As an executive coach and strategic advisor, I am in daily conversations with executives across a range of industries, and each one views AI differently. From the evangelical to the agnostic, their perspectives don’t just shape how they use AI themselves. Their views influence how their teams experiment, how decisions are made, and ultimately how much value their organizations realize from AI.
These conversations have pointed me to bigger questions, ones that extend beyond business and strategy to something much more innately human: How do we redefine ourselves when machines begin to do more of what once made us valuable? It’s a topic I want to explore in a future post.
For now, I’m interested in a different question:
Why are some organizations creating meaningful value from AI while others are simply adding another technology to the stack?
The Questions Beneath the Surface
In order to rethink how value is created, an organization and its leaders need time, and yet, the typical executive is being asked to simultaneously manage their business, lead their people and respond to constant change. Now layer on top of that the mandate to leverage AI for greater efficiency, higher quality and more seamless workflows. It’s no wonder that AI-related gains are often experimental and episodic.
But capacity is only part of the story. Beneath the surface lies a deeper set of questions about expertise, judgment, and what it means to remain relevant in a world where intelligence is increasingly abundant. Early conversations focused on whether AI was useful and where to begin. Today, leaders are wrestling with what this technology augments, what it replaces, and how it changes the way their organizations create value.
In my conversations with executives, the questions increasingly sound like this:
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If AI can do parts of my job better and faster, what’s left for me?
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How do I know when to trust AI and when to trust my own judgment?
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How do I know whether I’m reviewing someone’s thinking or AI’s?
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Are we redesigning work or simply layering AI on top of existing processes?
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Is AI helping me think better or doing the thinking for me?
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If AI saves time, what should we do with that extra time?
The questions themselves point to a larger reality. The organizations seeing the greatest value from AI are not necessarily the ones with the best tools. They are the ones asking deeper questions about expertise, judgment, quality, and how work gets done.
While every organization will answer those questions differently, there are several commonalities among organizations that are successfully integrating AI into their operations.
Acknowledge the Human Reality
AI adoption must address the psychological realities within organizations. While discussions often focus on productivity gains and efficiency, many employees are wrestling with more personal questions. Questions about expertise, relevance, confidence, and identity. Others are simply tired. Over the past several years, organizations have navigated a relentless pace of change, and for many leaders, AI feels like one more thing they are expected to understand, adopt, and champion.
These concerns are not always spoken aloud. Instead, they often show up in subtle ways: avoiding AI altogether, overusing it without critical thought, or quietly questioning one’s own relevance and expertise. Leaders who acknowledge these realities create the conditions for more honest conversations, greater trust, and ultimately more successful adoption.
Approach AI as an Organizational Capability, Not Just a Technology Initiative
Many clients we work with still approach AI through individual experimentation. Different teams test different tools, often without coordination or a shared understanding of how AI fits into the broader organization.
According to McKinsey, organizations generating the greatest value from AI are significantly more likely to redesign workflows, invest in training, establish governance, and integrate AI into business processes rather than rely on isolated experimentation.¹
This aligns with what I am seeing in my own work. Organizations creating meaningful value from AI tend to view AI not simply as a tool, but as a capability that influences how work gets done, how decisions are made, and how value is created.
They ask questions such as:
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Where should AI fit into existing workflows?
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Which decisions require human judgment?
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What work should be automated, augmented, or eliminated?
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How do we maintain quality and accountability?
Rather than layering AI on top of existing processes, these organizations thoughtfully redesign how work happens. As a result, AI becomes integrated into the operating rhythm of the business rather than remaining an isolated experiment.
Focus on Value, Not Productivity
Before selecting tools or launching pilots, understand the benefit to the organization or team. While many AI conversations begin with productivity, the organizations I see creating meaningful value from AI tend to connect it to something larger.
In some cases, that may mean freeing up capacity for more strategic work. In others, it may mean improving decision-making, accelerating innovation, enhancing customer experiences, or helping employees focus on the work that creates the greatest value.
The organizations I see creating meaningful value from AI are not simply asking, “How can we do the same work faster?” They are asking, “What becomes possible if time, expertise, and information are less constrained than they were before?”
Without a clear answer to that question, organizations often find themselves using AI to make existing work more efficient rather than rethinking how value is created.
Strengthen Human Judgment
Much of the conversation around AI focuses on what the technology can do. Less attention is paid to what it means for human judgment.
As AI becomes more integrated into daily work, leaders are confronting a new set of challenges. If a team member brings you something generated by AI, how do you evaluate its quality? How do you know whether the conclusions are sound? How do you know whether the person understands the work they are presenting? And how do you know when to trust AI and when to trust your own experience?
AI can produce answers. It can even produce answers that sound convincing. But someone still needs to decide what matters, what is accurate, what is missing, and what action should be taken.
The leaders who seem to navigate this transition most effectively recognize that expertise is no longer simply about having answers. Increasingly, it is about knowing which questions to ask, what to trust, and what matters.
As a result, developing judgment, critical thinking, and the unique strengths of employees becomes more important, not less.
The Opportunity
What stands out to me is that the organizations I see creating the most meaningful value from AI are asking questions that go well beyond technology.
AI will continue to evolve. New tools will emerge. Capabilities will expand. But organizations that focus exclusively on technology risk missing the larger opportunity.
The organizations that thrive will be those that invest as much energy in helping people work effectively with AI as they do in implementing the technology itself.
¹ McKinsey & Company, The State of AI research. Across recent studies, organizations realizing the greatest value from AI are more likely to redesign workflows, establish governance, invest in training, and integrate AI into core business processes rather than deploy AI in isolated use cases.
