Anna Bergman — Interim CFO for PE and VC-Backed Companies

The Fractional CFO Model: An Honest Assessment of When It Works and When It Doesn't

2026-03-24T00:00:00+00:00

I've been working as a fractional CFO for over a decade. I've had engagements that lasted a few months and one relationship that's spanned ten years across an entire portfolio of companies.

So when people ask me whether the fractional model works, my answer is: it depends. Which I realise is unsatisfying, so let me be more specific.

The fractional CFO model is genuinely brilliant in certain situations. In others, it's the wrong answer, and a good fractional CFO should tell you that. The problem is that the model has become fashionable, particularly in the PE and VC world, and fashion tends to obscure nuance. Everyone is selling fractional. Not everyone is being honest about its limitations.

This is my honest assessment. I have a commercial interest in the model working, obviously. But I have a greater interest in being trusted, and trust requires candour.

First, let's define our terms</h2>
This matters because people use "fractional," "interim," and "consultant" interchangeably. They're not the same thing, and the differences affect what you get.
A fractional CFO works with you on an ongoing, part-time basis. Typically one to three days a week, over a period of months or years. They're embedded in your business. They attend board meetings, manage your finance team, own the numbers. They're your CFO, just not full-time. This is what I did at Quasar Group, where I've been the fractional CFO across their portfolio for ten years. I was not visiting. I was part of the furniture.
An interim CFO fills a full-time role for a defined period. Cover for maternity leave, bridging a gap between permanent hires, or leading a specific transformation programme. The engagement is full-time but temporary.
A consultant advises. They produce reports, make recommendations, and leave. They don't own outcomes. They don't manage your team. They don't sit in the Monday morning leadership meeting when the cash forecast has gone sideways.
The distinction matters because each model creates a different relationship with accountability. A fractional or interim CFO owns results. A consultant owns a deliverable. Know which one you're buying.

When the fractional model works brilliantly</h2>
I've seen it work extraordinarily well in five specific situations.
PE portfolio companies needing transformation. This is the highest-value use case I've encountered. A PE sponsor acquires a business, identifies that the finance function needs significant improvement, and brings in a fractional CFO to drive that transformation. The engagement might be two or three days a week for 12 to 18 months. The CFO builds reporting, fixes the close process, implements controls, develops the team, and prepares the business for the next stage of the hold period.
This works because the work is inherently project-shaped even if it doesn't feel like it. There's a defined starting condition (messy finance function), a defined end state (PE-grade reporting and controls), and a timeline driven by the investment thesis. You need someone senior enough to challenge the board and credible enough to retain the PE sponsor's confidence. That profile commands £400-600 per hour in the market, or £20-35K per month on retainer. Most mid-market businesses can't justify that cost full-time, but they absolutely need that capability.
During my decade at Quasar Group, I focused on delivering transformation that sticks across their high-tech portfolio. Tech ventures require a unique kind of financial rigor; I've stepped in to lead everything from foundational rebuilds to strategic exit prep and restructuring through volatile periods. By operating fractionally, I provided the company with the senior capability needed to handle high-tech challenges without having to carry the full-time cost of permanent CFO.
Scaling businesses between funding rounds. At GeoVS, I was the fractional CFO for a high-growth tech startup for several years. We built the finance function from nothing, secured over £2m in funding, and I ultimately led the company through to a successful sale. The founder needed CFO-level capability from day one but couldn't afford or attract a full-time CFO. The fractional model gave him exactly the right level of finance leadership at each stage of growth, scaling up as the business scaled up.
Post-acquisition integration. When a business has just been acquired, the first 100 days are critical. The finance function needs to be assessed, aligned to the new owner's reporting requirements, and often restructured. This is intense, skilled work with a natural end point. A fractional CFO can lead the integration, build the new reporting framework, and hand over to the permanent team once the structure is in place.
Exit preparation. The 12 to 18 months before a sale are when finance functions either prove their value or cost the seller millions in lost enterprise value. A fractional CFO who has been through multiple exits knows exactly what buyers look for in due diligence, what "good" looks like, and how to get there. This is bounded, high-stakes work that suits the model perfectly.
Companies that need senior capability but can't justify a full-time CFO. This is the most common scenario, and the most straightforward. A business turning over £5-30m needs genuine CFO capability for strategic decisions, board reporting, banking relationships, and investor management. But it doesn't need that capability five days a week. Two days gives them everything they need. The alternative is hiring a less experienced full-time FD at the same cost, and hoping they grow into the role. Sometimes that works. Often it doesn't.
When it doesn't work</h2>
Here's where I lose the sales pitch, and I'm fine with that.
When the business needs daily operational finance leadership with no end point. Some businesses are complex enough, or early enough in their finance maturity, that they need someone in the chair every day. Not for a transformation sprint, but permanently. If the accounts payable process requires daily senior intervention, if there are cash management decisions every morning that need CFO judgement, if the business simply cannot function without a finance leader present five days a week, then fractional is the wrong model. Hire someone full-time.
When there's no internal team to develop. My philosophy is simple: I build the machine, train your people, then leave. But that requires people to train. If a business has no finance team and no intention of hiring one, the fractional CFO becomes a permanent crutch rather than a catalyst. The whole point is to build capability that outlasts the engagement. If there's nobody to build capability into, you need a different solution.
When the business wants a name on the org chart but won't give authority. I've seen this more than once. A PE sponsor or VC investor insists on "getting a CFO in," the management team grudgingly agrees, and then the fractional CFO arrives to discover they have no budget authority, no access to the board, and no mandate to change anything. They're a comfort blanket for the investor, not a functional leader.
This fails every time. A fractional CFO needs the same authority as a full-time one.

The "fractional" part refers to time, not to influence. If the CEO won't give a fractional CFO authority over the finance function, they shouldn't hire one. They should fix the underlying trust issue first. </blockquote>
The trust question</h2>
Every prospective client asks some version of the same question: "If we bring you in, won't we just become dependent on you?"
It's a fair concern, and I take it seriously. The answer is: not if the engagement is structured correctly.
Dependency happens when the fractional CFO becomes a key-person risk. When knowledge lives in their head, when processes depend on their presence, when the team can't function without them. I've seen it happen with other fractional professionals, and it's a failure of the model as practised, not of the model itself.
The way I prevent it is straightforward. Everything I build gets documented. Every process I create gets an owner on the internal team. Every decision framework I develop gets taught to someone who will be there after I leave. I actively work to make myself redundant. That sounds counterintuitive for someone whose livelihood depends on ongoing engagements, but it's actually the best business strategy I know. Clients who trust you to build genuine capability and then step back are clients who recommend you to everyone they know. And they call you back when the next challenge arrives.
How to structure a fractional engagement for success</h2>
Most fractional engagements that fail do so because of poor scoping, not poor execution. Here's what I've learned about getting the structure right.
Start with a diagnostic. Before committing to an ongoing engagement, invest in a proper assessment. Two to three days, looking at the finance function, the team, the systems, the reporting, the controls. This tells both sides what the actual need is, which may be different from what the client thinks they need. I've done diagnostics that led to six-month engagements and diagnostics that ended with me saying "you don't need me, you need to hire a management accountant and fix your chart of accounts."
Define outcomes, not activities. "Two days a week of CFO support" is not a scope. "Board-ready management accounts within 10 business days of month end, a 13-week rolling cash flow forecast, and the finance team cross-trained to run the close independently" is a scope. Outcomes create accountability on both sides. Activities create time sheets.
Build in a handover plan from Day 1. The engagement should include explicit milestones for transferring knowledge and responsibility to the internal team. If the plan doesn't include "and then I leave" at some point, something is wrong. At GeoVS, the handover was built into the engagement from the start. By the time the company was sold, the finance function could have run without me. It just so happened that I was the right person to lead the exit process, so I stayed for that too.
Set a review cadence. Every three months, both sides should ask: is this still the right model? Has the need changed? Should we scale up, scale down, or stop? The best fractional relationships adapt over time. The worst ones persist on autopilot long after the original need has been met.
Get the authority question settled upfront. The fractional CFO needs a clear mandate. Who do they report to? What decisions can they make? Do they have a seat at the leadership table? Can they hire and manage the finance team? Sort this out before the engagement starts, not three months in when it becomes a problem.
The AI question</h2>
I'd be dishonest if I didn't mention this. The market for fractional CFOs is changing, and AI is a significant part of the reason.
Recent data shows that 79% of CFOs are increasing their AI budgets, but only 10% are confident in their underlying data quality. That gap is enormous, and it creates a specific kind of demand for senior finance operators who understand both the technology and the fundamentals. You can't automate a finance function that doesn't have clean data, documented processes, and proper controls.

AI applied to chaos produces confident-sounding chaos. </blockquote>
The most in-demand fractional CFO profile right now is what the market calls an "AI implementation overseer." Someone who can assess a finance function's readiness for automation, clean up the foundations, oversee the implementation, and ensure the technology actually delivers value rather than just generating impressive demos.
This is where the fractional model has a genuine structural advantage. A full-time CFO who's been in the same business for five years may not have the breadth of exposure to know which AI tools actually work and which are expensive disappointments. A fractional CFO who works across multiple businesses sees what's working in practice across different contexts. That pattern recognition is valuable, and it's inherent to the model.
My philosophy</h2>
The goal is always to make myself redundant. Build the finance function, develop the team, put the systems and processes in place, and leave behind something that works without me. The best measure of my success is that the business doesn't need me any more.

