Biotech companies do not lack AI tools. They lack clarity on what to implement.
AI adoption becomes difficult when a promising idea meets the reality of company data, workflows, budgets, systems, security requirements and limited internal capacity. The hard question is not what AI can do — it is what you should actually implement, and whether it will work in your organization.
Too many options, too little decision support
DoE, predictive ML, Bayesian optimization, active learning, LLMs, computer vision, automation, specialist platforms and hundreds of vendors may all look promising. The challenge is knowing which approach fits a specific workflow.
Feasibility depends on company reality
An AI approach may be technically possible but still fail because the required data is incomplete, integrations are difficult, security approval takes too long, internal skills are missing or the economics do not justify implementation.
Past projects rarely improve the next decision
Project delays, approval bottlenecks, implementation failures and lessons often remain buried in reports, emails and individual experience. The next AI project starts from almost zero again.
Trial and error is expensive
Without a structured way to decide, teams fall back on trial and error — piloting approaches that do not fit, or delaying the decisions that would actually move R&D forward.
AI adoption should not depend on trial and error.