The Cleveland Clinic's success shows that AI thrives when domain experts (doctors) act as product managers, defining the problem and guiding the tech. This ensures technology serves the core mission, preventing the pursuit of vendor-pushed "magic beans" and grounding solutions in operational reality.

The successful approach to AI isn't applying the technology broadly and searching for value. Instead, leaders must first define a specific business outcome, such as improving pipeline conversion. From there, they can work backward to identify and procure the exact data needed to enable AI to solve that targeted problem.

Effective AI adoption isn't about force-fitting a new technology into a workflow. Leaders should start by identifying a significant business challenge, then assemble an agile team of business experts and technologists to apply AI as a targeted solution, ensuring the effort is driven by real-world value.

An effective AI strategy pairs a central task force for enablement—handling approvals, compliance, and awareness—with empowerment of frontline staff. The best, most elegant applications of AI will be identified by those doing the day-to-day work.

The most valuable AI champions within a company don't just promote tools. They act as 'internally deployed vibe coders,' embedding with business units to show what's possible by co-creating solutions and helping to fundamentally change workflows.

For successful enterprise AI implementation, initiatives should not be siloed in the central tech function. Instead, empower operational leaders—like the head of a call center—to own the project. They understand the business KPIs and are best positioned to drive adoption and ensure real-world value.

The key to driving AI adoption isn't always a dedicated technical team. It's about identifying internal champions in any department—even Legal—who have successfully automated their own processes. Embedding these individuals in other teams can effectively spread practical knowledge and inspire wider adoption.

Successful AI strategy development begins by asking executives about their primary business challenges, such as R&D costs or time-to-market. Only after identifying these core problems should AI solutions be mapped to them. This ensures AI initiatives are directly tied to tangible value creation.

To get teams to embrace AI, leaders should ditch generic mandates like "use more AI." Instead, focus on specific business transformations and highlight the customer value they create. Using company-wide forums for "show and tell" sessions where teams demonstrate unarguable successes makes adoption organic and outcome-driven, not a top-down chore.