We're not trying to create permanent dependency. We're trying to create permanent capability. </blockquote>
That might sound like a strange thing for someone in my position to say. But the fractional CFOs who build long, successful careers are the ones who are honest about this.
The honest close</h2>
If you're reading this and thinking a fractional CFO might be the right answer for your business, here's my advice: find one who will tell you when you don't need them.
The best fractional CFOs will turn down work that doesn't fit the model. They'll tell you if you need a full-time hire instead. They'll build in their own obsolescence from Day 1. They'll measure success by what the business can do without them, not by how many days they can bill.
If the person you're talking to can't articulate when the engagement ends, or gets uncomfortable when you ask, that tells you something. The model works. But only when both sides are honest about what it's for.

If the fractional or interim model sounds like the right fit, the Services page</a> explains how each engagement is structured and what it typically costs. If you're not sure which model applies to your situation, the Finance Diagnostic</a> is a good first step — it establishes what the finance function actually needs before deciding how to resource it.

EU AI Act: What Finance Leaders Need to Know Before August 2026

2026-03-17T00:00:00+00:00

The EU AI Act comes into full enforcement on August 2, 2026. That's five months from now. If you're a finance leader using AI in any capacity, or if your team is using it without your knowledge (more on that shortly), this deadline matters to you.
I'm not going to give you a legal treatise. There are plenty of those, and most of them are written by people who've never had to implement a controls framework under time pressure. What I want to do is translate this regulation into practical terms: what it actually requires, which finance use cases are affected, and what a proportionate response looks like for a mid-market business that doesn't have a dedicated compliance department.
What the Act actually requires</h2>
The EU AI Act takes a risk-based approach. Not all AI is treated equally. The regulation classifies AI systems into four tiers: unacceptable risk (banned), high-risk (heavy regulation), limited risk (transparency obligations), and minimal risk (essentially unregulated).
For finance, the high-risk category is where you need to focus. An AI system is classified as high-risk if it makes or materially influences decisions that affect people's access to financial services or their financial standing. The specific use cases called out include:
Credit scoring and creditworthiness assessment. If you're using AI to evaluate whether someone gets credit, how much, or at what rate, that's high-risk. This includes automated lending decisions, credit limit adjustments, and risk scoring models.
Fraud detection systems. AI that flags transactions or individuals as potentially fraudulent falls into the high-risk category. The logic is straightforward: a false positive can freeze someone's account, block a legitimate transaction, or trigger an investigation. Those are consequential decisions.
Anti-money laundering (AML) screening. Automated systems that assess AML risk, flag suspicious activity, or determine enhanced due diligence requirements are high-risk. If your AML tool uses machine learning to score transaction patterns, it's covered.
Financial forecasting that feeds material decisions. This one is less explicitly defined but increasingly being interpreted broadly. If AI-generated forecasts directly influence investment decisions, covenant assessments, or board-level strategy, the argument that these systems carry high-risk characteristics is gaining traction among regulators.
For each high-risk system, the Act requires: a risk management system, data governance and quality standards, technical documentation, record-keeping and logging, transparency to users, human oversight mechanisms, and accuracy and robustness standards.
That sounds like a lot. It is a lot if you're starting from nothing. But if you already have a decent controls environment, you're closer than you think.
The SOX connection most people miss</h2>
If your business has any SOX compliance obligations, or if you've built internal controls to a SOX-like standard (as many PE- and VC-backed companies do in preparation for exit), you've already done a significant portion of the groundwork.
SOX fundamentally requires human-in-the-loop for material financial processes. You need to demonstrate that a human with appropriate authority has reviewed and approved key outputs. The EU AI Act requires something very similar for high-risk AI: meaningful human oversight, the ability to intervene, and the capacity to override automated decisions.
The controls frameworks overlap substantially. Documentation requirements, audit trails, risk assessments, segregation of duties, exception handling. If you have a mature SOX-like environment, adapting it to cover AI governance is an extension of what you already do, not a new discipline entirely.
The frameworks for PE- and VC-backed companies that were designed with exit in mind and were built properly, with genuine rigour rather than just tick-box compliance, translate almost directly into AI governance frameworks. The gap is usually in AI-specific technical documentation and the formal risk classification process. The underlying governance muscle is already there.
The shadow AI problem</h2>
Here's the statistic that should concern every CFO: 78% of finance teams are using AI tools that haven't been approved by their organisation. And 75% have shared sensitive financial data with AI systems. Not maybe. Not occasionally. Three quarters of your team.
I've seen finance functions where the management accountant was using ChatGPT to draft variance commentary, the FP&A analyst had built a forecasting model using an AI tool nobody in leadership knew about, and the accounts payable team was using an unapproved browser extension to categorise invoices.
None of these people were being reckless. They were being resourceful. The tools made them faster and they adopted them. But from a regulatory perspective, each of those is a potential uncontrolled AI system processing financial data with no governance, no documentation, no risk assessment, and no audit trail.
Under the EU AI Act, the organisation is responsible. Not the individual who downloaded the tool. If an unapproved AI system is making or influencing financial decisions using your data, you need to know about it, assess its risk, and either bring it into your governance framework or stop it.

The CFO sees the approved tools on the IT roadmap. The controllers see what people are actually using day to day. </blockquote>
The perception gap makes this worse. Research shows 51% of CFOs believe they've achieved full AI adoption. Only 19% of controllers agree. That gap isn't about ambition. It's about visibility. If you're a CFO who thinks you have full visibility of AI use in your finance function, you almost certainly don't.
What a proportionate response looks like</h2>
I want to be direct about something: the EU AI Act compliance framework designed for a global bank is not what a PE- or VC-backed mid-market company needs. If you try to implement that, you'll spend a fortune, exhaust your team, and probably not finish before the deadline anyway. That is exactly the pattern behind the statistic that 42% of companies have abandoned the majority of their AI initiatives, up from 17% in 2024. Overscoping is the killer.
A proportionate response for a mid-market business looks like this:
Right-size the governance. You don't need a 50-page AI policy. You need a clear, practical framework that covers: what AI systems are in use, who owns them, how they're classified by risk, what controls exist around them, and who reviews them periodically. It typically is a 10-to-15-page governance framework with supporting templates. It should be something your finance team can actually maintain, not a document that lives in a drawer.
Focus on what's high-risk. Not every use of AI in your finance function will be high-risk under the Act. An AI tool that helps format Excel charts is minimal risk. An AI system that generates credit assessments is high-risk. Put your effort where the regulatory exposure actually sits. For most mid-market finance functions, that's probably two to five systems, not fifty.
Build on what you have. If you have an existing controls framework, internal audit function, or risk register, extend those to cover AI. Don't build a parallel governance structure. The Act doesn't require a separate AI governance team. It requires that AI risks are managed within your existing risk management approach.
Why PE- and VC-backed companies specifically need to care</h2>

Compliance risk gets priced into exits. Full stop. </blockquote>
When a buyer's due diligence team discovers that your finance function has been running AI systems without proper governance, that's not a minor finding. It's a material compliance risk that affects the valuation. The question isn't whether the buyer will find out. With August 2026 as a hard deadline, AI governance will be a standard due diligence question within twelve months. If it isn't already in your data room, it will be asked for.
There are PE and VC exits where compliance gaps in far less scrutinised areas led to price chips or extended warranty provisions. AI governance is going to be high on the diligence agenda precisely because it's new, it's high-profile, and the penalties for non-compliance are significant: up to 35 million euros or 7% of global turnover, whichever is higher.
The flip side is that having a clean AI governance framework actually strengthens your exit story. It demonstrates that the finance function is mature, well-controlled, and forward-looking. It signals that the management team understands regulatory risk and manages it proactively. That's exactly what buyers want to see.
Operating partners are starting to ask about this. Not all of them yet, but the ones with legal and compliance backgrounds are already adding AI governance to their portfolio review checklists. Getting ahead of that question is significantly easier than scrambling to answer it during a compressed exit process.
Three things to do this quarter</h2>
You have five months. That's not a lot, but it's enough to get the essentials in place if you start now and stay focused. Here's what I'd prioritise:
1. Conduct an AI use case inventory. Find out what's actually being used. Every AI tool, every automation, every browser plugin, every ChatGPT conversation that touches financial data. Survey your team. Be explicit that this isn't a witch hunt. You need honesty, not compliance theatre. Ask: what AI tools do you use, what data do you put into them, and what decisions do their outputs inform? The answers will surprise you. They always do.
2. Classify each use case by risk. Take your inventory and map each AI system against the Act's risk categories. High-risk systems need the full governance treatment. Limited-risk systems need transparency measures. Minimal-risk systems need to be documented but don't require heavy controls. For most mid-market finance functions, you'll find one or two genuinely high-risk systems, a handful of limited-risk tools, and a lot of minimal-risk usage that just needs to be visible.
3. Build your governance framework. For high-risk systems: document the purpose, the data inputs, the decision logic (as far as you can), the human oversight mechanisms, and the controls around accuracy and bias. For everything else: establish a simple register, an approval process for new AI tools, and a periodic review cycle. Assign ownership. Make it someone's job to keep this current.
This won't get you to full compliance by August 2, but it will get you to a defensible position. You'll know what AI is in use, you'll have assessed the risks, and you'll have a framework in place. That's a credible compliance posture, and it's miles ahead of most mid-market companies right now.
Governance is not the enemy of speed</h2>
One can say: "If we add governance around AI, we'll slow everything down and lose our competitive advantage."
I understand the instinct. But it's wrong.

