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AI readiness assessment: is your team ready to work with AI?
An AI readiness assessment measures whether a team has the data, processes, skills and governance to adopt AI well. This free version scores a team in five areas in about five minutes and returns a readiness score, a band from Exploring to Leading, and the three actions most likely to raise it.
- 20 questions in five areas
- About five minutes
- No sign-up and no email
Written by Intelogos. Published .
The assessment
Take the AI readiness assessment
Answer 20 questions, four in each of five areas, on a scale from 1 (not yet) to 4 (fully). Your score, band and three recommended actions appear as soon as you answer the last question, with a link you can share and a printable version.
Five areas
What does an AI readiness assessment measure?
It measures the foundations AI depends on: the data a team works from, how its work is organized, people's skills, the rules for using AI, and whether leaders can see where work time goes. This assessment scores each of those five areas with four questions.
| Area | What it asks | Why it matters for AI | Questions |
|---|---|---|---|
| Data | Is work data captured the same way across teams, is it accurate and reachable, and do you know where work time goes? | AI tools work from your data, and you can only judge what AI changes if you know how time is spent before it arrives. | 1 to 4 |
| Process | Are core workflows documented, have you found the repeatable tasks AI could take on, and do you know what they cost? | AI changes tasks inside workflows. Without written workflows and a shortlist of tasks, pilots start from tools instead of problems. | 5 to 8 |
| Skills | Have people been trained on AI for their own work, are there AI champions, and do people and managers have good habits with AI? | Tools don't change work, people do. Teams without training and shared habits either avoid AI or use it in ways nobody checks. | 9 to 12 |
| Governance | Is there an AI policy, a list of approved tools, clear rules on data, and human review of AI output used in decisions? | Employees often start using AI tools before any rules exist. Clear rules bring that use into the open and protect sensitive data and decisions about people. | 13 to 16 |
| Work visibility | Do you have a baseline of how work time is spent, can you see where AI could save time, and can you measure AI use and whether it pays off? | Without a measured baseline, you can't show whether AI saves time, where it could save more, or which teams use it at all. | 17 to 20 |
The AI readiness checklist: all 20 questions
The same questions as the form above, as a list. Each is answered from 1 to 4: 1 not yet, 2 starting, 3 mostly, 4 fully, with a short description of each level in the form.
Data
- Is work data captured the same way across teams?
- Do you know where your team's work time goes?
- Is that data accurate and current enough to make decisions with?
- Can the people who need work data get it without manual requests?
Process
- Are your core workflows documented, step by step?
- Have you identified the repeatable tasks AI could speed up?
- Have you estimated what those tasks cost today and what AI would need to save to pay off?
- Does someone own the changes AI brings to each workflow?
Skills
- Have people been trained to use AI tools in their own work?
- Do you have AI champions people can ask for help?
- Do people have good prompt habits: giving context, checking output and keeping sensitive data out?
- Are managers ready to lead AI use in their team, from setting expectations to reviewing AI-assisted work?
Governance
- Do you have a written AI acceptable use policy that employees have read?
- Is there a list of approved AI tools, and a way to request a new one?
- Do people know what information must never go into an AI tool?
- Does a person review AI output before it's used in decisions about people or customers?
Work visibility
- Do you have a baseline of how work time is spent today, before AI changes it?
- Can you see where AI could save the most time, such as the tasks and tools that take the most hours?
- Can you see which AI tools each team uses, and for how long?
- Can you show whether AI tools pay off, in time saved against what they cost?
Can I use it as an AI readiness assessment template?
Yes. Print this page or copy the 20 questions into a document or a spreadsheet, ask each team lead to answer them from 1 to 4, and score each team with the formulas in the next section. Scoring teams side by side shows where readiness differs inside the company, and repeating the assessment every quarter shows whether it is moving.
Scoring
How is the AI readiness score calculated?
Each answer scores 1 to 4, and the readiness score turns the total of all 20 answers into a number from 0 to 100: 0 if every answer is 1, 100 if every answer is 4. Each area gets its own score the same way, and the three lowest areas decide the recommended actions.
