# An AI readiness assessment that ends in a ranked list

> An AI readiness assessment is a review of your processes, data, systems, people and risk that ends in a ranked list of AI and automation opportunities, each with an estimated cost, effort and payback. The Method Crew runs a snapshot audit in 1 to 2 weeks for $3,000 to $5,000, or a full audit with feasibility tests in 3 to 4 weeks for $7,500 to $15,000.

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

We find where AI and automation would pay off in your company, check whether your data and systems can support it and price the first project. The report is short enough to read in one sitting.

## Key facts

- Price: $3,000 to $15,000 per project. Snapshot audit $3,000 to $5,000. Full audit $7,500 to $15,000, depending on teams, interviews and systems in scope.
- Timeline: 1 to 2 weeks (snapshot) or 3 to 4 weeks (full audit)
- Typical tools: Miro, Airtable, Google Sheets, Python, n8n, OpenAI API, Anthropic API, Loom
- 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

- Owners and COOs who suspect AI could help but cannot name the first project
- Teams of 10 to 500 people with work stuck in inboxes, spreadsheets and PDFs
- Companies about to sign an AI vendor contract who want a second opinion first
- Leaders who need a costed business case before asking for budget

## Signals you need it

- **Plenty of ideas, no order.** Your team has a list of 30 things AI might do. Nobody has checked which ones your data can support or what each would cost.
- **You are unsure your data is usable.** Customer records sit in a CRM, half the history is in email and the rest is in shared drives. You want to know if that matters before paying for a build.
- **A vendor is pitching you a platform.** The demo looked good. The audit tells you whether the problem it solves is in your top 5, and what a smaller build would cost.
- **Someone wants an AI plan by next quarter.** A board member, investor or parent company has asked. The audit gives you a ranked list with numbers you can defend in that meeting.

## What we hand over

- **Process maps.** The workflows in scope drawn step by step in Miro, with systems, hand-offs and time spent marked at each step.
- **Ranked opportunity list.** Every candidate we find, each with estimated hours saved per month, build cost, run cost, risk level and a payback estimate.
- **Data readiness scorecard.** For each top candidate: where the data lives, whether we can read it by API or export, how complete a sample is and what cleanup comes first.
- **Risk notes.** Which candidates touch personal, financial or client data, which need a person approving outputs and where your current AI rules have gaps.
- **First-build proposal.** Scope, timeline, acceptance test and price for the top-ranked project. A budget estimate after a snapshot, a fixed price after a full audit.

## How the engagement runs

1. **Map** (Week 1, or weeks 1 to 2 for a full audit): Kickoff, 45-minute interviews with the people who do the work, a systems inventory and a record of the AI tools staff already use and pay for.
2. **Prove** (Weeks 2 to 3): Full audit only. We pull 50 to 200 real records for the top 3 candidates and test whether a model or a rule-based workflow handles them at the accuracy the job needs.
3. **Build** (Final week): Scoring, the ranked list, the data scorecard and the first-build proposal, presented in a 60-minute readout where we defend the ranking and take challenges.
4. **Run** (30 days after the readout): 30 days of email follow-up while you decide. If you go ahead, the first build starts from the proposal; if your own team builds it, we brief them.

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

## What is an AI readiness assessment?

An AI readiness assessment checks where a company can put AI to work profitably, and whether its data, systems and people can support it. Ours looks at 5 things: the work itself, the data behind it, the systems that hold that data, the people who would use the result and the risk if an output is wrong.

Some assessments end in a maturity score, a number from 1 to 5 across a set of pillars. That is easy to produce and hard to act on. Ours ends in a ranked list of specific opportunities, each priced, with the data checks done. "Your data maturity is 2.4" tells you little. A line such as "invoice entry takes finance 60 hours a month, the PDFs are machine-readable and a pipeline would cost about $14,000 to build" tells you what to do next. (The figures in that line are illustrative.)

We also record which AI tools staff already use, what they cost and what data goes into them. That shapes both the shortlist and any usage policy that follows.

