You do not need a perfect data warehouse or a team of data scientists to get value from AI. You do need a few basics in place. Skip them and even a strong model will produce answers your team ignores.
Here are the five areas we check before any AI build, and what “ready enough” looks like in each.
1. Use cases: one clear job, one clear owner
Ready enough: you can name one workflow, the person who owns it, and how long it takes today.
Warning sign: the goal is “use AI more,” or a list of 30 ideas with no ranking.
Score each idea on value (hours saved, errors avoided), effort (data and systems involved), and risk (who sees the output). Start with high-value, low-risk work where a person still reviews the result.
2. Data: the fields that matter are filled and consistent
Ready enough: the handful of fields your first use case depends on are filled in most records and mean the same thing to everyone.
Warning sign: key information lives in free-text notes, attachments, or someone’s inbox.
You do not have to clean everything. Find the fields the use case touches and fix those. This is often the fastest return in the whole project, because good data helps your reports and your people, not only the AI.
3. Systems: the AI can read and write where the work happens
Ready enough: your core systems (Salesforce, Litify, accounting, document storage) have APIs or connectors, and you know which one is the source of truth for each record.
Warning sign: data moves between systems by export, email, or re-keying.
Integration is where most of the real time savings hide. If the AI’s output has to be pasted somewhere by hand, you have moved the work, not removed it.
4. Security and governance: answered before the build, not after
Ready enough: you know which data the AI may use, who may see its output, and whether the AI service trains on your data. Access follows the permissions you already have.
Warning sign: nobody has asked legal, compliance, or IT yet.
Write down where the data goes, what is logged, and who reviews output before it reaches a client. A one-page answer now saves months of delay later. Our Security & Trust page shows how BRIO handles the same questions.
5. People: the users helped design it
Ready enough: the people who do the work today are part of the pilot, and someone owns training and feedback.
Warning sign: the tool is announced by email and left for people to discover.
Adoption is the difference between a tool that exists and a tool that saves hours. Involve users early, show them where the AI’s answer came from, and make it easy to flag a bad one.
Putting it together
Score yourself from 1 to 5 in each area. Anything below 3 is your first project, often before the AI work itself. Anything at 4 or 5 is ready to build on.
Download the AI Readiness Scorecard to score all five areas and get a short list of starter use cases for your team.
