AI-ready data in 2026: the confidence gap

Executive summary

Leaders say they are ready, and in the same survey name readiness as their biggest problem. In a 2026 study of more than 500 senior data and analytics leaders, 88% said they had the data readiness AI needs, while 43% named data readiness as the biggest barrier to aligning AI with business goals.[4]
Most data functions are not built for what AI asks of them. Gartner’s 2026 CDAO survey finds 85% of data and analytics practices are not architected for scale, and only 12% of leaders hired as change agents feel fully prepared for the job.[2]
Data teams fear wrong answers more than slow ones. 71% of data practitioners and leaders worry that incorrect or hallucinated data will reach stakeholders, yet only 24% prioritise AI help with testing, observability and quality, against 72% for AI-assisted coding.[5]
Governance pays back in trust. 71% of organisations with a data strategy and governance programme report high trust in their data, against 50% without.[4]
Agents raise the bar. Gartner predicts that organisations that prioritise semantics in AI-ready data will improve agent accuracy by up to 80% and cut costs by up to 60% by 2027.[3]

Key findings

0188% of senior data leaders say their data is ready for AI, yet 43% name data readiness as their biggest barrier

0285% of data and analytics practices are not architected for scale

0371% of data teams worry that incorrect or hallucinated data will reach stakeholders

The data readiness confidence gap

Say their data is ready for AI

88%

Name data readiness as the biggest barrier

43%

Source: Precisely and Drexel LeBow, January 2026

High trust in data

With data strategy and governance

71%

Without

50%

Source: Precisely and Drexel LeBow, January 2026

What leaders should do

Agree definitions for the metrics that matter most before layering AI on top.
Retire low-use reports to cut cost and confusion.
Give domain teams ownership of their data products.

In the full report

The confidence gap
What the CDAO survey says about the function
Speed is winning over quality inside data teams
Why agents change the requirement
Governance as the thing that makes data trusted
What to do in 2027
Sources

Methodology

ConfX research brings together our own analysis, what we hear from senior leaders at ConfX summits, executive roundtables and briefings, and the latest published research from regulators, academic institutions, industry bodies and research firms. Analysis completed on 15 October 2026.

Every figure is attributed to its source. Samples, regions and definitions differ between studies, so figures from different sources describe different groups and should not be combined.