Solution · AI Operations
Audit your processes for AI: know what's a fit, in what order, and at what risk before you automate anything
Automating the wrong process is expensive, and automating blind is worse. Before you run AI you need to know which processes are a fit, which aren't, and in what order to bring them in — by volume, cost, variability, criticality, and available data. That audit isn't a slide deck you hand over once: it's a capability run continuously, because your processes and the models shift every quarter.
The problem
The problem isn't that AI doesn't work. It's that nobody has decided, with data, which process deserves AI.
- Every team pushes its own AI case, and the candidate list gets set by whoever shouts loudest — not by each process's volume, cost, or risk.
- Nobody has the process inventory with the numbers: how many times a month they run, what they cost, how much they vary, and what data they leave behind. Without that, prioritizing is guessing.
- Pilots get approved on critical processes, with no clean data, or tied to ten integrations — the ones most likely to break — while the truly fit ones wait in line.
- The suitability snapshot was taken once, in a consulting engagement, and it's already stale: the process changed, a new model shipped, and the priority list is still last year's.
Cost of staying the same
Without an audit, the order you bring AI into is decided by internal politics, not return. That carries two costs paid together: the fit, profitable process left waiting, and the pilot on a critical or data-poor process that breaks in production and burns the whole organization's trust in AI. And because processes and models change, a six-month-old suitability snapshot is worthless: what wasn't a fit yesterday is today, and vice versa. Without re-audit, every decision on what to automate is made with an old map.
The solution
We build and run the suitability audit — inventory, measurement, scoring, dependencies, risk, and re-audit — so what to automate stops being an opinion and becomes a data-backed decision
- 1We build the process inventory: what gets done, who does it, with which systems, and how often. Not a dead list — a living catalog with owner, inputs, outputs, and systems touched for each process.
- 2We measure each process by what matters for AI: volume and frequency, cost, repetitiveness versus variability, criticality if it fails, and what data trail it leaves. Without that measurement, scoring is an opinion dressed up as a table.
- 3We score suitability and cross it with the dependency and integration map: a fit process tied to six fragile systems isn't the first piece. Out of that comes a queue prioritized by return and risk, not by who pushes hardest.
- 4We flag risk and governance for each candidate — sensitive data, critical decision, supervision needed — and leave the re-audit running: when a process changes or a new model ships, suitability gets recalculated, not frozen.
What changes
What you stop losing
The order of automation stops being decided by internal politics: the queue comes from crossing volume, cost, variability, criticality, and available data — not from who pushes.
Mechanism
The pilots that break — critical process, no clean data, tied to too many integrations — get caught in the audit, before you spend on them, not in production.
Mechanism
The suitability snapshot stops expiring: continuous re-audit recalculates priorities when a process changes or a model ships, instead of deciding with a six-month-old map.
Mechanism
What we measure: processes inventoried and quantified versus those still run by eye, candidates prioritized by return and risk, pilots dropped in the audit before spending, and age of the last re-audit per process.
What we measure
Spec sheet
- Work it removes
- deciding by eye which process to automate with AI: no inventory, no numbers, and no looking again when processes or models change
- Typical setup
- 2–4 weeks for the first queue; re-audit on a continuous cadence
- Input
- a company with dozens of processes and pressure to bring in AI, but no data to decide where to start or at what risk
- Output
- a run audit capability: living inventory, measured processes, a queue prioritized by return and risk, a dependency map, and continuous re-audit
- Works with
- ERP & CRMTicketing & office softwareBPM & process mining toolsData stack
- Can connect to
- Your process systemsIntegration mapSuitability dashboard
- What we measure
- processes inventoried and measured vs by eyecandidates prioritized by return and riskpilots dropped in the audit before spendingage of the last re-audit per process
- Good fit for
- companies with many processes and pressure to adopt AI that want to decide what to automate with data, in the right order, without breaking the critical stuff
- Not a fit for
- anyone who already knows the one specific process and just wants to run it: that's automating, not auditing — a different job
Frequently asked questions
No. Consulting gives you a snapshot: a prioritized process list that expires the moment something changes. We build the capability and run it: the inventory stays alive, processes get measured with real numbers, and suitability gets recalculated when a process changes or a model ships. It's the difference between a report and a system that keeps working after we leave.
Depends on what a mistake costs you. Automating blind usually means putting AI into the process that makes the most noise, not the one with the most return, and finding out in production there was no clean data or that it depended on six fragile systems. The audit doesn't slow automation down: it puts it in order, so the first case is one that wins rather than one that burns the organization's trust in AI.
Auditing is deciding WHAT to automate and in what order; automating is DOING it. Two distinct, consecutive jobs. This layer is the step before and the one that continues: without it, you automate on intuition. Once the prioritized queue is clear, the winning process moves to operations automation — the service that runs it — and the re-audit keeps watching which one is next.
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You’ve pinned the problem. We ship the fix and leave it measured.