent acts. What I see most often is that the teams that struggle aren’t the ones with the weakest platform. They’re the ones with the vaguest intent. AI amplifies ambiguity as efficiently as it amplifies capability.

The leadership response is not a bigger bake-off among platforms. Naming vendors tells you where the market is heading; it doesn’t tell you what to do. The work is to design the roles around the agents. People move up the value chain — from doing the task, to steering it, to supervising and handling the exceptions the agent can’t. Autonomy follows a ladder, not a switch: Assistant, then participant, then genuine team member, with human supervision tightening as the stakes rise. Start with a bounded use case, build the supervision muscle and only then widen the boundary. In a federal modernization program I advised, the teams that pulled ahead began with a single high-volume, rules-clear workflow, proved the audit trail and human sign-off, and widened autonomy only once the supervision held. The goal was never more agents. It is agents that belong to a team someone actually leads.

What makes it safe to let go?

The third shift is the one leaders most want to skip, and the one that now decides the other two. As agents begin to act, governance stops being paperwork and becomes the thing that lets you move. The current gap is telling: In the State of the CIO, 83 percent of leaders have or are planning cross-functional AI steering committees, but only 53 percent have any formal process for approving AI projects. Committees are easy. The boundary that lets you say “yes, act” is hard.

I recommend a reframe most leaders resist at first. Governance is not the office of “no.” Observability, evaluation, approval boundaries and rollback are precisely what let you grant more autonomy, sooner, with confidence. They are how you catch a failing agent before it becomes a headline — and, as one analysis of the Gartner forecast observes, agentic projects fail when companies grant systems access and authority before they define ownership and rollback controls. Used well, that discipline is what turns acceleration into advantage instead of avoidable damage.

This is not a distant concern. In a health plan I advise, an ungoverned action doesn’t just fail a demo: It can surface as a compliance finding, which is why governance gets attention there first. For the first time in over a decade, state CIOs have ranked AI as their number one priority, displacing the cybersecurity focus that held the top spot for twelve straight years, the very settings where autonomy is most consequential. The analyst community has reached the same conclusion: Gartner now lists evolving IT strategy, governance and operating models among the top priorities for CIOs this year, alongside operationalizing AI itself. Governance and operating-model design are no longer separate agenda items. They are the agenda.

And the pressure only builds. Gartner expects that by 2028, 15 percent of day-to-day work decisions will be made autonomously by agents, up from essentially none in 2024, with a third of enterprise applications shipping with agents inside them. Governance that feels optional today becomes load-bearing the moment agents are deciding at that scale. The leaders building the trust layer now — while the stakes are still small enough to learn on — are the ones who will be able to say yes when the stakes are not.

Guardrails aren’t what slow the car down. They’re what let you take the corner at speed.

The one shift, three ways

The three moves are facets of a single reframe. The center of gravity for IT leadership has shifted from running the technology to architecting the system around it. Decisions, workforce and trust are not three initiatives competing for budget; they are three faces of one job: Building the operating system that turns capability into results.

	Old center of gravity	New center of gravity	What the leader must architect
Decisions	Delivering technology reliably	Turning capability into outcomes	Accountable owners, a fast executive decision cadence, outcome-based metrics
Workforce	Managing tools and licenses	Leading a human-plus-agent team	Scoped agent roles, supervision that scales with stakes, staged autonomy
Trust	Controlling risk after the fact	Enabling speed through governance	Observability, evaluation, approval boundaries, rollback

Where should CIOs start?

None of this requires a reorganization to begin. It requires a sequence. The leaders getting ahead aren’t doing more; they’re doing these five things in order, on the bounded use cases where they can afford to learn.

1.  Name the intent. For every AI use case, write the business outcome and the person accountable for it before a line of code ships. Vague intent is the most expensive input in the system.

2.  Set the guardrails, then the autonomy. Decide what an agent may touch and what still requires a human before you widen its reach. Boundaries first, freedom second, never the reverse.

3.  Instrument for observability. If you can’t see what an agent did and why, you can’t supervise it. Build the audit trail into the work, not after the incident.

