Patrick DePeters advises companies on modernizing finance operations, including using AI and modern systems to improve forecasting, reporting, profitability, and business value.
3.2K followersAI in financeBased on 12 posts · latest Oct 2026
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.
Key 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.
Check first: Confirm whether they offer paid sponsorship, pricing, inquiry route, and audience analytics.
Main ideas
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
Patrick DePeters advises companies on modernizing finance operations, including forecasting, reporting, and the use of AI.
Their posts connect those practical changes to cash flow, profitability, and business value.
For readers weighing AI in finance, their central point is that the operating model—not the tool—is the goal: define the finance function the business needs, then identify where AI can help.
DePeters makes finance decisions legible in business terms.
In a client finance transformation example, they describe replacing manual workflows and disconnected systems, reporting about $400,000 in annual savings, and translating that into roughly $2.4 million in enterprise value at a 6× EBITDA multiple.
They also frame working capital, profitability, and AI as connected parts of one operating model, rather than separate projects (how the three pillars reinforce one another).
Their AI advice is specific rather than universal: first define the desired finance operation—its forecast cadence, close cycle, dashboards, and reporting—then look for the few workflows where AI can make a meaningful difference.
They also argue that finance is an early proving ground because its work is data-rich, repeatable, and measurable, and that practices established there may spread to other functions (why finance may be an AI beachhead).
These posts help readers evaluate finance modernization through measurable operating and business outcomes, rather than technology adoption for its own sake.
Patrick DePeters writes about modernizing finance operations and argues that the useful unit of planning is the operating model, not the software.
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Across short LinkedIn posts, the recurring positions are consistent: define the finance function you want a buyer or board to see 18 months out, then decide where AI is the fastest route to each piece; justify finance change in enterprise value rather than hours saved; treat capital, profitability, and AI as one connected system; and talk to private-equity sponsors in the vocabulary of defensible forecasts, scenarios, and audit trails. They see finance as the first function to absorb AI—because the work is rules-based, data-rich, repeatable, and measurable—and expect patterns proven there to move into legal, marketing, HR, and operations.
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.
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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.
The posts list the pieces of that model concretely: forecast cadence, dashboards, close cycle, board pack, customer cohort view, scenario modeling depth, and how often the equity story is refreshed. This is a sequencing argument, not an anti-AI one: DePeters is not saying to skip AI, but that a tool decision made before the model exists has nothing to attach to.
Value finance automation in enterprise value, not hours saved
DePeters frames finance modernization as a valuation exercise.
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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.
A client finance department of about ten people, with roughly a million dollars a year in payroll and running on spreadsheets and disconnected systems, was rebuilt on modern systems with customized AI agents and workflows.
The stated result was about $400,000 a year in overhead savings, which at a 6x EBITDA multiple is described as roughly $2.4 million of enterprise value from one initiative in one department.
DePeters points readers to the manual close, the rekeyed invoice, and the hand-built report as places where this kind of value tends to hide.
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.
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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.
The case cited is a business doing over $30 million a year where growth consumes cash, so capital comes first, but on terms improved by the data and forecasting in place.
AI is then applied to finance, admin, customer service, and reporting as margin expansion rather than a cut into the core team.
DePeters notes that most companies they encounter are working on one pillar and treating the other two as someday problems.
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.
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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.
DePeters cites the Accordion 2025 survey of 200 PE sponsors and 200 portfolio CFOs, reporting that the word "innovation" is absent from the sponsor priority list; 85% of buyers now factor AI-enabled finance into valuation; 63% of sponsors monitor portfolio AI adoption during slow markets because they expect it to affect the exit multiple; and CFOs embedding AI in planning, forecasting, and reporting were twice as likely to reach smoother exits and higher perceived valuations.
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.
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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.
The post points to a May commitment of roughly $1.5 billion from Anthropic, Blackstone, and a group of private-equity firms to a services venture aimed at mid-size companies, with PE portfolio companies as the beachhead.
It predicts the first work inside those companies will be reconciliations, reporting, board materials, and audit preparation.
It also cites Deloitte's Q4 2025 CFO Signals survey, in which half of North American CFOs named digital transformation of finance their top priority for 2026.
DePeters says the timing could be wrong, but the direction is not.
This is the clearest statement of DePeters’ approach.
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To understand what they actually do for a company, start here: the post separates the finance operating model from the software that supports it.
