# AI consulting services from a team that also builds

> The Method Crew's AI consulting services help leadership teams at companies with 10 to 500 staff decide where AI pays off. We rank your use cases by return and risk, test the top 1 or 2 on your own data, and hand over a costed 12-month roadmap, an AI usage policy and a build plan. Each phase takes 3 to 8 weeks and costs $12,000 to $40,000.

- Page: https://methodcrew.com/services/corporate-ai-consulting
- Last updated: 2026-10-28
- Publisher: [The Method Crew](https://methodcrew.com/)
- Service: Corporate AI consulting
- Category: Strategy and advisory

We help leadership teams decide where AI is worth the money, test it on real data, then build it or hand the plan to whoever will. Every phase has a fixed price and ends with a written decision.

## Key facts

- Price: $12,000 to $40,000 per project. Priced per phase. One department with 1 pilot sits near the bottom; several business units with a full governance pack sit near the top.
- Timeline: 3 to 8 weeks per phase
- Typical tools: OpenAI API, Anthropic API, Amazon Bedrock, Microsoft 365 Copilot, ChatGPT Enterprise, n8n, Langfuse, Miro
- Change requests: $175 an hour, quoted in writing first
- Model and tool usage: billed to your own accounts at cost, no markup
- Ownership: code, workflows, prompts, accounts and data belong to you

## Who it is for

- CEOs and COOs whose board wants an AI plan, with no one internal to own it
- Leadership teams running several AI pilots with no way to compare them
- Companies of 10 to 500 staff without a data science team
- Finance leaders who need a budget figure before they commit
- Regulated firms that need a usage policy before client data meets AI tools

## Signals you need it

- **Every department is buying its own AI tools.** Marketing pays for one writing assistant, sales for another, and nobody has checked which contracts let a vendor train on your data.
- **Pilots start and never finish.** A chatbot demo impressed the leadership offsite 6 months ago. It still runs on someone's laptop, with no owner, no budget line and no measure of whether it helped.
- **The board wants a number.** You are asked what AI will save or earn next year, and today the answer is a guess. A ranked, costed list turns it into a figure you can defend.
- **Staff already paste client data into chat tools.** Without approved tools and a written policy, people use whatever is open in their browser. That is a data exposure problem first and a productivity question second.

## What we hand over

- **Use-case register.** Every AI and automation idea raised in interviews, scored on hours saved, money effect, data readiness, integration effort and risk. A live spreadsheet you own.
- **12-month AI roadmap.** The use cases worth funding, in order, each with build cost, monthly run cost, an owner and the quarter it starts. Written so your finance lead can drop it into next year's budget.
- **Pilot results memo.** For each pilot: what we built, the data it ran on, how it scored against the current process and a go or no-go recommendation with the reasoning.
- **AI usage policy and governance pack.** An acceptable use policy for staff, an approved tools list, a rule for which data can go into which tool and an intake process for new AI requests.
- **Build plan for the first project.** Architecture sketch, systems touched, test cases, timeline and a fixed price. Usable by us, by your internal team or by another vendor.

## How the engagement runs

1. **Map** (1 to 3 weeks): Interviews with 8 to 15 people, a review of your systems and current AI spend, then a scoring session that turns the findings into a ranked register.
2. **Prove** (1 to 3 weeks): Pilots of the top 1 or 2 use cases on your real data, measured against the current process. A pilot that misses the agreed bar comes off the roadmap.
3. **Build** (1 to 2 weeks): Roadmap, governance pack and first build plan, presented in a 90-minute leadership readout. A production build with us is scoped and priced as its own project.
4. **Run** (Quarterly, optional): A quarterly review of what shipped, what it costs to run and what to fund next, or a fractional AI lead retainer if you want someone in the room monthly.

Every engagement follows [The Method](https://methodcrew.com/approach): Map, Prove, Build, Run.

## What does an AI consultant actually do?

An AI consultant helps leadership decide which work to hand to AI, what it will cost, and who will build and run it. A good engagement ends with a decision you can fund, backed by a test on your own data.

Our work has 4 parts: finding the work by interviewing the people who do it and sampling real records, ranking it (covered below), proving the top 1 or 2 ideas with working pilots, and writing the rules on who can use which tools with what data. The strongest candidates are often unglamorous: invoice entry, inbox triage, weekly reporting, the same customer question answered 30 times a day.

The difference from strategy-only firms is that we build. Roadmap estimates come from the people who would quote the production work, so a line that says 4 weeks and $15,000 is a number we would sign. If you build in-house or with another vendor, the build plan is written for them to pick up. You own the register, the roadmap and every pilot, all on your accounts.

We also say no. If a rule-based workflow in a tool you already pay for does the job, the roadmap recommends that, because it is cheaper to run and easier to maintain. Expect the roadmap to contain both kinds of project.