If you want a first pass on your own, our [AI readiness assessment checklist](https://methodcrew.com/insights/ai-readiness-assessment-checklist) lists the questions we ask in week 1. It will tell you whether an audit is worth paying for.

## What does an AI readiness assessment include?

Both versions include interviews, a systems inventory, a ranked opportunity list and a readout. The full audit adds sampled data checks, feasibility tests on real records and a fixed-price proposal for the first build.

| Detail | Snapshot audit | Full audit |
|---|---|---|
| Duration | 1 to 2 weeks | 3 to 4 weeks |
| Price | $3,000 to $5,000 | $7,500 to $15,000 |
| Teams in scope | 1 | Up to 4 |
| Interviews | Up to 6 | Up to 20 |
| Data checks | Review of exports you send | Samples pulled from live systems |
| Feasibility tests | None | Top 3 candidates, on your data |
| First-build proposal | Budget estimate | Fixed price |
| Best for | A first look before any spend | Deciding what to build next quarter |

The snapshot suits a company that wants to know whether there is enough opportunity to justify more work. Its estimates carry wider ranges, because nothing has been tested.

The full audit suits a company ready to fund a build in the next 3 to 6 months. Feasibility tests remove the guesswork on the top candidates: we take real invoices, tickets or inbound emails and check whether a model or a plain rule-based workflow gets them right often enough for the job. A candidate that fails is marked down, with the reason and what would fix it.

## How do I know if our data is ready for AI?

Your data is ready enough when the inputs for a specific task exist in a system we can read, in a form a person could work from. It does not need to be clean everywhere, only for the first use case. What counts as ready depends on the task:

- Workflow automation, such as routing leads or filing invoices, needs structured fields and an API or export. Gaps are fine if a person can fill them today.
- Document processing needs the documents, ideally as digital PDFs. Scans work with more checking.
- A knowledge assistant built with [retrieval-augmented generation](https://methodcrew.com/glossary/retrieval-augmented-generation) needs your documents somewhere we can index, with current versions marked. Watch for duplicate policies that give conflicting answers.
- AI agents that take actions need API access scoped to what the agent should touch.

You can run 3 quick tests before talking to anyone. Pick the task, then ask: could we export last month's inputs to a spreadsheet in under an hour? Could a new hire do the task from that export alone? Can we describe what a correct output looks like? If the first 2 answers are no, start with data work. If the third is no, define the task before automating it.

The data readiness scorecard runs these checks properly, on samples from your systems, and prices any cleanup next to the build.

## How much does an AI readiness assessment cost?

A snapshot audit costs $3,000 to $5,000 and a full audit $7,500 to $15,000, both fixed price.

What moves the price within each band:

- Teams in scope. Each extra team adds interviews, a process map and its own candidates.
- Systems. Pulling samples from a CRM with a clean API is quick. Read access to an on-premise ERP or a legacy database takes longer.
- Regulated data. If samples include health, financial or personal data, we work inside your environment under your controls, which adds setup.
- Feasibility tests. The full audit tests 3 candidates. Testing more is possible and priced per candidate.

Model usage during feasibility tests is billed at cost with no markup; at a few hundred records it is a small line.

For comparison, the [Layer3 Labs AI consulting rates guide (October 2026)](https://www.layer3labs.io/guides/ai-consulting-rates-pricing) puts AI discovery and audit work at $2,000 to $5,000, and the [Leanware AI consultant cost guide (October 2026)](https://www.leanware.co/insights/how-much-does-an-ai-consultant-cost) puts small AI strategy assessments at $5,000 to $25,000. Our snapshot sits in the upper half of the first range because it includes a ranked list and a budget estimate for the first build.

## When is an AI readiness audit the wrong first step?

Skip the audit if you already know the task and it touches 1 or 2 systems. A team that wants inbound leads qualified and routed into HubSpot needs a build quote, and the Map stage of that build covers the same ground for a single workflow. Ask for a scoping call about [sales and CRM automation](https://methodcrew.com/services/sales-crm-automation) instead.