DePeters argues that founders asking "how should we think about AI in finance" are asking the wrong question: it points them toward a tool or roadmap deck instead of the business.
They recommend asking what the operating model should look like 18 months out to produce the cash flow, clarity, and optionality a board or buyer would want.
They lay out the model’s components: forecast cadence, dashboards, close cycle, board pack, customer cohort view, scenario modeling depth, and how often the equity story gets refreshed.
Only then do they ask where AI is the fastest route to each component, noting the answer may be two or three specific places—or nowhere yet.
For brands planning a campaign
Brands targeting founders or finance leaders can brief against this framing: start with a designed operating model and place the product at one named step, rather than opening with AI capability. It also sets the vocabulary DePeters would bring to a sponsored piece or webinar.
This is the most specific example of the work and its outcome.
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It shows the scale of DePeters’ work and how they translate operating savings into a valuation figure.
DePeters describes a client finance function of about ten people and roughly a million dollars a year in payroll, running on spreadsheets and systems that did not talk to each other.
They rebuilt the function on modern systems in place of a legacy ERP, added customized AI agents and workflows for repetitive work, and kept the strongest people who wanted to move from doing everything by hand to supervising a system.
The stated result was about $400,000 a year in overhead savings, which at a 6x EBITDA multiple is roughly $2.4 million of enterprise value.
They say the hours saved are a bonus and the multiple is the point, then ask where manual work like a hand-built close or a rekeyed invoice is hiding in the reader’s business.
For brands planning a campaign
Useful for vendors whose products touch close, reporting, or AP workflows: the post offers a reusable ROI frame (annual savings times the exit multiple) and a human-in-the-loop adoption story. Before using the numbers as a benchmark, ask for details of the client case.
This post shows how DePeters frames an engagement when several demands arrive at once, as they often do in a fractional finance brief.
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A CEO asked DePeters for three things in one meeting: help raising a working capital facility, a path to profitability, and an AI roadmap.
They treat these as one system, not three projects.
In their telling, the business does over $30 million a year and growth consumes cash, so capital comes first.
But lenders price what they can see, and instrumented forecasting and clean data improve the terms.
Profitability then comes from applying AI to workflows where overhead sits, such as finance, admin, customer service, and reporting—expanding margins without cutting into the core team.
More profit reduces dependence on outside capital, making growth less fragile.
DePeters notes that most companies they meet are working on one pillar and treating the other two as someday problems.
For brands planning a campaign
A strong hook for sponsors or platforms serving growth-stage companies: the post shows how to position a product within a combined capital, margin, and data agenda instead of as a standalone AI pitch. It also identifies the buyer: a CEO managing all three at once.
This post is the clearest evidence of how DePeters speaks to and about the private-equity audience, a useful signal for any sponsor-facing brief.
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DePeters writes that sponsors do not want AI experiments; they want EBITDA impact, faster diligence, and a cleaner equity story.
They cite the Accordion 2025 survey of 200 PE sponsors and 200 portfolio CFOs, reporting that the word "innovation" is absent from the sponsor priority list, while CFOs embedding AI in planning, forecasting, and reporting are said to be twice as likely to reach smoother exits and higher perceived valuations.
The post also cites figures of 85% of buyers factoring AI-enabled finance into valuation and 63% of sponsors monitoring portfolio AI adoption during slow markets because they expect it to affect the multiple at exit.
DePeters sums up the sponsor vocabulary: prove the forecast holds, prove scenarios are defensible, and prove the audit trail is verifiable in an afternoon.
For brands planning a campaign
Frames a sponsor or portfolio-company pitch around defensibility and exit value rather than transformation language. Before reusing the survey figures commercially, ask for the source and attribution details.
This is the broadest-argument piece: it explains why finance leads the shift and what that implies for other functions, providing useful context before the more tactical posts.
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DePeters argues that finance is going first with AI for structural reasons: the work is rules-based, data-rich, and repeatable; its outputs are verifiable; and its return on investment can be measured in terms a board understands.
They point to a May commitment of roughly $1.5 billion from Anthropic, Blackstone, and a group of private-equity firms to a services venture aimed at mid-size companies, with PE portfolio companies as the initial target.
They predict the first work inside those companies will be reconciliations, reporting, board materials, and audit preparation.
They also cite Deloitte's Q4 2025 CFO Signals survey, in which half of North American CFOs named digital transformation of finance their top priority for 2026.
