AI Tool Finder & Automation ROI Calculator

How the tool finder scores the catalog

Every item in our 26-tool catalog gets a fit score from 0 to 100 for your answers. The score is a sum of seven rule-based parts, and none of them looks at commissions:

Part	Max	Rule
Category fit	40	Your task categories map to catalog categories with fixed weights, e.g. meetings → meeting-notes (1.0) and calendar (0.6); project coordination → project (1.0), task (0.7); writing → writing (1.0), AI assistant (0.8); research → PKM (1.0), AI assistant (0.8); scheduling → calendar (1.0); data/reporting and admin → automation (1.0). The highest matching weight × 40 is used.
Role	10	A small boost for categories typical of your role (freelancers → time tracking, managers → project tools, researchers → PKM).
Team size	12	"solo" and "teams" tags from the catalog; 21+ people add points for an "enterprise" tag.
Technical fit	8	Self-hostable automation (n8n) scores 0 for non-technical users and 5 for no-code users, because it assumes server or scripting comfort.
Integrations	10	API/webhooks, self-host and CRM requirements are checked against tags. Google Workspace, Microsoft 365 and Slack are not recorded in the catalog, so they receive half credit and a visible warning.
Privacy	12	EU preference rewards EU-based companies (tag privacy-eu); "no third-party AI on sensitive data" excludes general AI assistants and flags tools with AI features; "self-host only" keeps only self-hostable or offline tools; SOC 2 is tag-based, not a certification check.
Budget	8	Known price × seats (team-size midpoint) compared with your band. Missing prices are treated as unknown and shown as "see pricing" — we never guess a price.

Tools you already use are marked keep in their category instead of being replaced; the rest of the stack is chosen to sit beside them. The stack has three to six slots, one per category you need, each with up to three alternatives.

The ROI formula, with an example

The calculator estimates, it does not measure. The chain is:

1. Hours automatable per week = repetitive hours × automation share. The default share depends on technical level (non-technical 25%, no-code 40%, scripts 55%, developer 65%) multiplied by a frequency factor (a few per week 0.8, daily 1.0, many per day 1.1). The slider in the result lets you override it.
2. Monthly value of time saved = hours × your hourly value × 4.33 weeks.
3. Setup effort = new tools × a per-tool setup time (1.5–3 h by technical level) plus the starter recipes (1, 3 or 6 h by difficulty, scaled by level). It is subtracted in the first month only.
4. Three scenarios: conservative (50% of the baseline hours), baseline, optimistic (130%).

Example: a no-code-comfortable consultant with 6 repetitive hours a week, daily repetition and a $60/h rate: 6 h × 40% = 2.4 h/week; 2.4 × $60 × 4.33 ≈ $624 a month. Three new tools and four recipes come to about 14 setup hours (≈ $840), so the first month is negative and payback lands around six weeks at baseline — if the 40% assumption holds. Known subscription costs are subtracted; prices we have not recorded are listed as unknown so the total is a lower bound.

Why we do not use vendor case studies as evidence

Vendor case studies select their best customers, rarely report the baseline measurement, and never publish the failures. They are marketing, which is fine, but not evidence for your situation. We therefore anchor the default automation shares to controlled studies and keep them deliberately modest. If your own measurement differs, the slider is the right place to put that knowledge.

What the research says about AI and productivity

The picture is mixed and task-dependent, which is exactly why the calculator shows a range rather than a number.

• In a pre-registered online experiment, professionals who used ChatGPT for mid-level writing tasks finished about 40% faster and produced output rated higher in quality, with the largest gains for weaker writers.^[1] B It was a lab setting with short, well-defined tasks.
• In a field study of 5,000+ customer-support agents, access to a generative AI assistant raised issues resolved per hour by about 14% on average, with much larger gains for novice and low-skilled workers and little for the most experienced.^[2] A
• A field experiment with consultants found a "jagged technological frontier": on tasks inside the AI's competence, consultants were faster and produced higher-quality work; on a task outside it, those using AI were less likely to get the right answer.^[3] B
• In a randomized controlled trial with experienced open-source developers working in their own repositories, developers allowed to use early-2025 AI tools took about 19% longer to complete tasks, even though they believed they had been faster.^[4] B

Taken together: routine, well-specified, high-volume tasks are where the automation share is realistic; expert work in unfamiliar territory can show no gain or a loss. The calculator cannot see which of your tasks fall on which side, so treat the baseline as a hypothesis to test for a month.

Limitations

The catalog has 26 tools and no integration or compliance database, prices are being filled in, and the hours you enter are self-reported. The result is a starting point for a trial, not a business case. Everything shown is an estimate, nothing is guaranteed, and we earn nothing from the links; if that changes, the score will still not depend on it.

Frequently asked questions

Does the ranking depend on affiliate commissions?

No. The seven scoring parts are listed above with their weights and none of them reads commission data. Several catalog items (Obsidian, ChatGPT, Claude) have no affiliate program at all and are ranked by the same rules.

Why are some prices shown as "see pricing"?

Our catalog records a starting price only after it has been checked on a dated source. Until then the price is null and we show "see pricing" rather than guessing. The subscription total therefore says "≥ $X + n unknown".

Where do the 25–65% automation shares come from?

They are deliberately modest defaults by technical level, informed by the controlled studies cited below (roughly 14% to 40% gains on routine tasks, and losses for experts outside the tool's competence). They are assumptions, which is why the result has a slider to replace them with your own number.

Can the calculator check whether a tool works with my Google Workspace, Microsoft 365 or Slack?

Not yet. The catalog records API, self-host and EU tags, not vendor-by-vendor integrations. Those requirements get half credit and a warning, and you should verify with the vendor before buying.

Is the result a business case I can hand to my manager?

It is a starting point: a CSV and a printable page with every assumption visible. Treat the baseline as a hypothesis, run the top recipe for a month, measure, and then update the share slider with what you found.

Sources and evidence

1. Experimental evidence on the productivity effects of generative artificial intelligence — Noy, S.; Zhang, W. (2023), Randomized online experiment, Science 381(6654) A doi.org/10.1126/science.adh2586 (retrieved 2026-11-09)
2. Generative AI at Work — Brynjolfsson, E.; Li, D.; Raymond, L. (2025), Field study, Quarterly Journal of Economics A doi.org/10.1093/qje/qjae044 (retrieved 2026-11-09)
3. Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality — Dell'Acqua, F.; McFowland, E.; Mollick, E. et al. (2023), Field experiment, SSRN working paper B doi.org/10.2139/ssrn.4573321 (retrieved 2026-11-09)
4. Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity — Becker, J.; Rush, N.; Barnes, E.; Rein, D. (METR) (2025), Randomized controlled trial, arXiv:2507.09089 B arxiv.org/abs/2507.09089 (retrieved 2026-11-09)