Catalysts: AI’s Next Challenge Isn’t Adoption, It’s Leadership

For the past two years, organizations have been racing to adopt AI. Teams have experimented with pilots, automated workflows, tested use cases, and launched innovation initiatives at unprecedented speed. But in many companies, these initiatives are disconnected and AI adoption has outpaced governance, operating models, and even clear strategic direction.  

As part of the seventh edition of Prophet’s Catalysts research, we’re exploring whether AI’s next challenge is no longer adoption, but leadership. Through our qualitative research, several early hypotheses are beginning to emerge that we’ll continue to test through our upcoming quantitative research. 

As the initial wave of experimentation matures, a new reality is emerging: AI isn’t just changing how organisations operate; it’s changing the nature of work itself. To make the most of this opportunity, organisations must be structured, aligned, and upskilled enough to harness it. 

In last year’s Catalysts research, we explored the importance of building the human infrastructure around AI, emphasizing that transformation could not simply be technical, but needed to be cultural as well. That idea still holds true. But the conversation has evolved. In other words, the challenge is no longer just adapting to AI. It is fundamentally shifting how we organize and structure our work based on a new AI-enabled paradigm. 

Organizations are now moving beyond questions of readiness and adaptability toward something more fundamental: how strategy, talent, culture, governance, and operations work together as an interconnected system, and how to redesign work, not just layer AI onto outdated processes.  

This demands intentional focus from leadership and points to a broader shift: competitive advantage may increasingly come from an organization’s ability to continuously adapt, rather than simply adopt. We’ll be exploring how organisations can do this across the next four blog posts. 

What we’re seeing from the new research: 

1. AI Leadership is becoming change leadership

To succeed in the age of AI, leaders can no longer rely on certainty, fixed answers, or static transformation maps. As AI evolves rapidly, organizations need leaders who can guide through ambiguity, align teams around moving targets, and create momentum without having complete clarity. 

“You have to take into consideration all the human change management aspects.”

Business Leader, International Food Business

2. Leaders need to set the vision 

The most effective leaders are starting from the business need, not the tool, asking how AI accelerates what the organization is already trying to achieve and seizing the opportunity to innovate on the systems and processes that currently exist. 

“We had to create a visual of all the agents we had in existence to prevent people from building the same solution several times in different parts of the business.”

AI leader, Global Industrial Conglomerate

3. Leaders need to communicate openly

For employees to be engaged and excited around AI, leaders must build trust around it and communicate openly, about what AI can help the organization do, what it means for how people work and their jobs, and what they themselves are still learning or don’t yet know. 

“We’ve had to get everyone over the AI anxiety hump because everyone was talking about AI coming to take their jobs, but what does that really mean and what skillsets and mindsets do we need to develop to keep pace with this change?”

HR Leader, B2B Marketing & Sales 

What’s next for business leaders?

1. Get organized around AI, not just excited about it 

In last year’s Catalysts research, we emphasized the importance of building workforce readiness and fostering human-centered AI adoption. This year, we’re seeing organizations move beyond awareness and experimentation into a new challenge: organizational coordination. 

The companies pulling ahead are not necessarily the ones with the most AI tools; they are the ones creating alignment across strategy, governance, operations, talent, and leadership. The challenge is no longer simply adopting AI; it’s organizing the enterprise around it. 

2. Develop leaders who can operate through uncertainty  

Our 2025 research highlighted the need for leaders to build trust and create human-centered transformation environments. This year, we’re seeing leadership expectations evolve even further. Organizations increasingly need leaders who can navigate “known unknowns” without pretending to have fixed answers. Leaders that are embedding experimentation, iteration, and learning directly into how work happens, rather than treating transformation as a separate initiative running alongside the business.  

3. Normalize experimentation and course correction 

Organizations know they need experimentation, but many are still operating with cultures and systems optimized for predictability and risk reduction. 

In our last research, we emphasized experimentation as an innovation muscle. This year, it is increasingly becoming an operational requirement. Organizations need to create environments where teams can test, learn, adapt, and share insights quickly, while still operating within clear strategic guardrails. The strongest organizations are not eliminating uncertainty. They are building the institutional stamina to move through it faster.