An AI consulting framework that runs

The Foundry Method turns a repeatable consulting process into software: gather outside-in and company evidence, identify opportunities, explain the ranking, plan adoption, simulate safely, and prepare the best ideas for engineering.

Use the discovery interview guide Review a sample discovery brief

What this establishes

The decision in view.

Principle 1

Six steps: Scan, Source, Evidence, Review, Prove, and Build.

Principle 2

A broad library of tested opportunity patterns helps find where AI may create value.

Principle 3

Every priority has written reasons covering value, practicality, and how hard it is to copy.

Principle 4

Reusable materials include a discovery brief, ranked roadmap, evidence-gap list, adoption plan, simulation report, and pilot handoff package.

Principle 5

Clear ready, needs-work, or unsafe results control the path from idea to agent handoff.

What is The Foundry Method?

The Foundry Method moves from broad AI ambition to a decision a sponsor can fund and an engineering team can act on. It is designed to run repeatedly across clients, portfolio companies, business units, and departments without rebuilding the assessment in a new spreadsheet and slide deck each time.

Who uses it

Consultants and advisors who need a repeatable client assessment without sacrificing evidence quality.
Transformation leaders who need one way to compare ideas and required preparation across departments.
Investment and portfolio teams who need a comparable view of possible AI value across companies.
Product and strategy leaders who need to separate practical AI ideas from those that only sound attractive.

The six-step framework

Scan — start with a company URL and automatically gather dated public evidence about customer, competitive, regulatory, supplier, talent, and capital pressure.
Source — add company pages, documents, spreadsheets, interviews, and optional consent-first workflow capture.
Evidence — build a structured map of workflows, pains, data, skills, assets, offerings, constraints, and relationships. Machine-drafted facts remain proposals until reviewed.
Review — compare the evidence with a broad library of opportunity patterns, show which ideas the evidence supports, rank them with written reasons, expose missing proof, and separate quick wins from larger bets and required preparation.
Prove — test economics and system readiness, plan adoption, and simulate suitable agents on approved historical or clearly labeled synthetic cases before live work.
Build — prepare agent designs, test suites, supervised pilot plans, starter code, and a production handoff package after the required checks pass. Your technical owner decides what is deployed.

A framework that runs

Well-known consulting frameworks such as BCG's 10-20-70 rule, McKinsey's Rewired capabilities, and Deloitte's Trustworthy AI dimensions help leaders structure transformation and governance. The Foundry Method addresses a narrower execution question: which AI opportunities does this company have evidence to pursue, in what order, and what must be true before each one is built?

The method runs in a working assessment system. The same rules gather and review evidence, test opportunity patterns, calculate scores people can inspect, order required preparation, and generate delivery materials. That makes results more consistent across engagements without making recommendations generic.

What you walk away with

A source-linked discovery brief that sharpens the first meeting.
A ranked roadmap with quick wins, larger bets, required preparation, evidence gaps, and dropped generic ideas.
Written score reasons that sponsors and delivery teams can challenge directly.
An adoption plan showing foundations, owners, controls, readiness questions, and rollout routes.
A simulation report and pilot handoff package for suitable ideas that pass the readiness checks.

Worked example

The Meridian Fab examples on this site show the method in practice: a sample discovery brief starts the assessment, a sample manufacturing roadmap shows how priorities are chosen, adoption planning orders the change, and agent readiness shows how the best ideas are simulated and prepared for a supervised pilot.

The controls behind the method

Automatic discovery drafts evidence; a person controls what enters the company record.
Fixed evidence checks and initial scores keep the AI model from silently changing opportunity order.
Generic ideas are demoted when company-specific pain, data, or advantage is absent.
Readiness results come from evidence and fixed checks, not from an AI grading its own output.
Public signals remain sourced questions until validated against company reality.

Where consultants use it

Advisors can begin before the first interview with a source-linked discovery brief, use workshops to close the highest-value evidence gaps, and leave the engagement with a roadmap and build package rather than a presentation alone. The method also supports repeatable portfolio reviews, transformation programs, and function-level discovery.

Start with a practical asset

If you are evaluating the framework rather than buying software immediately, start with the AI discovery interview guide, compare it against a consulting template, then review one complete sample roadmap. That sequence shows the method as a working system instead of a slide title.

Side by side

Compare the decision process.

Criterion	Use Case Foundry	Static consulting framework
Primary output	Discovery brief, ranked roadmap, evidence gaps, and reviewed pilot handoff	Method description plus manually produced slides or worksheets
Evidence lineage	Sources and accepted facts stay attached to the ranking logic	Reasoning often lives in analyst notes or workshop memory
Repeatability	The same workflow runs across clients, teams, and portfolio companies	Depends on the consultant recreating the process each time
Implementation handoff	Selected opportunities continue into tests, pilot plans, and starter code	Roadmap output usually stops before technical handoff

Questions buyers ask

Resolve the practical concerns.

What is an AI consulting framework?

An AI consulting framework is a repeatable method for gathering business evidence, identifying and prioritizing AI opportunities, testing readiness and risk, and turning selected opportunities into an execution roadmap.

What is The Foundry Method?

The Foundry Method is Use Case Foundry's six-step consulting process: Scan, Source, Evidence, Review, Prove, and Build. It runs as software instead of living only in a template or slide deck.

How is The Foundry Method different from BCG or McKinsey AI frameworks?

Those frameworks address broad transformation and organizational change. The Foundry Method focuses on finding use cases from evidence, explaining priorities, ordering the work, and preparing a clear handoff to implementation. The approaches can work together.

Can consultants use The Foundry Method with clients?

Yes. It is designed for repeatable client assessments, from a pre-discovery public-evidence brief through workshops, ranked roadmaps, pilot packages, and engineering handoff. Contact Use Case Foundry about practice and client-delivery options.

Can consultants white-label The Foundry Method?

Use Case Foundry is designed for repeatable advisor and transformation workflows. Contact info@usecasefoundry.com to discuss practice delivery, client rollout, and white-label or co-branded assessment options.

Does the framework deploy AI agents automatically?

No. It generates reviewed plans, tests, pilot materials, and starter code. It never creates credentials, connects to customer systems, or deploys without the customer's technical review and approval.