The AI Transformation Ticker
The definitive institutional signal for enterprise AI execution.
7 questions · 3 minutes
Q2 2026
Last updated
4 pillars
Ticker formula
27
out of 100
AI Transformation Ticker
Most enterprises are investing in AI. Only 25% have moved pilots into production, and just 5% are generating substantial value at scale.
How this is calculated
The Numbers That Matter
The clearest signal on where enterprise AI stands today.
Adoption vs. Execution
88%
of organizations report regular AI use in at least one business function.
But in practice
25%
have moved a meaningful share of their AI pilots into production. Most are still experimenting.
What this means: Deployment is not the same as execution. The gap between piloting AI and scaling it is where most organizations stall.
Usage vs. Readiness
45%
of workers now use AI regularly at work, up 13 points in a single year.
Yet at the same time
-18%
Worker confidence in using AI technology fell 18% over the same period. People are using tools they don't feel equipped to use.
What this means: Usage is accelerating faster than readiness. Organizations are handing people tools without building the skills to use them well.
Both figures: ManpowerGroup, Global Talent Barometer, Jan 2026
Investment vs. Priority
$2.5T
in worldwide AI spending projected for 2026. Up 44% year over year. The investment is accelerating fast.
Yet the #1 barrier
Skills.
Insufficient workforce skills rank as the top obstacle to integrating AI into workflows, above technology, budget, and leadership buy-in.
What this means: AI spending is up 44% in 2026. Training budgets? Up just 5% — and average employee learning time is actually falling. Organizations are deploying powerful tools without adequately investing in skills for the people who need to use them.
$2.5T, 44% growth: Gartner, Jan 15, 2026
Skills barrier: IDC via Deloitte, State of AI 2026
5% training growth: Training Magazine, 2025 Training Industry Report
Governance Principles vs. Practice
75%
of organizations have a dedicated AI governance committee in place. The structures exist.
Yet in practice
12%
describe their AI governance efforts as mature. Most organizations have the structure. Almost none have the capability.
What this means: Having a governance committee is not the same as governing AI. Only 23% of IT leaders say they're very confident their organization can govern AI in practice — a number that lines up almost exactly with the maturity gap.
ROI Achievement
5%
of enterprises are generating substantial AI value at scale. BCG calls them “future-built.” The other 95% are still working toward it.
Future-Built
5%
Systematically generating substantial value from AI across functions. These organizations have built the capabilities — and it shows in their results.
Scalers
35%
Scaling AI efforts and beginning to see gains. Making real progress, but not yet generating consistent enterprise-level impact.
Laggards
60%
Reporting minimal revenue or cost gains from AI. Investments are being made, but the value isn't materializing.
What this means: Most enterprises are still working toward AI ROI because they haven't built the human and organizational capabilities required to use it well. The gap between 60% and 5% is a skills and execution gap. Tech investment is not enough.
All figures: BCG, The Widening AI Value Gap, Sep 2025 (n=1,250 senior executives, 9 industries, 25+ sectors)
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