This is a draft version! Do not share the link externally!

Article

test

test

No items found.

Data readiness predicts AI maturity

% reporting advanced or
transforming AI maturity

1.0%
Bottom
quartile
10.6%
2nd
quartile
24.3%
3rd
quartile
39.4%
Top
quartile

By quartile of data readiness  |  n = 415

Exhibit 1: Share of respondents reporting advanced or transforming AI maturity, by data-readiness quartile.

Motivation defines integration depth

Cost and model
optimization
65%
35%
Both equally
51%
49%
Workflow redesign
for impact
53%
47%
Standalone / adjacent (periphery) Partially / fully embedded (deep)

Primary stated motivation for embedding AI more deeply  |  n = 415

Exhibit 2: Deployment depth reached, by primary stated motivation for embedding AI more deeply.

A third of organizations are exposed on both fronts

Confident in
containment

5.5% Ungoverned &
confident
16.6% Governed &
confident
32.8% Ungoverned &
low-confidence
45.1% Governed &
low-confidence

Low confidence in
containment

Ungoverned for
agentic AI
Governance in place
(partial or dedicated)

n = 415. Magenta cell marks the double-exposure segment.

Exhibit 3: Governance status against confidence in containment. The magenta quadrant is the double-exposure segment.

Governance maturity flips the accelerator-to-constraint ratio

Accelerator Constraint
59%
20%
Mature governance
(partially/fully automated)
32%
34%
Immature governance
(ad hoc / manual)

Share describing AI governance as an accelerator vs. a constraint on deployment  |  n = 415

Exhibit 5: Share describing AI governance as an accelerator versus a constraint on deployment, by governance maturity.


More to Explore

No items found.
No items found.
No items found.
No items found.
No items found.