# Patrick DePeters

Patrick DePeters advises companies on modernizing finance operations, including using AI and modern systems to improve forecasting, reporting, profitability, and business value.

- Profile: https://b2binfluencer.marketing/creators/patrick-depeters/
- LinkedIn: https://www.linkedin.com/in/patrickdepeters
- LinkedIn followers: 3,167
- Topics: artificial-intelligence, ai-in-finance
- Based on 12 dated posts, latest 2026-10-07

## Summary

Most useful for making finance modernization and AI legible in business terms.

- Best for: PE-backed services firms seeking fractional CFO support for finance modernization and exit readiness.
- Not for: High-reach future-of-work campaigns for HR and operations buyers with guaranteed impressions.
- Check first: Confirm whether they offer paid sponsorship, pricing, inquiry route, and audience analytics.

### Takeaways

- Design the finance operating model before choosing AI tools.
- Value finance automation in enterprise value, not hours saved.
- Capital, profitability, and AI are one system, not three projects.

## Main ideas

- **Design the finance operating model before choosing AI tools**: DePeters argues that founders asking how to think about AI in finance are asking the wrong question: it points them to a tool, a vendor, or a roadmap deck rather than the business. The question they prefer is what the operating model should look like 18 months out to produce the cash flow, clarity, and optionality a board or buyer would want. Only after writing those pieces down do they ask where AI is the fastest route to each one. The answer, they observe, is sometimes two or three specific places and sometimes nowhere yet.
- **Value finance automation in enterprise value, not hours saved**: DePeters frames finance modernization as a valuation exercise. Moving repetitive work from people to systems and AI workflows creates recurring savings, and a recurring saving is worth a multiple. They describe keeping the strongest performers as the human in the loop rather than removing the function, and see the multiple—not the time saved—as the reason to do the work.
- **Capital, profitability, and AI are one system, not three projects**: DePeters argues that when a CEO asks for a working capital facility, a path to profitability, and an AI roadmap in the same meeting, those are not three separate mandates. In this account, the pillars feed each other: lenders price what they can see, so instrumented forecasting and clean data improve borrowing terms; profitability comes from applying AI where overhead sits; and more profit reduces reliance on outside capital, making growth less fragile. Their summary: one pillar is a project, while three working together make an operating model.
- **Sponsors want defensible numbers, not transformation language**: DePeters argues that private-equity sponsors do not want AI experiments; they want EBITDA impact, faster diligence, and a cleaner equity story. The vocabulary they report from buyers is narrow and verifiable: prove the forecast holds, prove scenarios are defensible, and prove the audit trail of decisions can be checked in an afternoon. An AI strategy that still sounds like a TED talk has, in this framing, been priced at zero.
- **Finance goes first with AI, and other functions follow the pattern**: DePeters tells people in legal, marketing, HR, and operations to watch what is happening to CFOs as a preview of their own function. The reasoning is structural: finance work is rules-based, data-rich, repeatable, and produces verifiable outputs with a return on investment a board can measure, which is why the capital went there first. They expect patterns proven in finance—forecasting copilots, document abstraction, instrumented dashboards, and AI-drafted narratives with human review—to land in other functions within a year or two.

## Work to read

- ["Stop thinking about AI": design the operating model first](https://www.linkedin.com/posts/patrickdepeters_cfo-fractionalcfo-financialintelligence-activity-7469529126278963200-5oIm)
- [One finance department rebuilt: $400K of overhead, $2.4M of enterprise value](https://www.linkedin.com/posts/patrickdepeters_cfo-financialintelligence-activity-7468441967614521344-jfV7)
- [Three requests, one system: capital, profitability, and AI](https://www.linkedin.com/posts/patrickdepeters_cfo-financialintelligence-activity-7469077653719810048-LV3z)
- [What private-equity sponsors actually ask for](https://www.linkedin.com/posts/patrickdepeters_privateequity-cfo-fractionalcfo-activity-7469440039094124544-Nm25)
- [Why CFOs are the first function to absorb AI](https://www.linkedin.com/posts/patrickdepeters_cfo-financialintelligence-activity-7469166739424067584-l3aK)

