Guide
What fractional leadership actually costs
A fractional engagement is priced very differently from a salary. This guide walks through typical day rates by discipline, how retainers are structured, what a genuine senior expert expects, and the three questions that reveal whether a candidate is a real operator or a CV with good titles. It closes with a simple worksheet for comparing a fractional hire against a full time role at your stage.
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Guide
Is your business ready for a fractional CFO?
Most growing businesses feel the finance constraint long before they name it. This guide covers the ten signals: pricing that no longer reflects cost, board packs that take weeks, cash forecasting that lives in someone's head, and more. It then sets out what a good fractional CFO does in the first 30, 60 and 90 days, so you can hold the engagement to account.
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Guide
The 90 day AI adoption plan
AI adoption fails when it starts with tools instead of problems. This guide sets out a 90 day sequence: map the processes that cost you most, pick two with clear measures, run a contained pilot with the people who do the work, and decide with evidence whether to scale, adjust or stop. Includes a one page brief you can hand to any expert you engage.
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Case study
Launched without ever testing on real users
A company brought a CE marked product to market, designed to improve infection control and reduce needlestick injuries. It looked ready, but the fundamentals had never been done: the device had not been tested on its intended use. Every test had been carried out in a lab environment that simulated what the designers assumed would happen in the real world, instead of testing the product in the conditions it would actually face. When our team joined as part of the launch and took the product to real world users within the first few weeks, the results were catastrophic. The product was not viable, investors lost a substantial amount, and the business had to be scrapped. The lesson: people sat behind closed doors inventing products without ever being on the shop floor understanding the real pain points. Had the team been instructed sooner, none of it would have happened. Until a product is tested in a live environment and solves a problem people genuinely have, there will be no adoption.
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Case study
The right algorithm, the wrong ward
A company set out to predict patient deterioration six hours ahead, for use in intensive care. The idea was good, but the people designing it had clearly never worked inside a real intensive care unit. The parameters the algorithm relied on were already altered by the very treatment the patients were receiving: respiration rates distorted by ventilation, oxygen saturation distorted by oxygen support. The measurements could never be accurate for those patients, and by the time a patient reaches intensive care it is already too late. The product had to be reworked and was relaunched with a different purpose, different branding and a different fit: assessing a patient's potential acuity from admission, before they are receiving any vital support. It shows why you must understand your customer and their workflow, and why somebody would actually adopt the product, rather than expecting it to work from a lab.
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Case study
The product the market had moved past
An insurance company wanted to provide information on demand. The problem: large language models had made information on demand largely irrelevant, so the product was aimed at the wrong opportunity entirely. Unpacking the real pain points showed where the value actually was: in the data being created and how it could be used. A whole new, data driven business model was built around that insight, and the end product was completely different from what the founder anticipated. It solved a bigger problem than the one they thought they wanted to solve, and in time helped save insurance premiums. The lesson: it is easy to sit and think there is a problem. You have to get out, do real world testing, and build the evidence behind who, what, when and why. And if you are taking investment money, remember your investors want a return; without an exit in sight you are not an investable business.
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