The formulas
Readiness score = (sum of the 20 answers - 20) ÷ 60 × 100
Area score = (sum of the area's 4 answers - 4) ÷ 12 × 100
Both are rounded to the nearest whole number. Subtracting 20 (or 4) makes the lowest possible total zero, and dividing by 60 (or 12), the distance from the lowest total to the highest, turns the result into a share of the way to fully ready. A total of 50, for example, scores (50 - 20) ÷ 60 × 100 = 50.
The band comes from the readiness score: Exploring 0 to 39, Building 40 to 59, Ready 60 to 79, Leading 80 to 100. A total of 44 or more reaches Building, 56 or more Ready, and 68 or more Leading.
How the three actions are chosen
- The three areas with the lowest area scores get one action each, lowest first.
- When areas tie, a fixed order decides: Governance, Work visibility, Data, Process, Skills. Governance comes first because employees often start using AI tools before any rules exist; work visibility next, because without a baseline you can't measure what AI changes.
- Within an area, the action addresses the question with the lowest answer. If two questions share the lowest answer, the one listed first wins.
- If every answer in an area is 4, the action is about keeping that area current rather than fixing it.
- Each action links one resource: the AI acceptable use policy template for governance, the capacity planning template for process, the shadow AI guide or the guide to measuring AI adoption for skills, and the guide to measuring AI adoption plus a demo of Intelogos for data and work visibility. Where the gap is cost or value, the AI ROI calculator.
The four bands
What do the AI readiness results mean?
Your readiness score places your team in one of four bands, from Exploring to Leading. The band says how much of the groundwork for AI is in place; the three actions say where to work next.
- Exploring0 to 39
- Building40 to 59
- Ready60 to 79
- Leading80 to 100
Exploring
0 to 39AI use is mostly informal. Foundations such as a policy, measured work data and documented workflows are missing or patchy.
Next: Set basic rules and get a picture of how work is done today before buying more AI tools.
Building
40 to 59Some foundations are in place, usually in a few teams. AI pilots can start, but gaps in the weakest areas will limit what they prove.
Next: Close the gaps in your three lowest areas before you scale beyond a pilot.
Ready
60 to 79Most foundations are in place across teams. You can roll AI out more widely and measure the results against a baseline.
Next: Expand to more teams, and fix the remaining weak spots as you go.
Leading
80 to 100Data, rules, skills and visibility are in place across teams, and AI use can be measured.
Next: Keep the foundations current as tools change, and report what AI returns.
Example result
Example result: what does a finished assessment look like?
Here is the assessment filled in with sample answers for an illustrative team, scored with the same formulas as the tool. It scores 43 out of 100, in the Building band, with its first action in work visibility.
The sample answers and the arithmetic
| Area | Answers | Area score |
|---|---|---|
| Data | 3 + 2 + 3 + 1 = 9 | (9 - 4) ÷ 12 × 100 = 41.7, so 42 |
| Process | 3 + 2 + 1 + 3 = 9 | (9 - 4) ÷ 12 × 100 = 41.7, so 42 |
| Skills | 3 + 3 + 3 + 3 = 12 | (12 - 4) ÷ 12 × 100 = 66.7, so 67 |
| Governance | 3 + 3 + 2 + 1 = 9 | (9 - 4) ÷ 12 × 100 = 41.7, so 42 |
| Work visibility | 2 + 1 + 2 + 2 = 7 | (7 - 4) ÷ 12 × 100 = 25, so 25 |
- Sum of the 20 answers: 9 + 9 + 12 + 9 + 7 = 46.
- Readiness score: (46 - 20) ÷ 60 × 100 = 43.3, rounded to 43.
- 43 falls between 40 and 59, so the band is Building.
- The lowest area is Work visibility at 25. Governance, Data and Process tie at 42; the tie order keeps Governance and Data.
Open this example in the tool to see it as a live result, then change any answer.
Sample readiness score
43out of 100
BuildingSome foundations are in place, usually in a few teams. AI pilots can start, but gaps in the weakest areas will limit what they prove. Close the gaps in your three lowest areas before you scale beyond a pilot.
