Catalysts: Getting More Value out of AI Means Looking Beyond Tech

Evaluating AI Through a Human-Centered Lens

As part of our 2026 Catalysts research, we’re exploring how organizations are progressing on their AI journeys through Prophet’s Human-Centered Transformation Model. Rather than looking at AI adoption through a technology lens alone, the framework examines how strategy (DNA), capabilities (Mind), operating models (Body), and behavior (Soul) must evolve together to enable lasting transformation.  

Here we focus specifically on our findings under ‘DNA’. 

As organizations move beyond the initial wave of AI experimentation, a common challenge is emerging: many are still starting with the solution before considering the business problem that needs to be addressed. Across our Catalysts 2026 research, leaders described organizations overwhelmed by new tools, use cases, and possibilities, yet struggling to connect AI investments back to strategic priorities and measurable business outcomes. 

The organizations generating the greatest value from AI are taking a different approach. Rather than treating AI as a standalone initiative, they are embedding it within broader business, brand, customer, and growth strategies. 

What we’re seeing from the new research: 

AI Initiatives are Operating Alongside Strategy, Rather Than Within It 

Many organizations have established dedicated AI programs, roadmaps, and innovation initiatives, but these efforts are not always clearly connected to broader business objectives. Leaders described AI as a parallel workstream rather than a capability embedded within the organization’s strategic agenda. 

“Don’t know that we will have an AI strategy, but we will have an AI role that is helping the larger strategy.”

Business Leader, International Food Business

Organizations are Struggling to Connect AI Investments to Enterprise Value.  

As AI opportunities multiply, leaders are finding it increasingly difficult to distinguish between experimentation that creates meaningful value and experimentation that simply creates noise. 

“A nose for value…there are a lot of great ideas and capabilities being developed, but we are trying to understand how we actually extract value out of it.”

HR Leader, Pharmaceuticals

AI Adoption is More Effective When Embedded Into Existing Workflows. 

Organizations seeing benefit from AI are focusing on solving business and operational issues to create impact while positioning AI as part of a larger story about growth, transformation, customer experience, or operational excellence. Rather than presenting AI as a set of standalone tools, they are helping employees understand how it supports the organization’s broader direction and embedding it into or reworking existing processes and workflows. 

“Customer experience is the guiding principle for us so whatever we do should be about making the customer experience better.”

 Business Leader, International Food Business

What’s Next for Business Leaders?

1. Don’t build an AI strategy. Build a business strategy powered by AI.

Our 2025 Catalysts research highlighted the importance of aligning AI with organizational purpose, values and strategic direction. That finding still holds true, but the urgency has changed. Organizations can no longer afford to treat AI as a parallel innovation agenda. The leaders pulling ahead are embedding AI into the core business strategy, ensuring every AI initiative is tied to the enterprise agenda before fragmented investments become harder to align. 

2. Stop measuring AI activity. Start measuring enterprise value. 

In 2025, we encouraged organizations to focus AI efforts on meaningful business outcomes rather than technology deployment alone. As AI adoption accelerates, that principle becomes even more critical. The next challenge is no longer generating AI ideas, it’s making disciplined choices about where AI creates differentiated value and having the confidence to stop investing where it doesn’t. 

3. Make governance an accelerator, not a gatekeeper. 

Governance can no longer focus solely on responsible AI and risk management. As organizations move beyond isolated pilots, governance must become the mechanism that aligns priorities, coordinates investment and enables scale. The organizations that move fastest won’t necessarily be those running the most experiments, they’ll be the ones most effectively organizing around them.