In 2023, Bill Gates called AI the most important technological advancement since the graphical user interface; we agree. AI doesn’t function like most technologies. It changes how work happens, how decisions get made, and how value gets created.
That means adoption is less about installing something, and more about organizing around it.
A leadership lens we frequently apply in our client work is to examine an organization’s strategy, structure, and talent in tandem. Like other major organizational challenges, this lens is especially useful when extended to AI adoption.
Strategy: What are we solving for?
AI adoption should start with a handful of strategic priorities—not a long list of tools.
Useful questions include:
Where do we need speed most?
Where do we need consistency and quality most?
Where are we making expensive decisions with incomplete information?
Where are the highest-cost handoffs or bottlenecks in our workflows?
The goal isn’t “use AI.” The goal is to create value that aligns with an organization’s mission, vision, strategy, and objectives. AI is simply a capability that can accelerate desired outcomes.
Structure: How does work actually flow?
Once priorities are clear and calibrated, AI adoption becomes an operating model decision: How should work be done differently?
This doesn’t mean replacing people. It means redesigning how work gets done so teams can eliminate redundant work, improve cycle time, reduce decision friction, and elevate focus to higher-value judgment.
Meaningful progress often requires moving beyond ad hoc experimentation toward enterprise-level operating rhythms that integrate data and workflows, standardize outputs, and make use auditable. Without this shift, AI remains fragmented, difficult to scale, and hard to trust.
Talent: What capabilities do leaders and teams need now?
Organizations that scale AI effectively don’t just train people on prompts; they build leadership and team capabilities that make AI outputs usable, trustworthy, and repeatable. This means identifying strengths and weaknesses, then tailoring development opportunities to individuals across the organization. Practically, it may include capability-building in the following ways:
Judgment and discernment (knowing when to trust outputs and when to challenge them)
Problem framing (understanding how to formulate the right questions)
Workflow ownership (treating AI as part of the process, not an ancillary tool)
Quality control habits (simple checks that prevent downstream issues)
AI increases capacity only when teams have clarity on standards, accountability, and review points.
What This Means for Leadership Teams
Senior leadership teams are cautioned not to over-index on a handful of leaders to serve as AI champions.6 Instead, leading researchers describe the need for every senior leader to become an AI “shaper,” embedding AI into strategy and day-to-day work.
Ultimately, AI adoption is a leadership decision, not an IT decision. Leaders set the tone, and have to carry it personally, not delegate it.
In this way, industry-leading organizations do not treat AI as a standalone initiative. They approach it as a strategic force that demands the same fundamentals as any major shift: a clear “why,” disciplined choices, an operating rhythm that supports execution, and the leadership maturity to use new capability fully and responsibly.
At ExecLead, we believe making superior decisions is one of the most important responsibilities of leadership. AI presents enormous advantages for leaders who embrace the technology and institutionalize effective use for informed decision-making. The teams that make progress are the ones who treat AI as a strategy-and-operating-model shift: aligning priorities, clarifying decision rights, and building the talent systems required to scale new ways of working.
References
Kenny, G., & Oosthuizen, K. Don’t Let AI Reinforce Organizational Silos. HBR.org (Sep 18, 2025).
Valentine, M., Politzer, D. J., & Davenport, T. H. How to Make Enterprise Gen AI Work. HBR.org (Sep 18, 2025).
van den Broek, R., Hellauer, S., & Wang, D. What Companies with Successful AI Pilots Do Differently. HBR.org (Sep 12, 2025)
Hoque, F. Two Frameworks for Balancing AI Innovation and Risk. HBR.org (Mar 6, 2025).
Stave, J., Kurt, R., & Winsor, J. Agentic AI Is Already Changing the Workforce. HBR.org (May 22, 2025).
Lawler, B., D’Silva, V., & Arora, V. What Companies Succeeding with AI Do Differently. HBR.org (Jan 9, 2025).
Niederhoffer, K., Rosen Kellerman, G., & Lee, A. AI-Generated “Workslop” Is Destroying Productivity. HBR.org (Sep 22, 2025).