AI strategy / A practical business guide
AI Readiness Assessment: Is Your Business Ready?
Assess your AI readiness across workflows, data, tools, ownership, and measurement. Use a practical checklist to choose a useful first project for your business.
The quick answer
AI readiness means having a specific business problem, usable information, access to the right tools, and someone responsible for the outcome. You do not need an AI project in every department. You need enough clarity to run one useful test.
What is an AI readiness assessment?
An AI readiness assessment reviews whether a proposed use case can become a workable business process. It looks beyond software selection to the workflow, information, permissions, people, and measures that a solution depends on.
For a small business, the assessment can start with a single recurring problem: too much time organizing inquiries, inconsistent answers to common questions, or repeated document entry. The goal is to decide what to test, what must be fixed first, and what should stay with a person.
Readiness is specific to a use case. Your business might be ready for a public website assistant using approved service information while still being unprepared for a system that acts on private customer records. Treat those as different projects with different requirements.
Use this five-part AI readiness checklist.
1. Can you describe the workflow?
Write down what starts the task, what information arrives, what your team does, and what a useful output looks like. Include exceptions. If different people follow different processes, agree on a baseline before trying to automate it.
2. Is the information usable and approved?
Find the relevant forms, documents, and business knowledge. Check whether they are current, consistent, and available in a usable format. Identify material the system must not access. Decide who will keep the information accurate after launch.
3. Can the tools connect?
List the systems involved and who controls them. Check available integrations, subscription restrictions, permissions, and ownership of the accounts. An attractive demo is not enough if the proposed connection is unavailable in your actual software plan.
4. Who owns decisions and exceptions?
Name a business owner and the people who will review outputs. Define when a person must approve an action and what happens when the system is uncertain. If no one is responsible for monitoring failures, the process is not ready for unattended operation.
5. How will you judge the pilot?
Capture a baseline before launch: task volume, total handling time, common errors, and what the receiving team considers useful. Then choose acceptance criteria that reflect the business problem. Count review and correction effort, not just the time needed to generate an output.
A simple way to use the checklist
For each area, mark clear, partly defined, or unknown. This is a planning aid, not a validated scoring model. Fix unknowns that block safe testing before committing to a build.
How do you choose the first AI project?
Compare candidate projects on usefulness, effort, and consequences of error. A high-volume task with consistent inputs may be a better pilot than a complex task that spans many systems. A low-risk review queue may be a more manageable starting point than an action that changes customer commitments.
| Candidate | Questions to resolve | Possible first test |
|---|---|---|
| Lead follow-up | Where do inquiries arrive, and who responds? | Draft a summary for human review |
| Website questions | Are service answers current and approved? | Answer a bounded set of public questions |
| Document processing | Which fields matter, and what errors are costly? | Propose extracted values for verification |
These examples illustrate how to narrow scope. Feasibility still depends on your data and systems. The NIST Generative AI Profile provides background on risks such as inaccurate generated output and privacy. Use the consequences of a mistake to decide which safeguards a pilot needs.
Turn a list of ideas into a practical roadmap.
The Manage Peak AI Opportunity Sprint is $499 one-time. It includes a 90-minute workshop, review of up to three workflows, a prioritized roadmap, one pilot scope recommendation, and a 30-minute walkthrough.
What should an AI discovery engagement deliver?
Look for concrete decisions: a prioritized opportunity list, a recommended pilot, the systems and information required, the people responsible, and the issues that must be resolved before implementation. A useful recommendation may be to simplify a process first or use existing software features instead of building something new.
A proposal for the next stage should distinguish included work from dependencies. Ask about tool subscriptions, AI usage charges, testing, training, ongoing updates, and the boundaries of support. This makes the investment easier to assess before work begins.
AI readiness questions.
Is AI readiness only relevant to large companies?
No. A smaller team can assess a narrow workflow without a company-wide program. The same practical questions about information, access, ownership, and success criteria still apply.
Do we need to choose an AI vendor first?
Start with the problem and requirements. Tool selection is easier once the job, information boundaries, and integrations are understood.
Does a readiness assessment include implementation?
Manage Peak’s $499 Sprint is a discovery and planning engagement. Implementation is separate: the AI Pilot Build starts at $2,499. Final scope, third-party costs, and applicable taxes are confirmed in a proposal.
Explore what a pilot could look like in our guides to small-business AI automation and website AI chatbots.
Your next step
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