Key takeaways
- A good assessment scores use cases on value, feasibility and risk, not on how impressive the demo looks.
- Most small and mid-size businesses finish one in two to four weeks.
- Typical industry pricing runs from a few thousand dollars for a small team to tens of thousands for a multi-department company.
- The output should name a first pilot, its owner, its data sources and the success metric.
What an AI readiness assessment covers
An AI readiness assessment for companies looks at four things: the work, the data, the tools and the people. The work means the recurring tasks that eat hours, such as invoice entry, lead research, support triage or weekly reporting. The data means where the inputs live and how clean they are. The tools means whether your CRM, accounting system and inbox expose APIs an agent can use. The people means who owns each process and who would approve an agent's output.
A readiness assessment framework turns those four areas into a consistent score. Each process gets rated on hours spent, error cost, data availability, integration effort and risk if something goes wrong. That scoring matters more than the questionnaire itself, because it stops the loudest request from jumping the queue. Small businesses rarely lack ideas for AI. They lack a way to compare ideas on the same terms and pick the one that will return time or money within a quarter.
How to prioritize AI use cases with an evaluation matrix
An AI use case evaluation matrix plots each candidate on two axes: value and feasibility. Value combines hours saved, errors avoided and revenue affected. Feasibility combines data quality, integration effort and how reversible the action is. Use cases in the high value, high feasibility corner become the first pilot. High value but low feasibility ideas usually need a data or process fix first, and the matrix makes that visible instead of letting it surface halfway through a build.
Risk is scored separately. Anything that sends money, emails customers or changes records gets a human approval step by default, which lowers its risk score without killing the use case. In practice the first pick is often boring: document intake, inbox triage, CRM hygiene or month-end reconciliation prep. These have clear inputs, measurable outputs and a person who already checks the work, so an agent can take over the drafting while that person keeps the final say.
What you get at the end: an AI implementation roadmap
The deliverable of a useful assessment is an AI implementation roadmap, not a slide deck of trends. It should list the ranked use cases, the first pilot with its scope and owner, the systems it touches, the data it reads, the approvals it needs and the metric that decides whether it worked. It should also flag anything that must change first, such as a missing integration, messy customer records or a process nobody owns.
Cost estimates belong in the roadmap too, as ranges tied to scope rather than a single number. A good roadmap also says what not to do yet, which saves as much money as the recommendations. Some teams take the roadmap and build internally; others hire a partner. Either way, the assessment has done its job if the next step is obvious and someone has agreed to own it, with a date for the pilot review already on the calendar.
How it works
- 1
Interview process owners
We spend a few hours with the people who run each department to list recurring tasks, volumes and pain points.
- 2
Map systems and data
We check which tools you use, what their APIs allow and where the data for each task actually lives.
- 3
Score every use case
Each candidate is scored on value, feasibility and risk in one matrix so the ranking is transparent.
- 4
Write the roadmap
You get a ranked list, a scoped first pilot, cost ranges and the prerequisites for each later phase.
- 5
Launch the pilot
If you proceed, the first agent goes live on real work with a person approving every irreversible action.
Before and after
Typical ranges from comparable deployments. Your baseline is measured before anything is built.
Tools it works with
- Microsoft 365
- Google Workspace
- HubSpot
- Salesforce
- QuickBooks
- Xero
- Notion
- Slack
- Claude
- OpenAI