Seven numbers that explain adopting AI and being ready for AI are not the same thing.

Enterprise investment in artificial intelligence is accelerating, but across Gartner’s research, another pattern is emerging alongside that investment: the data, architecture and operating practices required to support AI are not advancing at the same pace.

Gartner describes AI-ready data as data that is appropriate for a specific AI use case and supported by the metadata, governance and data-management practices necessary to qualify and maintain it. Readiness is therefore not a one-time data-cleaning exercise. It is an ongoing organizational capability. Gartner

1. Just 4% of organizations have AI-ready data

Gartner reported in 2025 that just 4% of organizations have AI-ready data, even as AI adoption continues to grow. The finding appears in Gartner research focused specifically on establishing data governance frameworks capable of supporting AI. Gartner.

At 4%, data readiness remains far from an established enterprise capability. As organizations expand their use of AI, the question increasingly becomes whether the data environments supporting those investments are developing at the same pace. 

2. Gartner predicts 60% of AI projects unsupported by AI-ready data will be abandoned

In February 2025, Gartner predicted that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data. The finding accompanied research based on a July 2024 survey of 1,203 data-management leaders. Gartner

Gartner's prediction specifically concerns AI projects unsupported by AI-ready data. AI use cases introduce requirements around representativeness, metadata, governance, pipelines, observability and the ability to continually assess whether data remains appropriate for its intended use. Gartner

The consequence is often operational before it is technical. When data cannot reliably support an AI use case, people compensate by validating outputs, finding missing context, reconciling sources or maintaining parallel workflows. What looks like an AI problem can become an ongoing operating-cost problem.

3. 63% lack, or are unsure they have, the right data-management practices for AI

The same Gartner research found that 63% of organizations either do not have, or are unsure whether they have, the right data-management practices for AI. Gartner

Much of the problem exists at the functional level. Platforms, teams and vendors use different definitions, taxonomies and collection practices, leaving people to reconcile them before the data can be used together. Poor data management therefore creates both a data-quality problem and the recurring human cost of making fragmented data usable.

4. 90% of CDAOs say their data architecture needs an overhaul for new AI use cases

By 2026, the issue extends beyond individual datasets and practices. Gartner reports that 90% of chief data and analytics officers say their current data architecture needs an overhaul to support new AI use cases. Gartner

Gartner's guidance describes AI-ready architecture as a layered capability spanning data discovery, access, quality and governance. It also emphasizes metadata, semantics, provenance and context as AI and agentic use cases become more sophisticated. Gartner

The implication is structural: organizations are increasingly asking existing data environments to support uses for which those environments were not designed. Meeting those demands requires organizations to reconsider how data is structured at the functional level. 

5. More than 75% of organizations are prioritizing AI-ready data investments

Organizations appear to recognize the problem with Gartner reporting that more than 75% of organizations are prioritizing investments in AI-ready data, identifying lack of data readiness as a top barrier to AI. Gartner

The shift in investment suggests that data readiness is becoming an explicit part of AI strategy rather than a problem addressed after implementation begins. As we see in market-facing functions like marketing and communications, investment is also increasing at the functional level, suggesting that AI readiness will require coordination between enterprise data strategy and the environments where data is actually created, managed and used. Gartner

The investment response signals where the next phase of AI readiness is moving: into the data environments where sufficient functional context exists to support AI properly.

6. Only 30% of CMOs report mature or fully developed AI-readiness capabilities

Gardner demonstrates these challenges in market facing functions like communications and marketing. Their 2026 CMO Spend Survey found that 70% of CMOs consider becoming an AI leader a critical goal for 2026, while only 30% report mature or fully developed AI-readiness capabilities. The survey included 401 CMOs and other marketing leaders across North America, the United Kingdom and Europe. Gartner

At the same time, CMOs are allocating an average of 15.3% of marketing budgets to AI initiatives. Gartner also found that 70% acknowledge their internal marketing processes are not mature enough to effectively implement and scale AI. Gartner

Marketing’s readiness challenges sit within a function where data and context are often fragmented across platforms, vendors and systems. Gartner identifies data foundations, processes, governance and talent as capabilities that must develop alongside AI investment – areas directly affected by that fragmentation. Gartner  As we explore in Michelin-star article, this has direct implications for the quality of data available to AI.

7. 47% of communications leaders struggle to demonstrate their impact on organizational objectives

Communications carry a related challenge. Gartner reports that 47% of communications leaders struggle to demonstrate the true impact of their work on organizational objectives despite strong performance. Gartner

Gartner's 2026 CCO Spend Survey found that 34% of communications leaders plan to increase spending on data and analytics. Measurement and analytics also increased from 2.9% to 4.1% of communications budgets. Gartner connects the remaining measurement gap to the broader difficulty communications teams face linking their work to organizational outcomes. cco-budget-report-2026

This is not itself an AI-readiness measure, but it exposes an existing weakness as communications increases its investment in AI: the function already struggles to connect its data to organizational outcomes. AI can analyze the relationships preserved in that data, but it cannot recover context and connections that the underlying data environment does not capture. 

Readiness is becoming the work

Taken independently, these numbers measure different things. They come from different Gartner studies, populations and areas of the enterprise. They should not be collapsed into a single measure of enterprise AI readiness.

Taken together, however, they point to a common operational problem: AI is advancing faster than the environments supplying it with data.

As AI becomes more embedded in these functions, the infrastructure surrounding their data will increasingly shape what organizations are able to do with it.

AI readiness, in that sense, is not simply a technology question. It is a question of whether the organization beneath the technology is ready to support it.