FEATUREDApplication Development & Modernization
Drew Godwin | Field CTO
The hype phase is over.
Boards, CFOs, and CEOs are done funding experiments that don’t move the business. AI is no longer a science project. It’s a line item that must perform.
Here’s what that looks like and what separates the companies that win from those that keep waiting.
1. Most AI Projects Will Die. The Right Ones Will Finally Live.
Most AI initiatives won’t survive this year. Not because AI failed. Because they were never built to succeed.
Projects without an owner will be cut. Projects without a measurable outcome will be cut. Projects still “in pilot” after twelve months will be cut.
This is good news.
The companies that win in 2026 will run fewer projects and get more from each one. They’ll pick three bets that matter instead of twenty that don’t.
The choice: Kill your weakest pilots now and double down on what’s working, or wait for your CFO to do it for you.
2. Your Data Problem Is Your AI Problem
Models aren’t the bottleneck. Tools aren’t the bottleneck. Your data is.
Most enterprises still treat data as something to report on, not something to run the business on.
The valuable information sits locked in documents, emails, and contracts. Governance is inconsistent. Access depends on who you know, not what you need.
AI can’t fix this. AI exposes it.
The choice: Build strong data foundations, cataloging, access, quality, or keep facing stalled AI projects.
3. Your Infrastructure Is Quietly Strangling Your AI
AI doesn’t tolerate fragmentation.
Legacy systems sprawled across data centers. Cloud accounts no one audited. Platforms adopted for one project and never retired. Every shortcut you took in the last decade now makes AI slower and more expensive.
The math is simple: complexity costs money. Every extra integration, every redundant platform, every manual workaround, you’ll pay for it in AI budgets that balloon and timelines that slip.
The choice: Standardize your hybrid infrastructure this year, or watch competitors move twice as fast at half the cost.
4. Responsible AI Becomes Real or Becomes Irrelevant
Everyone has a policy. Almost no one has controls.
In 2026, intent isn’t enough. Regulators are watching. Customers are asking. Boards want answers.
What “operational governance” looks like: Every AI output traces back to the data it used and the human who approved it. Access controls tied to identity, not job titles. Monitoring that catches drift before customers do.
If you can’t show an auditor exactly how your AI makes decisions, you don’t have governance. You have a slide deck.
The choice: Build the controls now while you design the systems, or bolt them on later at triple the cost and half the trust.
5. IT Strategy and Business Strategy Are Now the Same Thing
The wall between technology and business outcomes is gone. AI tore it down.
Every technology decision is now visible at the board level. Every delayed project, every security gap, every integration that doesn’t work shows up in the numbers. There’s nowhere to hide.
In 2026, successful organizations will prioritize focus over breadth. They’ll fund what moves the business and stop funding what doesn’t. They’ll measure technology the way they measure sales: by results.
The choice: Align your tech priorities to three business outcomes that matter, or keep explaining why the investment isn’t paying off.
The Real Prediction
The gap between companies that execute and companies that experiment will widen dramatically this year.
AI doesn’t reward curiosity. It rewards clarity. It rewards follow-through. It rewards the discipline to finish what you start.
By December, you’ll either have AI systems in production that your CFO can defend or you’ll have a pile of pilots no one remembers funding.
2026 isn’t about adopting more technology.
It’s about proving that the technology you adopted works.
