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.

Data shows that AI adoption has the least positive momentum when owned solely by the IT department, with only 47% of such companies reporting progress. Initiatives led by dedicated AI leadership or executives are far more successful, framing AI adoption as a strategic challenge, not just a technology rollout.

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.

Moving past chaotic "hackathons," effective AI implementation needs a designated leader who knows the team's processes inside and out. This person shepherds the strategy, ensuring agents are built on a solid foundation and integrated smoothly, preventing a proliferation of uncontrolled, low-quality bots.

A technical AI background isn't required to be a PM in the AI space. The critical need is for leaders who can translate powerful AI models into tangible, human-centric value for end users. Your expertise in customer behavior and problem-solving is often more valuable than model-building skills.

An effective AI strategy in healthcare is not limited to consumer-facing assistants. A critical focus is building tools to augment the clinicians themselves. An AI 'assistant' for doctors to surface information and guide decisions scales expertise and improves care quality from the inside out.

AI models lack novel context and frequently produce errors. The success of an AI-first product hinges on leveraging domain experts to build the model's "muscle," provide essential context, and constantly validate its output to ensure accuracy and value.

The most successful AI automation projects are identified by employees who perform the manual workflows day-to-day, not by executives. A top-down approach often fails to account for practical data and implementation challenges that front-line workers and technical teams understand best.

Instead of a centralized AI team pushing solutions, Pfizer makes business unit leaders directly accountable for using AI to transform their own domains (e.g., manufacturing, research). The central function provides infrastructure, but the responsibility for creating use cases lies with the leaders who must deliver results.