Operators and investors,

If your FP&A team still ships a month-end pack that reads like a museum catalog, you are leaving EBITDA on the table. Too many portfolio companies treat Finance as the historian of the P&L rather than the builder of the operating model. Last quarter, I sat in a review where FP&A had 60 slides of variance analysis without a single driver tree, without one modeled scenario, and without any clear linkage to the sales and delivery workload that actually produces margin.

The stakes are higher this year because value creation is harder, holding periods are longer, and boards want operating proof rather than narratives. Bain's Global PE Report 2026 points to a record $3.8 trillion in unrealized buyout value, which translates to more time in the trenches and less room for lagging insights. When FP&A is the sharpest tool in your operating shed, you catch bad news early and compound the wins that would otherwise go unnoticed.

This issue is my practical take on rebuilding FP&A inside PE-backed companies. I break down where reporting-heavy teams fall short, what a driver-based function actually looks like, how to fix the data plumbing, and how to run rolling re-forecasting that your operating partners can steer against. The answer is rarely a bigger team and almost always a different operating model.

FP&A must move from reporting to driver-based operating models

The thesis is simple, and many will disagree because it changes who owns the plan. FP&A belongs in the engine room, translating operating levers into P&L outcomes that leaders can steer in real time, rather than sitting downstream as a monthly reporter.

Boards often accept a pretty budget book in January, then live with twelve months of explanation theatre. That approach has run out of road in 2026. Driver-based planning flips the script, so you model the business on controllable inputs rather than aggregates. Pipeline creation rate, win rate by segment, implementation cycle time, utilization by role, renewal likelihood by cohort. Then you tie those to CAC payback, contribution margin, and cash. Frank Slootman's bias for operating cadence is the right mental model here, because the goal is more cycles of change tied to a few clear inputs, rather than more slides explaining the last cycle.

Pulling FP&A into that seat requires a different meeting culture, where weekly working reviews replace monthly slide decks. Ops and GTM leaders own the inputs, and FP&A owns the math and the consequences. I want the CFO and the VP Sales arguing about a modeled step-up in SDR productivity next quarter, because it changes hiring, cash, and quota coverage in measurable ways. That is a useful argument and it reflects how committed operating companies actually work.

• Driver tree first: map the handful of controllable inputs that explain 80% of your P&L movements, then assign owners to each input

• One version of truth: publish a single, shared driver-based model that sales, marketing, ops, and finance edit together, rather than parallel spreadsheets that drift apart by week two

• Replace budget theatrics: run a 45-minute weekly drivers call where input owners update assumptions, and FP&A shows the EBITDA and cash impact in real time

Data plumbing beats wizardry: automate collection, standardize definitions

FP&A cannot be forward-looking when it spends most of the week cleaning CSV files. Many finance teams are trapped here through no fault of their own, because disconnected systems, duct-taped integrations, and a CRM that does not match the GL will kill any chance of proactive analysis. Gartner's 2026 CFO and Finance Executive Survey reported that finance teams still spend roughly 40% of their time on data collection and reconciliation rather than analysis. That ratio puts a hard ceiling on how much value the function can create.

Fixing this is boring engineering work that pays back in months. Start with data contracts and canonical keys, because if your CRM account does not map cleanly to your billing account and your project management client object, everything downstream becomes a guessing game. Use ELT into a warehouse, transform to a semantic model with defined business logic, then crank reverse ETL back into your systems of action. The work resembles building a paved road through the operating company, so that every quarter is no longer a fresh hike through the jungle.

Standardization is the only way to compare real-world performance over time. You would be surprised how many mid-market companies "improve retention" by quietly changing the definition. Whether you use retained billings or retained logos matters less than picking one, publishing it, and instrumenting it in the warehouse. Then make sure it shows up consistently in FP&A's model, the RevOps dashboard, and the board pack. Vista Equity is known for this level of KPI discipline, particularly around standardized metric definitions enforced across every portfolio company, which is part of why their operating playbooks travel so well.

