Many corporate leaders praise generative AI's potential, yet struggle to realize its benefits. A recent analysis found that while 374 S&P 500 companies mentioned AI on earnings calls, their annual reports detailed more risks than tangible gains. This gap highlights a critical issue: success with AI depends less on the technology itself and more on leadership's ability to drive organizational change.
Experts argue that the failure to capture value from new technologies is rarely a technical problem. Instead, it stems from a failure to adapt business processes, align technology with core strategy, and empower teams to work in new ways. To navigate this transformation, leaders must develop a specific set of skills that differ from those that made them successful in the past.
Key Takeaways
- Effective AI integration requires leaders to focus on organizational transformation, not just technology adoption.
- Leaders must develop five key competencies: boundary spanning, organizational design, team orchestration, talent coaching, and leading by example.
- Success stories from companies like Microsoft, SAP, and Amazon show that redesigning workflows and decision-making processes is essential.
- Personal, hands-on use of AI by executives is critical for building fluency and encouraging widespread adoption within the company.
1. Span Organizational Boundaries
True fluency in artificial intelligence is not gained from reading reports but through direct exposure and diverse networks. Research has consistently shown that individuals connected to varied groups of people access a wider range of information, making them more innovative.
For executives, this means actively building relationships outside their immediate industry. Engaging with regulators, startup founders, and technologists provides a deeper, more practical understanding of AI's opportunities and risks. This process of observing credible peers is a classic driver of technology adoption.
The Power of Diverse Networks
According to diffusion of innovations theory, technology spreads when people see it used successfully by others they trust. By stepping outside their corporate bubbles, leaders can gain the tacit knowledge needed to apply AI effectively to their specific business challenges.
Leaders must also create these opportunities for their teams. When Satya Nadella became CEO of Microsoft, he invited the CEOs of small tech companies Microsoft had acquired to the annual strategic offsite, a meeting previously reserved for top executives. This move injected new perspectives into high-level discussions and demonstrated the value of boundary-spanning.
2. Redesign the Organization
Generative AI only creates significant value when companies are redesigned to harness its capabilities. Simply adding AI tools to existing, outdated workflows often results in minimal returns. Decades of research confirm that productivity gains come from complementary changes to business processes, incentives, and organizational structures.
Leaders must act as organizational architects, deciding where to automate tasks, where to augment human judgment, and where to maintain full human control. This goes beyond simple cost-cutting to fundamentally rethinking how the business operates.
Real Value Beyond Automation
Research indicates the most significant benefits of Generative AI come from redesigning business processes, enabling hyper-personalization for customers, and creating entirely new business models, rather than just reducing employee headcount.
At SAP, CFO Dominik Asam led a major initiative to integrate generative AI into core functions. His team automated significant portions of finance and back-office operations, which freed employees to concentrate on higher-value strategic tasks. Similarly, the executive search firm Russell Reynolds encourages managers to treat AI agents as team members, assigning them simpler tasks to upgrade the work performed by human employees.
PepsiCo took a bold step by merging its strategy, transformation, and technology departments. This allowed its chief strategy and transformation officer, Athina Kanioura, to leverage AI to redesign the organization from a unified perspective, identifying process inefficiencies and new opportunities across the company.
3. Orchestrate Team Collaboration
A key test of modern leadership is the ability to integrate AI into high-stakes team decisions. This involves more than just using AI-generated reports as an input; it requires treating AI as an active participant in collaborative discussions.
Finance leaders at Amazon now use generative AI for complex tasks like tax analysis and revenue modeling. The AI's outputs are then synthesized into briefings that feed directly into senior team meetings. This allows executives to debate strategic tradeoffs with a richer and more current evidence base than human analysts could provide alone.
AI as a Team Member
Recent experimental research supports giving AI a more active role in decision-making teams. One study demonstrated that a large language model could be deployed as a "devil’s advocate" to challenge group consensus, thereby improving critical thinking and preventing groupthink.
This means leaders must become skilled orchestrators, carefully managing the balance between human and algorithmic inputs. They must also foster an environment of psychological safety, where team members feel comfortable questioning AI-driven scenarios, sharing failures, and learning from the collaborative process.
4. Coach and Develop Talent
Successful AI adoption depends on leaders who can effectively coach their employees through a period of significant change. People need guidance and the freedom to experiment, make mistakes, and develop new skills to complement the technology.
The nature of management itself is shifting. A large-scale analysis of 34 million U.S. managerial job postings revealed that since 2007, employers have tripled the share of listings that emphasize skills like collaboration and coaching, while reducing the emphasis on traditional supervision.
"Instead of interrogating subordinates, he modeled listening and guided managers to redirect their time toward customers and learning."
Jean-Philippe Courtois, a former executive at Microsoft, provides a powerful example. He dismantled a long-standing "inspection culture" built around exhaustive forecasting rituals. He replaced it with a coaching culture supported by real-time digital dashboards and mandatory coaching training for all managers. This shift freed up thousands of hours for client engagement and learning, just as the company was leaning more heavily on automated analytics.
As AI handles more routine work, leaders must act less like inspectors and more like teachers, helping their teams adapt and thrive in a new environment.
5. Lead by Example
When asked how leaders can remain relevant when they are not the technical innovators themselves, Courtois gave a simple answer: "Use AI every day, in your personal and in your professional life."
Personal, hands-on experience is non-negotiable. Donna Morris, Walmart’s chief people officer, exemplifies this approach. She uses ChatGPT to initiate searches for senior leaders and finds the results often align with her team's existing candidates. She also uses AI for everyday personal needs, from getting travel recommendations to researching medical information for her family.
This regular use builds personal fluency and signals to the entire organization that experimentation is encouraged. One study found that while senior leaders are often more excited about AI than their employees, they use the technology less than their public statements suggest. This can create a perception that they are managing impressions rather than genuinely modeling adoption.
By personally using AI tools, leaders also become better at recognizing "workslop"—content that appears polished but lacks substance or accuracy. More importantly, making this personal use visible creates the social proof needed to accelerate adoption and build a culture of curiosity and continuous learning across the company.