The audit is also the wrong fit when:

- You need a company-wide AI strategy with governance, board reporting and a usage policy. That is [corporate AI consulting](https://methodcrew.com/services/corporate-ai-consulting), which adds pilots and a 12-month roadmap.
- Your core systems are mid-migration. If the CRM or ERP is being replaced in the next 6 months, audit once the new one is live, or the data checks will describe a system you are about to switch off.
- You want a security or compliance audit. We do not run cybersecurity audits, penetration tests or certification readiness work. Our risk notes cover AI-specific risks, such as data sent to model providers and outputs that need human review.

What the audit does not include: building anything beyond feasibility tests, vendor selection RFPs, staff training or ongoing advice. Each of those exists as its own service, and none is required to act on the findings.

## What changes for your team

- You know the first AI project to fund, what it costs and when it should pay back.
- Ideas that fail on data or risk are off the list, with the reason recorded.
- Data problems are named and priced, so cleanup can start before the build.
- Leadership shares one scored view of where time goes in the teams reviewed.
- Vendor pitches can be checked against your own ranked list.

## Tools we work with

Miro, Airtable, Google Sheets, Python, n8n, OpenAI API, Anthropic API, Loom

## What it costs

$3,000 to $15,000 per project. Snapshot audit $3,000 to $5,000. Full audit $7,500 to $15,000, depending on teams, interviews and systems in scope.

- 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

- [Invoice processing pipeline](https://methodcrew.com/blueprints/invoice-processing-pipeline): built in 6 to 9 weeks, $15,000 to $35,000 per project.
- [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.
- [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 happens after the assessment?

You decide. The readout ends with a recommendation for the first build and, after a full audit, a fixed-price proposal for it. You can hire us, hand the proposal to your own team or another vendor, or do nothing for now. We answer questions about the findings by email for 30 days after the readout.

### Who in the company needs to be involved?

A sponsor who can approve the next step, usually the COO, CFO or owner, plus the people who do the work in each team in scope. Interviews take about 45 minutes per person. Someone in IT or operations grants read-only access or sends exports. The sponsor spends about 3 hours across the audit, including the readout.

### Do we need clean data before an AI readiness audit?

No. The audit is how you find out which data needs cleaning, and for which use case. Most automation work runs on data as it is today. A knowledge system needs your documents in one place we can read, and the audit tells you how much cleanup that takes and what it costs.

### Is an AI maturity assessment the same thing?

They overlap. A maturity assessment scores a company against a framework across areas such as strategy, data, skills and governance. Our readiness audit covers the same ground but ends in specific, priced opportunities. If your board or parent company requires a maturity score, we can add one to the report using the same evidence.

### How is our data handled during the audit?

We use the minimum data needed and, where possible, work inside your environment. Samples are pulled with read-only access and deleted from working copies when the audit ends. Feasibility tests go through API access, which OpenAI and Anthropic do not use to train their models by default, and we name every provider involved in the scope.

### Can the audit run remotely?

Yes. Interviews run over Zoom, Google Meet or Microsoft Teams, and people who find it easier to show than describe can record a Loom walkthrough of their process. On-site days are available for teams on a shop floor, in a warehouse or in a clinic, with travel billed at cost.

### How long does an AI readiness assessment take?

1 to 2 weeks for a snapshot and 3 to 4 weeks for a full audit, from kickoff to readout. Access is the main variable: if read-only accounts or exports take a week to approve, the audit waits. We send the access list before kickoff so approvals can start early.

## Related services

- [Corporate AI consulting](https://methodcrew.com/services/corporate-ai-consulting): Use cases ranked by return, the best 1 or 2 piloted on your data, then a costed roadmap. $12,000 to $40,000 per project.
- [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.
- [Business process automation](https://methodcrew.com/services/business-process-automation): One cross-team process, such as client onboarding, rebuilt so the hand-offs run on their own. $5,000 to $40,000 per project.

## Further reading

### Guides

- [What AI consulting costs in 2026: rates, packages and price drivers](https://methodcrew.com/insights/ai-consulting-cost)
- [How to build a company knowledge assistant that cites its sources](https://methodcrew.com/insights/build-rag-knowledge-assistant)
- [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)

### 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.

## Next step

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