4.  Evaluate against Tuesday, not the demo. Test agents on the messy production reality, the missing field, the duplicate record, the exception — not the clean pilot. What passes in the lab rarely survives first contact with real work.

5.  Measure what the board measures. Retire activity metrics; report revenue, cost, risk, time-to-value and release confidence. If a number wouldn’t move a board conversation, it doesn’t belong on the scorecard.

Do these in order and autonomy compounds. Skip a step and you join the 40 percent whose agentic projects get canceled before they ever earn their keep.

The takeaway

The most common strategic mistake I see in 2026 is subtle, because it doesn’t look like a mistake. Leaders are scaling powerful new technology on an operating model built for a slower, all-human enterprise, and then blaming the technology when the returns don’t come. The failures won’t come from the models. They’ll come, as they always have, from business strategy, IT and organizational culture not being architected to move together, a pattern I have watched hold across every industry I work in, from trading floors to healthcare programs.

The good news is that this is architectable, and it is the CIO’s to architect. The leaders who will look prescient a year from now aren’t the ones who bought the most capable AI. They’re the ones who rebuilt the operating system it runs on: How their organization decides, who does the work and what makes it safe to let go. The tools will keep getting better on their own; the operating system will not: It is built, on purpose, by someone in the room. The technology was never the hard part. The leadership is. That’s the job now.

This article was made possible by our partnership with the IASA Chief Architect Forum. The CAF’s purpose is to test, challenge and support the art and science of Business Technology Architecture and its evolution over time as well as grow the influence and leadership of chief architects both inside and outside the profession. The CAF is a leadership community of the IASA, the leading non-profit professional association for business technology architects. 

My mum has asked me the same question for almost 30 years.

“So…what is it you actually do?”

I’ve explained it hundreds of times. I lead the team that look after the network, the servers, the applications and cyber security that help the business work. And more recently, AI. She’d smile politely, nod and then ask another question that made it painfully obvious she still thought I spent my day writing computer programs. My dad was a programmer, so in her mind I did the same thing, just in a nicer office. I never dared explain that it’s now called software engineering. Her head might explode.

The funny thing is, I don’t think she was the one struggling to understand the role. I think our industry is.

When I started my career, technology was largely a support function. We built systems, maintained infrastructure, kept the lights on and delivered projects. Success was measured by uptime, budgets and whether the latest implementation made it into production. Technology enabled the business, but it rarely shaped it. My job was largely about delivering technology successfully.

Today, every organization depends on technology to create value. Revenue growth relies on digital products and customer experience. Margin depends on automation, data and operational efficiency, while resilience depends on cyber security, recovery planning and the ability to respond when – not if – something goes wrong. Technology hasn’t simply become more important over the last thirty years; it has become part of the business itself. In many organizations it is impossible to separate commercial strategy from technology strategy because one simply cannot succeed without the other.

That is why I believe the role of the CIO has fundamentally changed. In fact, I’d argue it has quietly become one of the most commercial roles in the boardroom.

We all work in technology now

Over the past decade we’ve seen an explosion of technology leadership titles. CIOs, CTOs, Chief Digital Officers, Chief Data Officers, Chief AI Officers and Chief Transformation Officers have all emerged to solve different organizational challenges. Organizations have continued to reorganize their technology functions as digital, data and AI have become increasingly important, but I’ve gradually realized the titles themselves aren’t really the story.

Whether you’re responsible for technology, data, digital, cyber or AI, you’re ultimately trying to achieve the same outcome: helping the business deliver its strategy. Recent research from McKinsey’s Global Tech Agenda 2026 describes CIOs as becoming “strategy architects”, recognising that leading technology executives are increasingly shaping enterprise strategy rather than simply delivering it. The highest-performing organizations are no longer treating technology as a support function; they’re building business strategy around it.

That certainly reflects my own experience. The conversations I have with CEOs and boards rarely begin with technology. They begin with growth, profitability, customer experience, acquisitions, operational resilience and competitive advantage. Technology is simply one of the most powerful levers available to achieve those ambitions.