Governance doesn't slow you down. Chaos slows you down. The company that deployed five different AI tools across finance with no coordination, no data standards, and no oversight? They're the ones who are slow, because every tool produces slightly different numbers, nobody trusts any of them, and the CFO spends half her time reconciling AI outputs that should agree but don't. </blockquote>
The company with a clear governance framework knows exactly which AI systems are running, what they do, and how to verify their output. When something goes wrong, and it will, they can identify and fix the problem quickly. When a new use case emerges, they can assess it, approve it, and deploy it through a known process rather than having someone download a tool and hope for the best.
Governance is what lets you move fast with confidence. It's the difference between driving fast on a road you know and driving fast in fog. Both are fast. Only one gets you where you're going.
The companies that will get the most value from AI in finance over the next five years won't be the ones that adopted earliest. They'll be the ones that adopted smartly, with proper oversight, clear accountability, and the ability to prove to regulators, auditors, and buyers that they're in control.
That's what the EU AI Act is really asking for. Not perfection. Control. And for any well-run finance function, that shouldn't be a foreign concept. It's what we do.

If the EU AI Act compliance timeline is relevant to your finance function, the AI Readiness Assessment</a> covers governance frameworks, risk classification, and the data and process foundations that compliance depends on — as well as a practical roadmap for getting there before August 2026.

Why 80% of AI Projects in Finance Fail (And What to Fix First)

2026-03-03T00:00:00+00:00

Roughly 80% of AI projects in finance fail to deliver their expected value. That statistic should make every CFO pause. Not because the technology doesn't work. It does. The large language models are impressive. The automation platforms are capable. The dashboards are beautiful.
They fail because the finance function underneath them isn't ready. And most people are looking at the wrong reasons.
The assumption vs the reality</h2>
When an AI project stalls in finance, the default narrative is that the technology wasn't right, or the vendor oversold, or the use case was too ambitious. Sometimes those things are true. But in my experience, after 25 years of building and fixing finance functions, the actual reasons are far more mundane.
Data quality. The AI needs clean, consistent, well-structured data. Most finance functions don't have that. They have data scattered across multiple systems, inconsistent naming conventions, manual workarounds that introduce errors, and reconciliations that exist precisely because the data can't be trusted.
Process immaturity. AI automates processes. If your processes are inconsistent, poorly documented, or dependent on individual knowledge, there's nothing stable to automate. You're asking AI to learn a process that changes depending on who's doing it and what day of the month it is.
Organisational unreadiness. Even when the data is clean and the processes are solid, the organisation needs to be ready to work differently. That means trust in the output, willingness to change workflows, and leadership that understands what AI can and cannot do.
These three factors account for the vast majority of AI failures in finance. And none of them are technology problems.
The perception gap</h2>
There's a fascinating disconnect in how organisations see their own AI readiness. Research consistently shows that around 51% of CFOs claim their organisations have achieved full AI adoption. But when you ask the controllers and finance managers — the people actually doing the work — only about 19% agree.
That's not a small gap. That's a chasm. And it tells you something important: the people at the top think AI is working. The people in the middle know it isn't.

The CFO sees the dashboard. The controller sees the manual workaround that feeds it. </blockquote>
I've seen this play out repeatedly. A PE-backed business invests in an AI-powered forecasting tool. The CFO presents the output at board meetings. What nobody mentions is that the management accountant spends two days each month manually adjusting the inputs because the underlying data isn't structured in a way the tool can consume. The AI is technically running. It's just not doing what anyone thinks it's doing.
What "AI-ready" actually means</h2>
When I assess a finance function's AI readiness, I look at six dimensions. This isn't a theoretical framework. It's what I've found actually determines whether AI will work in practice.
1. Data foundation. Is your data clean, consistent, and accessible? Do you have a single chart of accounts across all entities? Are your naming conventions standardised? Can you extract data from your systems without manual intervention? Most finance functions score poorly here, especially those that have grown through acquisition.
2. Process maturity. Are your core finance processes documented, standardised, and repeatable? Could someone new follow them without guidance from the person who usually does the work? If your month-end close depends on institutional knowledge rather than documented procedures, you're not ready.
3. Controls environment. Do you have proper controls around data input, processing, and output? AI doesn't just need good data — it needs data you can prove is good. That means audit trails, approval workflows, and exception handling that's systematic rather than ad hoc.
4. Systems architecture. Are your systems integrated, or are you running parallel systems with manual bridges between them? AI works best when it can access data through clean APIs and automated feeds, not when it depends on someone exporting a CSV and reformatting it.
5. People and skills. Does your team understand what AI does and doesn't do? Are they willing to work alongside automated processes? Do you have anyone who can evaluate AI output critically, or will people blindly trust whatever the system produces?
6. Governance framework. How will you manage AI-related risks? Who's responsible for the accuracy of AI-generated output? How do you handle the EU AI Act requirements that are already affecting financial services? Most finance functions haven't even started thinking about this.
The Marie Myers principle</h2>
Marie Myers, the former CFO of HP, articulated something I think about constantly: "The first move wasn't to switch on AI, but to redesign the work."
That's the insight most organisations miss. They think AI adoption is about choosing the right tool and implementing it. It's actually about redesigning how work gets done so that AI can be effective. That means standardising processes before you automate them. Cleaning data before you feed it to algorithms. Building controls before you hand decisions to machines.
The organisations that succeed with AI in finance almost always follow this sequence: fix the foundation, then add the intelligence. The ones that fail do it the other way round.
Why this matters for PE and VC specifically</h2>
If you're in a PE- or VC-backed business, AI readiness isn't just an efficiency question. It's a value question.
Compliance risk gets priced into exits. If your finance function can't demonstrate proper controls around AI-generated output, that's a risk factor in due diligence. If you're using AI for forecasting or reporting and you can't explain how the numbers are produced, buyers will discount your projections.
Operating partners are already asking about AI readiness. I'm seeing it in every portfolio review I'm involved with. Not because they want to see flashy AI demos, but because AI readiness is a proxy for finance function maturity. A finance function that's AI-ready is, by definition, one with clean data, standardised processes, proper controls, and capable people. That's what creates value.
The flip side is also true.

A finance function that's deployed AI on top of messy foundations is a liability. It looks modern, but it's fragile. And sophisticated buyers can see through it. </blockquote>
The foundation is the work</h2>
Most of what it takes to be AI-ready is straightforward finance operations. Not technology. Not data science. The unglamorous work of process standardisation, data governance, controls design, and documentation.
Standardise your chart of accounts across all entities. Document your close process so anyone can follow it. Clean up your master data: customer names, cost centres, GL codes. Build proper reconciliation processes with audit trails. Create controls around data input that prevent errors rather than just catching them.
This work isn't exciting. It doesn't make for good conference presentations. But it's what makes AI actually work.
The technology layer, selecting tools, integrating systems, training models, is easier than it's ever been. The tools are mature, the integration options are extensive, and the vendors have largely figured out how to deploy in mid-market finance functions. The technology isn't the hard part any more. The foundation is.
Three questions before you buy any AI tool</h2>
Before you sign that contract or approve that budget, ask yourself:
1. Can I describe the process this will automate in a standard operating procedure? If you can't write down exactly how the work gets done today — step by step, with no "and then Sarah does her thing" gaps — then you don't have a process mature enough to automate. AI can't standardise a process for you. It can only execute one that already exists.
2. If I gave the AI's input data to a new hire, could they produce the right output manually? This tests both data quality and process clarity. If a competent accountant couldn't produce the right answer from the data the AI will receive, the AI won't either. Machines don't have the institutional knowledge your team uses to compensate for bad data.
3. Who will be accountable for the AI's output, and how will they verify it? If the answer is "nobody" or "we'll trust the system," you're not ready. Every AI output in finance needs a human owner who understands what they're looking at and can spot when something's wrong. That requires both skills and governance structures.
If you can't answer all three clearly, you're not ready to buy. You're ready to fix your foundation.
Realistic timeline expectations</h2>
The vendors will tell you 7-12 months to full deployment and ROI. In my experience, for a PE- or VC-backed mid-market business, the realistic timeline is more like 2-4 years for meaningful return.
That breaks down roughly as:

Months 1-6: Foundation work. Data cleanup, process standardisation, controls design, team training. This is where the real value starts. Your finance function gets better before AI even enters the picture.</li>
Months 6-12: Pilot phase. One or two carefully chosen use cases, usually in areas where the data is cleanest and the process most standardised. Accounts payable automation or basic reporting are common starting points.</li>
Months 12-24: Expansion. Apply what you learned in the pilot to additional use cases. Adjust processes that didn't work as expected. Build internal capability to manage AI tools.</li>
Months 24-36: Maturity. AI is embedded in core finance processes. The team trusts and understands the output. You can demonstrate to buyers or auditors exactly how AI-generated numbers are produced.</li> </ul>
That timeline might sound long. But compare it to the alternative: buying a tool tomorrow, spending 12 months trying to make it work with messy data and immature processes, then writing it off as a failed project and starting over. I've seen that cycle repeat three times in a single organisation.
Fix the foundation first</h2>
The technology works. I want to be clear about that. AI in finance is not hype. It's capable of transforming how finance functions operate. Automated reconciliations, predictive analytics, natural language reporting, anomaly detection — these things work when the foundation is right.
But the foundation has to be right first. Clean data. Standard processes. Proper controls. Capable people. Clear governance.
That's the bulk of the work. And it's work that makes your finance function better regardless of whether you ever deploy AI.

A finance function with clean data, standardised processes, and strong controls creates value at exit whether it uses AI or not. </blockquote>
Fix the foundation first. Then the AI works.

If you're trying to figure out whether your finance function is actually ready for AI — or what needs to happen first — the AI Readiness Assessment</a> is a focused 2–3 week engagement designed specifically for this. No vendor bias, no technology agenda. Just a clear view of where you stand and a practical roadmap to get AI-ready.

