Work with me
Treasury and finance workflows, redesigned — with AI where it holds up
I take one slow, manual, exception-heavy workflow — bank statement processing and reconciliation, the daily cash position, payment approvals in SAP BCM, hedge accounting, month-end — and redesign it end to end: what stays a rule, where AI reads or drafts, and which decisions stay with a named person. The same method works elsewhere in the enterprise; this is where I move fastest, after eighteen years inside SAP, finance and treasury programmes.
How it starts
- A free 30-minute call. Tell me what the workflow is and what makes it painful. I'll say honestly whether I can help — NDA first if you need one.
- A written scope within a week. Which engagement fits, what is in and out, and a fixed fee.
- The work. Weeks, not months. Remote by default, on site where the work needs it.
The main engagement
AI Workflow Redesign Sprint
One complex workflow. Redesigned end to end around AI.
For a workflow you already know is a problem: slow, manual, exception-ridden, or quietly expensive.
We map how the work actually runs — not the process diagram, the real thing, including the parts nobody documents — and find where the elapsed time and the rework genuinely sit. Then we separate what must stay deterministic (calculations, limits, postings, segregation of duties) from what AI does well (reading, synthesising, classifying, drafting, spotting the exception) from the decisions that need a named human. What comes out is the version of the workflow that would be designed today: one that owns its own state, handles the exception path instead of ignoring it, and can be audited.
Before
- Work arrives by email and spreadsheet, and someone chases it
- Most of the elapsed time is waiting, not working
- Exceptions are handled by whoever remembers how
- The reasoning behind a decision lives in someone's inbox
After
- Input is read and structured on arrival; the chase is the system's job
- The deterministic core is still deterministic — and still auditable
- Exceptions are a queue with owners, thresholds and escalation
- Every decision carries its evidence, its author and its version
What it covers
- Current state, with the time and rework where they really are
- The deterministic core that must never be handed to a model
- Where AI reads, drafts, classifies or proposes — and where it stops
- Decision rights: who approves what, on what evidence
- The state the workflow has to hold, and the exception path
- The architecture and controls the redesign implies
- A business case in cycle time, rework and risk, using your numbers
- A roadmap: what to build first, and what to leave alone
You end up with: A redesign your team can build against, with the trade-offs written down rather than discovered in production.
Talk about your workflow →If a sprint isn't where you are yet
Not sure which workflow?
AI Workflow Opportunity Scan
We go through the real workflows in a function and assess each against the things that decide whether AI changes its economics: how often it runs, where the human hours go, how much of the input is unstructured, how repeatable the judgement is, the exception rate, what a wrong answer costs, and who would have to own it.
What it covers
- A ranked shortlist of candidate workflows, with the reasoning visible
- For each: automate, augment, autonomy — or leave alone, and why
- The data, state and control implications of each candidate
You end up with: A written assessment you can take to a steering committee — and a defensible reason for the two or three you decide to pursue.
Ask about the AI Workflow Opportunity Scan →Already building something?
Enterprise AI Architecture Review
The hard part of enterprise AI is rarely the model. It is where the capability sits relative to the systems of record, what it may write and under whose identity, how its actions are logged and reconciled, what happens when an upstream system is unavailable, and what rollback means. This is the review that asks those questions while they are still cheap to answer.
What it covers
- Placement: read-only, propose-and-queue, scoped write, orchestrating
- Identity, permissions and who owns which record
- Controls, segregation of duties, audit trail and observability
You end up with: A written review with the risks named, the gaps prioritised, and the specific changes that would let it run in production.
Ask about the Enterprise AI Architecture Review →Common questions
What does an enterprise AI workflow redesign engagement cost?
A fixed fee, quoted in the written scope that follows the first call. There is no package price because the scope depends on the workflow and how much of the surrounding landscape is in play — a published figure would fit almost nobody. The first call is free, and if the work turns out smaller than a sprint, I will say so.
How long does an AI workflow redesign sprint take?
A sprint runs in weeks rather than months, and the opportunity scan and architecture review are shorter than that. The shape matters more than the number: if a piece of work cannot be done inside that window it is almost certainly two engagements rather than one long one, and saying so at the start is cheaper for everyone than discovering it halfway. Remote by default, on site where the work genuinely needs it.
Will you sign an NDA before I describe the workflow?
Yes, and before rather than after. Anything you send is treated as confidential, and I am happy to sign your NDA before you describe the workflow — describing the problem is usually the part that involves the sensitive detail, so an agreement that only arrives once the engagement is agreed has been signed too late to protect the conversation that decided it.
What happens if AI is not the right answer for my workflow?
Then I say so, and that is a good outcome rather than a failed one. A scan that comes back with two workflows worth pursuing and honestly none of the rest has done its job — it has saved the cost of the other five. The expensive version is the engagement where nobody is willing to say it, and the programme spends a year proving something that was answerable in a fortnight.
Do you have client references for AI transformation work?
No, and I will not invent them. The eighteen years behind this are enterprise systems experience — SAP, finance, treasury, programmes that had to survive a control environment, a cutover and an audit — not eighteen years of AI transformation, and there are no client logos, testimonials or saved-millions figures to show for a practice this new. What there is instead is everything published here: the method I would apply to your workflow, the architecture questions I would ask, and years of field notes. Read a few and you will know whether I am the right person before we ever speak.
Get in touch
Tell me about the workflow: what it is, roughly how it runs today, and what makes it painful. If you'd rather read first, everything I'd apply is published — the workflow-design method, the architecture and controls questions, and the guides they come from.