## Partnerships

Nothing in the five posts available for this page describes a sponsorship, paid partnership, or brand-collaboration offering, or points to a place where a brand can inquire. The posts are standalone commentary on finance operations: modernising a finance department with AI and modern systems, the cash-flow case for rebuilding finance inside a PE-backed or growth business, and what private-equity sponsors say they want from portfolio finance teams. They carry no sponsor tags, partner disclosures, rate or format information, or booking contact. A buyer considering Patrick DePeters for a campaign should ask directly whether they take on paid work and, if so, what the format, delivery and cost would be.

## Alternatives

- Deva I J.: Deva is the better choice when the buyer's problem sits on the plant floor or in the field, not in the finance function, or when the real blocker is adoption across a portfolio rather than forecasting and close discipline. Two newsletter issues make the case. One covers physical AI — robotics, automation, and AI-enabled equipment moving into factories, warehouses, inspection lines, equipment yards, and field operations — and asks what a sponsor should settle before deployment: what the system is allowed to do, where human judgment enters, who can override it, what happens when the system is uncertain, and who is accountable when it acts. It ends with a readiness scorecard for assessing one robotics or automation use case in a value-creation plan. The other argues that AI plus people plus execution equals realized value, and offers a 30-day toolkit to survey AI usage, remove the top three friction points, and track adoption. For the PE-backed services brief, Deva is the stronger pick when the portfolio company's EBITDA case rests on operational technology or getting operating teams to use tools that already exist, and the audience is COOs, CIOs, CTOs, and operating partners rather than the CFO's office.
- Anooshay Arif: Arif is the better choice when the finance problem sits lower in the stack — bank reconciliations, accounts payable and receivable, variance commentary, and the reporting package — or when a subscription business has numbers nobody trusts. One post describes reversing the common split in which finance teams spend 80% of their time producing numbers and 20% explaining them: AI flags reconciliation anomalies while a human reviews and approves each one; pattern matching handles the obvious 70% of AP/AR so the team can focus on the messy 30% that needs judgment; AI drafts variance commentary for someone who knows the client's business to refine; and a person writes the reporting-package narrative. The other post makes a first-90-days case for CFOs joining SaaS companies: the root problem is rarely the numbers but that nobody trusts them. The board and sales carry different versions of ARR, revenue recognition lives in a spreadsheet, and clean dashboards that do not match expectations get ignored. Arif's answer is to sit with sales, product, and go-to-market before building anything. For the same PE-backed services brief, Arif is the stronger pick when metric trust and cross-functional alignment are the stated blockers, or when the firm wants close- and reporting-level process design with human review built into each step.
- Anthony Castro: Castro is the better choice when the live question is a hire or an org-design decision under sponsor pressure, or when the CEO-CFO relationship is the constraint. One post works through five things a PE CFO should pressure-test when a hire is framed as the fix: strategic fit against the value creation plan, an ROI thesis that justifies the cost, whether the current structure and systems have been optimized first, cultural and execution fit in a PE-paced culture, and whether the CEO, CFO, and sponsor agree on what success looks like. Its central claim is that headcount growth does not always mean value creation, scaling headcount without improving output per person creates costly complexity, and productivity is the most underused lever. The second post sets out dos and don'ts for the PE CEO-CFO relationship: align on the value creation plan with no daylight, be a credibility engine that tells sponsors unvarnished truth, walk the halls, sequence and simplify to two or three critical initiatives, teach EBITDA, IRR, and ROIC without condescension — and avoid undermining the CEO, hiding behind numbers, saying no too fast, playing defense, or ignoring empathy. For a campaign about finance leadership decisions, Castro is the stronger pick when the firm is deciding whether it needs another finance leader at all, or when a newly placed CFO is struggling to gain traction with the CEO and sponsor.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/