Why AI investment is outrunning impact, and ways to close the gap
Organizations are investing heavily in AI, but many are still struggling to turn that investment into measurable impact.
of the C-suite say their workforce can effectively combine human and machine capabilities today
of employees are concerned about losing their job to AI
Too often, AI is layered onto existing ways of working instead of being used to rethink the work itself. At the same time, broad, generic AI training misses the mark — it rarely builds the judgment, adaptability, and confidence people need to use AI effectively. Growing regulatory expectations are also adding more pressure, with organizations needing to build AI literacy and show evidence of the steps they’re taking.
A different approach is needed — one that helps organizations build AI agility across the workforce and turn investment into real value.
What is AI agility?
AI agility is the ability to work effectively with AI — to collaborate with it, critically evaluate it, and apply it responsibly to solve new problems.
But many organizations still treat AI use and AI capability as the same thing. They are not. Access and adoption matter, but they do not tell you whether people can adapt, apply judgment, and reshape work as AI changes the task.
To understand AI agility clearly, organizations need to look across three levels at once:
- 1 Individual
- 2 Team
- 3 Organizational
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Where the real gap lies
Why AI investment is not yet translating into impact, and the three barriers holding organizations back. -
How to diagnose your organization
A practical framework to assess AI agility at the individual, team, and organizational levels. -
What to do next
A three-step approach to measure capability, develop AI agility over time, and redesign work to optimize AI’s impact. -
What progress looks like
Examples of how different functions can move from AI adoption to meaningful, sustained AI agility.
Why AI agility is falling short
Senior Partner, Workforce Global Leader