IDEA IN BRIEF
What the data show
Employees are ahead:
McKinsey data shows employees use gen AI at 3× the rate leaders estimate (13% vs 4% for 30%+ of daily work). Nearly half expect to reach this level within a year.
Leadership cannot align:
Adecco surveyed 2,000 C-suite leaders and found only 10% of companies are “future-ready.” Over half of CEOs (53%) say their teams struggle to align on AI strategy.
Value remains elusive:
BCG reveals 74% of companies have yet to demonstrate tangible value from AI; only 4% have cutting-edge capabilities across functions.
Trust is high but fragile:
McKinsey found 71% of employees trust their employers to deploy AI ethically, yet only 1% of companies describe their AI deployment as mature.
Your employees are ready for AI. Your leadership team is not.
This is not speculation. It is the conclusion of three independent research studies published in the last six months by McKinsey, BCG, and Adecco Group. Each approached the question from a different angle. Each arrived at the same answer.
Frontline teams are deploying AI tools at three times the rate leadership assumes. Seventy-four percent of companies have yet to show tangible value from their AI investments. Fifty-three percent of CEOs cannot get their executive teams to align on strategy. And only 10% of organizations qualify as future-ready for the disruption ahead.
This is not a story about lagging adoption—adoption is widespread.
This is a story about the widening gap between employee capability and leadership readiness.
THE DATA
The Research Convergence: Three Studies, One Conclusion
When independent research teams studying different populations arrive at the same conclusion, it demands attention.
McKinsey
Surveyed 3,613 employees and 238 C-suite leaders to examine daily AI usage and readiness.
Finding: C-suite executives underestimate employee AI usage by 3×.
BCG
Assessed AI maturity across 30 enterprise capabilities in 59 countries.
Finding: 74% of companies have not demonstrated measurable AI value.
Adecco Group
Surveyed 2,000 C-suite leaders across 17 industries to measure leadership alignment and workforce readiness.
Finding: Only 10% of organizations qualify as future-ready.
The convergence is clear:
The problem is no longer awareness.
The problem is execution, alignment, and readiness.
Exhibit 1. The AI Readiness Gap
Employees adopt AI at 3× the rate leadership estimates—yet value creation remains stalled.
Key Gaps:
3× underestimation of daily AI use
2× expectation gap in projected usage
Only 10% of leaders consider their organizations future-ready
74% of companies have not demonstrated tangible AI value
53% of CEOs struggle to align their teams
Bottom-up adoption is accelerating. Top-down readiness is not.
The Perception Gap Is Widening
Leadership underestimates how deeply AI is embedded into daily work.
Employees: 13% use AI for 30%+ of tasks
Leaders estimate: 4%
Employees also expect rapid acceleration: 47% expect to reach deep AI usage within a year, while only 20% of leaders believe this.
Millennial managers (ages 35–44) report the highest AI fluency—62% saying they are highly familiar with gen AI—further accelerating bottom-up adoption.
AI LITERACY AND LEADERSHIP CAPABILITY
The Alignment Crisis
The majority of CEOs (53%) say their teams struggle to align on AI strategy. This is the hidden crisis beneath adoption statistics.
Leadership confidence in AI strategy has fallen from 69% to 58% in one year.
This decline comes at the exact moment employees are accelerating usage.
Executives cite talent gaps and resource constraints, but the data shows the real constraint is leadership clarity and alignment.
Exhibit 2. The Leadership Alignment Crisis
Top barriers identified by CEOs:
53%: cannot align teams on strategy
–11 pts: decline in confidence
48%: unclear outcomes + metrics
42%: inconsistent governance
46%: talent readiness concerns
38%: resourcing constraints
The AI Value Paradox
Investment is rising. Value is not.
BCG findings:
74% of companies have not captured measurable value
Only 4% have enterprise-wide, cutting-edge AI capabilities
An additional 22% have strategy + emerging capabilities
Leaders outperform peers with
1.5× revenue growth
1.6× shareholder returns
1.4× ROIC improvement
Value does not come from AI tools. Value comes from redesigning work, not automating tasks.
Exhibit 3. The AI Value Paradox
AI leaders (4%)
Cutting-edge capabilities → significant measurable value.
Emerging performers (22%)
Advanced capabilities + early value.
Majority (74%)
Tools deployed, but no workflow redesign → no value.
The Trust Advantage — and the Risk
Employees trust employers (71%) more than:
universities
tech companies
startups
Yet:
Only 66% of companies have usage policies
Less than 50% offer formal AI training
Nearly 50% of employees want more
60% of leaders expect employees to upskill themselves
Exhibit 4. Trust vs. Readiness Gap
Insight:
Employees are not resisting AI.
They are asking for clarity, training, and direction.
Measure | % |
|---|---|
Employees trusting employers to deploy AI ethically | 71% |
Companies with clear policies | 66% |
Employees wanting more AI training | ~50% |
Companies offering formal training | <50% |
Leaders expecting employees to self-upskill | 60% |
What Leaders Must Do Now
1. Audit real usage — not assumptions.
Shadow adoption is widespread. Understanding current state is the prerequisite to designing future state.
2. Align at the top before scaling.
53% of CEOs say alignment is the barrier.
Acceleration without consensus creates chaos.
3. Shift investment to people and processes.
AI leaders follow a 70–20–10 rule:
70% → people + processes
20% → technology + data
10% → algorithms
Most companies do the reverse.
Exhibit 5. The 70–20–10 Allocation That Predicts AI Winners
AI Leaders Spend:
70% on people + processes
20% on technology + data
10% on algorithms
Typical Companies Spend:
30–40% people/processes
40–50% tech/data
20–30% algorithms
FAQ
Q: How can leaders audit AI usage when adoption is decentralized?
Start with anonymous surveys, IT logs, and workflow interviews. Shadow usage is already 3× higher than leaders assume.
Q: What does “alignment” actually mean?
Agreement on:
Which workflows to transform
What outcomes define success
Who is accountable
Q: Why do most companies fail to show AI value?
Because they automate existing processes instead of redesigning them.
Q: What is the difference between AI adoption and AI maturity?
Adoption = usage.
Maturity = governance, consistency, scaling, and measurable impact.
Q: What is the 70–20–10 rule?
AI leaders put most of their investment into people and processes, not technology.
Research Sources
McKinsey & Company: Superagency in the Workplace (Jan 2025)
Boston Consulting Group: Where’s the Value in AI? (Oct 2024)
Adecco Group: Leading in the Age of AI: Expectations vs. Reality (May 2025)
All statistics are directly sourced from these primary research studies.
