After years of squinting at big, macro trends in the larger culture, I find myself now looking through a much narrower lens at the enterprise IT world again, this time through the filter of compliance call recording at Numonix. From this unique perspective, however, I’m rubbing elbows with very interesting people with sharp insights and opinions on weighty topics. Thus, this post. Start with the CIO and what’s on their minds. There’s no shortage of AI optimism in the market right now. But when you step away from vendor decks and analyst reports and listen to CIOs talking to each other, a different set of concerns comes into focus. So, let’s look briefly at the big CIO trends in 2026.
Most organizations have already crossed the adoption threshold. Employees are using generative AI tools every day. Pilots are running. Experiments are underway.
The difficult part comes after that.
I recently had the opportunity to sit down with Ken Jarvis, the CEO of nfinity3 – a consultancy that works with business leaders and CIO’s delving into strategic and operational issues – and Mike Levy, the inimitable CEO of Numonix, where I serve as CMO. The 3 of us talked over the biggest challenges CIO’s face today and how smart companies, like Numonix, step into this blender of uncertainty to make sense of an IT landscape spinning quickly out of control.
AI Is Easy to Launch – Hard to Scale Responsibly
One of the most common frustrations I hear is the gap between individual productivity and enterprise value. AI absolutely helps people work faster. What it doesn’t automatically do is improve revenue, accelerate innovation, or change business outcomes. That requires governance, ownership, and alignment – things that are far harder than tool deployment.
“There’s been an enormous amount of hype around AI, enormous investment in AI, and the business benefits are not flowing through. Individual productivities are flowing through, but the business benefits aren’t,” Jarvis explained.
As AI tools multiply, consistency becomes a real problem. Different models produce different answers. Bias is unavoidable. Without structure, AI becomes difficult to trust at scale.
Data Is the Real Constraint
The strongest AI outcomes I’ve seen are tied directly to enterprise data – especially customer interactions.
Most organizations already have vast repositories of call recordings, service conversations, and operational signals. Applied thoughtfully, AI can surface insights that materially improve products and customer experience.
“We’re still at the opening phase of how massive amounts of enterprise data are being pulled in intelligently and meaningfully to actually impact the rest of the organization,” Denny (me) added.
But unlocking that value takes time. It requires clean data, secure pipelines, and a willingness to invest beyond surface‑level productivity wins.
“I hear often, ‘We need AI.’ And I’m like, ‘What AI? What do you want?’ And they’re like, ‘No, we just need AI,’” Levy explained.
Cloud Costs Are Becoming the Silent Friction Point
AI is also intensifying an existing issue: cloud cost unpredictability.
Consumption‑based pricing models put CIOs in a difficult position. Usage is driven by the business, but accountability still sits with IT. AI workloads only amplify the variability.
“This is an unbudgetable item, but I’m expected to pay for it at the end of every month,” Jarvis explained.
As a result, predictability is becoming just as important as performance when enterprise buyers evaluate technology platforms.
Security and Trust Remain Non-Negotiable
Security never left the top of the CIO agenda – but the context has changed.
AI, regulation, and consumer skepticism have pushed trust into the foreground. Security posture is increasingly the first thing evaluated, not the last.
Organizations that can’t demonstrate responsible data stewardship often don’t make it into serious discussions at all. If this doesn’t ring every possible bell from our earlier discussions on the intersection of culture and technology and the collapse of trust, and of “seeking control in an out-of-control world,” it should. We’re in a C2B world at this point and the lines between our individual sensibilities and our corporate responsibilities are blurred – we don’t trust the institutions around us, and it only gets worse when we mention AI, data sovereignty, and what’s happening with our personal data.
Key Insights & Takeaways:
- AI is delivering productivity gains, but consistent enterprise‑wide business value remains a work in progress.
- Effective AI adoption now hinges on governance, ownership, bias management, and consistency.
- Cloud cost volatility is increasing, particularly as AI workloads scale, driving demand for predictable models.
- Security, risk, and compliance remain permanent, top‑tier priorities and are now the first gate in technology decisions.
The Bigger Shift
What’s becoming clear is that the CIO role is converging. Innovation, economics, and trust are no longer separate concerns.
AI isn’t failing – but it’s growing up. And for CIOs, the next phase is less about experimentation and more about accountability. I recently explored these themes in more depth in a longer CIO trends post, which you can read here.