• Define canonical entities: agree on accounts, products, projects, and cohorts, then enforce IDs across CRM, billing, delivery, and support

• Instrument upstream: capture the operational events that feed your drivers, such as demo completed or go-live date, inside your systems rather than spreadsheets

• Automate the month-end: close with a pre-built pipeline that refreshes your warehouse, validates joins, updates the driver dashboard, and flags anomalies

Integrate operational and financial metrics or you will miss the real levers

My consistent frustration is watching FP&A run a smart model while ops, sales, and marketing run a different play entirely. When your financial drivers are disconnected from real operational measures, the forecast remains a guess dressed up in spreadsheet formatting. The magic happens when the telemetry from the workbench updates the P&L view directly, without human storytelling in the middle.

We helped a PE-backed vertical SaaS company in property operations, mid eight-figure ARR with a little over a hundred people, rebuild its FP&A model around actual product usage and service capacity. Their finance plan pictured "implementation cycle time" as a constant, while on the ground those cycles varied by 3x based on property type and vendor integrations. Sales kept booking deals that overloaded one implementation pod and left another idle. We mapped each SKU to a standard work package, set realistic cycle times by segment, and fed product telemetry into a capacity model that FP&A owned. The result went beyond a nicer report, because sales changed pricing for high-friction segments, services rebalanced pods, and FP&A stopped treating backlog as revenue glue.

When the metrics are integrated, the conversation shifts in useful ways. Marketing pipeline transforms from a pretty funnel slide into a workload forecast that adjusts hiring and cash needs. Support tickets evolve from anecdotes about quality into lead indicators for gross margin compression. Leaders start seeing the business as it runs, rather than as it gets summarized for the board pack.

• Tie pipeline to capacity: connect stage-weighted pipeline by segment to implementation, support, and CSM workload, then budget time alongside dollars

• Track unit economics at the edge: instrument SKU-level or region-level contribution margin, then roll up to the P&L so leaders see where wins or leaks live

• Bring CS into the math: measure expansion, downgrade, and churn propensity by cohort, then let FP&A simulate NRR's impact on hiring and CAC payback

Replace static budgets with rolling re-forecasting and scenario orchestration

Annual budgets behave like theatre in volatile markets. You need a rolling re-forecast that turns operating signals into financial reality within weeks rather than quarters. The structure matters here. A weekly flash P&L catches trend breaks, a 13-week cash view protects the downside, and a 12 to 18 month driver-based forecast updates every month. Put hard gates on when you change course, rather than allowing ad hoc edits to hit a number you liked in January.

The goal is rarely perfect prediction. The real value comes from modeling the sensitivity of your outcomes to the few inputs you can push. A change in win rate by three points, an extra week of implementation cycle time, a delayed enterprise contract, a price rise that sticks in one segment but not another. When FP&A can show the P&L and cash impact within the week, operators move faster. The 2024 SaaS Benchmarks Report from High Alpha and partners, which carries on the series originated by OpenView, has consistently shown double-digit median forecast misses on new ARR for many mid-market SaaS companies. That gap is the cost of vanity forecasting, and a tighter driver loop closes most of it.

One final point worth making here. Run two to three named scenarios and assign them owners, using Base, Push, and Protect as a starting frame. Assign thresholds that flip you from one to another, so the model carries the operating decision rather than the room. If the last two months show utilization below target for three weeks in a row, flip to Protect, pause the non-critical hires, and shift marketing to segments with proven close velocity. When the trend recovers for four weeks, return to Base. Those rules turn re-forecasting from an art into an operating system.

• Build the cadence: weekly drivers call, monthly re-forecast, quarterly reset of the scenario thresholds, all working from the same shared model

• Set triggers rather than vibes: define concrete conditions that move you between Base, Push, and Protect, then publish them to the exec team and the board

• Close the loop: every re-forecast should list three operating moves and three budget reallocations, with owners and dates, alongside the new set of numbers

Here is the action I want you to take this week. Book a 90-minute working session with your CFO, head of RevOps, and head of delivery. Draw your top ten operating drivers on a whiteboard, from pipeline to utilization to churn. Pick three that you can measure cleanly, and ask FP&A to rebuild next month's re-forecast around those three inputs. No slides, a live model, and a concrete change it will drive.

