How AI is changing strategy and research
Strategy and research are about making good decisions under uncertainty: where to compete, what customers want, which products to build and how much to invest. They depend on gathering and interpreting large amounts of information, such as reports, interviews, surveys, competitor activity and financial data. AI can read and organise that material far faster than a team can, but it cannot supply judgment, accountability or fresh insight about your customers on its own.
- Reading at scale: summarising reports, filings, articles and interview transcripts.
- Finding patterns: grouping feedback, spotting themes and weak signals.
- Modelling: testing scenarios and quantifying uncertainty.
- Structuring thinking: drafting options, assumptions and counter-arguments for people to challenge.
Explore the four areas
AI in Business Strategy
Competitive analysis, opportunity assessment and AI use-case selection.
Open guideAI in Market Research
Survey analysis, trend monitoring and customer insight.
Open guideAI in Product Research & Innovation
Feedback analysis, idea generation and prioritisation.
Open guideAI in Business Planning & Decision-Making
Scenario modelling, forecasting and decision support.
Open guidePractical use cases
- Summarising dozens of industry reports into a comparison for leadership.
- Coding thousands of open-ended survey answers into themes.
- Clustering customer feedback to see what to fix first.
- Running best-case, expected and worst-case financial scenarios.
- Drafting a pre-mortem listing how a plan could fail.
- Scoring candidate AI projects by value, feasibility and risk.
Technologies behind these use cases
| Capability | What it does | Typical building blocks |
|---|---|---|
| Language models | Summarise, compare, draft, extract | Provider APIs, open-source models |
| Text analytics | Cluster and classify feedback | Embeddings, TF-IDF, scikit-learn |
| Retrieval and search | Query your document collections | Vector databases, RAG, AI search |
| Simulation and forecasting | Test scenarios and uncertainty | Python, NumPy, spreadsheets, time-series tools |
A sensible way to start
- Name the decision the research must inform.
- Gather trusted sources and note dates and origins.
- Use AI to organise and summarise, citing the source for every claim.
- Verify key facts against the original documents.
- Let people decide, and record the reasoning and assumptions.
Limits and risks to plan for
Models can state false facts with confidence, miss recent events, and echo the biases of their training data. They are poor substitutes for talking to real customers. Competitive research must stay within legal and ethical limits, and confidential strategy documents should not go to services you have not vetted. Sound conclusions come from combining AI speed with human scrutiny.
Treat AI-generated market claims as leads to verify, not findings to publish.