20 Days to 5: What a 75% Month-End Close Reduction Actually Looks Like

2026-02-24T00:00:00+00:00

When I walked into this business, the month-end close was taking 20 working days. And it closed simply because another one has started. That meant the board pack wasn't landing until almost the end of the following month. The PE sponsor was getting increasingly vocal about it. Their operating partner had started attending monthly finance calls, which is never a good sign.
The finance team was working flat out. Late nights at period end, weekends lost to reconciliations, a controller who hadn't taken a proper holiday in two years because nobody else could do what she did. And despite all that effort, the numbers were still being questioned at every board meeting.
This is the story of how we got that close down to 5 days. Not with a big technology transformation. Not by throwing more bodies at it. By treating the close like the operations problem it actually is.
The mess we inherited</h2>
Let me paint the picture properly, because the starting conditions matter.
The business had grown through acquisition — three bolt-ons in four years. Each acquired entity had kept its own chart of accounts, its own processes, its own way of doing things. There was no standardised close process across the group. Each entity closed at its own pace, and consolidation happened last, manually, in a spreadsheet that only one person understood.
Month-end involved 47 manual journal entries. Forty-seven. Some of them were reversing journals that existed solely to fix other journals. Several reconciliations were being performed on accounts that hadn't moved in over a year — legacy items nobody had thought to question. The bank reconciliation alone took three days because it involved matching transactions across seven bank accounts with no automated feed.
The board pack was a 60-page document that took the FD three days to compile after the numbers were "final." And I use that word loosely, because there were usually at least two restatements before the PE sponsor accepted them.

Everyone was busy. Nobody was effective. </blockquote>
What we fixed first — and it wasn't technology</h2>
The instinct when you see a 20-day close is to look at the systems. Can we automate something? Can we buy a tool? That's usually the wrong starting point.
The first thing we did was map the close anatomy. Every single task that happened between Day 1 and Day 20. Who did it, how long it took, what it depended on, and what depended on it. We used a whiteboard, sticky notes, and three sessions with the finance team where I asked them to walk me through exactly what they did, in what order, and why.
This exercise alone was revelatory. The team had never seen their own close process laid out end to end. They could see for the first time where the bottlenecks were, where tasks were being done sequentially that could run in parallel, and — critically — where tasks were being done that added no value at all.
We found 11 reconciliations that could be eliminated entirely. Accounts that hadn't moved, balances that were immaterial, reconciliations that existed because "we've always done them." We found that the revenue recognition process was waiting for a report from operations that wasn't needed until two weeks later. We found that the intercompany elimination was being done twice — once at entity level and once at group — because of a miscommunication that had become embedded as process.
Building the close calendar</h2>
From the anatomy map, we built a close calendar. Not a list of tasks with deadlines — a proper visual calendar showing every task, its owner, its duration, its dependencies, and the critical path.
The critical path is the key concept here. In any close process, there's a sequence of tasks that determines the minimum possible close time. Everything else is either parallel work or has slack. If you don't know your critical path, you're optimising blindly.
Our critical path ran through: revenue recognition, cost accruals, intercompany, consolidation, management accounts, board pack. Everything else — fixed asset registers, prepayments, payroll postings, VAT — could theoretically run in parallel. But it wasn't, because nobody had ever mapped it that way.
The six things we changed</h2>
1. Shifted pre-close tasks before month end</h3>
This was the single biggest win. We identified every task that could be performed before the month actually closed. Prepayment schedules, depreciation runs, recurring accruals for known amounts, intercompany billing — none of these need to wait for Day 1 of the close. We moved 12 tasks into the final week of the current month, effectively giving us a 3-day head start.
2. Eliminated stale reconciliations</h3>
Those 11 reconciliations I mentioned? Gone. We also simplified several others. The bank reconciliation moved from a three-day manual exercise to a same-day process once we set up automated bank feeds. The total time saved was roughly 4 days of effort across the team.
3. Parallelised the critical path</h3>
Once we had the close calendar, we reorganised who did what and when. Tasks that had been running sequentially were reassigned to run in parallel. This required cross-training — the controller couldn't be the only person who understood consolidation. We paired her with a management accountant for two months until the knowledge was shared.
4. Introduced hard deadlines with escalation</h3>
Previously, tasks were "done when they were done." We introduced specific deadlines for each task on the close calendar, with an escalation protocol. If a task wasn't completed by its deadline, it escalated to me within two hours. Not to punish anyone, but to unblock them. Usually the delay was caused by waiting for information from outside finance, and a quick conversation with the relevant operations manager sorted it.
5. Built a close cockpit</h3>
We created a simple tracking dashboard — nothing fancy, a shared spreadsheet initially — that showed every close task, its status, its owner, and whether it was on track. Updated twice daily during close. This gave everyone visibility and created a healthy sense of accountability. When your task is the only red item on a board that the CFO reviews every morning, you find a way to get it done.
6. Only then did we add automation</h3>
After the process was clean, we automated what made sense. Automated bank feeds. Automated recurring journals. A consolidation template that pulled from the standardised chart of accounts we'd implemented across all entities. We also built automated variance analysis that flagged anything outside expected ranges, which cut the review time significantly.

Automation applied to a broken process just gives you faster broken output. </blockquote>
We fixed the process first.
The results</h2>
Within 90 days of starting:

Close cycle: 20 days to 5 days. The board pack now lands within 10 business days of month end.</li>
Manual journals reduced by 40%. From 47 to 28. Every remaining journal has a documented reason for existing.</li>
Zero restatements in the six months following the change. The numbers are right first time because the process is right.</li>
Team overtime eliminated. No more weekends, no more late nights at period end. The controller took her first two-week holiday in three years.</li>
PE sponsor confidence restored. The operating partner stopped attending monthly finance calls. That's the real measure of success.</li> </ul>
What I'd do differently</h2>
If I did this again — there's one thing I'd change: I'd start the change management work earlier.
The process redesign took about six weeks. The cultural shift took closer to four months. People don't change how they work just because you give them a new calendar. The controller who'd been running the close her own way for eight years needed time to trust that the new process would work. The management accountants who'd never been responsible for meeting a specific deadline needed support, not just a deadline.
I spent too long assuming that a better process would sell itself. It doesn't. You have to bring people along. Explain the why, not just the what. Celebrate early wins publicly. Make it safe to flag problems without feeling like you're admitting failure.

The technical work of close optimisation is relatively straightforward. The human work is where it gets hard. If I had my time again, I'd run a proper change programme alongside the process redesign from Day 1, not bolt it on when I noticed resistance at Week 4. </blockquote>
The takeaway</h2>
The close isn't a finance problem. It's an operations problem. Treat it like one.
Map the process. Find the critical path. Eliminate waste. Parallelise where you can. Set clear ownership and deadlines. Build visibility. Only then automate.
And start the people work on Day 1. Because a 5-day close that nobody believes in is just a target on a slide. A 5-day close that the team owns and runs without you — that's a finance function that creates value.

If your close is still measured in weeks, the Finance Diagnostic</a> is where to start — it maps your specific process and identifies exactly where the time is going. If you're already clear on the problem and need the transformation, the Exit-Ready Finance Transformation</a> programme covers close optimisation as its first phase.

The First 100 Days After Acquisition: Why Finance Integration Fails (And How to Get It Right)

2026-02-17T00:00:00+00:00

I've seen post-acquisition finance integration across Central Europe and through a series of bolt-on acquisitions at a PE-backed consultancy. The pattern is always the same. The deal closes, everyone celebrates, and then someone turns to the finance team and says: "Right, when can we have one set of numbers?"
The honest answer is usually "longer than you think." And the reason is almost never the one people expect.
It's not a systems problem</h2>
When a business acquires another business, the instinct is to treat finance integration as a technology project. Which ERP wins? How do we migrate the data? Can we just put everyone on the same system?
These are valid questions. They're also the wrong starting point.
At another client, we had multiple legacy systems across several countries, each with its own reporting conventions, its own language (literally), and its own way of interpreting group accounting policies. The natural impulse was to start with systems convergence. We resisted that impulse, and it was the best decision we made.
The real problems in finance integration are human. They're about two teams who've never worked together, who have different definitions of the same terms, who do the same tasks in different ways, and who are frightened about their jobs.

