How to Build an AI Market Intelligence Workflow for Your Team

Build a market-intelligence workflow that combines sources, AI research and human judgment in one persistent artifact instead of generating isolated summaries.

2026-09-15 · 3 min read

Market intelligence has never suffered from a shortage of information. AI increases the volume again: more sources can be processed, more questions can be asked and more summaries can be generated.

The scarce resource becomes attention.

A useful AI market-intelligence system should therefore do more than collect and summarize. It should maintain a shared understanding of the market and make changes visible to the people responsible for decisions.

The use case: intelligence for a team, not a private AI conversation

This workflow is aimed at strategy, product, marketing, sales, investment, innovation and enterprise intelligence teams.

The system may monitor companies, categories, regulation, customer signals or technologies. The important requirement is that multiple people need to consume and act on the result over time.

The opportunity: one intelligence object, many machine contributors

Different parts of the workflow can use different tools.

A crawler can inspect known sources. A research provider can investigate broad questions. Claude or ChatGPT can synthesize findings. An agent can compare the new state with the old. An automation can decide when to run.

The team should not have to care which provider produced each update. It needs one trustworthy intelligence artifact.

What is Stated?

Stated is an AI-agnostic artifact and publishing layer. It gives models, research tools and automations a persistent destination that humans can read, share and interact with.

That makes it suitable as the delivery layer for a multi-provider intelligence workflow.

Start with the intelligence question

Create a Stated market-intelligence Page for [TEAM] covering [MARKET]. Organize it around the decisions the team makes, not around the source list. Preserve citations and provenance, separate observed changes from interpretation, identify what changed since the previous state, and ask the team which uncertainty should drive the next research cycle.

Copy the prompt and try it in ChatGPT or Claude with Stated connected.

The system becomes:

Design around decisions

Do not start by asking what data you can collect. Start with what the team decides.

A product team may care about feature and positioning changes. A strategy team may care about category structure and new entrants. Sales may care about competitive objections. An investment team may care about signals that alter a thesis.

This determines what deserves monitoring and what is noise.

Use the right research mechanism

Known public pages are good candidates for targeted scraping. Broad questions are better suited to sourced research. Internal systems may provide proprietary evidence. AI models can synthesize across the permitted inputs.

Keep provenance intact through the pipeline. A human should be able to tell whether a statement came from a source, a calculation or an AI interpretation.

Make change a first-class object

The report should not rewrite the whole market on every run. Highlight what changed since the previous accepted state.

This reduces cognitive load and makes recurring intelligence useful. Stable context remains available; new evidence receives attention.

Stated in an enterprise intelligence stack

For example:

The artifact provides continuity across all of those execution paths.

Who this is for

Use this architecture when intelligence is shared and recurring. For a one-off private research question, a model conversation may be enough. The artifact layer becomes useful when the knowledge needs organizational memory and a human feedback loop.

Try the shared-intelligence pattern

Maintain one Stated market-intelligence artifact for [TEAM]. Accept validated updates from authorized research tools and agents, preserve provenance, emphasize changes that affect [DECISIONS], retain stable context, and collect the team's next priority as an explicit response. The artifact should remain canonical even if the underlying model, research provider or orchestration system changes.

The goal is not an AI that knows everything about the market. It is a system that helps the team notice what changed and why it matters.