DePeters expects patterns proven in finance to reach legal, marketing, HR, and operations within a year or two, and says the direction is more certain than the timing.
For brands planning a campaign
The post supports a placement beyond finance teams by explicitly telling other functions to watch this as a preview. It also offers a natural sponsorship angle for vendors selling into CFO offices, the earliest adopter segment.
DePeters answers a founder's question about AI in finance by reframing it.
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The useful question, they say, is what the operating model should look like 18 months out, defined by specifics: forecast cadence, dashboards, close cycle, board pack, customer cohort view, scenario modeling depth, and equity story refresh rhythm.
Then ask of each piece where AI is the fastest way to get there.
Sometimes the answer is everywhere, sometimes nowhere yet, but usually two or three places where the lift is obvious while the rest can wait.
A direct response to a founder's question, with a checklist readers can apply to their own business before choosing vendors or tools.
For brands planning a campaign
For a firm buying finance transformation help, this positions the first deliverable as a target operating model, not software selection, making the engagement measurable before spending on tools.
Turning finance overhead savings into enterprise value
A client's ten-person finance department ran on about a million dollars a year in payroll, spreadsheets, and disconnected systems.
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DePeters rebuilt it with modern systems, using AI agents and workflows for repetitive work while keeping the strongest performers in the loop.
The result was about $400,000 a year in overhead savings, equivalent to roughly $2.4 million in enterprise value at a 6x EBITDA multiple.
Their framing: the hours saved are a bonus; the multiple is the point.
DePeters finds the same pattern across companies of different sizes, pointing to the manual close, rekeyed invoices, and hand-built reports as places where EBITDA is hidden in the org chart.
For brands planning a campaign
This gives buyers a repeatable way to size a finance modernization program in valuation terms, not cost savings—the language a board or sponsor responds to.
Capital, profitability, and AI as one operating system
A CEO asked DePeters for a working capital facility, a path to profitability, and an AI roadmap in the same meeting.
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They treat the three as one system.
Capital comes first: a $30M+ business whose growth eats cash cannot scale without a credit line.
But lenders price what they can see, so instrumented forecasting and clean data improve borrowing terms.
AI then supports profitability across workflows where overhead hides, including finance, admin, customer service, and reporting, expanding margin without cutting into productive capacity.
More profit reduces reliance on outside capital and makes growth less fragile.
Their point: one pillar is a project; all three working together form an operating model.
Uses a single client conversation to lay out a three-pillar model, showing how each pillar feeds the next rather than treating them as separate projects.
For brands planning a campaign
A fractional engagement can be proposed as three linked workstreams with a shared measurement frame, making the case more defensible to a sponsor than a standalone AI pilot or fundraising mandate.
DePeters argues that private equity sponsors want EBITDA impact, faster diligence, and a cleaner equity story—not AI experiments.
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They cite the Accordion 2025 survey of 200 PE sponsors and 200 portfolio CFOs: the word "innovation" does not appear on the sponsor priority list; 85% of buyers now factor AI-enabled finance into valuation; 63% of sponsors monitor portfolio AI adoption during slow markets; and CFOs embedding AI in planning, forecasting, and reporting were 2x more likely to achieve smoother exits and higher perceived valuations. The buyer's vocabulary, they say, is narrower: prove the forecast holds, prove scenarios are defensible, and prove the audit trail of decisions is verifiable in an afternoon.
DePeters reflects the buyer's own words rather than arguing for transformation, and calls an AI strategy priced at zero when it sounds like a TED talk.
The figures come from the cited Accordion survey and are reported by DePeters, not presented as results DePeters measured personally.
For brands planning a campaign
Campaigns around finance AI for a PE audience should use this evidence-and-verification vocabulary rather than innovation or future-of-work framing.
Finance goes first; the pattern moves to other functions
DePeters tells people in legal, marketing, HR, and operations to watch what is happening to CFOs as a preview of their own functions.
Read more
The reasoning is structural: finance work is rules-based, data-rich, repeatable, produces verifiable outputs, and has ROI a board can measure, which is why the capital went there first.
They point to a May commitment of roughly $1.5 billion from Anthropic, Blackstone, and a group of PE firms to a services venture aimed at mid-size companies, with PE portfolio companies as the beachhead, and predict the first landing spots inside those companies will be reconciliations, reporting, board materials, and audit prep.
They also cite Deloitte's Q4 2025 CFO Signals survey, which found that half of North American CFOs rank digital transformation of finance as their top 2026 priority.
