The Mismatch Problem: Why Most Firms Sell EPM Software but Can't Answer Your Profitability Question

How to choose a profitability advisory firm that actually transforms financial decision-making — not just implements technology.

Last year, I sat across from a CFO at a mid-sized bank in the USA. She had spent $1.8 million over fourteen months on what her board had approved as a "profitability transformation." The consulting firm — a well-known technology implementer — had delivered exactly what they'd promised: a fully configured Oracle EPM environment with dashboards, data pipelines, and 42 custom reports.

The CFO pulled up one of those reports on her laptop, turned it toward me, and said, "Pedro, I can tell you that Branch 14 processed 3,200 transactions last month. What I still can't tell you is whether Branch 14 is making us money or costing us money."

That moment crystallized something I've observed across twenty years of profitability work in The Americas and Spain: the market is flooded with firms that sell EPM software and call it profitability consulting. The technology gets installed. The reports get generated. But the fundamental question — where are we actually creating and destroying value? — remains unanswered.

This is the mismatch problem. And if you're a CFO evaluating advisory firms right now, understanding this gap could save you millions and months.

Three Questions Every CFO Should Ask Before Hiring a Profitability Advisor

1. Does this firm sell software implementation or financial decision capability?

2. Can they connect operational cost drivers to strategic value creation — or do they stop at cost allocation?

3. Will their work change how my leadership team makes decisions on Monday morning — or just produce better-looking reports?

1. Implementation vs. Decision Capability: The $1.8 Million Gap

The consulting market in the Americas has a structural problem. Most firms that position themselves as "profitability advisors" are, in practice, technology implementers. They know how to configure EPM platforms — Oracle, SAP, OneStream — but they lack the financial architecture expertise to design a profitability model that actually drives decisions.

This isn't a criticism of technology. EPM platforms are essential infrastructure. The problem is sequencing: when you lead with technology rather than the decision framework, you end up with a perfectly implemented system that answers the wrong questions.

A McKinsey study on finance transformations found that 73% of EPM implementations delivered "technical success" — the system worked as designed — but only 28% achieved "decision impact," meaning leadership actually changed how they allocated capital, priced products, or managed costs based on the new information. The gap between technical success and decision impact is precisely where the mismatch lives.

"We passed every technical acceptance test," the CFO told me. "Every data feed was validated. Every report matched the specifications. But when our credit committee met to review the consumer lending portfolio, they used the same spreadsheets they'd been using for five years. The new system sat there, technically perfect and strategically irrelevant."

What to ask a prospective advisor:"Show me three examples where your profitability work directly changed a client's pricing structure, channel strategy, or product portfolio — not three examples of successful software deployments." If they can't answer with specific business outcomes, they're an implementer, not an advisor.

2. From Cost Allocation to Value Architecture: The Driver-Based Difference

The second mismatch runs deeper. Most profitability models in the market are built on cost-allocation logic: take a pool of indirect costs, select an allocation base (headcount, square meters, or transaction volume), and spread the costs across products, customers, or channels. The math is clean. The result is misleading.

Cost allocation tells you how costs were distributed. It doesn't tell you what drives those costs or how changing a business decision would change the cost structure. When a regional retail bank in Colombia allocated IT costs to branches based on headcount, Branch 14 appeared expensive. When we rebuilt the model using activity drivers — actual transaction volumes by type, channel utilization rates, and customer complexity scores — the picture inverted entirely. Branch 14 was processing the bank's most profitable customer segment through its lowest-cost channel mix. The branch wasn't expensive; it was underinvested.

This is the difference between cost allocation and what I call value architecture: building a model in which every cost traces back to a causal driver, and every driver connects to a revenue or margin impact. The framework requires three integrated components:

Driver-Based Planning (DBP): Instead of budgeting by line item, you model how demand drivers (customer acquisition, transaction volumes, product mix) cascade through resource consumption to produce financial outcomes. Change a driver assumption, and the entire P&L recalculates.

Funds Transfer Pricing (FTP): For financial institutions, this is the second pillar — properly pricing the internal cost of funds so that asset-generating and liability-generating units see their true economic contribution, not an accounting artifact.

Profitability & Cost Management (PCM): The measurement layer that traces actual results — by product, customer, channel, and segment — back to the same driver structure used in planning.

