AI readiness, by industrySame AI pitch. Different data underneath.
A churn model in SaaS and a churn model in retail are sold with the same slide. The data under them is nothing alike. Each page below sets out the AI plays put forward in one industry, the systems a mid-market company there runs, one record traced across three of them, and the gaps that sink the plays.
Starts with a 30-minute call. Commits you to nothing. No sector averages: your own records decide your verdict.
Industries
Pick your sector.
- SaaS and technology Account, subscription and workspace rarely share an ID. Churn and ARR plays depend on it. Traced across: CRM · Billing · Product
- Financial services Accurate ledgers, weak joins. Fraud and onboarding AI stall on the client ID. Traced across: Core platform · CRM · Finance
- Healthcare Built by acquisition: each site has its own system and codes. Operational AI needs them to agree. Traced across: Practice system · Finance · Rota and payroll
- Logistics and distribution Physical data is accurate; the seams are not. Pricing and automation plays run across them. Traced across: TMS · ERP and finance · Carrier bill
- Manufacturing Shop floor, engineering and ERP grew up apart. Planning and pricing AI need them to agree. Traced across: MES · ERP · Quality
- Professional services Time, billing, CRM and HR rarely agree on the client or the hour. Forecasting depends on it. Traced across: Time and practice · Finance · CRM
- Retail and e-commerce Sales are accurate; customers and products fragment across channels. Personalisation needs them whole. Traced across: Web store · Email platform · ERP and finance
Where each play comes up
One use case, many industries.
The AI use cases each industry page covers. Follow a row to see what the use case needs; follow a column to see the industry. Each one is tested the same way: a Truth Score™ (a score out of 100 for how far your data can be trusted) and a Leverability read (which AI ideas you can use now, after fixes, or not at all).
| Use case | SaaS and technology | Financial services | Healthcare | Logistics and distribution | Manufacturing | Professional services | Retail and e-commerce |
|---|---|---|---|---|---|---|---|
| Revenue forecasting | Covered | Not covered | Not covered | Not covered | Not covered | Covered | Not covered |
| Lead scoring | Covered | Not covered | Not covered | Not covered | Not covered | Covered | Not covered |
| Customer churn prediction | Covered | Covered | Not covered | Not covered | Not covered | Covered | Covered |
| Pricing and rebate optimisation | Covered | Not covered | Not covered | Covered | Covered | Not covered | Covered |
| Personalisation | Not covered | Covered | Not covered | Not covered | Not covered | Not covered | Covered |
| 13-week cash flow forecasting | Not covered | Not covered | Covered | Covered | Covered | Covered | Not covered |
| Financial close and FP&A | Not covered | Covered | Covered | Not covered | Covered | Covered | Not covered |
| Fraud detection | Not covered | Covered | Not covered | Not covered | Not covered | Not covered | Covered |
| Document intelligence | Not covered | Covered | Covered | Covered | Covered | Covered | Not covered |
| Process automation | Not covered | Covered | Covered | Covered | Covered | Not covered | Not covered |
| Demand forecasting | Not covered | Not covered | Covered | Covered | Covered | Not covered | Covered |
| Customer service automation | Covered | Covered | Covered | Covered | Not covered | Not covered | Covered |
| Natural-language analytics | Covered | Not covered | Not covered | Not covered | Not covered | Covered | Not covered |
Which plays each page covers, not how common they are.
Also by
Start from the AI or the system instead.
- 13 use cases AI use case readiness What each AI use case needs from your data, where it breaks between systems, and what to ask the vendor selling it.
- 21 system guides Data quality for AI, by system What each system gets right, its common data problems for AI, and the gaps between it and its neighbours.
What you get back
See what comes back: a real sample memo.
One fictional B2B software company, Example Co: 72,837 records in 7 exports from its CRM, finance and HR systems, run through the same engine and the same checks as a paid Truth Score Read. Each system passes its own checks. Matched across systems, only 13% of deals tie to an invoice.
Not a software company? The sample is one, but the method doesn’t change; only the systems do. Each industry page traces one record across that sector’s own systems.
Questions
Industries, answered plainly.
Anything else, ask on the 30-minute call. It commits you to nothing.
Book the callDo you publish AI readiness scores by industry?
No. We don’t publish sector averages or benchmarks: a score only means something for your own records. These pages describe the AI plays, systems and gaps to test in each industry; a Truth Score Read tells you where your company actually stands.
My industry isn’t listed. Does that matter?
No. The method is the same for any business that runs on systems that export: match the customers, people, deals and invoices across them, check every red flag by hand, and test the AI in question against what holds. These seven sectors are a starting point, not a limit.
Who are these pages written for?
CEOs and CFOs of mid-market companies, roughly €20M to €500M revenue, and the investors and operating partners behind them, at the moment an AI proposal or budget is on the table.
Can you test the AI plays in our investment plan?
Yes. Send the business plan, value creation plan or investment thesis with your exports. We pull out the AI plays and show, play by play, whether the data they need is there and can be trusted.
Your sector sets the plays. Your records set the verdict.
Exports from two or more systems, plus the plan or proposal if there is one. A signed verdict within 5 working days of complete exports. Starts with a 30-minute call. Commits you to nothing.
Starts with a 30-minute call. Commits you to nothing.