Fractional CFO · Manufacturing, Supply Chain & Energy
A fractional CFO practice for manufacturers, distributors, and energy operators — multi-plant, cross-border, roughly $50M to $500M — where cost, capacity, and cash move with the plant floor, and where AI software has started making calls someone will eventually have to defend.
Twenty years inside Fortune 10 and private-equity-backed operators taught me performance doesn't happen by magic — structure, cadence, and accountability deliver it: $25 to $30 million in cost savings, year over year, and this same discipline applies equally to seed-stage capital raises. Every engagement is tool-agnostic, built to your priorities, on your level and your terms.
Cost you can defend. Standard-cost environments, landed cost, plant and network economics, capacity and labor planning, and the KPI structure underneath all of it. The work is making the model match the factory, then making the factory legible to everyone who has to decide something.
Forecasting, pricing, capital cases, footprint and sourcing choices, diligence support, and the reporting a board or a lender will actually accept. Finance earns its seat by improving the next decision, not by describing the last quarter.
A control environment for the models now sitting inside pricing, forecasting, sourcing, and the close: where the controls belong, what gets recorded at the moment the AI model answers, who approves what, and how all of it is stated in language an auditor accepts. It is the newest discipline in the practice. The approach is set out below.
Usually two of these are true at once.
Models now shape pricing, forecasts, inventory and credit calls, vendor selection, and increasingly the close itself. In a regulated business that raises a question no vendor demonstration answers: when the decision is challenged — by an auditor, a regulator, a customer, or a court — what can be proven?
For most organizations the honest answer is very little. The prompt is gone. The model version changed without notice. The retrieved documents were never retained. Nobody can say whether a person reviewed the output or waved it through. The control environment that took twenty years to build around the ERP does not yet exist around AI tools.
This is a finance problem before it is a technology problem. It belongs on the same shelf as segregation of duties, spend authority, and revenue recognition, and it will be examined the same way.
"Regulated" doesn't mean banks. It means anyone whose numbers face an auditor, a lender, a customer contract, a privacy regime, a legal challenge, or the AI legislation now arriving. That is nearly everyone.
Why the old financial risk controls don't cover it
The existing rulebooks were written for machines that behave.
The checks companies already run assume software that gives the same answer every time and changes only when someone schedules it. These systems do neither. So the practical questions go unanswered: where does a control belong, what do you keep as proof, and what do you hand the auditor when they ask.
What a finance function actually needs is narrower and more practical: controls for systems that will not give the same answer twice, expressed in the language your auditor already speaks — not the language of the AI industry. Guidance is arriving quickly from regulators and standard-setters — faster, in most cases, than finance functions can absorb it, and none of it is specific to your process. The translation work is what we engage with you on.
What the work covers
What is recorded the moment the AI model answers, so a decision can be reconstructed a year later: inputs, model and version, retrieved context, output, reviewer, disposition. Written once, tamper-evident, retained on the schedule of the accounting record.
Which decisions require a person, at what threshold, holding what authority — documented like any other approval, not left to whoever is closest to the screen.
Concentration, change management, contractual audit rights, and a workable exit. Few AI vendor agreements give a CFO all four.
AI usage behaves like an expense without cost controls. It needs an owner, a budget, and unit economics before it needs agent autonomy.
Why this practice
The practice maintains its own working implementation of this control stack — an attestation and obligation-ledger layer that records what a model was asked, what it returned, which sources it drew on, and who accepted the result. It was built because the advice had to be tested against something that runs, and because nothing off the shelf produced the evidence the work required. Specifics are available under NDA.
Fixed scope, typically two to four weeks, ending in a written finding and a sequenced plan with owners and dates. Some engagements are designed to stop here.
A standing seat. Planning cadence, board and lender reporting, and the decisions that need a finance owner. Defined days per month, defined scope, reviewed quarterly.
One hard thing, scoped and dated: an ERP cost design, a tariff response, a footprint move, or the AI control environment described above — designed, installed, and handed over.
Engagements begin with a conversation, not a proposal. If the work isn't a fit, I'll tell you on the first call.
The evidence a finance function needs at the moment of AI response, and why it is almost never captured.
Why the bank-era framework does not transfer, and what a controller needs instead.
Controlling scope gets set early, quietly, and by people who will not own the margin report.
Longer-form work is published under Kenneth Mangum. All writing →
Describe the situation in a few sentences. If a call makes sense, let's schedule it; if it doesn't, I'll let you know what I think or steer you somewhere else — either way you'll get a response directly from me within two business days.