Research AI adoption use cases
Use this page to scan AI adoption opportunities across the research workflow. The use cases are grouped by stage so you can decide where AI is likely to improve speed, quality, or cost before you commit to a rollout.
Scope
Review scope use cases in the research process, then pick the ideas worth testing against real work.
Bar-setter from prior decisions
Surface historical decisions and outcomes from internal archive; reveal implicit bar past calls applied
Value drivers: Quality
Value 5/5 · Effort 5/5
Criteria stress-tester
LLM critiques proposed criteria for gameability, overlap, missing dimensions
Value drivers: Quality
Value 1/5 · Effort 2/5
Hypothesis tree + criteria translator
Convert one-paragraph brief into MECE hypothesis tree and quantifiable screening criteria
Value drivers: Quality
Value 4/5 · Effort 3/5
Pre-mortem generator
"Imagine this round produced a useless answer, what went wrong?" Multi-persona prompt
Value drivers: Quality
Value 2/5 · Effort 1/5
Stakeholder/landscape intelligence
Deep-research agent reads 20-50 external sources to map what peers are scoping toward
Value drivers: Speed
Value 3/5 · Effort 4/5
Which of these is worth automating for you?
Every use case here has a generic effort rating. Which ones pay back for your team depends on where the hours actually go today, and most teams are guessing. DoneThat reconstructs that automatically, with no timers to forget, so you can measure the baseline before committing to a project and check the gain afterward.
Source
Review source use cases in the research process, then pick the ideas worth testing against real work.
Continuous source monitor
Watch defined sources and push new candidates into intake
Value drivers: Speed
Value 1/5 · Effort 4/5
Domain-specific discovery
Semantic search over the right corpus (Elicit/Consensus, Exa, Research Rabbit)
Value drivers: Quality
Value 5/5 · Effort 2/5
Grey-literature + practitioner sweep
Targeted sweep of practitioner reports, registries, retractions
Value drivers: Quality
Value 2/5 · Effort 3/5
Multi-engine deep research
Same query through ChatGPT, Gemini, Claude Deep Research; merge results
Value drivers: Quality
Value 3/5 · Effort 1/5
Outcome-linked prior-art retrieval
Retrieve prior internal work, with the decision and what happened next
Value drivers: Quality
Value 4/5 · Effort 5/5
Triage
Review triage use cases in the research process, then pick the ideas worth testing against real work.
Adversarial cull-check
Re-score sample of dropped candidates with an "advocate" LLM persona
Value drivers: Quality
Value 2/5 · Effort 3/5
Auto-knockout filter
Rules engine rejects candidates failing hard criteria before LLM scoring
Value drivers: Cost
Value 4/5 · Effort 1/5
Decomposed-criteria scorer
Score each candidate on each sub-criterion independently, then recombine
Value drivers: Quality
Value 5/5 · Effort 4/5
Disagreement flagger + pre-meeting brief
Surface high-variance candidates; brief on divergence and questions to resolve
Value drivers: Speed
Value 3/5 · Effort 2/5
Embedding-based deduplication
Vector similarity to merge near-duplicates and aliases
Value drivers: Cost
Value 1/5 · Effort 5/5
Shallow Assess
Review shallow assess use cases in the research process, then pick the ideas worth testing against real work.
Claim-grounded source verifier
For each claim, identify cited passage; flag where citation doesn't support claim
Value drivers: Quality
Value 3/5 · Effort 4/5
Comparison matrix auto-fill
Define columns once; LLM fills cells from each source
Value drivers: Speed
Value 1/5 · Effort 1/5
Sensitivity-driven research scoper
Ranks which inputs would most reduce decision uncertainty
Value drivers: Quality
Value 4/5 · Effort 5/5
Structured one-pager auto-draft
LLM compiles standard 1-pager per candidate from public and parsed inputs
Value drivers: Speed
Value 5/5 · Effort 2/5
Theory-of-change generator + validator
LLM drafts ToC (inputs, activities, outputs, outcomes, assumptions) from candidate brief; validates each link against evidence
Value drivers: Quality
Value 2/5 · Effort 3/5
Deep Assess
Review deep assess use cases in the research process, then pick the ideas worth testing against real work.
Code-grounded quant red-team
Runs alternative parameterisations against the actual model; reports where conclusions flip
Value drivers: Quality
Value 3/5 · Effort 5/5
Expert finder
Surfaces named experts from publications, citations, conference talks, LinkedIn relevant to the deep-dive
Value drivers: Quality
Value 1/5 · Effort 1/5
Interview prep + transcription + synthesis
Per expert: generate tailored question set from deep-dive draft. Post-interview: auto-transcribe, synthesise themes across many interviews, surface quotes per theme with source link
Value drivers: Speed
Value 5/5 · Effort 3/5
Methodology compliance + completeness check
LLM checks deep-dive against declared protocol (PRISMA, pre-reg, stated rubric); flags missing sections and unjustified deviations
Value drivers: Quality
Value 2/5 · Effort 2/5
Multi-agent adversarial review
Combines falsification prompts, heterogeneous personas (skeptical methodologist, domain expert, base-rate thinker), and Hypothesis-Verifier-Quantifier pipelines. Single architecture, multiple configurations depending on depth needed
Value drivers: Quality
Value 4/5 · Effort 4/5
Decide
Review decide use cases in the research process, then pick the ideas worth testing against real work.
Calibration tracker
Tracks each rater's scores against final outcomes over rounds; surfaces systematic over/underrating
Value drivers: Quality
Value 5/5 · Effort 5/5
Decision capture + disagreement extraction
Record meeting; extract which assumption each side rests on, what evidence resolves it
Value drivers: Quality
Value 4/5 · Effort 3/5
Forecasting tournament on outcomes
Registered forecasts on 12/24-month outcomes; scored when they resolve
Value drivers: Quality
Value 2/5 · Effort 4/5
Pre-meeting steelman pair
Per finalist: strongest case for and against, distributed before meeting
Value drivers: Quality
Value 3/5 · Effort 1/5
Probabilistic decision register
Capture decisions as probability distributions, not binary
Value drivers: Quality
Value 1/5 · Effort 2/5
Hand Over
Review hand over use cases in the research process, then pick the ideas worth testing against real work.
Audience-tailored briefs
One corpus into exec, technical, operational versions; caveats unhideable
Value drivers: Speed
Value 3/5 · Effort 1/5
Implementation feedback loop
Structured check-ins with recipient: what was validated, what was wrong, synthesised back into KB
Value drivers: Quality
Value 4/5 · Effort 5/5
Living-document refresh trigger
Periodically re-run key claims against new evidence; flag when conclusions go stale
Value drivers: Cost
Value 1/5 · Effort 4/5
Open-question packager
Extract unresolved uncertainties; package each as actionable research brief
Value drivers: Quality
Value 2/5 · Effort 2/5
Searchable cross-round repository
Embedding-based search across all decision artifacts with grounded answers
Value drivers: Quality
Value 5/5 · Effort 3/5
Which of these is worth automating for you?
Every use case here has a generic effort rating. Which ones pay back for your team depends on where the hours actually go today, and most teams are guessing. DoneThat reconstructs that automatically, with no timers to forget, so you can measure the baseline before committing to a project and check the gain afterward.