DePeters advises readers to watch what the strongest finance teams do this year rather than read predictions about their own discipline, treating finance as a leading indicator for other functions.
DePeters says the timing could be off, but the direction is not.
For brands planning a campaign
For a services firm, this supports positioning finance transformation as the first phase of a wider operating-model program, with the CFO's office as the pilot and other functions as later expansions.
The posts document no sponsorship, affiliate arrangement, or named client relationship.
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Anthropic and Blackstone are cited as backers of a roughly $1.5 billion services venture aimed at mid-size companies, while Deloitte's CFO Signals survey and Accordion's 2025 private-equity survey provide data behind their arguments about AI in finance.
They describe rebuilding the finance function at an unnamed client with about ten people and roughly $1 million in payroll, cutting around $400,000 in annual overhead.
The posts show founders, CEOs, and sponsors asking them for guidance but do not say whether they own or work for an advisory firm.
Ask them to clarify their firm role and disclose any relevant paid relationships.
Patrick DePeters publishes short LinkedIn posts about modernising the finance function, mainly for founders, CFOs and private-equity-backed companies.
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A central argument runs through the work: decide what the finance operating model should look like 18 months out — forecast cadence, close cycle, board pack, cohort reporting and scenario depth — then identify where AI can get each piece in place fastest.
Three threads are worth reading in sequence.
One links finance savings to enterprise value, as in a ten-person department rebuilt for roughly $400,000 a year in overhead savings, worth about $2.4 million at a 6x multiple.
Another treats capital, profitability and AI as one system rather than three separate projects.
The third translates the same thinking into the language private-equity sponsors use: forecasts that hold, defensible scenarios and an audit trail that can be checked in an afternoon.
They argue that finance is first in line for AI because its work is rules-based, data-rich and measurable, and that proven patterns will reach legal, marketing, HR and operations within a year or two.
For the underlying numbers, see the cited Accordion 2025 sponsor survey and Deloitte's Q4 2025 CFO Signals.
AI in financeFinance operating modelFinance automationPrivate equityEBITDA and enterprise valueFractional CFOCFOs and finance leadersFoundersLinkedIn posts
Deva I J.
Writes about AI adoption and value creation at private-equity-backed portfolio companies for operating partners and portfolio leaders, turning those ideas into recurring newsletter issues with frameworks and readiness scorecards.
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Where DePeters focuses on the finance function, operating models and valuation, Deva examines how people adopt AI in their work, including physical AI and robotics in factories and field operations, as well as governance and trust in human-machine handoffs.
Covers how to redesign finance-team workflows with AI while keeping a person in the loop on reconciliations, AP/AR and variance commentary, as well as what a CFO should prioritise in the first 90 days.
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Arif writes about day-to-day close work and CFO onboarding from the accounting-practice perspective. DePeters frames finance change around the operating model, EBITDA and the exit multiple.
Writes for private-equity-backed CFOs about value creation plans, hiring decisions and the CEO-CFO relationship, using sponsor-facing language focused on accountability and measurable impact.
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Castro focuses on the CFO role: when to hire, how to pressure-test a hire and how a CFO works with a CEO. The emphasis is not on AI tools or finance automation.
The LinkedIn profile shows 3,167 followers as of October 7, 2026, and five June 2026 posts focus on finance operations, AI-enabled finance, fractional CFO work, and private-equity EBITDA priorities.
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These topics target finance leaders, founders, and PE sponsors.
For a campaign aimed at a PE-backed services firm, request recent LinkedIn analytics or an audience survey covering seniority, industry, company size, and geography, along with recent engagement on posts about AI-enabled finance transformation and EBITDA improvement.
Specify whether the campaign needs to reach PE sponsors, portfolio CFOs, or operating finance leaders.
LinkedIn followers
3,167
LinkedIn profile count in creator the research · 2026-10-07
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.
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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.
A PE-backed services firm with 500–1,500 employees wants fractional CFO support to modernize finance operations, embed AI in planning, forecasting, and reporting, and improve EBITDA and exit readiness.
Reasons
DePeters's most direct post tells founders to stop asking how to think about AI in finance and instead design the operating model 18 months out. The specifics — forecast cadence, dashboards, close cycle, board pack, customer cohort view, scenario modeling depth, and equity story refresh rhythm — map directly to a fractional CFO diagnostic and a finance transformation roadmap.
