Something interesting has happened in the first half of 2026. Every major analyst firm spent the past two years telling businesses to get their AI strategy sorted. Most did. They hired consultants, ran pilots, wrote policies, and set budgets.

Then the execution started. And the problems showed up.

Info-Tech Research Group released its Best of 2026 Mid-Year Report today, and the headline is not what you might expect from a year that has seen record AI investment. The most-accessed research across the first six months of 2026 was not about which model to use or how to run an AI pilot. It was about data quality, data governance, cybersecurity, and workforce readiness.

In other words: the fundamentals.

What CIOs Are Actually Wrestling With

The report finds that AI has moved from a strategic ambition to an execution challenge. Organizations have stopped asking whether to adopt AI. The question now is whether their infrastructure, their data, and their people are ready to make it work.

The strongest demand in H1 2026 was for guidance on:

• AI execution — how to actually move from proof of concept to production
• Cybersecurity — how to harden systems as AI increases the attack surface
• Data quality and governance — how to ensure AI has clean, reliable data to work with
• Workforce readiness — how to build the skills required to operate in an AI-augmented environment
• Technology buying decisions — how to evaluate tools without being overwhelmed by vendor noise

That last one is telling. When organizations are focused on buying decisions, it means they have moved past exploration and into investment. But it also means they need better filters for an increasingly crowded market.

The Four Data Priorities That Define AI Success

Info-Tech’s companion Data Priorities 2026 report put it plainly: AI adoption is exposing gaps in data quality, governance, accountability, and literacy that were always there, but nobody had to care about until now.

The report outlines four priorities for leaders who want AI to actually work:

1. Establish unified governance for data and AI. Separate frameworks for data governance and AI governance create gaps. Organizations need a single framework that covers both.

2. Build data products around measurable outcomes. Data teams that treat internal datasets as products — with owners, SLAs, and quality standards — are consistently outperforming those that treat data as a byproduct of operations.

3. Create a trusted, AI-ready data supply. This means investing in data pipelines, metadata management, and access controls before deploying agents, not after.

4. Cultivate data champions across the organization. AI success cannot rest on a single data team. The organizations that are scaling AI effectively have people in finance, marketing, operations, and HR who understand their data and can work intelligently with AI tools.

Why This Should Not Surprise Anyone

This is not a new problem. Organizations that have tried to shortcut their way to AI — skipping data foundations, skipping skills development, skipping governance — are discovering in 2026 that shortcuts have costs. The AI pilot worked. The production deployment did not. The model was fine. The data was not.

What has changed is urgency. With AI now consuming meaningful budget in most mid-to-large organizations, the cost of getting data wrong is no longer theoretical. It shows up in failed projects, wasted compute spend, and agents that produce unreliable outputs.

The organizations that are scaling AI with confidence share a common profile. They have invested in their data foundation. They have people who can work with data — not just data scientists, but business users across every function who understand what their data means and how to question it. And they have governance frameworks that let them move fast without flying blind.

What This Means for Business Leaders

The Info-Tech report is validation for something a lot of business leaders have been reluctant to hear: AI strategy and data strategy are the same thing. You cannot have one without the other.

If your AI pilots are not converting to production deployments, the problem is probably not the model. It is the data the model is working with, or the skills of the people interpreting its outputs, or the absence of governance that would let you trust what you are seeing.

The businesses that are winning with AI in 2026 are not necessarily the ones with the most sophisticated models or the biggest vendor contracts. They are the ones that did the unglamorous work of getting their data house in order first.

That work is harder to talk about at board level. But it is the only work that actually produces results at scale.

---

Enterprise DNA helps organizations build the data literacy and AI foundations required for sustainable AI adoption. Explore EDNA Learn for data skills training, or speak with our team about Omni Advisory to assess your organization’s AI and data readiness.