Step 3: Scale impact

Moving from successful pilots to organization-wide adoption takes structured training that builds real capability, centers of excellence that include scientific domain experts, and governance that scales with adoption while protecting patient safety and compliance.

Turn pilots into a launchpad for AI upskilling

Scaling success requires building deep AI capability across every role, from the C-suite to your bench scientists. Treat training not as compliance modules but as an investment in your people's ability to deliver better science with less administrative drag.

Don't overlook experiential learning. Hackathons bring energy and experimentation that top-down training never achieves. When teams compete to solve real problems with AI, learning happens organically and enthusiasm builds.

Consider peer learning and mentorship that pairs experienced users with colleagues just starting out, through regular check-ins, shadowing in real workflows, and problem-solving sessions. You can also create certification programs that validate competency through real-world assessments rather than multiple-choice tests.

Establish centers of excellence

Create specialized teams, or centers of excellence, dedicated to sustaining and expanding AI capability. They develop best practices across functions, provide technical support and troubleshooting, and systematically test new use cases.

Structure them with clear accountability and cross-functional representation that prevents siloed thinking. Include technical architects who understand integration and data flows, domain experts from each major function who translate scientific needs into AI opportunities, and data scientists who tune performance and spot emerging capabilities.