AI use case · Is your data ready for process automation?Automation does what your data says. Every time, at speed.
Agents and automation promise to run whole processes without a person checking each step. That raises the stakes on the data. If the process is recorded three different ways, exceptions live in email and the keys break between order, invoice and payment, automation multiplies the errors a person used to catch.
Starts with a 30-minute call. Commits you to nothing.
What it needs
What process automation needs from your data.
Each requirement, the systems it lives in, and what we check. The AI is the easy part; this list is the hard part. Each check feeds the Truth Score™, a score out of 100 for how far your data can be trusted.
Truth Score pillars that gate it mostGovernanceConsistencyThe six pillars
Where it breaks
Every system looks fine. The gaps between them don’t.
Where process automation breaks: not inside one system, but where two of them disagree about the same customer, deal or invoice.
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CRM ↔ Billing
Orders that never became invoices
What it does to the AIAn order-to-cash agent skips the cases that most need a person.
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Spreadsheets ↔ Operations
Exceptions tracked in a spreadsheet
What it does to the AIThe automation can’t see them, so it runs straight through them.
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HR ↔ Operations
Approval routes that point at people who have left
What it does to the AIWork stalls, or is approved by whoever is left in the chain.
Gaps to test for this use case, not client findings.
Before you sign
Questions to ask the vendor selling it.
Four questions that separate a demo from a deployment. Bring the answers, or the proposal, to the Truth Score Read and we test them against your records.
- Q1
What does the agent do when a record is missing a field?
Stop, guess or skip are three very different answers. Only one is safe by default.
- Q2
Which version of the process did you design for, and how many do we actually run?
Demos use the happy path. Your records will show the variants.
- Q3
Is every automated action logged, and can it be reversed?
Speed without an undo is how small errors become large ones.
- Q4
Who is accountable when it acts on the wrong record?
Agree it before go-live, in writing. Afterwards is too late.
How a Truth Score Read tests it
Process automation, tested against your own records.
Our engine matches the customers, people, deals and invoices across the exports from the systems behind the decision. Every red flag is checked by hand against sample records before sign-off, and the verdict comes in writing. Here is what that means for this use case.
- 01
Follow the keys
Orders, invoices and payments from each export, chained together so you can see exactly where the process breaks.
- 02
Count the variants
Status values and step sequences compared, so the real process, not the documented one, is visible.
- 03
Find the side channels
Exceptions and approvals that live in spreadsheets, with approvers checked against the HR export.
- 04
Give the verdict
Which steps an agent can take now, which need fixes, and which should keep a person in the loop.
Two systems are enough to start. We send the exact field list after the call.
How the Truth Score Read worksWhat 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.
Questions
Process automation, answered plainly.
Anything else, ask on the 30-minute call. It commits you to nothing.
Book the callWhy is data quality more critical for automation than for reporting?
A report with bad data gets questioned in a meeting. An automation with bad data acts on it, at volume, before anyone looks. The tolerance for error falls as the human checks disappear.
How do we know a process is ready to automate?
When it is recorded the same way every time, its keys carry from step to step, exceptions are in the system, and every approval has a current owner. Each of those can be checked from exports.
Should we start with one process?
Yes. Start with one segment: one process, or one variant of it. Test it end to end, automate the clean path, and keep exceptions with a person until they are recorded properly.
Does this apply to AI agents as well as older automation?
More so. Rules-based automation fails loudly when data is missing. Agents are designed to keep going, which means they can fill a gap with a plausible guess. The data has to be better, not worse.
Is your data ready for process automation? Find out before you sign.
Exports from two or more systems in. A signed verdict on this use case 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.