The starting point is the part of the workflow where manual losses, delays, errors or weak control already exist.
AI Audit
AI audit as the design of the first governed AI contour
Logicot AI audit is used when a company needs to understand not where AI looks fashionable, but where it should actually operate inside workflows, data, roles and control.
What the AI audit is
The first question is where AI should work, not simply where it can be added.
The AI audit helps define which workflows, documents, requests, roles and data should enter the first AI contour. If the answer is a rollout, it continues through the implementation model with a clear first scenario rather than an expensive experiment.
We verify which data sources and documents AI should rely on and what is still missing for a real launch.
We define where AI can act on its own, where a person has to confirm and how the result must be logged.
The audit ends with the first pilot scope: process, participants, limits, metrics and the next step.
What the company gets
Three outputs are enough for a meaningful first launch.
We do not end with an abstract report deck. The output has to translate into a concrete next step: pilot, module, integration or phased rollout.
Where AI can create value: documents, requests, sales, service, knowledge, control or analytics.
The first working contour: process, users, data, limits, metrics and success criteria.
Access, data sources, integrations, roles, rules, human-in-the-loop and data quality requirements.
Where the audit is most useful
The first AI contour usually appears where data and execution are already disconnected.
The AI audit is especially useful in B2B environments where work already sits between documents, requests, CRM, ERP, spreadsheets and manual approvals.
Document checks, field extraction, approval routes and the jump from file to a working action.
Lead qualification, routing, next-step preparation and status control around the actual scenario.
Links between 1C, CRM, ERP, website, portal and mail where manual data transfer already slows work down.
Process summaries, deviations, exceptions and management views built on current operational data.
How the audit works
The audit runs as a short controlled route, not as a long consulting cycle.
Each step should reduce uncertainty: from the current stack and limits to the first scenario that is actually worth launching.
We map workflows, systems, roles, manual handoffs and the real points of lost time or control.
We collect data sources, documents, access rules, security limits and the areas where AI cannot act without control.
We separate real working scenarios from attractive but weak ideas with no owner, no data base or no operational value.
We define the pilot scope: participants, scenario, human approvals, metrics and stop-or-scale criteria.
We decide what follows: a Logicot OS pilot, a separate module, an integration contour or phased implementation.
When the audit is not needed
The audit is not needed if the first pilot is already specified and ready to launch.
If the task is already defined, the data is prepared, the workflow owner is assigned and success criteria are agreed, it is better to move directly into pilot and implementation. In all other cases the audit lowers the risk of an expensive launch without results.
It is clear which workflow goes into the pilot and why it matters right now.
The required sources, context owners and technical access for the first contour are already in place.
A workflow owner is assigned, metrics are agreed and the first rollout can start without a long discovery phase.
What can come next
After the audit, the path should become shorter, not more complicated.
A good audit should not end as a standalone PDF. It should quickly move the company into one practical next format of work.
If the company needs one working contour with portal, workflows, documents, roles and AI in one system.
If the right next step is one working contour without full platform scope and without a heavy first launch.
If the main problem is broken data flow between systems and the first priority is one reliable working data contour.
Related reading
Journal materials
AI automation starts where a business step changes: a document is checked, a next action is prepared, a role confirms the step and the result is captured inside the workflow.
The strongest AI value in B2B appears less in impressive demos and more in reducing handoffs, preparing the next action and keeping the process under control.
Automation projects often fail not because the thesis is wrong, but because the launch is too broad. A phased rollout reduces risk and creates an earlier proof of value.
Next step
Need the first AI contour without an expensive experiment?
We can review the current operating model, define the first contour and decide what should actually launch first: a pilot, a module, an integration or phased implementation.