Finance
Brings the workflow, process knowledge, review judgement, and operating ownership.
How implementation works
The engagement starts with one real finance workflow, understands how it works today, redesigns it around an AI work layer, proves it in parallel, then transfers the capability to finance inside IT-governed rails.
This is not a generic AI workshop. The engagement starts with one named workflow that is valuable, repeated, and specific enough to prove.
The engagement in one map
The map shows how the work moves from one named workflow to current-state understanding, AI work-layer redesign, parallel proof, finance capability transfer, and IT-governed operation.
Specialist delivery partner
Distils the workflow, designs the first AI-supported version, builds the proof, and transfers capability.
IT / security
Approves tools, access, environments, connections, visibility, controls, and rollback.
This is a detailed map. Open it full-size to read the annotations comfortably.
The engagement starts with one workflow
Good engagements start with one specific workflow in mind. It might be a treasury process that is difficult to run across systems, or a transfer pricing workflow where the finance team wants to understand what AI could practically change.
The first step is to choose one workflow that is repeated, valuable, and specific enough to prove. The goal is not to “find an AI use case” in the abstract. The goal is to redesign one real piece of finance work.
Repeated
Evidence-rich
Cross-system
Valuable
Bounded
Understand the workflow before changing it
Before building anything, the engagement separates the workflow from the way it happens today.
That means setting out the business reason, the core steps, the key inputs and outputs, the critical data, and the human decisions that must remain.
Then we map the current operating reality: source data, existing systems, files, controls, hidden logic, and the final authoritative output.
Build and prove in parallel
Once the workflow is understood, the specialist delivery partner builds the first AI-supported version: the workflow setup, workflow-specific tools, required connections, evidence trail, and review points.
This needs early IT involvement. Finance, IT/security, and the delivery partner agree what tools can be used, what environments are acceptable, what systems can be connected, and what access model is allowed.
Early versions should be low-risk: read-only, draft-only, or propose-actions-first before direct system changes are considered.
Parallel proof before production
Your team continues the normal workflow while the new workflow produces comparable outputs. That gives a direct basis for judging accuracy, speed, cost, control quality, and exception handling.
Transfer operating capability to the finance team
The aim is not for the workflow to become another external dependency. The finance team needs to be able to run it, review it, understand its evidence trail, identify failures, and specify improvements.
Finance owns the workflow logic and operating judgement; the AI work layer helps execute the work; IT owns the technical rails.
Handover to Governance
Once the first workflow has been proved, the detailed controls move to Governance so IT can define how to operate it safely and repeatedly.
What your team needs to bring
The engagement works best when your organisation can provide clear workflow ownership, clear IT/security ownership, and a safe route to test the redesigned workflow.
This does not require a full transformation programme to begin. It requires one real workflow and the basic approvals needed to prove whether the agentic model is useful.
Inputs for the first workflow
- named finance workflow owner;
- named IT/security owner;
- approved AI provider, tool-use approval, and data boundaries;
- route to sandbox, cloned system, or safe test data.
Have a workflow in mind?
If you have one finance workflow that is painful, repeated, and worth proving, that is enough to start the conversation.
Discuss one workflow