Upload job titles and departments. In under two minutes you get a score for every role and a training plan you can act on.
Market research runs on research, segmentation, survey write-ups, performance analysis, and recommendations. AI can help with most of that.
AI changes the daily work of some roles and barely touches others. Without a map of which is which, the budget gets spread evenly across both.
different job titles in a typical 10,000-person organization. They overlap and are written many different ways, so sorting them by hand is not realistic.
AI training budgets are split by headcount or by department, rather than by where the training would pay off.
is when leaders get asked where the AI plan starts. Talvio gives you an answer you can explain.
We compared the Training Priority score against two independent sources. Both results, and their limits, are below.
Two limits. Agreement with the Felten AIOE index is 0.97. We treat that as a consistency check rather than evidence, because AIOE is built from the same O*NET data. And a national occupation profile is not your organization: your titles and your real work will differ. Match Review lets you correct any match before you act on it.
Counting each job by how many people hold it, the average US worker scores 4.2 out of 10. Half score under 3.5, mostly in hands-on jobs. Pick an industry to see where its workers fall.
All US workers average 4.2; 19% score 7 or above.
Worker counts from the Bureau of Labor Statistics, May 2025. Scores rate how much of the work current AI can help with. See every industry and how this was built →
No integration, no IT project, and no personal data about employees.
An Excel or CSV file with job titles and departments, one row per employee, up to 10,000. No names, IDs, or salary data.
Each title is matched to a U.S. Department of Labor O*NET occupation, then scored on the work activities that job involves.
You get a treemap, department totals, a breakdown by AI skill, and the three training groups.
Every role scored and ordered, with a cost estimate and the reasoning behind each number.
Roles with the most writing, data, communication, and decision work. Ordered by score.
Some AI skills fit the work but have no approved tool behind them yet. Those are listed separately, so you do not train people for something they cannot use.
The strongest work activity becomes the first topic, alongside a skip-for-now list and a cost estimate you can edit.
The analysis is the same in each case.
You have been asked for an AI workforce plan. You get a role-by-role map, the outside checks on the numbers, and a costed first group.
See the leadership view →You need to train people in order rather than all at once. The three groups and the tool-gap split show where the work and the approved tools line up.
Explore the full dashboard →Multi-client workspaces, white-label PowerPoint exports, client viewer links, and a methodology pack. Priced per engagement.
See the consultant plan →Pay when you are ready to run your own file.
Early-access pricing is available during beta. Secure checkout via Stripe.
Start with the demo on sample data, or run your own file. Either way it takes about two minutes.