Mario

🛶 Even on a school trip in Venice, I'm screening CVs and bumping into Bulgarian IT managers on the street. Hiring never pauses, and when AI agents handle the volume 24/7, passion becomes the only filter that matters. DevriX is hiring in Sofia - determination and hard work, non-negotiable.

🎤 I'm not a TikTok pop fan, but Dara's Eurovision 70 win is a product engineering masterclass. Eurovision is entertainment, trends, and politics packaged as a song contest, and you build for all three or you lose. The full storytelling end to end was a masterpiece - the same playbook operators need in mixed-criteria markets.

Digital sovereignty is becoming a fundable thesis. The Sovereign Tech Fund is putting over €1 million into KDE to harden the security behind one of Linux's core desktop environments, a signal that European public capital is now underwriting open-source alternatives to hyperscaler-dependent stacks.

Frontier AI capital concentration keeps accelerating. Recursive Superintelligence emerged from stealth at a $4.65B valuation with fewer than 30 staff, underscoring the valuation gap between frontier AI bets and the applied AI work that actually moves portfolio EBITDA.

Skilled labor shortages are now a material constraint on industrial value creation. Gyver raised €1.4M to scale AI hiring tools for electricians as the EU faces a 5.8 million worker gap by 2030.

Regulatory scrutiny on insurer private credit exposure is intensifying. Munich Re disclosed €2 to €2.5bn in private credit, around 1% of its portfolio, while BaFin flags that some German insurers carry over 25% in private debt, a signal that LP capital flowing into private credit funds will face tighter constraints.

Email Automation SaaS: A lightweight Gmail automation SaaS that sends recurring emails via a Chrome and Workspace extension. 99% profit margins, 1,400 active subscribers, and 3% churn - a simple yet lucrative model priced at $350,000.

422% Growth Golf SaaS: AI-powered golf operating system helping courses boost revenue and run more efficiently. Verified financials via Xero with strong traction and profitability. $450,000 ($10,000 minimum investment)

High-Growth Automotive CRM SaaS: 2-year-old B2B CRM SaaS built for the automotive sector, positioned as a low-touch platform with strong market traction. Backed by framework contracts with industry giants and claims zero ad spend to date. $706,844

Automated Marketing Platform: A 10-year-old automated marketing platform generating roughly $900K in annual revenue with exceptional 95% profit margins. Producing over $72K in monthly profit with more than 3,000 subscribers, it’s selling for $1,700,000.

Working with me

🌐 Scaling $30M - $100M+ mid-market companies with value creation through RevOps, data engineering, and WordPress. DevriX provides full RevOps consulting + delivery with GTM enablement for PE-backed portfolio companies, traditional tech, healthcare, finance, and professional service businesses pacing toward revenue growth initiatives. Our standard retainers between $10K and $60K include revenue lifecycle services for marketing and sales leaders, FP&A for financial teams, pipeline enrichment through websites and dozens of lead sources, automations and delivery integrations, CRO and ongoing testing, product delivery and platform integration solutions, and more through our consulting solutions.

🚀 1:1 Advisory retainers. At Growth Shuttle, I run two popular plans: Async Advisory ($3,500/mo) for $3M - $30M founders and executive teams and the smaller Strategic Growth Circle ($997/mo) for $100K - $500K entrepreneurs, agency founders, scale ups. My fractional executive plan is also available here.

📈 Building US LLCs from Europe. I help European and Asian founders scale faster through doola and their “Business in a Box” model. Also suitable for US citizens (given their bookkeeping solution), but in very high demand across Europe.

📊 Post-Merger Integration. We take on M&A initiatives with Flippa. Working closely on PMI retainers for PE companies and fast-growing startups integrating new companies within their portfolios, enabling data pipelines, and securing more deals through my personal network.