- Exploring0 to 39
- Building40 to 59
- Ready60 to 79
- Leading80 to 100
Sample score by area
- DataFocus area42
- Process42
- Skills67
- GovernanceFocus area42
- Work visibilityFocus area25
Each area is scored from 0 to 100. The three lowest, highlighted, get a recommended action.
The example's three recommended actions
One for each of the three lowest areas, lowest first, chosen by the weakest answer in that area.
1. Work visibility: 25 out of 100
Find the hours AI could save, from measured time
Look for the tools and types of work that take the most hours in each team, such as documents, email or data entry, and start your AI pilots there.
Based on the lowest answer in this area, question 18: Can you see where AI could save the most time, such as the tasks and tools that take the most hours?
2. Governance: 42 out of 100
Require human review of AI output in decisions about people
Write down that decisions about hiring, pay, performance and customers never rest on AI output alone, and name who reviews AI-assisted work before it's used.
Based on the lowest answer in this area, question 16: Does a person review AI output before it's used in decisions about people or customers?
3. Data: 42 out of 100
Give each work data source an owner and access rules
List where work data lives, who owns each source and who may see what. It is the groundwork for connecting AI tools to your data safely and for letting managers answer their own questions.
Based on the lowest answer in this area, question 4: Can the people who need work data get it without manual requests?
Where Intelogos helps. Intelogos is a workforce analytics platform that shows how work time is spent across apps and websites, without screenshots or keystroke logging, and lets managers ask questions about it in plain language. It groups AI apps and websites in a built-in AI time category, so you can filter its Tools page to AI and to a department and see which AI tools that department uses and for how long.
Book a demoNext steps
How do you improve AI readiness?
Start with your lowest area, not your strongest: one weak foundation limits what every AI tool can do. Each area below has one place to start, the same resource the assessment recommends.
How do you improve data readiness for AI?
Capture work data the same way in every team, and measure where work time goes before AI arrives. That gives AI tools consistent data to work from and gives you a baseline to judge them against. Start with the questions you can't answer today, such as how many hours each team spends in documents, email or spreadsheets, and record the answers automatically instead of through timesheets filled in from memory.
How do you prepare processes for AI?
Write down the workflows AI will change first, shortlist the repeatable tasks inside them and estimate the hours each one takes. A capacity plan shows how much of each person's week goes to that work, and lets you decide in advance what the hours AI frees up will be used for. Put a cost and a payback on each use case before you buy a tool.
How do you build AI skills in a team?
Train people on their own tasks with the tools you've approved, name a champion in each team, and teach three habits: give the tool context, check every output, and keep sensitive data out. When training skips the rules, people guess, and guessing is how unapproved AI use starts. Track adoption by team, so you can see where training worked.
How do you set up AI governance?
Write a short AI acceptable use policy that names the approved tools, the information that must never go into an AI tool, when to disclose AI use and when a person must review the output, and ask everyone to acknowledge it. Keep a register of approved tools with the data allowed in each, give people a way to request new ones, and review both every quarter, because new AI tools and new AI features in existing apps keep arriving.
How do you get visibility into work time and AI use?
Record a baseline of how each team's time splits across apps, websites and types of work before AI arrives, then compare the same measures as AI tools roll out. That shows where AI could save the most time, which teams use which AI tools, and whether the time saved is worth what the tools cost.
Intelogos is a workforce analytics platform that shows how work time is spent across apps and websites, without screenshots or keystroke logging, and lets managers ask questions about it in plain language. It records the AI apps and websites people use on work computers like any other tool and groups them in a built-in AI time category for AI tools and assistants. On the Tools page, you can filter to the AI category and to a department to see which AI tools that department uses and for how long, and the Time Categories chart on the Dashboard and Profile pages shows time in AI tools next to other kinds of work.
Intelogos doesn't take screenshots. Intelogos doesn't record what people type; it records only whether keyboard or mouse input happened. So it shows how much time a team spends in AI tools, not what anyone typed into them.
Need to put a value on the time AI saves? Use the AI ROI calculator.
FAQs
Frequently asked questions
How long the AI readiness assessment takes, what it costs, how it differs from an AI maturity assessment, and whether you need an AI policy first.
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