1%.
McKinsey finds that nearly every company plans to increase AI investment over the next three years, but only 1% of leaders describe their organizations as “mature” in AI deployment. That maturity level means AI is integrated into real workflows and drives significant business outcomes.
The gap that matters more than your AI budget
McKinsey surveyed 3,613 employees and 238 C-level executives in October and November 2024, with most responses coming from the United States. That matters because the 1% figure is not just an abstract tech metric; it reflects a leadership execution gap in daily operations.
In the same report, McKinsey states that the main obstacle to scaling AI is that leaders move too slowly, while employees are better prepared than leaders believe.
AI spending rises fast. ROI expectations rise faster.
Over the next three years, 92% of executives surveyed expect to boost spending on AI, and 55% expect investment increases of at least 10% from current levels.
That creates a CEO problem.
• Your peers increase budgets, so the performance bar rises even if your strategy remains unchanged.
• Your board hears “AI investment” everywhere, prompting them to ask for measurable outcomes connected to workflows, owners, and timelines.
• Your teams see delays as signals about priorities, which can alter retention risk in the critical talent pools needed for growth.
The constraint looks like speed and operating model, not curiosity
McKinsey shows that many leaders perceive the development and release of gen AI tools within their organizations as moving too slowly. In their US C suite survey, 47% say development and release is too slow. When asked why, the top reasons include talent skill gaps at 46% and resourcing constraints at 38%.
This is a CEO level systems issue.
Speed comes from decisions that remove friction across legal, security, data, procurement, and risk. Speed comes from a clear operating model that turns pilots into owned workflows.
Employees already told you what would increase daily usage
McKinsey asked US employees which company initiatives would make them more likely to increase their day-to-day use of gen AI tools. The top items look practical and operational, not inspirational.
• Formal generative AI training provided by the organization at 48%
• Seamless integration into the current workflow at 45%
• Access to generative AI tools at 41%
• Incentives and rewards at 40%
That is your playbook: training, workflow integration, and tool access. Then reinforce adoption with incentives that drive measurable outcomes.
Your natural AI operators already lead teams
McKinsey highlights a specific age cohort as a leverage point. Millennials aged 35 to 44 show high confidence in the abilities of gen AI at 90%. They also report higher levels of extensive familiarity with gen AI at 62%.
If you want speed, this cohort is a practical starting layer for scaled adoption.
• They already manage teams.
• They already use tools confidently.
• They can turn capability into consistent team routines.
A CEO diagnostic you can run this week
You can use these questions to find where your organization is on the 1% maturity curve.
Strategy and value
• Which 10 workflows have the highest cost, time, and risk exposure today
• Which 10 workflows generate the most revenue and pipeline velocity
• Which 5 AI use cases correspond to those workflows with measurable benchmarks
Operating model
• Who owns AI outcomes by function and has the authority to change process and tooling
• The path from idea to production, including security, legal, and data approvals
• Your default build pattern for AI within workflows, including human review and audit logs
Talent and enablement
• What percentage of employees completed formal generative AI training in the past 90 days
• Which roles access workflow-integrated tools rather than standalone chat experiences
• Which managers conduct weekly adoption reviews with metrics and usage patterns
What the 1% figure should change in your next exec meeting
Treat AI like an execution system.
Budgets will rise across your peer set. The scarce asset becomes leadership throughput.
If you want to move toward AI maturity, focus on three levers that McKinsey’s data keeps pointing back to.
• Speed of decision-making and release cadence
• Workflow integration rather than tool experimentation
• Training and manager-led adoption programs