Start with a systems migration before you've addressed those issues and you'll automate the dysfunction. </blockquote>
I've seen it happen. It's expensive and demoralising.
The cultural dimension nobody budgets for</h2>
Here's something that never appears in the integration plan: the acquired finance team is terrified.
They don't know if they'll have a job in six months. They don't know if the acquiring company thinks they're competent. They don't know if the way they've been doing things for the last ten years is about to be declared wrong. So they do what any rational person would do. They keep their heads down, protect their processes, and resist change at every opportunity. Not because they're difficult. Because they're human.
Integrating finance teams across Central Europe taught me this lesson the hard way. We had brilliant people in the acquired entities who had deep knowledge of local regulations, customer relationships, and operational nuances. In the first few weeks, we almost lost several of them because the integration felt like an occupation rather than a collaboration.
What changed things was simple. We asked people about their processes before we told them about ours. We identified what the acquired teams did better than us (there's always something) and adopted those practices instead of imposing our own. We made explicit commitments about roles and timelines so people could stop catastrophising and start contributing.
We didn't show the presentations about "the group way." We listened instead. What works well here? What are you proud of? What frustrates you? Those conversations surfaced more useful information than any due diligence report.
The chart of accounts problem</h2>
If you're running a buy-and-build strategy, this will be familiar. Every acquired business arrives with its own chart of accounts. Its own cost centre structure. Its own revenue categories. Its own definition of what constitutes an overhead versus a direct cost.
At the PE backed consultancy, after four acquisitions, we had four different charts of accounts. One entity categorised subcontractor costs as direct costs. Another categorised the same costs as cost of sales. A third split them between two categories depending on the contract type. When we tried to produce consolidated management accounts, the numbers were technically correct at entity level and completely meaningless at group level.
This problem compounds over time. The longer you leave each entity on its own chart of accounts, the harder convergence becomes. People build reports, budgets, and KPIs around their local structure. Changing it feels like changing everything.
The solution is to converge early, but converge intelligently. Don't just impose the acquiring company's chart of accounts. Build a group chart that works for all entities, informed by what you find in each one. If the acquired business has a smarter way of categorising project revenue, use that. The goal isn't to make them like you. It's to build a common language that serves the group.
At the PE backed consultancy, we ultimately built a unified chart of accounts that borrowed elements from three of the four entities. It took about eight weeks of detailed work with the finance leads from each business. It was tedious. It was also the single most important thing we did for the integrity of group reporting.
The first 100 days: a practical sequence</h2>
Based on what I've learned across multiple integrations, here's the sequence that works. The temptation is to do everything at once. Don't.

Integration is sequential because trust is sequential. You can't standardise processes with people who don't trust you yet. </blockquote>
Days 1-14: Listen and stabilise. Don't change anything. Run listening sessions with every member of the acquired finance team. Understand their processes, their pain points, their concerns. Simultaneously, make sure the basics are working: can you produce management accounts, can you pay suppliers, can you meet payroll. If the acquired business was well-run, the answer is yes. Leave it alone for now.
Days 15-30: Map and compare. Document both finance functions side by side. Every process, every report, every reconciliation. Identify where processes differ and, critically, why they differ. Sometimes the acquired team does something differently because they're wrong. Sometimes they do it differently because they're dealing with a complexity you don't have. You need to know which is which.
Days 30-60: Design the target. Define the target operating model for the integrated finance function. Unified chart of accounts. Standardised close process. Common reporting pack. Agreed KPI definitions. Involve people from both sides in designing this. It takes longer than doing it yourself, but the result is something people will actually follow.
Days 60-90: Implement the foundation. Migrate to the unified chart of accounts. Implement the standardised close process. Run the first integrated month-end. It will be messy. That's fine. You're learning.
Days 90-100: Review and adjust. After the first integrated close, do a thorough retrospective. What worked? What didn't? Where are the remaining pain points? Adjust the plan. You're not done at Day 100, but you should have a functioning integrated finance operation that produces reliable group numbers.
The mistakes I see every time</h2>
Waiting too long to start. Every month you delay integration is a month of duplicated effort, inconsistent reporting, and growing divergence between how the two teams work. I've seen businesses wait 12 months before starting finance integration. By that point, the acquired team has settled into their existing processes even more deeply, and the political cost of change has tripled.
Trying to do everything at once. A PE operating partner once asked me to deliver full finance integration, including systems migration, in 60 days. I explained that we could have it fast, or we could have it right, but not both. We agreed on 100 days for process and reporting integration, with systems migration to follow over the next six months. That sequencing worked. The alternative would have been chaos.
Ignoring the human side. This is the most common and most expensive mistake. You can build the perfect target operating model, but if the people who need to operate it feel excluded, unheard, or threatened, it won't work. I've seen finance teams lose their best people within weeks of an acquisition, not because the integration was bad, but because nobody told them they were valued.
Imposing rather than integrating. "We're the acquirer, so we do it our way" is a recipe for passive resistance and talent loss. Integration means finding the best approach from both sides and building something new. Once, some of the most effective processes in the integrated function originated in the acquired entities. If we'd simply imposed the existing corporate way, we'd have lost those improvements and the people who created them.
The real cost of getting it wrong</h2>
Let me be specific about what failure looks like, because it's not abstract.
Extended timelines mean the PE sponsor is flying blind. If you can't produce reliable consolidated numbers for six or nine months after an acquisition, the board is making decisions based on incomplete or inconsistent data. I've seen investment decisions delayed and follow-on acquisitions paused because the finance function couldn't provide a credible view of group performance.
Lost talent is permanent damage. A good management accountant who leaves three months after acquisition takes years of institutional knowledge with them. In one integration I observed (not one I ran), the acquired entity lost its entire finance team within four months. The cost of rebuilding, including temporary contractors, recruitment fees, and lost productivity, was roughly £350K. That wasn't in anybody's integration budget.
Unreliable numbers erode trust.

Once a PE sponsor stops trusting the numbers, every board meeting becomes an interrogation rather than a discussion. </blockquote>
I've seen this dynamic take 18 months to repair. The finance team produces good numbers, but the board questions everything because they got burned once. That's a terrible environment for everyone.
What good looks like</h2>
The integration programme I led delivered a 70% increase in efficiency across the finance function and a 50% reduction in administrative spend. Those aren't theoretical numbers. That's real headcount optimisation, real process elimination, real time saved. It was enough to win Finance Leader of the Year, which was gratifying, but the real measure of success was simpler: we could produce one set of numbers that everyone trusted.
At another client, we went from four separate finance functions producing four separate sets of accounts to a single integrated team producing consolidated group reporting within 10 days of month end. Each acquisition integration got faster because we had a playbook and, just as importantly, because the team had been through it before and knew what to expect.
The common thread in both cases was the same. We started with people. We built trust before we built processes. We built processes before we changed systems. And we involved the acquired teams in designing the solution rather than imposing one.
Finance integration after acquisition is hard. But it's not complicated. The sequence is straightforward: listen, map, design, implement, review. The discipline is in resisting the urge to skip steps, especially the human ones.
If you're about to close a deal, or you closed one recently and the finance integration is stalling, the first question to ask isn't "which system should we use?" It's "have we actually talked to the people who do the work?"
Start there. The rest follows.

If you've recently closed a deal and the finance integration is stalling — or you're about to close and want to get ahead of it — the Post-Acquisition Finance Integration</a> service covers the full first 100 days and beyond. The Finance Diagnostic</a> is also useful immediately post-close to establish a clear baseline across both entities.

5 Finance Red Flags That Cost Sellers 10-15% of Enterprise Value

2026-02-10T00:00:00+00:00

I've sat on both sides of the due diligence table. I've been the interim CFO preparing a business for exit, and I've reviewed finance functions as part of acquisition assessments. The pattern is remarkably consistent: certain finance weaknesses reliably cost sellers 10-15% of enterprise value.

On a £100m exit, that's £10-15m. Not theoretical money. Real money that the seller leaves on the table because their finance function wasn't ready for scrutiny. </blockquote>
The frustrating part is that every one of these red flags is fixable. They just need time, typically 12-18 months. Which means the cost of not preparing early is enormous.
Here are the five red flags I see most often, why they matter, and what to do about each one.
Red flag 1: Close cycle longer than 10 days</h2>
When a buyer's due diligence team asks how long your month-end close takes and the answer is anything north of 10 working days, alarm bells ring. Not because they care about your close process per se, but because of what a slow close signals.
A long close cycle tells buyers that the finance function lacks control over its own data. If it takes you 15 or 20 days to produce a set of management accounts, there are almost certainly manual workarounds, data integrity issues, and process bottlenecks embedded in that timeline. Every one of those is a risk factor.
More practically, a slow close makes the due diligence process itself painful. Buyers need data. They need it quickly, in consistent formats, with clear audit trails. If your team is spending three weeks producing monthly numbers, they're going to struggle to respond to DD information requests at the pace buyers expect. That extends the DD timeline, which increases costs and creates opportunities for the deal to stall.
I worked with a business where the 18-day close was explicitly cited in the buyer's DD report as a material weakness. The buyer used it as leverage to negotiate a £2m price reduction on a £30m deal, arguing that the finance function would need significant investment post-acquisition. The seller had known about the slow close for years. Fixing it would have cost a fraction of what it ultimately cost in lost value.
What good looks like: 5-7 day close cycle. Management accounts produced within 10 business days. No restatements. Board pack delivered on a predictable schedule.
Red flag 2: No consolidated reporting across entities</h2>
This one is almost universal in buy-and-build strategies, and it's a deal-breaker more often than people realise.
When a PE-backed business has made multiple acquisitions but hasn't consolidated the finance function, you end up with separate charts of accounts, different reporting formats, inconsistent KPI definitions, and a consolidation process that runs on spreadsheets and hope. I've seen businesses where the "group accounts" are literally a series of copy-paste operations from entity-level reports into a master spreadsheet, with manual adjustments that only the FD understands.
Buyers see this as a fundamental integration failure. If you've owned these businesses for three years and you still can't produce a single, unified view of group performance, what does that say about your management capability? What does it say about the reliability of the numbers you've been presenting?
For trade buyers, it also signals significant post-acquisition integration cost. They're going to have to do the consolidation work you didn't, and that cost gets priced into the deal. For secondary PE buyers, it raises questions about whether the reported EBITDA is truly comparable across periods, which directly affects the multiple they're willing to pay.
One business I worked with had four entities on three different accounting systems with no unified chart of accounts. The DD process took seven months instead of the expected three because every number had to be verified at entity level. By the time the deal closed, the buyer had negotiated a retention holdback and a price adjustment that together cost the seller roughly 12% of the original valuation.
What good looks like: Single chart of accounts across all entities. Automated consolidation. One set of group management accounts that any qualified accountant could understand. Intercompany transactions eliminated cleanly with an audit trail.
Red flag 3: Key-person dependency</h2>
This is the silent killer. Every finance function has its hero — the controller who's been there since the first acquisition, who knows where everything is, who can produce any number on request because it all lives in her head.