When these three systems operate in a closed loop, the CFO doesn't just see what happened. She sees why it happened, what would change if she adjusted a driver, and where the next dollar of investment creates the most value.

What to ask a prospective advisor: "Walk me through your cost modeling methodology. Do you use allocation bases or causal drivers? Can your model simulate what happens to profitability if I change pricing on one product by 200 basis points?" If the answer involves spreading costs by percentages rather than tracing them through activities and drivers, you're looking at a 1990s methodology wrapped in a modern dashboard.

3. Reports vs. Decisions: The Monday Morning Test

The third mismatch is the most consequential. A profitability project succeeds or fails based on one criterion: does the executive committee make different decisions after the project than they made before it?

I call this the Monday Morning Test. When leadership walks into their weekly operating review, are they using the new profitability insights to challenge assumptions, reallocate resources, and reprice offerings? Or are they glancing at a dashboard, nodding politely, and reverting to the same intuition-driven process they've always used?

In my experience across PwC, Deloitte, IBM, and two decades of independent advisory work, the firms that fail this test share a common pattern: they treat the profitability model as a reporting deliverable rather than a decision system. They hand over the keys to a configured platform and move on to the next engagement.

The firms that pass it do something fundamentally different. They embed the profitability framework into the organization's existing decision rhythms. The monthly pricing committee doesn't get a new report — it gets a new agenda structured around customer-level pocket margins. The quarterly strategic review doesn't add a profitability slide — it replaces the old P&L waterfall with a driver-based variance analysis that shows which decisions created or destroyed value.

A practical example: when we worked with a multi-country insurance group, we didn't just build a product profitability model. We redesigned their product approval process so that every new product proposal included a driver-based margin simulation. Within eight months, the product committee rejected two proposals that would have passed under the old system — products that looked profitable at the gross margin level but destroyed value when distribution costs and claims servicing complexity were properly traced. Estimated value preserved: $4.2 million annually.

What to ask a prospective advisor:"After your engagement ends, which specific decisions in my organization will be made differently? Name the meeting, the decision, and the data that will drive it." Vague answers about "better visibility" or "improved reporting" are red flags. You want specifics: "Your credit committee will use customer-level cost-to-serve data to set differentiated pricing tiers by risk segment."

The real cost of the mismatch isn't the consulting fee you paid. It's the eighteen months of decisions your leadership team made using the same flawed assumptions they had before the project started — while believing the problem was solved.

Synthesis and Action

Three ideas to carry forward:

1. The market conflates EPM implementation with profitability advisory. They are different disciplines requiring different expertise. A firm that excels at configuring software may have no capability in financial decision architecture.

2. Cost allocation is not profitability modeling. True profitability insight requires causal driver logic that connects operational decisions to financial outcomes — and the ability to simulate the future, not just report the past.

3. The only valid measure of a profitability analytics engagement is whether it changes decisions. Not reports delivered, not dashboards built, not data pipelines configured — decisions changed.

What to do this week:

If you're currently evaluating profitability advisory firms, ask each candidate to present a case study structured around three elements: the business decision that changed, the financial impact measured twelve months later, and the driver methodology that made it possible. Any firm that can't articulate all three is selling you technology, not transformation.

If you've already completed a profitability project, apply the Monday Morning Test: identify three strategic decisions your leadership team made last quarter. For each one, determine whether the profitability model influenced the decision. If fewer than two out of three were informed by the model, your investment hasn't yet delivered its potential — and the gap is methodology, not technology.

Looking ahead:

Over the next 12–24 months, three forces will reshape profitability advisory in Latin America. First, AI and machine learning will enable the identification of cost drivers directly from transactional data, compressing what used to be a six-month modeling effort into a matter of weeks. Second, cloud-native EPM platforms will lower the technology barrier, making the advisory layer — the decision architecture — the true differentiator. Third, regulatory pressure (IFRS 18, Basel III.1, open banking) will force institutions to demonstrate granular profitability by product and customer segment, turning what was once a strategic nice-to-have into a compliance requirement.

The firms that thrive won't be the ones with the best software certifications. They'll be the ones that can answer the question that CFO in The Americas still couldn't answer after spending $1.8 million: Is Branch 14 making us money?

Pedro San Martín Principal – Asher psanmartin@asheranalytics.com