evidence

Most AI ROI claims are marketing, not measurement. This lesson builds a rigorous framework: how to set baselines and counterfactuals, why RCTs beat vendor case studies, the real cost components of AI deployment, and how to avoid the attribution traps that make bad investments look good on paper.

Strip away the hype. This lesson unpacks the real adoption and impact data: McKinsey's State of AI surveys, the MIT 2025 GenAI Divide, and why roughly 95% of enterprise GenAI pilots show no measurable P&L impact — plus where value has actually landed and what the pilot-to-production chasm looks like in practice.

What measurable benchmarks say agentic software agents can actually do in 2026, what production deployments have publicly disclosed, the strongest skeptic case on the books, and how the EU AI Act reshapes the deployment math.

Where the evidence for AI in scientific discovery is strongest, where it is weakest, and how to tell them apart. Anchored on AlphaFold's peer-reviewed record, the 2024 Nobel Prize in Chemistry, and what AI-first drug-discovery companies have actually disclosed to investors.

Evidence on AI in legal practice: the Stanford RegLab hallucination benchmark of Westlaw and Lexis+ AI, the Mata v. Avianca and Park v. Kim sanctions cases, and what published ethics rules require.

Hard evidence on AI coding assistants: the Microsoft Research RCTs, METR's surprising 2025 slowdown finding, SWE-bench Verified, and why benchmark scores keep outrunning real-world value.

An evidence-led look at AI agents in customer support: the Brynjolfsson NBER RCT, Klarna's 2024 victory lap and 2025 walk-back, and what generalises beyond a single firm.