The question is no longer whether AI can create value. It's why so many organizations struggle to capture it.
A recent global survey found that 73% of enterprises are using AI regularly across their business, yet only 10% say AI is core to how their organization operates. Most haven't transformed the systems, workflows, and operating models needed to realize its full value.
AI isn't exposing a technology problem. It's exposing an operating model problem.
Every leadership team we meet with is trying to solve the same dilemma. Move too slowly, and the organization falls behind. Move too quickly, and AI initiatives become fragmented, risky, and difficult to scale.
Many organizations make the understandable mistake of simply plugging AI into the operating model they already have. When that creates friction, they often swing between too little coordination and too much bureaucracy. Neither approach unlocks AI at scale.
The organizations creating the greatest value aren't simply adopting AI. They're redesigning how work moves through the organization.
AI Requires a Different Way of Working
For years, organizations have refined operating models designed to deliver traditional technology initiatives. They emphasize planning, predictable delivery, formal governance, and clearly defined requirements. Those approaches have served organizations well.
AI demands a different way of working.
Ideas emerge from every corner of the business. New capabilities appear almost weekly. Solutions require rapid experimentation, close collaboration between business and technology, and continuous learning. The speed of change often outpaces the processes organizations rely on to manage it.
When AI is forced through operating models built for a different kind of work, friction follows.
Some organizations struggle with disconnected experimentation. Teams solve the same problems in isolation, purchase overlapping tools, and create solutions that are difficult to scale or support. Valuable lessons remain trapped within individual departments, and leadership has little visibility into where AI is creating value.
Other organizations respond by adding more process. More approvals. More committees. More documentation. While intended to reduce risk, these additions often slow decision-making, discourage experimentation, and drive innovation outside established channels.
Neither extreme produces lasting results. The good news is that leading organizations are showing there's a better way.
The organizations making the greatest progress with AI are designing operating models built for the realities of AI work. They create a clear path for ideas to enter the organization. They establish lightweight guardrails instead of heavy bureaucracy. They empower teams to experiment within defined boundaries. They create transparency around priorities and decisions. And they intentionally connect teams so successful approaches can be shared, improved, and scaled across the enterprise.
The operating model doesn't slow AI down. It enables AI to move faster with greater confidence.
Take Action
Building an effective AI operating model doesn't require reinventing your organization overnight. It starts by intentionally redesigning how AI work moves from idea to impact.
Consider these six principles:
Create one visible path for AI opportunities. Employees should know exactly where ideas go, how they're evaluated, and what happens next. A transparent intake process creates visibility while reducing duplication.
Build an AI community, not just an AI committee. AI relies on people to scale. Create a network of AI champions who identify and advance opportunities, community facilitators who connect people and share learnings, and enablement leads who provide reusable tools and guidance. Together, they become the connective tissue that helps AI scale across the organization.
Design for experimentation. AI initiatives shouldn't follow the same path as every enterprise technology project. Create lightweight processes that allow teams to test ideas quickly while learning before making larger investments.
Empower teams within clear guardrails. Establish simple expectations around security, data, architecture, and responsible AI, then push decisions to the lowest accountable level whenever possible.
Stand up mechanisms to learn and scale. Establish regular forums to share lessons learned, reusable assets, successful approaches, and emerging risks. Organizations that learn together improve together.
Continuously evolve the operating model. AI will continue changing. Your operating model should as well. Regularly evaluate where work gets stuck, where unnecessary friction exists, and what new capabilities or structures will help the organization move faster.
If you're wondering where your organization stands, our AI Readiness Assessment can help identify the operating model capabilities that are enabling AI—and the ones that may be holding it back.
The competitive advantage won't come from access to AI. It will come from the operating models organizations build around it.
Organizations that get this right won't simply deliver more AI projects. They'll build an organization that can continually turn emerging AI capabilities into lasting business value.
