AI readiness check · 10 questions · about 5 minutes
Where are you in your AI journey?
Answer ten questions about your AI tools, your data and your controls. You'll get a readiness score, the archetype that fits your situation and a specific first step. You'll see your results straight away. No email required.
Question 1 of 10
Press 1 to 4 to answer
Your archetype
Licensed, but stuck
You've invested in AI tools, but they can't reach the data that matters, so usage has stalled at email and documents. The tools aren't the problem. Access is.
Shadow AI exposure
People are already using AI with company data, and you have limited visibility or control. That's not a discipline problem. It's an access problem: give people a governed option and the shadow use drops.
Fresh start
You haven't chosen an AI partner yet, and you're under pressure to bring AI in. That's an advantage: you can start with chat, agents and apps on one set of access controls, instead of stitching tools together later.
Ready to expand
AI is already doing useful work on some of your data, with reasonable controls. The next gain comes from extending the same governance to more systems, more people and repeatable agents and apps.
AI readiness 0 / 100
Your four dimensions
AI adoption
0
AI isn't part of how people work yet, at least not officially. That leaves room to choose one approach on purpose, before tools arrive one team at a time. People are trying AI, mostly for email and documents. Useful, but it stays shallow until the tools can see the data your teams actually work from. AI is in regular use and some of it touches business data. The next gain comes from moving from answers to repeatable work. AI is part of daily work, including workflows that act on business data. The job now is keeping control as usage spreads.
Data access
0
Your AI tools can't reach the systems where the business runs, so they can't answer the questions that matter. This is usually where AI rollouts stall. AI can see email and documents, and maybe one system through custom work. Every new system is its own project, which caps how far AI can go. Several systems are reachable, with some controls. The gap is the systems still outside AI's reach, often the on-prem and legacy ones. Most of your systems are reachable, with access controls in place. Data access no longer decides the pace.
Governance and control
0
You can't yet see who uses AI or on what data, and you can't limit what it sees. If your board, auditor or insurer asks, the answer today is reassurance rather than records. There are controls on some tools, but limited visibility overall. Shadow use fills the gaps wherever people lack an approved option. Your main AI tool is logged and access is limited for some systems. The next step is one set of per-user policies across every tool, not one per vendor. You can show who asked what and which data was used, across tools, with record-level access. That lets you say yes to new use cases without starting a review each time.
Team capacity
0
No one has the time to run an AI rollout. That doesn't rule one out, but it means the first deployment needs to be run for you. IT would run a rollout alongside everything else. Keep the first scope small enough that it doesn't compete with the day job. A small data team can carry a rollout. Their time is best spent on access and policies, not on building interfaces people already have. A dedicated data and AI team can own the rollout. The constraint shifts from capacity to choosing which workflows come next.
Your recommended first step
Connect the AI tool you already own to one dataset for one team with Converse, in a launch workshop of 3 days or less.
Three things to do next
- 1 Pick one team whose daily questions live in a single system, such as dispatch in the TMS or claims in the policy admin system.
- 2 Keep your existing AI tools. Datafi Converse connects them to your data over MCP, so there's no new interface to learn.
- 3 Start read-only, measure usage for 30 days, then add users and datasets.
Your recommended first step
Start with a fixed-scope shadow AI risk review, then give one team a governed alternative.
Three things to do next
- 1 Find out which AI tools are in use, by whom, and with what data.
- 2 Set per-user, row-level policies before widening AI access to business systems.
- 3 Turn on event logging so you can answer your board, auditor or insurer with records, not reassurances.
Your recommended first step
Start on Datafi Activate with its built-in business chat, and scope one agent for your team's most repetitive task.
Three things to do next
- 1 Choose one team and one recurring question they answer by hand today.
- 2 Use Datafi's business chat for everyday questions. No separate AI license needed to start.
- 3 Include a few Standard licenses at launch, so the first workflow is automated from day one.
Your recommended first step
Extend governed access to your next two datasets, and add agents for the workflows people already run by hand.
Three things to do next
- 1 Apply one set of per-user policies across every AI tool, not one per vendor.
- 2 Add the systems still outside AI's reach, especially on-prem and legacy ones.
- 3 Move to Activate for work that repeats daily: agent and app builders inside your AI tool, plus Datafi Studio.
Optional
Get a copy and a tailored plan
We'll send your results and a one-page plan for your first use case. A person will reply within one business day.
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Your results are on the way. If you'd like to talk sooner, plan a workshop.