That hero is a catastrophic risk, and buyers know it. </blockquote>
Key-person dependency in finance means that institutional knowledge isn't documented, processes aren't standardised, and the business is one resignation away from chaos. In DD, buyers assess this through interviews, process documentation reviews, and sometimes by asking team members to explain processes that the key person usually handles. The results are often telling.
The risk gets priced into the deal in two ways. First, the buyer may require a retention package for the key person, funded by the seller, typically through an escrow or holdback. Second, and more expensively, the buyer may discount the valuation to reflect the cost of building proper documentation, cross-training, and process resilience that should already exist.
I once saw a DD report that explicitly stated: "The finance function is operationally dependent on a single individual. Should this individual depart within 12 months of completion, the buyer estimates a cost of £400K-£600K to rebuild institutional knowledge and stabilise operations." That estimate was deducted from the purchase price.
What good looks like: Documented processes that anyone qualified could follow. At least two people capable of performing every critical function. A team structure that survives any single departure without service disruption. Clear handover documentation maintained as a living asset, not something created in a panic.
Red flag 4: Manual reconciliations with no audit trail</h2>
Reconciliations are where data quality lives or dies. And in DD, they're one of the first things the buyer's accountants examine.
When reconciliations are manual — spreadsheet-based, performed by copying data from one system, pasting it into another, and manually matching transactions — two problems emerge. First, the error rate is inherently higher. Humans make mistakes, especially when they're working under time pressure during a close. Second, there's no systematic audit trail. The reconciliation exists as a spreadsheet on someone's laptop, with no version control, no approval workflow, and no way to demonstrate to a buyer that the process was consistently followed.
This matters because reconciliation quality is a direct proxy for data reliability. If a buyer can't trust the reconciliations, they can't trust the numbers. And if they can't trust the numbers, they either walk away or they discount.
I've seen DD teams spend days working through manual bank reconciliations, finding unexplained differences dating back months. Differences that the finance team had been "parking" in suspense accounts with the intention of investigating later. Later never came. Those unexplained differences became adjustments in the DD completion accounts, always in the buyer's favour.
What good looks like: Reconciliations performed in a system with an audit trail — ideally automated where possible. Exceptions flagged and resolved within the period. Zero unexplained balances. Reconciliation sign-off by someone other than the preparer. A history that shows consistent execution, not just a clean snapshot at the DD date.
Red flag 5: No forward-looking financial model</h2>
What surprises people is that due diligence isn't just about proving the past. It's about demonstrating the future. If you can't show buyers a credible, well-structured financial model that tells them where the business is going, they'll assume it's going nowhere.
I don't mean a budget. I mean a proper three-statement model — P&L, balance sheet, and cash flow — with clearly stated assumptions, scenario analysis, and sensitivity testing. A model that can answer questions like: what happens to margins if raw material costs increase 15%? What's the cash impact of that acquisition pipeline? How does the EBITDA bridge from current performance to the investment case?
Without this, the buyer builds their own model. And their model will always be more conservative than yours, because they don't have the context you have, and they're incentivised to see downside, not upside.
One seller I worked with had been running the business on a single-year budget with no balance sheet or cash flow projections. When the buyer asked for a three-year model during DD, the finance team scrambled to build one in two weeks. It was inconsistent with the historical data, the assumptions weren't documented, and it fell apart under questioning. The buyer concluded that management didn't have a credible view of the future, which directly affected the multiple they were willing to pay.
What good looks like: A rolling three-year model, updated quarterly. Three statements that balance. Clearly documented and defensible assumptions. Scenario analysis that shows upside, base case, and downside. A model that the CFO can walk through line by line under questioning without hesitation.
The due-diligence ready checklist</h2>
If you're preparing for exit, here's what "ready" actually looks like. This isn't exhaustive, but it covers the items that most commonly cause problems:
Financial close and reporting:

Month-end close completed within 5-7 working days</li>
Management accounts produced within 10 business days</li>
Consolidated reporting across all entities with a unified chart of accounts</li>
Zero unexplained restatements in the trailing 12 months</li>
Board pack delivered on a consistent, predictable schedule</li> </ul>
Data integrity and controls:

All material reconciliations performed systematically with an audit trail</li>
No unexplained balances in suspense or intercompany accounts</li>
Automated bank feeds and reconciliation where possible</li>
Segregation of duties in place for all material processes</li>
Clear approval workflows for journals, payments, and accruals</li> </ul>
People and process:

All critical finance processes documented in standard operating procedures</li>
No single point of failure — at least two people trained on every critical process</li>
Finance team structure appropriate for the business's complexity</li>
Clear roles and responsibilities documented and understood</li> </ul>
Forward-looking capability:

Three-statement financial model updated quarterly</li>
Assumptions documented and defensible</li>
Scenario analysis available (base, upside, downside)</li>
Cash flow forecasting with 13-week rolling visibility</li>
EBITDA bridge from current to projected clearly articulated</li> </ul>
Due diligence logistics:

Virtual data room structured and maintained (not built in a rush)</li>
Historical financial data easily extractable in consistent formats</li>
Tax, statutory, and management accounts reconciled</li>
Key contracts, leases, and commitments documented and accessible</li> </ul>
The timeline: what to fix and when</h2>
If you're 12-18 months from a potential exit, here's the order I'd address these in:
Months 1-3: Foundation. Standardise the chart of accounts across all entities. Document all critical processes. Start the data cleanup — master data, intercompany balances, suspense accounts. This is the unglamorous work that everything else depends on.
Months 3-6: Close and reporting. Optimise the month-end close. Build consolidated reporting. Eliminate manual workarounds. Get the close cycle below 7 days and the board pack below 10 business days.
Months 6-9: Controls and resilience. Implement proper controls and audit trails. Cross-train the team to eliminate key-person dependencies. Build the reconciliation framework.
Months 9-12: Forward-looking. Build or rebuild the three-statement model. Establish rolling forecasting. Develop the EBITDA bridge and management presentation narrative.
Months 12-18: Polish and prepare. Structure the data room. Run a mock DD with your advisers. Stress-test the finance function's ability to respond to information requests at pace. Fix whatever breaks.
This timeline assumes you're starting from a position of reasonable maturity. If you're starting from a 20-day close with no consolidated reporting, add 6-12 months. The earlier you start, the more value you protect.
The bottom line</h2>
The best time to prepare for due diligence is the day after acquisition. The second best time is today.
Every one of these red flags is fixable. None of them require exotic technology or enormous budgets. They require focus, discipline, and time. The finance functions that create value at exit aren't the ones with the fanciest systems. They're the ones where the basics are bulletproof.

Look at your finance function through a buyer's eyes. If what you see makes you nervous, it will make them nervous too. And nervous buyers pay less. </blockquote>
Fix it now. The return on that investment will be the best your portfolio has ever seen.

The Finance Diagnostic</a> will tell you exactly where your finance function stands against each of these red flags — with enough time to fix them before they cost you at exit. If the assessment confirms you need a full programme, the Exit-Ready Finance Transformation</a> is designed specifically for this.

What Your PE Sponsor Actually Wants From the Board Pack (And What They're Too Polite to Say)

2026-01-27T00:00:00+00:00

I've presented board packs to PE sponsors dozens of times. Among others at Quasar Group over a decade of fractional CFO work. In interim roles where I was parachuted in specifically because the board had lost confidence in the numbers.
Here's what I've learned: most finance teams fundamentally misunderstand what the board pack is for.
They think it's a report. A comprehensive, thorough, look-at-all-the-work-we-did report. So they produce 60 pages of tables and charts and commentary that took three days to compile, land it three weeks after month end, and wonder why the PE partner flicks to page 40 and starts asking questions the pack doesn't answer.
The board pack isn't a report. It's a conversation tool. And if you understand that distinction, everything about how you build it changes.
What PE sponsors actually read first</h2>
It's not the P&L. I know that's where most finance teams start, because it's the most familiar ground. Revenue, gross margin, overheads, EBITDA. The comfort zone.
PE sponsors flip straight to cash. Every time.
This makes perfect sense when you think about it from their perspective. They've leveraged the business. There's debt to service. They need to know whether the cash is there, whether the forecast is reliable, and whether there are any surprises coming. A business can report a decent EBITDA and still be haemorrhaging cash through working capital, capex overruns, or one-off items that somehow recur every quarter.
After cash, they look at the bridge. EBITDA actuals vs budget, broken down into its component variances. Not because they love variance analysis, but because the bridge tells them whether management understands the drivers. Can the CFO explain why EBITDA is £200k below plan and attribute it to specific, identifiable causes? Or is there a vague line called "timing differences" that accounts for half the gap?
Then they look forward. The forecast, the rolling 13-week cash flow, the pipeline. They already know what happened last month. They were there. What they need from you is a credible view of what happens next.
The P&L? They'll glance at it. They might not read it at all if the bridge and cash position tell a clear story.
The gap between reporting and storytelling</h2>