## How do we decide which AI use cases to fund first?

Score every candidate on the same 5 criteria, then fund the best ratio of payback to risk. That usually means starting with work that is frequent, well documented and easy to check. The criteria:

1. Hours per month, measured from system logs or a week of tracking.
2. Money effect: revenue protected or gained, such as faster quotes or inbound leads answered within 5 minutes.
3. Data readiness: whether the inputs sit in a system we can read, and how messy they are.
4. Integration effort: how many systems the work touches and whether they have usable APIs.
5. Risk: what happens when the output is wrong, who would notice and whether a person must approve it first.

Each criterion gets a score from 1 to 5 with a written reason. We still argue through the top 10 with you, because scores miss context: a process that looks perfect on paper might be redesigned next quarter.

Two rules keep the list honest. Anything customer-facing or financial starts with a person approving each output (see [human-in-the-loop](https://methodcrew.com/glossary/human-in-the-loop)), and that step comes off only after the error rate has been measured. And the first project should pay back inside 12 months on conservative numbers. Ambitious projects can follow once the first one is live and your team trusts the method.

## How long does an AI strategy engagement take?

A first phase takes 3 to 8 weeks from kickoff to leadership readout. Where you land depends mostly on how many teams we interview and how long it takes to get pilots access to the systems they need.

A single-department phase usually runs like this:

| Weeks | What happens | What you have at the end |
|---|---|---|
| 1 to 2 | Interviews, systems review, data samples | Draft use-case register |
| 3 | Scoring session with your leadership team | Ranked shortlist of 5 to 8 use cases |
| 4 to 5 | Pilots on the top 1 or 2 | Results measured against the current process |
| 6 | Roadmap, policy and build plan | Readout and a go or no-go on the first build |

Group-wide engagements run longer, mostly because interviews span sites and IT security reviews take time. The scope names the approvals that could slow things down.

Access causes more delay than anything else. If a pilot needs production data, someone on your side has to approve a read-only account or an export. We request it in the first week and keep a list of what is still blocked, with an owner next to each item.

After the first phase, the work moves into delivery: the roadmap names the first build with its price, and production builds typically take 2 to 8 weeks. For a lighter first step, the [AI readiness audit](https://methodcrew.com/services/ai-readiness-audit) covers discovery alone in 1 to 4 weeks (1 to 2 for a snapshot, 3 to 4 for a full audit), without pilots or governance work.

## How much do AI consulting services cost, and what changes the price?

Our AI consulting services cost $12,000 to $40,000 per phase, fixed in writing before work starts. The price only moves if the scope does.

What moves a phase toward the top of the band:

- Business units. One department with 8 interviews is a different job from 4 units across 2 countries.
- Pilots. A phase without one costs less, but the roadmap then rests on estimates. We recommend at least 1.
- Systems access. Pilots that need data from an ERP, a core banking platform or a clinical records system take longer to set up safely.
- Governance depth. A short usage policy is quick. A full governance pack mapped to the [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) or [ISO/IEC 42001](https://www.iso.org/standard/42001) takes longer.
- Regulated data. Healthcare and financial services work adds review time with your compliance team.

Model usage during pilots (OpenAI, Anthropic and similar) is billed at cost with no markup, on its own invoice line.

For context, [Leanware's October 2026 AI consultant cost guide](https://www.leanware.co/insights/how-much-does-an-ai-consultant-cost) puts small AI strategy assessments at $5,000 to $25,000 and mid-size opportunity analyses at $25,000 to $100,000. Our full breakdown of rates and packages is in [what AI consulting costs](https://methodcrew.com/insights/ai-consulting-cost).

## When is AI consulting the wrong choice?

Skip a consulting phase if you already know the job you want done. If the brief is "answer inbound leads within 5 minutes and book them into the calendar", you need a build, and a strategy phase would charge you to rediscover what you already know. Go straight to [AI automation](https://methodcrew.com/services/ai-automation), and we will tell you on the scoping call if something upstream needs fixing first.

It is also the wrong choice when:

- You have fewer than 10 staff. The process knowledge sits in 2 or 3 heads, and a workshop plus 1 well-chosen build will do more for less.
- There is no budget to build anything in the next 12 months. A roadmap that cannot be funded for a year will be out of date by the time it can.
- You want a vendor-neutral verdict on a large platform purchase. We will give you a view, but we are a delivery firm and we say so up front.

What is not included: cybersecurity audits, penetration tests, legal opinions on AI regulation and consumer app builds. We do none of those. Your counsel should review the governance documents before you adopt them. The production build sits outside the consulting fee too; it is scoped and priced separately, and you are free to give it to someone else.

## What changes for your team

- One ranked list of AI work replaces scattered pilots and department wish lists.
- Next year's AI budget has line items with build cost, run cost and a named owner.
- Staff know which AI tools are approved and which data must stay out of them.
- The first build starts with evidence, because the use case was already tested on your data.
- Pilots that were not paying back are stopped, with the reason written down.

