Services
Two ways to work together
In the project I turn AI in the Office of Finance into impact, from diagnosis to delivery. In the mentoring I am your ongoing, confidential mind for the AI agenda. Both independent and vendor-neutral.
The project
From diagnosis to impact
In four steps, from the first read to measurable steering. The entry point is a fixed-price first-phase package with a money-back guarantee.
The mentoring
An AI mentor at your side
Ongoing, one to one, confidential. For CFOs and owners, and equally for boards and advisory boards on decisions about finance and AI.
The project
My method has four steps.
First, the diagnosis. I look at where the money goes, where projects stall, and where IT and the business talk past each other. Before we discuss solutions, I show you your problem clearly.
Second, the target state. With the CFO and the owner, I set out what technology and AI in the Office of Finance should actually deliver, in the language of the business.
Third, the roadmap. This is where I supply the missing link: which initiatives, in which order, clearly prioritised, and what is left aside on purpose. You end up with a quantified roadmap you can put in front of the owner or the board.
Fourth, delivery to impact. I stay with it until the plan turns into measurable benefit, and I keep the two sides aligned.
The entry point is a first-phase package of diagnosis, target state and roadmap, at a fixed price agreed in advance, with a money-back guarantee. If you see no clear diagnosis and no usable roadmap afterwards, you do not pay. The delivery phase follows on request, in stages with fixed review points.
Like a movement, AI only runs once the parts mesh.
From my practice
Where AI works in the Office of Finance
In my mandates I meet the same fields again and again. The map below shows where AI in the Office of Finance truly moves the needle, and what it needs. Which two or three matter most for you is what we find together in the diagnosis, the first step of the method above.
That impact rarely rides on the tool. It rides on three things: a clean data basis, sound governance, and the link that holds the Office of Finance and IT together. Where those are missing, even the best software stays inconsequential.
Planning and analysis
AI answers questions on financial and operational data in plain language, builds forecasts and scenarios, and explains variances down to the cause. That shortens preparation and sharpens the basis for decisions. It needs an integrated, clean data basis and a human check before any number reaches the board.
The close and accounting
AI reconciles accounts, categorises transactions, and drafts first versions of reporting commentary. The close gets faster and easier to follow. It works best where the processes are standardised and auditable.
Transactional processes, procure to pay and order to cash
Incoming invoices run through with little manual touch, and in receivables AI spots payment patterns and exceptions early. That lowers processing cost and improves the working-capital effect. Clear ownership and controls are a must, because errors in the payment process are expensive.
Treasury
AI pulls data from several systems, forecasts cash flow, and watches the working-capital chain continuously. That lowers funding cost and makes liquidity visible earlier. The benefit stands or falls with the connection to bank, payables and receivables data.
Tax
AI makes tax data accessible in plain language and drafts memos with sources. That saves search time and makes documents more consistent. Because the data is sensitive, access control and professional review are non-negotiable.
Audit, risk and compliance
AI checks transactions continuously rather than only at period end and spots anomalies early. Risks surface sooner and the review effort drops. For this to hold, it needs defined risk indicators and a clear governance frame.
Investor relations
AI drafts consistent communication in the company's style and tone, from the presentation to the mandatory filing. That saves writing time and keeps the message uniform. For anything that moves the capital market, human sign-off stays mandatory.
Knowledge and operational assistance
AI searches internal finance knowledge, summarises, and answers questions in plain language. It is often the lowest-risk entry point, because no sensitive numbers have to be exposed. It needs a maintained document base and control over the sources.
The sequence decides
The same pattern shows up across every area. AI can do a lot, yet the value only appears with data, governance, and the link between the business and IT. So the sequence holds: first the question of what you want to steer, then the data, then the software.
Rather than many experiments, two or three use cases with the greatest impact are what pay off. Which ones those are for your company is what the diagnosis settles.