Numbers without narrative are just noise. </blockquote>
I've seen board packs where every single line of the P&L had a variance commentary, and not one of those commentaries said anything useful. "Revenue is £50k below budget due to lower than expected sales." That's not insight. That's a tautology.
What PE sponsors want is the story. What happened, why it happened, what we're doing about it, and what it means for the rest of the year. In that order.
At one of the clients, when I inherited the board pack, it was a data dump. Beautifully formatted, meticulously reconciled, completely useless as a decision-making tool. The PE sponsor's operating partner told me, quite directly, that he stopped reading it after page 5 and just saved his questions for the board meeting. That's a failure of the pack, not the reader.
We restructured the entire thing around narrative. The first two pages became an executive summary: here's what happened this month, here's what matters, here's what we need to discuss. No tables. Just words. Clear, direct, written by someone who understood the business, not auto-generated from a template.
The operating partner started reading the whole pack. Board meetings got shorter because the pre-read was actually doing its job. That's what good looks like.
The seven deadly sins of board packs</h2>
I've seen enough bad board packs to compile a definitive list. Most finance teams are committing at least three of these at any given time.
Too long. If your board pack is over 30 pages, nobody is reading it cover to cover. I've seen packs of 80+ pages. That's not thorough. That's a sign you can't distinguish signal from noise. A good board pack for a mid-market PE-backed business should be 15-25 pages. Everything else goes in appendices that exist for reference, not for reading.
Too late. If the pack lands more than 10 business days after month end, it's stale. The PE sponsor has already formed their own view from the flash numbers, the weekly KPIs, and conversations with the CEO. Your pack is now either confirming what they already know (pointless) or contradicting it (alarming). At one of my clients, the board pack was consistently arriving close to three weeks after month end. We got it to within 10 business days. That required fixing the close process first, but the board pack timeline was the forcing function.
Too backward-looking. A pack that's 90% historical and 10% forward-looking has the ratio backwards. The past is useful context. The future is where decisions get made. At minimum, your forecast should get as much space and rigour as your actuals.
No narrative. Pages of numbers with no interpretation. If the board has to do its own analysis during the meeting, you've failed. Your job is to do the analysis, draw the conclusions, and present them clearly enough that the board can challenge your thinking, not do your thinking for you.
Inconsistent format. If the board pack looks different every month, confidence erodes. PE sponsors are pattern-matchers. They want to open the pack and know exactly where to find the cash position, the EBITDA bridge, the forecast. A consistent structure builds trust over time because it demonstrates process discipline.
Buried bad news. I'll come back to this one. It deserves its own section.
No actions or decisions. The board pack should make clear what the board is being asked to decide, approve, or note. A pack with no explicit asks is a pack that generates vague discussion instead of clear outcomes.
What a good board pack actually looks like</h2>
After years of iteration across multiple PE-backed businesses, here's the structure I use. It's not the only way, but it works consistently.
Page 1-2: Executive summary. Written in prose, not bullet points. What happened this month, what the key issues are, what's changed in the forecast, what decisions the board needs to make. This is the most important part of the pack and should be written last, by the CFO personally. Not delegated. Not templated.
Page 3-4: EBITDA bridge. Actual vs budget, actual vs prior year. Broken into meaningful categories with clear, specific commentary on every material variance. "Commercial revenue £120k below plan due to delayed completion of Project X, now expected in Month 8" is useful. "Revenue below budget due to timing" is not.
Page 5-7: Cash. Actual cash position, net debt, covenant headroom, and the rolling 13-week cash flow forecast. Include a cash bridge showing EBITDA to free cash flow conversion. PE sponsors care deeply about cash conversion, and the bridge makes it visible.
Page 8-10: KPIs. The 8-12 metrics that actually drive value in this specific business, with trend lines and RAG status. Not 40 KPIs. Not every metric you can measure. The ones that matter, consistently tracked, with commentary on anything off-trend.
Page 11-13: Forecast and outlook. Updated full-year forecast with key assumptions clearly stated. Risks and opportunities quantified. Scenario analysis if the range of outcomes is wide.
Page 14-15: Strategic items. Anything requiring board input or decision. M&A pipeline, capital allocation, major contracts, people issues. Each with a clear recommendation and an explicit ask.
Appendices: Everything else. Detailed P&L, balance sheet, aged debtors, project-level detail. There for reference if someone wants to drill down. Not there to be read page by page.
Total: 15-20 pages of core content plus appendices. Delivered within 10 business days. Consistent format every month.
Why cash matters more than EBITDA to your PE sponsor</h2>
This deserves emphasis because I still meet finance teams who treat the cash flow forecast as an afterthought. Something bolted onto the back of the pack, produced hurriedly from an indirect method that nobody fully trusts.
PE sponsors live and die by cash. They have fund reporting obligations. They have debt covenants to monitor. They have portfolio cash management to optimise. They need to know, with reasonable confidence, what the cash position will look like 13 weeks from now. Not a vague "we'll be fine." A specific number, built from specific assumptions, that they can stress-test.
A good 13-week cash flow forecast is built bottom-up. Receipts based on the actual debtor book, with collection assumptions by customer or category. Payments based on the actual creditor book and committed expenditure. Known one-offs explicitly called out. Debt service on its actual schedule. Tax payments on their actual due dates.
When I build these, I include a sensitivity table: what happens to the cash position if collections slip by 5 days? What if that big contract payment is delayed? What if we need to pull forward a capex item? PE sponsors don't expect you to predict the future perfectly. They expect you to show them you've thought about what could go wrong and quantified it.
The 13-week forecast is also one of the most powerful early warning tools you have. If you're updating it properly every week, you'll see cash pressure coming months before it arrives. That's time to act, not react.
How to present bad news</h2>
There is always bad news. Always.

If your board pack contains nothing but good news, the PE sponsor doesn't think you're doing well. They think you're hiding something. </blockquote>
I've watched CFOs try to bury bad news on page 47 of a 50-page pack. I've watched them present a variance as "broadly in line with budget" when it was 8% below. I've watched them attribute a miss to "timing" for three consecutive months, at which point it stops being timing and starts being a trend.
None of this works. PE sponsors have seen every trick. They were doing financial analysis before most of us had our professional qualifications. Trying to obscure bad news destroys the one thing you need above all else: trust.
Here's how I present bad news. It goes on page 1, in the executive summary. Plainly stated, with context. "EBITDA is £180k below plan this month, driven by [specific cause]. Here's why it happened, here's what we're doing about it, and here's the impact on the full-year forecast." Three sentences. No spin.
Then in the relevant section of the pack, the full detail. The root cause analysis. The corrective action, with timelines and owners. The updated forecast reflecting the revised position.
PE sponsors don't panic when they see bad news presented this way. They've invested in dozens of businesses. They know things go wrong. What makes them panic is the sense that the management team either doesn't know there's a problem or is hoping nobody will notice. Both are worse than the problem itself.
I learned this early in my career and it has never let me down. The faster you surface a problem, the more credible you are when you surface the solution.
The board pack as a trust-building tool</h2>
Everything I've described comes down to one thing: trust.
The board pack is the primary mechanism through which a PE sponsor forms their view of the finance function, and by extension, of management's grip on the business. A pack that's timely, accurate, well-structured, and honest builds trust month after month. A pack that's late, confusing, or evasive erodes it.
Trust compounds. When you've delivered 12 consecutive months of clean, on-time board packs with honest commentary and reliable forecasts, you've earned something valuable. The PE sponsor starts giving management the benefit of the doubt. Board meetings become more strategic and less forensic. When you need something (more capex, a revised covenant, an add-on acquisition), the answer is more likely to be yes because the board has confidence in the numbers behind the ask.
The opposite also compounds. Miss your forecast three months running with vague explanations, and suddenly every number gets challenged. The operating partner starts requesting weekly calls. The board meeting turns into an interrogation. The management team spends more time defending the past than planning the future.
I've seen both dynamics play out.

The difference between the two is rarely the quality of the business. It's the quality of the reporting. </blockquote>
Your board pack is not a compliance exercise. It's not something the finance team produces because they have to. It's the most important communication tool you have with your most important stakeholder. Treat it that way, and it will pay for itself many times over.

Fixing the board pack is usually part of a broader reporting and governance overhaul. The Exit-Ready Finance Transformation</a> covers this end-to-end — close cycle, reporting framework, board pack design, and the governance structures PE sponsors expect. If you're not sure where your reporting stands, the Finance Diagnostic</a> gives you the honest picture.

Building a Finance Function From Nothing: A Practical Guide for Founders Who'd Rather Not Think About Finance

2026-01-13T00:00:00+00:00

I once took on a client — a high-growth tech startup called GeoVS — that had no finance function at all. No management accounts. No chart of accounts. No controls. No cash flow forecasting. The founder, a brilliant technologist, was running the company's finances from a combination of his bank app, a spreadsheet he'd started two years earlier and stopped updating, and memory.
He wasn't negligent. He was busy building a product, winning customers, and trying to hire developers in a market where everyone was trying to hire developers. Finance was the thing he'd get to next quarter. It had been "next quarter" for about six quarters.
This is more common than people think. And I say this without judgement, because the instinct to focus on product and customers is exactly right.

