What would an AI find in your data?
Before you automate anything, find out what is underneath: how often the same customer appears twice, how many systems tell different stories and who can export the whole database. Answer with what really happens in your operation, and we'll tell you what your data can support today and what to fix first. This tool looks at your internal data; to check your public website, use Website AI Readiness.
- Free
- Under 5 minutes
- Email only at the end
What is assessed and how it is counted
A score about your own data is only useful if you can take it apart. These are the rules, with the arithmetic in plain view. If one doesn't fit your case, you know how much it is worth.
Rule 1 of 5
Size and systems decide what is asked
Before the first question you choose two things: how large the operation is and where the data lives today. This isn't sales profiling. It is the only thing that decides the questionnaire that follows.
- Size changes the list of systems on offer. A small team isn't asked about a data warehouse, and a large enterprise doesn't have it hidden.
- The systems you select turn questions on or off. With a single system there are no questions about silos, because there can't be any.
- Nobody gets a low score for something they don't have. It is the same rule that turns off the ticket questions in the HubSpot Assessment when the portal doesn't use Service Hub.
Rule 2 of 5
Four areas, not one overall score
A database is rarely bad at everything: it is usually clean but isolated, or complete but messy. A single number hides the exact area someone came here for.
- Quality and duplicates: weight 3; structure and fields: weight 2; integrations and silos: weight 3; governance and permissions: weight 2. The heaviest are the areas that lead a model to wrong answers.
- Each area's weight is fixed and doesn't change with company size. What changes with size is what gets asked.
- An area with no questions that apply is left out of the score. It doesn't count as zero: it disappears.
Rule 3 of 5
From one answer to the total
Each question has four answers worth 0 to 3 points, and a weight within its area. Each area gets a percentage of the most it could score, and the total is the average of the areas, weighted by each area's weight.
- The options aren't “poor, fair, good”: they describe concrete situations, so it is hard to answer from memory.
- Weighting stops a cheap area from making up for an expensive one just because both have the same number of questions.
- Only the areas that apply are counted, so two companies with different systems can still be compared.
Rule 4 of 5
What you take away
The score tells you where you stand; the action list tells you what to do next. Each answer that wasn't the best one creates a specific action, sorted by what it costs to leave it as it is.
- Actions are split into three priority levels, as in the Manual Work Cost calculator and the HubSpot Assessment.
- The order comes from the question's weight times how far your answer was from the best one, not from a fixed list.
- If you pick the best option every time, the list comes out empty. That is a result too.
Rule 5 of 5
What it isn't, and how it differs from Website AI Readiness
This doesn't read your database. It asks for no access to any system, connects to nothing and checks nothing you answer. It is a questionnaire, and the result is only as good as the honesty of your answers.
- Website AI Readiness measures your website on its own, by reading it. This tool asks about your company's internal data, which can't be read without access.
- Answering with how you would like things to be gives a nice score and a useless action list.
- Nobody sees your answers until you ask for your results. At that point they are recorded with your email, as with any form on this site.
Four areas, and only the ones that apply to you
Silos only count when there is more than one system. The other areas apply to any company that keeps customer data, even in a spreadsheet. The weight shows how much each one moves the final score.
Quality and duplicates
weight 3Whether you can trust what is stored. A model can't tell a good record from an outdated one: it blends them and states both with the same confidence.
4 questions
Structure and fields
weight 2Whether the data is where it can be found. Free text can be read but not grouped, and what can't be grouped can't be analyzed.
4 questions
Integrations and silos
weight 3How many systems store the same customer, and whether they agree. This area decides whether an answer draws on the whole operation or only on the piece it happened to see.
4 questions
Governance and permissions
weight 2Who is responsible for the data, who can export it and what can be sent outside. It is what separates an AI project that gets going from one that stalls in legal review.
4 questions
Size and systems decide the rest
This isn't sales profiling: it is the only thing that decides what gets asked. A small team isn't offered a data warehouse, and nobody with a single system is asked about silos.
