Explore 8 articles covering in-depth insights and practical guidance on ai for scientific discovery.
Critical KPIs for AI-driven scientific discovery across materials science, drug development, climate modeling, and clean energy research—with 2024-2025 benchmark ranges and guidance on measuring real research acceleration.
What's working, what isn't, and what's next — with the trade-offs made explicit. Focus on data quality, standards alignment, and how to avoid measurement theater.
A step-by-step rollout plan with milestones, owners, and metrics. Focus on KPIs that matter, benchmark ranges, and what 'good' looks like in practice.
A concrete implementation with numbers, lessons learned, and what to copy/avoid. Focus on implementation trade-offs, stakeholder incentives, and the hidden bottlenecks.
A practical primer: key concepts, the decision checklist, and the core economics. Focus on data quality, standards alignment, and how to avoid measurement theater.
Signals to watch, value pools, and how the landscape may shift over the next 12–24 months. Focus on data quality, standards alignment, and how to avoid measurement theater.
Myths vs. realities, backed by recent evidence and practitioner experience. Focus on KPIs that matter, benchmark ranges, and what 'good' looks like in practice.
A practitioner conversation: what surprised them, what failed, and what they'd do differently. Focus on implementation trade-offs, stakeholder incentives, and the hidden bottlenecks.