DePeters reports a client case at a relevant scale: a ten-person finance function with about $1 million in annual payroll, running on spreadsheets and disconnected systems. The rebuild used modern systems, customized AI agents and workflows, and a leaner team of stronger performers as the human in the loop. The stated result was about $400,000 a year in overhead savings, which at a 6x EBITDA multiple is roughly $2.4 million of enterprise value. Treat the figures as one reported client example, not a guarantee.
A second post treats capital, profitability, and AI as one operating system for a business doing more than $30 million a year. Capital comes first because growth eats cash, but lenders price what they can see, so instrumented forecasting and clean data improve borrowing terms. Profitability then runs through AI applied to finance, admin, customer service, and reporting workflows. That aligns with the brief: a services firm needing capital discipline, margin expansion, and better reporting at the same time.
DePeters speaks the sponsor audience's language. The private-equity post says sponsors do not want AI experiments; they want EBITDA impact, faster diligence, and a cleaner equity story. It cites the Accordion 2025 survey of 200 PE sponsors and 200 portfolio CFOs, including the finding that 85% of buyers now factor AI-enabled finance into valuation and that 63% of sponsors monitor portfolio AI adoption during slow markets. The stated sponsor test is specific: prove the forecast holds, prove scenarios are defensible, and prove the audit trail of decisions is verifiable in an afternoon.
The audience section shows 3,167 LinkedIn followers as of October 7, 2026, with posts concentrated on finance operations, AI-enabled finance, fractional CFO work, and private-equity EBITDA priorities. Reach is small; the case rests on topic and seniority relevance, not mass audience size.
Caveats
The posts do not document sponsorship formats, pricing, or an inquiry route. If the firm wants a paid creator sponsorship rather than a fractional CFO engagement, establish those commercial terms separately.
The posts suggest relevance to founders, finance leaders, and PE sponsors, but provide no audience demographics or measures of employer, job title, seniority, company size, or geography. A buyer needing guaranteed sponsor or portfolio-CFO reach should request LinkedIn analytics or an audience survey.
The available work consists of five short LinkedIn posts, with no long-form articles, videos, or transcripts. Ask for additional samples if those formats matter to the brief.
When to choose a specialist
Poor fit for this brief.
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Brief
A workplace-tech vendor wants a high-reach creator to front a future-of-work campaign across short-form video, live events, and paid social, targeting HR and operations buyers at mid-market companies, with guaranteed impressions and optimistic transformation messaging.
Reasons
DePeters's available material focuses on finance operations and private-equity valuation, not workplace culture, HR technology, or broad future-of-work trends. The one post that mentions legal, marketing, HR, and operations argues that finance goes first and other functions follow. It is written for people watching finance, not as HR-audience content.
DePeters explicitly rejects transformation and innovation language for sponsor audiences. The private-equity post says the word “innovation” does not appear on the sponsor priority list, and that an AI strategy sounding like a TED talk has been priced at zero. That conflicts with a campaign built on optimistic future-of-work messaging.
The five short text posts provide no video, event, or short-form visual work samples. Ask for samples before assessing a campaign requiring short-form video, live-event appearances, or paid-social creative.
DePeters had 3,167 LinkedIn followers as of October 7, 2026. The posts provide no employer, job-title, seniority, company-size, or geography data, so they do not support a guaranteed mid-market HR and operations audience. Request audience analytics to assess that reach.
The partnerships section documents no sponsorship formats, rate information, partner disclosures, or inquiry route. Establish those details before planning a high-reach creator campaign.
What would change the decision
Audience analytics showing meaningful HR, operations, or workplace-technology buyer concentration.
Video, event, or short-form work samples demonstrating the required formats.
A documented sponsorship offer with format, pricing, disclosure policy, and an inquiry path.
Messaging evidence that DePeters can carry a transformation theme without contradicting the sponsor-facing vocabulary in the posts.
A worked campaign example
Angle
Run a three-pillar operating-model engagement rather than a standalone AI pilot. The first pillar is capital readiness; the second is profitability through AI in the workflows where overhead hides; the third is investor-ready reporting and decision visibility. Start with a short finance operating-model diagnostic before selecting tools.
Brief
A PE-backed services firm doing more than $30 million a year, with growth consuming cash, needs fractional CFO support to modernize finance operations, embed AI in planning and reporting, and improve EBITDA and exit readiness.