The 3 sizes
Small team
Up to about fifteen people. Almost everything runs through two or three tools, and someone knows them by heart.
4 systems on offer
Mid-sized company
Several departments, each with its own way of working. Some systems are already more than one person can keep track of.
5 systems on offer
Large enterprise
Several business units or countries, with an IT department and its own rules for access to information.
6 systems on offer
The 6 systems
Spreadsheets and files
Excel, Google Sheets, lists someone keeps on the side.
A CRM
HubSpot, Salesforce, Pipedrive, Zoho or any other.
Website or online store
Forms, chat, shopping cart, event sign-ups.
Invoicing, collections or ERP
The system where an invoice is issued or a payment is recorded.
Help desk or tickets
Zendesk, Freshdesk, a shared inbox with rules.
Data warehouse or BI
BigQuery, Snowflake, Power BI, your own data warehouse.
What your score means
A number on its own gets filed away. These are the four possible readings and what kind of AI project the data can support at each one, including the top one, where the honest answer is that the problem is somewhere else.
0–39
An AI would make things up here
The data is spread out and doesn't match, so any automated answer would come out with half the story, stated with complete confidence. A better model or more context won't fix this. The work is to bring together what lives in three places today and decide which version wins. It is dull work, and it is the only thing that works.
40–64
Good for reading, not for deciding
You can use AI to summarize, draft and search within what already exists, and that alone gives hours back. What the data can't support yet is letting AI decide on its own, such as qualifying, prioritizing or answering a customer, because the gaps are not where you think and nobody will check them one by one.
65–84
Ready for narrow use cases
The data can support one specific use case with specific data: an agent that answers questions about one part of the business, automatic classification, a summary that someone signs off on. What is missing is a few specific areas, which is why the list below is short. Nothing needs rebuilding: close two or three things before you widen the scope.
85–100
Data isn't the problem
Your data is not what is holding you back. At this level the bottleneck is usually somewhere else: which process you pick, who reviews what the model produces and how you measure whether it worked. If something on the list below surprises you, start there. If not, the next conversation is not about data.
Questions about data quality
Does this connect to my CRM or database?
No, and that is on purpose. Asking for access to another company's database for a public tool is a barrier almost nobody crosses, and a responsibility we don't want over your customers' data. Here you answer yourself, in under five minutes, without installing anything or authorizing any app.
How is this different from Website AI Readiness?
In what they look at and who answers. Website AI Readiness analyzes your website on its own: it reads it, checks what it finds and returns findings you can verify. It tells you what search engines and AI crawlers see of you. This tool asks about your company's internal data, which no program can read from outside, and tells you whether that data can support an AI model on top of it. You can take both: they don't overlap.
Does it work if we keep everything in spreadsheets?
Yes, and that is probably where it helps most. Spreadsheets count as a system: in many companies they are the real database, and pretending otherwise makes the assessment skip exactly what needs fixing. Select only what you use and the score is calculated on that.
How is the score calculated?
Each answer is worth 0 to 3 points, and each question has a weight from 1 to 3 within its area. Each area gets a percentage of the most it could score, and the total is the average of the areas that apply, weighted by each area's weight. Quality and duplicates: weight 3; structure and fields: weight 2; integrations and silos: weight 3; governance and permissions: weight 2. The step-by-step breakdown is in the markdown version of this page.
Does a low score mean we can't use AI yet?
No. It means you can't let it decide on its own yet. Summarizing, drafting, searching documents and classifying with human review work just as well with imperfect data, and they are often the first use cases that pay for themselves. What messy data can't support is an agent that answers without anyone checking.
What happens to my answers?
They stay in your browser while you answer. When you press “See my results”, they are sent with your email and recorded in our CRM. That is what lets us write to you about your case instead of sending a generic text. The result can't be edited: to try other answers, you start over.
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