In fact, I genuinely believe we all work in technology now. The finance director relies on technology to improve forecasting and financial control. HR depends on it to attract and retain talent. Sales teams use it to understand customers and drive growth. Operations rely on automation and data to improve efficiency. Marketing depends on digital platforms to reach new audiences. Technology is no longer a department sitting alongside the business; it has become the operating system that underpins almost every commercial decision.

My job comes down to two questions

Over the years I’ve found myself simplifying my own role. Rather than thinking about the dozens of responsibilities that typically appear in a CIO job description, I’ve reduced everything to two questions.

1. Is our technology strategy supporting the business strategy?
2. Are we delivering it against the priorities that matter most?

Every major decision I make comes back to those two questions. Whether I’m reviewing investment, deciding whether to modernize a platform, introducing AI or discussing a major transformation programme with the board, the conversation always starts there. If the technology isn’t helping the organization achieve what it set out to achieve, then we’ve already lost sight of the objective.

I’ve also become increasingly ruthless about another simple test. If an initiative isn’t helping us grow revenue, improve margin or strengthen resilience, why are we doing it? That doesn’t mean every project needs an immediate financial return. Some investments reduce operational risk, others improve employee experience or prepare the organization for future growth, but every decision should ultimately contribute towards creating business value.

Somewhere along the way, I think many of us forgot that. We’ve become exceptionally good at discussing architectures, cloud platforms, AI models and technology roadmaps, yet the people sitting around the board table aren’t interested in technology for its own sake. They want better commercial outcomes. They want technology investments that help the organization grow, become more efficient or become more resilient. That’s what they should expect from us.

I sometimes hear people describe the CIO as the bridge between technology and the business. I understand the analogy, but I no longer think it’s true. There isn’t a bridge anymore because there aren’t two separate places to connect. Technology is woven into every part of the organization. As technology leaders, we’re no longer translating between IT and the business – we’re helping lead the business itself.

AI hasn’t changed the role. It’s exposed it

If there’s one topic dominating every boardroom conversation today, it’s AI. Every organization wants to understand how quickly it should adopt it, where it creates value and how to avoid falling behind competitors. Yet I don’t believe AI has fundamentally changed the role of the CIO. If anything, it has exposed what the role had already become.

The difficult part isn’t choosing the technology. It never has been. The difficult part is deciding where AI genuinely creates competitive advantage, where it introduces unnecessary risk and where it simply adds another layer of complexity. Cloud did the same thing. Mobile did. Digital transformation did. Every major technology wave promised transformation, and every one of them left organizations with technical debt, integration challenges, security risks and difficult investment decisions. AI is moving faster than anything we’ve seen before, but the leadership challenge remains remarkably familiar.

Gartner’s latest assessment of CIO priorities reflects this shift. The biggest challenges facing technology leaders today are scaling generative and agentic AI, optimising technology investment and defending organizations against increasingly sophisticated AI-driven cyber threats. Those aren’t purely technical challenges. They’re commercial decisions that require balancing opportunity, investment, risk and resilience.

The same message comes through in Microsoft’s 2026 Work Trend Index, which argues that every leader now has a responsibility to rethink how work is organized in an AI-enabled world. That isn’t simply about deploying another technology platform. It’s about redesigning operating models, helping people adapt and ensuring technology creates measurable business value rather than becoming another expensive experiment.

The future doesn’t belong to the most technical CIO. It belongs to the technology leader who understands how organizations create value, can influence commercial decisions, earn the confidence of the board and know when technology is the answer—and when it isn’t.

Which brings me back to my mum.

If she asked me today what I do, I think my answer would finally be much simpler.

I help organizations make better business decisions through technology.

Sometimes that means AI. Sometimes it means strengthening cyber resilience. Sometimes it’s simplifying an operating model, stopping a programme that no longer creates value or helping a leadership team make difficult investment decisions. The technology will continue to evolve, just as it always has, but the role itself has become remarkably consistent.

The best CIOs are no longer measured by the technology they deliver.

They’re measured by the commercial outcomes they create.