Measured impact
Measure annual run-rate overhead saved, days to close, forecast accuracy, scenario turnaround time, audit-trail verification time, and estimated enterprise value created using the company's actual exit multiple. DePeters's reported client example is an illustration only: about $400,000 in annual savings from one department, at a 6x multiple, equals roughly $2.4 million of enterprise value. For the actual client, use measured savings and the applicable multiple.
Next step
Start with a 45-minute scoping call with the CEO, CFO, and sponsor representative, followed by the 30-day diagnostic. If the firm also wants a public content component, develop a co-authored post or webinar on the three-pillar model separately and agree partnership terms first; the posts do not describe a sponsorship offer.
Phases
Detail
Define the 18-month target for forecast cadence, close cycle, dashboards, board pack, customer cohort view, scenario modeling depth, and equity story refresh rhythm. Map current-state processes, systems, manual close steps, rekeyed invoices, hand-built reports, and audit-trail gaps. Deliver a target operating model, a prioritized AI workflow backlog, and a first-pass EBITDA opportunity map.
Name
30-day finance operating-model diagnostic
Detail
Pick two or three places where the lift is obvious, such as reconciliations, reporting, board materials, or audit preparation. Build human-in-the-loop workflows with named process owners. Instrument the results to show what changed and by how much. Deliver a 13-week cash forecast, a revised close calendar, and documented forecasting assumptions.
Name
90-day AI workflow build in finance
Detail
Turn the rebuilt finance function into sponsor-facing evidence: a defensible forecast, scenario models, a clean EBITDA bridge, and an audit trail that can be verified quickly. Package the work for a board or sponsor review so the finance transformation informs diligence and valuation conversations, not just internal cost savings.
Name
Investor-ready reporting and exit preparation
Risks
Savings depend on the processes selected and whether the finance team adopts the new workflows.
Sponsors may not automatically credit AI-enabled finance in the multiple; support any valuation effect with evidence.
The engagement requires enough CFO, controller, and finance-team capacity to run the diagnostic and build phases without disrupting the close.
Ask for the sponsorship format and pricing; neither is documented. This worked example assumes a fractional CFO engagement, not a paid creator campaign.
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.
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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.
The material is newsletter-led and framework-heavy.
Branded models and acronyms carry much of the argument, and the posts promote subscriptions and a scorecard download rather than describe a completed engagement.
There is no reported client case, close cycle, forecast cadence, board-pack work, or EBITDA or valuation arithmetic.
A firm buying finance-function delivery would still need to source the CFO-side work elsewhere.
Buyers seeking evidence of DePeters' hands-on finance rebuild should ask for client examples.
Their focus is the finance operating model and the sponsor's exit vocabulary; Deva's is adoption and operating-technology governance.
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.
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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.
The posts describe a services provider's client work, not a PE portfolio engagement.
They offer no sponsor or exit-multiple framing, reported dollar savings, capital or working-capital line, or documented fractional CFO scope at PE-backed scale.
The examples specify which tasks to automate and where a human stays in the loop, but stop short of the valuation argument a sponsor buys.
A firm looking to show up better in diligence and at exit would get useful process-level design, but should ask for the capital, profitability, and AI-as-one-system framing DePeters' posts supply.
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.
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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.
These are leadership and decision-hygiene posts, not a delivery model.
They offer no AI-in-finance workflow, forecasting, close, reporting, audit-trail build, or quantified outcome.
A firm that has decided to modernize finance operations should ask for a plan.
Castro is the right lens for the decision that comes before DePeters' — whether to add or change finance leadership, and how that leader should operate — but not once the answer is yes and the work turns to systems, workflows, and measurable savings.
Patrick DePeters's five LinkedIn posts from the first week of June 2026 cover finance modernization, AI adoption in finance, fractional CFO work, and private-equity priorities.
The posts describe a client example, survey figures, and company news, with attribution to the Accordion 2025 sponsor survey and Deloitte's Q4 2025 CFO Signals rather than to DePeters.
Follower count (3,167 on LinkedIn as of October 7, 2026) is listed on LinkedIn.
The posts here are short-form LinkedIn posts only.
They do not include long-form articles, videos, or transcripts, so they reflect recent posts rather than a complete body of work.
The posts do not state audience demographics (employer, job title, seniority, company size, or geography), or whether DePeters owns or works for a named advisory firm.
The posts do not mention sponsorship formats, pricing, partner disclosures, or an inquiry route, and the client project described is unnamed.