## Tools we work with

OpenAI API, Anthropic API, Amazon Bedrock, Microsoft 365 Copilot, ChatGPT Enterprise, n8n, Langfuse, Miro

## What it costs

$12,000 to $40,000 per project. Priced per phase. One department with 1 pilot sits near the bottom; several business units with a full governance pack sit near the top.

- Change requests: $175 an hour, quoted before work starts
- Model and tool usage: billed at cost, no markup

All 20 price bands: https://methodcrew.com/pricing

## Related blueprints

- [Inbound lead qualification agent](https://methodcrew.com/blueprints/inbound-lead-qualification-agent): built in 4 to 6 weeks, $12,000 to $25,000 per project.
- [Support knowledge assistant](https://methodcrew.com/blueprints/support-knowledge-assistant): built in 6 to 12 weeks, $20,000 to $60,000 per project.
- [Weekly ops report automation](https://methodcrew.com/blueprints/weekly-ops-report): built in 3 to 6 weeks, $8,000 to $20,000 per project.

## Common questions

### What is the difference between AI consulting and an AI readiness audit?

An AI readiness audit is the discovery part on its own: 1 to 4 weeks (1 to 2 for a snapshot, 3 to 4 for a full audit), a ranked list of opportunities and a short report, from $3,000. Corporate AI consulting adds pilots on your real data, a governance pack, a 12-month roadmap with budget lines and a leadership readout. Start with the audit if you need evidence before committing to a larger phase.

### Should we hire an AI consultant or build an in-house AI team?

Use a consultant to decide what to build and to ship the first 1 or 2 projects. Hire in-house once AI work needs decisions every day. A senior AI hire needs a roadmap to work from and a budget to spend, and a consulting phase produces both. A fractional AI lead at 2 to 5 days a month covers the gap in between.

### Who owns the code, prompts and data after the engagement?

You do. Pilots run on accounts you own, from your cloud workspace to your OpenAI or Anthropic API keys and your n8n instance. The use-case register, roadmap, policy documents, prompts and pilot code are handed over at the end of the phase with notes on how to run them. Nothing we keep would stop you working with another vendor.

### Which AI models and tools do you recommend?

Whichever does the job at the lowest running cost with acceptable risk, chosen per use case. We build with OpenAI and Anthropic models through their APIs, Amazon Bedrock where you run on AWS, and Microsoft 365 Copilot where you already pay for it. Usage is billed at cost with no markup, so we gain nothing from steering you to one vendor.

### What do you need from us during the engagement?

A sponsor on the leadership team who can make decisions, 45 minutes each with 8 to 15 people for interviews, read-only access to the systems in scope and a 90-minute scoring session in week 3. Plan on 2 to 3 hours a week from the sponsor.

### How do you handle staff worries about AI replacing jobs?

We say plainly what each use case changes. Work that scores well on our criteria tends to be repetitive keying and chasing, which few people want to keep. The roadmap states the hours freed per team. Where a role would change a lot, we flag it early so you can plan that conversation with your people.

## Related services

- [AI readiness audit](https://methodcrew.com/services/ai-readiness-audit): An assessment that ranks your AI opportunities by return and prices the first build. $3,000 to $15,000 per project.
- [Fractional AI lead](https://methodcrew.com/services/fractional-ai-lead): An AI lead in your leadership meetings 2 to 5 days a month, owning the roadmap and budget. $4,000 to $15,000 per month.
- [AI automation](https://methodcrew.com/services/ai-automation): Workflows where a language model reads and sorts, and your existing systems do the rest. $4,000 to $35,000 per project.

## Further reading

### Guides

- [What AI consulting costs in 2026: rates, packages and price drivers](https://methodcrew.com/insights/ai-consulting-cost)
- [AI readiness assessment: the checklist we use and how to score it](https://methodcrew.com/insights/ai-readiness-assessment-checklist)
- [Fractional chief AI officer: what the role covers and what it costs](https://methodcrew.com/insights/fractional-chief-ai-officer)
- [AI acceptable use policy template for employees](https://methodcrew.com/insights/ai-acceptable-use-policy-template)

### Terms explained

- [Agentic AI](https://methodcrew.com/glossary/agentic-ai): Agentic AI is the umbrella term for systems where language models plan steps and take actions through tools, with less step-by-step direction from people.
- [Fine-tuning](https://methodcrew.com/glossary/fine-tuning): Fine-tuning is further training of an existing language model on your own examples, so it learns a specific format or task without long instructions every time.
- [Large language model (LLM)](https://methodcrew.com/glossary/large-language-model): A large language model is a neural network trained on huge volumes of text to predict the next token, which lets it write, summarize, classify and answer questions.

## Next step

Request a 30-minute scoping call: https://methodcrew.com/contact. Or email contact@methodcrew.com.