Start with the operating problem
AI can support drug development, production planning, maintenance, and logistics, but broad enthusiasm does not establish a useful implementation sequence. Identify bottlenecks and decisions where improved information or automation could create measurable value.
Complex AI adoption differs from configuring rules-based enterprise software. Models require iterative testing, feedback, and continuing attention to the quality and reliability of their output.
Build readiness into the plan
Assess whether data is accurate, complete, consistent, and accessible. Evaluate employees’ data literacy and their ability to recognize errors, understand limitations, and use insights responsibly. Trust depends on those capabilities as much as the technology itself.
Develop a roadmap that makes prerequisites, resource needs, investment, complexity, and expected value explicit. Prioritize achievable use cases, learn through controlled application, and expand when the evidence supports it. The opportunity is substantial, but durable benefit comes from disciplined execution and informed adoption.