AI Planning vs Execution Readiness

There is a finding buried in Deloitte’s 2026 State of AI in the Enterprise report that deserves more attention than it has received. Most coverage of the report focuses on the acceleration story: more workers with AI access, more pilots moving to production, more investment, more confidence. All of that is, of course, real.

What the coverage tends to skip is a directional finding that cuts against the optimism. When Deloitte asked leaders to rate their organization’s preparedness across five dimensions — technology infrastructure, strategy, data management, risk and governance, and talent — something counterintuitive showed up in the year-over-year comparison.

Strategic preparedness went up three percentage points. Risk and governance preparedness went up six. Both are areas driven primarily by executive decision-making and policy development, areas where intent and communication can move the needle quickly.

But technology infrastructure preparedness went down four points. Data management preparedness went down three points. Talent preparedness went down two points. These are the operational dimensions — the ones that require sustained investment, structural change, and time to build.

Read that again slowly. Organizations are simultaneously getting more confident about their AI strategy and less operationally ready to execute it. The gap between what leadership believes is true and what the organization can actually deliver is widening, not narrowing.

That is not a technology problem. That is a leadership problem. And it is one of the most important signals in the report.

Why Confidence and Readiness Are Moving in Opposite Directions

The pattern makes sense once you understand what drives each dimension. Strategic confidence is relatively easy to build. You hire a Chief AI Officer. You publish an AI strategy document. You announce a set of AI initiatives in an all-hands meeting. You read the same reports your competitors are reading and reach the same conclusions about where the market is going. All of that activity registers as strategic preparedness, and it should — direction-setting matters.

What it doesn’t do is build data pipelines. It doesn’t modernize legacy infrastructure. It doesn’t retrain the workforce. It doesn’t create the governance frameworks that allow AI to operate safely at scale. Those things require a different kind of investment: slower, less visible, more expensive, and much harder to report in a board presentation.

The head of AI strategy at a major European bank described this tension directly in the Deloitte report. He noted that many organizations prepared for an AI future by building infrastructure and governance for traditional AI models. Then large language models (LLMs) arrived and upended those investments entirely. Suddenly, nearly 80 to 90 percent of new use cases were generative AI, and the infrastructure organizations had built was designed for a different future.

That observation points to something important beyond the technology shift. Organizations that were genuinely operationally prepared for the previous generation of AI still found themselves underprepared for the current one. Preparation is not a destination you arrive at. It is a continuous process of closing gaps that keep moving.

The Talent Number Is the Most Alarming

Of the five preparedness dimensions, talent is the lowest and the only one that declined year over year. Only 20% of organizations report that their talent is highly prepared for AI. That number was higher last year.

Think about what that means in context. Organizations have spent the past two years running AI training programs, deploying AI tools, hiring AI specialists, and making public commitments to workforce development. All of that activity, and the percentage of organizations that feel their talent is highly prepared for AI went down.

There are a few ways to read this. The most charitable interpretation is that the bar keeps moving. As AI capabilities expand rapidly into agentic and physical AI, what counts as “highly prepared” gets harder to achieve. Organizations that felt prepared for generative AI are now measuring themselves against a broader and more demanding standard.

The less charitable interpretation is that most AI training programs are not producing meaningful capability gains. Organizations are running awareness events and calling it workforce development. They are measuring training completion instead of behavior change. They are building AI fluency without building AI capability. As I started reading through and writing about some of my insights from the Deloitte report, I wrote about why training without reinforcement, role-specific application, and management accountability fails to produce lasting behavior change. The talent preparedness data is what that failure looks like at scale.

The most honest interpretation is probably some combination of both: the standard is rising faster than the capability is building, in part because the capability-building work has been approached less seriously than the deployment work.

The BigDATAwire analysis of the Deloitte report framed this bluntly: organizations are operationally not prepared to achieve their AI goals, and this widening execution gap is the core theme of the 2026 findings. That is not a fringe interpretation. It is what the data says.

The Data Foundation Problem

The data management preparedness decline deserves its own conversation because it sits underneath almost every other AI challenge organizations face.

You cannot build reliable AI systems on unreliable data.

You cannot govern AI outputs that you cannot trace to their inputs.

You cannot scale AI workflows built on data that was adequate for dashboards but inadequate for automated decisions.

These are not new observations — they have been true throughout the history of enterprise analytics — but the stakes are higher now because the consequences of bad data are no longer limited to a flawed report. They extend to decisions made autonomously at scale.

As one analyst put it, “We have data” is not the same as “we have data we trust, with known provenance, with clear usage rights.” In 2026, the most effective enterprise AI strategies start with the foundation question: what data can we actually trust, and what needs to be fixed before we automate decisions based on it?

Gartner’s estimate is pointed: 60% of agentic AI projects will fail in 2026 due to a lack of AI-ready data. That is not a projection about model quality or tool capability. It is a projection about the data foundations that organizations have spent years not fully investing in — and that AI, unlike more forgiving technologies, cannot compensate for.

The Deloitte data shows that data management preparedness declined three points year over year, even as AI deployment accelerated. Organizations are building on foundations they have not sufficiently strengthened. The pilots may be working. The production deployments are where the data problems become visible and expensive.

The Widening Gap

Taken together, the talent and data findings point to the same underlying dynamic: organizations are accelerating on the surface — more tools, more pilots, more announcements — while the foundational work that makes acceleration sustainable is falling further behind.

That is not a comfortable place to be. And it raises a harder question than the data alone can answer: if leadership knows the operational gaps exist, why are they widening rather than closing?

That question moves us from diagnosis into leadership behavior, and that is where the real conversation begins. In Part 2 of this post, I’ll examine why strategic confidence without operational honesty is not just a planning problem. It is a leadership liability — and one of the most consequential blind spots I’ve observed across 35 years of enterprise technology adoption.