The problem is that "no finance function" doesn't stay harmless. It compounds. </blockquote>
And by the time it becomes a crisis — a failed funding round, a cash shortfall nobody saw coming, an HMRC inquiry — the cost of fixing it is dramatically higher than the cost of building it properly in the first place.
I worked with GeoVS on a fractional basis for several years. We built the entire finance function from scratch, secured over £2M in VC and grant funding, achieved zero audit qualifications across the whole period, and ultimately I led the company through its sale — due diligence, data room, negotiations, completion. The founder later told me: "As a first-time founder I didn't know what I didn't know about finance, investments, grants, VCs, reporting and taxes. I'd have been completely lost without Anna."
This article is everything I learned from that engagement and others like it. The sequence. The priorities. The things that actually matter versus the things that can wait.
Why founders avoid finance</h2>
Let's be honest about this. Most founders started their companies because they're good at something — technology, design, sales, a specific domain. Nobody started a company because they were excited about month-end reconciliations.
Finance feels like overhead. It feels like bureaucracy. It feels like the opposite of the fast, scrappy, build-things-and-ship-them culture that makes startups work. And in the very early days, when it's two people and a credit card, that instinct is mostly correct. You don't need a chart of accounts when you have twelve transactions a month.
But companies grow. And finance complexity doesn't scale linearly — it scales exponentially. You hire people, and suddenly there's payroll, pensions, employment taxes. You win a grant, and now there's reporting obligations and eligible expenditure tracking. You raise VC funding, and investors want monthly reporting packs. You start selling internationally, and there's multi-currency accounting and VAT registration thresholds.
Each of these individually is manageable. Together, without a system to handle them, they become a tangle that eats founder time, creates risk, and — most damagingly — leads to decisions made on bad information.
What "no finance function" actually looks like</h2>
I've seen enough of these to describe the pattern. It's remarkably consistent across companies and sectors.
The bank account is the accounting system. Cash in the bank equals "we're fine." Cash getting low equals "we need to invoice someone." There's no distinction between revenue and cash, no understanding of accruals, no visibility into what's been committed but not yet paid.
The bookkeeping is either months behind or has been done by someone whose qualifications extend to "being good with numbers." No offence to those people — they're often incredibly capable in other areas. But bookkeeping done badly is worse than bookkeeping not done at all, because it creates a false sense of having financial information when what you actually have is unreliable data.
VAT returns are filed, usually late, by an external accountant who has minimal context on the business. Year-end accounts are prepared reactively, often with surprises. Nobody knows the company's burn rate with any precision. Nobody knows what the cash position will be in three months. The founder is making investment decisions based on gut feel, which works until it doesn't.
At GeoVS, the first thing I did was a full financial health check. It took about a week. The founder's response was something along the lines of: "I didn't know it was that bad." It usually isn't catastrophic — it's just that nobody has been watching, and entropy does its thing.
The minimum viable finance function</h2>
You don't need to go from nothing to a fully staffed finance department overnight. You need the financial equivalent of an MVP: the minimum set of things that give you control, visibility, and credibility.
Here's what that looks like:
A proper set of books. Current, accurate, reconciled. This is non-negotiable. If your books aren't up to date, everything else — reporting, forecasting, funding applications — is built on sand.
A chart of accounts designed for your business. Not the default one that came with your accounting software. One that reflects your revenue streams, your cost structure, and the way you need to see your numbers. Getting this right early saves enormous pain later. I've seen companies three years in still using a generic chart of accounts that makes it impossible to understand gross margin by product line.
Monthly management accounts. Not annual. Not quarterly. Monthly. A profit and loss, a balance sheet, and a cash flow statement, produced within 10-15 business days of month end. This is how you see problems before they become crises.
A cash flow forecast. At minimum, 13 weeks rolling. This is the single most important financial tool for any early-stage company.

More startups die of cash starvation than of anything else. You need to see what's coming. </blockquote>
Basic controls. Approval workflows for payments. Segregation between who raises invoices and who approves them. A process for expense claims. These aren't bureaucracy — they're how you prevent the mistakes and fraud that sink small companies.
That's it. That's your MVP. Everything else — investor reporting, budgeting, scenario modelling — builds on top of these foundations.
The sequence: what to build and when</h2>
Order matters here. I've done this enough times to know that building things in the wrong sequence creates rework and frustration. Here's the order I follow.
Step 1: Clean up the books and bank accounts. Get the bookkeeping current. Reconcile every bank account. Fix whatever the previous bookkeeper got wrong. Set up proper bank feeds into your accounting software. This is unglamorous, sometimes tedious work, and it's the foundation everything else sits on. At GeoVS, this took about three weeks of concentrated effort. There were two years of transactions to work through.
Step 2: Design the chart of accounts. Build a chart of accounts that reflects how the business actually operates. Revenue broken down by meaningful categories. Cost of sales separated from overheads. R&D expenditure tracked in a way that supports potential tax claims. This requires understanding the business, not just the accounting — which is why it needs to involve someone who asks questions about the commercial model, not just someone who knows debits and credits.
Step 3: Monthly reporting. With clean books and a sensible chart of accounts, you can now produce meaningful management accounts. Start simple: P&L, balance sheet, cash flow, and a one-page commentary that explains what happened and why. The commentary matters. Numbers without narrative are just numbers. The founder needs to understand what the numbers mean for the business.
Step 4: Controls. Now that you have a reporting rhythm, put controls around it. Payment approval processes. Journal approval. Bank reconciliation sign-off. Expense policies. These don't need to be heavy. They need to exist. A two-person startup needs different controls than a fifty-person company, but both need some.
Step 5: Cash flow management. Build a proper cash flow forecast. At GeoVS, this became the most-used financial tool in the business. The founder went from checking his bank app nervously to knowing, with reasonable confidence, what the cash position would be 13 weeks out. That changes how you make decisions. You hire with confidence instead of anxiety. You invest in growth knowing you can afford it.
Step 6: Investor and grant reporting. If you're funded or applying for funding, you need reporting that meets investor and grant body expectations. This isn't the same as management reporting — it's a specific format, with specific KPIs, delivered on a specific schedule. But if you've done steps 1-5 properly, this is just repackaging information you already have. If you haven't done steps 1-5, this is where the pain becomes acute, because you're trying to produce credible investor reports from unreliable data.
The funding angle</h2>
This is where it gets commercially important. VCs, angel investors, and grant bodies all need to see financial rigour. Not because they enjoy reading spreadsheets (some do, but that's another matter), but because financial discipline is a proxy for management quality.
When I was helping GeoVS secure VC funding, the quality of our financial reporting was explicitly cited as a positive factor. The investors could see that the numbers were reliable, the forecasts were grounded in reality, and there was someone credible overseeing the finances. That's not a nice-to-have. That's a prerequisite.
Grant funding is even more demanding. Innovate UK, Horizon Europe, and similar bodies require detailed financial reporting against eligible expenditure categories. If your finance function can't track costs at that level of granularity, you either can't apply for the grant or you can't claim the money you're entitled to. I've seen companies leave tens of thousands of pounds on the table because they couldn't evidence their expenditure to the required standard.
At GeoVS, we secured over £2M across VC rounds and grants. That wasn't because the product wasn't good enough to win on its own merits — it was. But good product with poor financial governance doesn't get funded. Investors have been burned too many times.
Hire, outsource, or go fractional</h2>
This is the question every founder asks, and the answer depends on where you are.
Pre-revenue to about £1m revenue: You don't need a full-time finance person. You need a good bookkeeper (outsourced is fine, 1-2 days a week) and a fractional CFO who can set up the structure, produce the reporting, and provide strategic input. This is the model I used at GeoVS for the first couple of years. Total cost: significantly less than a full-time hire, with significantly more capability.
£1m to about £5m revenue: You probably need a part-time or full-time bookkeeper/management accountant in-house, plus a fractional CFO. The volume of transactions and the complexity of the business now justify someone being closer to the day-to-day. But you still don't need a full-time CFO, and you probably can't afford one at the level of experience you actually need.
£5m and above: You're approaching the point where a full-time finance hire at a senior level makes sense. But even here, a fractional CFO can bridge the gap while you recruit, or can provide specific expertise — funding rounds, exit preparation, system implementations — that a permanent hire may not have.

The mistake I see most often is founders hiring too junior too early. </blockquote>
They bring in a bookkeeper and expect strategic finance. Or they hire a finance manager and expect CFO-level insight. The result is a finance function that processes transactions but doesn't inform decisions. Getting the seniority right matters more than getting the headcount right.
What good looks like at each stage</h2>
Seed stage: Books are current and reconciled. Monthly P&L produced. Cash flow forecast maintained. Founder can tell you the burn rate and runway without checking anything. Grant and investor reporting delivered on time.
Series A / post-first-institutional-funding: Full monthly management accounts with commentary. 13-week rolling cash flow forecast. Budget in place with variance analysis. Board reporting pack that investors actually read. Controls appropriate to the size of the team. Clean audit or independent review.
Growth stage / pre-exit: Everything above, plus: scenario modelling. Departmental or project-level P&L. Working capital management. A finance function that can withstand due diligence scrutiny. At GeoVS, by the time we reached the company sale, the finance function was robust enough to go through full buyer due diligence with zero audit qualifications. That doesn't happen by accident. It happens because you built it right from the start.
The goal</h2>
The goal of a finance function in a startup is not to create work for accountants. It's to give the founder the information and confidence to make good decisions, and to create the financial credibility that unlocks funding and, eventually, a successful exit.
Done properly, the founder shouldn't have to think about finance daily. They should receive a monthly pack that tells them what they need to know. They should have a cash flow forecast that lets them sleep at night. They should have someone they trust handling the detail so they can focus on building the business.
At GeoVS, that's exactly what we achieved. The founder focused on product, customers, and growth. I handled the finance. When it came time to sell the company, the finance function was an asset, not a liability. The due diligence was clean. The data room was ready. The deal completed.
That's what good looks like. And it starts with getting the basics right, in the right order, as early as you can.

If you're building from scratch and want an honest assessment of where you are, the Finance Diagnostic</a> is the right starting point — it takes 2–3 weeks and gives you a clear, prioritised picture of what to fix first. For more on the person who'd be doing this work, the About page</a> covers the background and credentials in full.

Anna Bergman — Interim CFO for PE and VC-Backed Companies

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