AI Research Agents for Strategy Teams: A Practical Guide
Where research agents help strategy teams, where they fall short, and how FifthRow's Large Consulting Model runs them with source checks and a judge agent.
Research agents make strategy teams faster, and they can also make their thinking narrower. In a 2026 study of AI-generated product ideas, ideas from large language models were 7 times more likely to rank in the top 10% on purchase intent, but less novel and less diverse as a set than human ideas (study). Speed is the easy part to get. Range and reliability take design.
AI research agents for strategy teams work best on research that repeats: scanning markets, profiling competitors, sizing categories and tracking signals over time. The team sets the questions and the method, the agents do the reading and the first draft, and people decide what it means.
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What a research agent does for a strategy team
Given a question, a research agent finds and reads sources, extracts the facts that answer it and returns a structured, sourced draft. Several agents chained together can run a whole study: scope, search, analysis, synthesis and review.
The difference from a general AI assistant is the method. An assistant answers whatever it is asked, in whatever shape. The agent runs the same steps, against the same sources, to the same output format every time, so two studies can be compared and the third one starts from the first two.
Where research agents work well
Agents earn their place on work with many sources, a stable method and a need for repeat runs. That covers most of a strategy team's desk research.
| Strategy task | What the agent does | What the team still does |
|---|---|---|
| Market sizing and category analysis | Collects estimates, extracts drivers, compares methods | Picks the estimate the plan rests on |
| Competitor tracking | Reads filings, releases, hiring and product pages on a schedule | Decides which moves need a response |
| Technology and partner scouting | Finds companies by problem, scores them against criteria | Chooses who to meet |
| Foresight and scenarios | Gathers signals and drafts scenario inputs | Sets the scenario logic and the implications |
| Opportunity screening | Runs the same screen on every idea in the backlog | Decides what to fund |
Where they fall short
Agents are weakest where the job is judgement. They will answer the question they were given, even when it is the wrong question.
METR measures AI capability with a 50%-task-completion time horizon: the length of task, in human time, that a model completes half the time. A strategy team needs far better than half, which is why longer jobs have to be split into checked steps rather than handed to one agent end to end.
Plan for three limits:
- Narrow range. Agents converge on the obvious answer. Ask for options explicitly, and have a person add the non-obvious ones.
- Source quality. An agent can cite a weak source with the same confidence as a strong one. Set which sources count.
- Context. Agents do not know your board's history, your risk appetite or last year's failed launch unless you tell them.
How to set up research agents in a strategy team
Start with one recurring study the team already runs, encode it, and measure it against the manual version before adding more.
- Choose a study that repeats. A quarterly competitor review or a monthly market scan is a good first candidate.
- Write down the method. Questions, trusted sources, steps and output format, as your best analyst runs it.
- Build in checks. Every cited fact is matched against its source, and a reviewer step reads the output before anyone sees it.
- Run it side by side. Compare one agent run with the last manual study. Fix the method where they differ.
- Open it up. Once the team trusts the output, let people outside strategy run the same study on their own questions.
How FifthRow's Large Consulting Model runs research agents
FifthRow's Large Consulting Model (LCM) is "a consistent, repeatable, fact-checked way to run consulting work". It builds the setup steps above into one platform, so a strategy team does not have to assemble the checks itself.
Each part of the LCM covers a job the team would otherwise do by hand:
| LCM part | What it does | The job it covers |
|---|---|---|
| Concierge | A chat agent that answers from your company's research, with sources, or starts new research | Asking the question |
| Answers | A research agent that searches multiple search engines and strips out made-up citations | Reading the sources |
| Fact checking | Breaks each source into reusable facts and keeps them in a facts store | Matching facts to sources |
| Quality control | A judge agent that reads every output | The reviewer step |
| Apps | A multi-agent team that repeats the same consulting job, with the same inputs and a structured output | Encoding the method |
| Docs | A writing surface that pulls the work into one report | The deliverable |
Two parts matter most for strategy work. Apps fix the method, so this quarter's competitor review runs the same way as last quarter's and the two can be compared. And each run builds on the work before it: facts and answers from earlier studies are reused, so the tenth study starts from what the first nine found.
The LCM also works where the team already works. Apps, facts and answers are available in Copilot, Teams, ChatGPT and Claude, and over MCP and the API. Your data stays yours: FifthRow does not train on, resell or package it.
An example: 124 studies run by the people who needed them
A $5B+ global materials science company runs front-end innovation inside its global R&D function. Questions came from scientists, commercial leads and marketers across three regions, and each one meant somebody doing the desk research by hand.
FifthRow set up a shared catalogue of research systems the company's own people run. A plain-language question returns a multi-step, source-traced study in a consistent format. 14 people in three regions now run their own research, and they have run 124 distinct studies, almost none repeated.
We deliver the outcomes, or you run the work on FifthRow. See what agents do for strategy and foresight teams and
market intelligence teams.
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Frequently asked questions
What can AI research agents do for a strategy team?
They can run desk research at volume: market sizing inputs, competitor tracking, technology scouting, signal gathering and opportunity screening. They return structured, sourced drafts on a fixed method. The team keeps the questions, the judgement and the decision.
How is a research agent different from ChatGPT?
A general AI assistant, even in a deep-research mode, plans its own steps and picks its own format each time. An agent built for research runs a fixed method against chosen sources, checks its citations and returns the same output format every time, so studies can be compared and reused.
What is FifthRow's Large Consulting Model?
The Large Consulting Model (LCM) is FifthRow's consistent, repeatable, fact-checked way to run consulting work. It combines a chat agent, a research agent, fact checking, a judge agent, multi-agent Apps and a writing surface. Strategy teams can use it on the platform or inside Copilot, Teams, ChatGPT and Claude.
How do you stop research agents giving the same obvious answer?
Ask for options explicitly, set a wide source list, and keep a person on the step where ideas are generated and ranked. A 2026 study found AI-generated product ideas less diverse than human ones, even from newer models, so treat range as something to design for.
Which strategy work should stay with people?
Framing the question, choosing between conflicting sources, reading internal politics and making the recommendation. Agents can draft the evidence for each of these, but the call belongs to the person whose name is on it.
The takeaway
Research agents suit the repeatable desk research behind strategy work. Encode one study the team trusts, build in source checks and a review step, and keep the questions and the decision with people. FifthRow's Large Consulting Model runs the method and the checks, so the team's time goes to the decision.
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