Thom Hornback has spent 20 years inside large organizations running the kinds of complex implementation projects that transformation programs are built around. He is a Six Sigma black belt, a project management professional and someone who thinks primarily about the people side of change. When he first encountered a process modeling approach that evaluated business processes through the lens of the information they generate, consume and destroy, he tried to get it approved at GE Power, where he ran professional services for one of its major platforms. But his request didn’t get much airtime. “The value of information is not a concept that organizations tend to track or value for that matter,” he says.
Most transformation frameworks are built to fixate on cost: what a process consumes, where it slows, which steps can be eliminated or automated. Those are legitimate questions, and the methodologies like business process modeling (BPM) or activity-based costing (ABC) that answer them are genuinely useful. What they are not designed to ask is what a process is worth, such as how revenue is influenced, how risk is absorbed, what options are preserved and especially anything about the information that is generated. Efficiency captures what a process no longer costs. It says nothing about what it produces of economic value. A generation of transformation investment has produced returns that reflect exactly that tilt. However, today’s organizational and business complexity demands more than simply squeeze-the-denominator type transformations, especially if executives hope to innovate.
The cost of not knowing what things cost
Process transformation has been a fixture on the enterprise agenda for 30 years, moving through successive waves: business process reengineering, Lean and Six Sigma, robotic process automation and now agentic AI. Each arrived with genuine methodology, a consulting infrastructure and case studies demonstrating impressive operational results.
However, the actual returns paint a different picture. Recent Bain research found that 88% of business transformations fail to achieve their original ambitions. Organizations that have become demonstrably better at executing processes have not, in most cases, materially benefited from it.
The explanation most commonly offered is implementation failure: change management gaps, technology underperformance, organizational resistance, insufficient executive sponsorship. These are real factors, but ones that perhaps take a back seat to how and by whom transformation programs define success. In other research, Gartner reported that 67% of CFOs believe their digital spending is underperforming against expected outcomes, primarily due to poor CFO-CIO alignment, and that only 30% of CFO-CIO relationships can be described as strong digital partnerships. That gap isn’t a communication problem. Rather, it speaks to the need for a better framework for defining what a process is worth before the program launches.
The optimization trap
Efficiency measures output per unit of input, making it indifferent to whether the output is worth producing. A process that delivers the wrong product faster is more efficient and less valuable. A process that removes friction from a workflow that shouldn’t exist in the first place is one that optimizes a waste. The biased logic of efficiency improvement assumes the process being improved is already doing something worth doing, at a scale worth doing it, in a configuration that makes economic sense. It is an assumption that transformation programs rarely examine, which is why so many produce measurable operational gains and negligible economic returns — returns that executives can’t even agree upon.
Most organizations carry, embedded in their process portfolios, activities that consume significant resources and generate little economic value: approval layers whose risk rationale has not been reviewed in years, reporting cycles producing outputs nobody uses, coordination processes that exist because two functions never aligned on accountabilities, quality checks duplicated at successive handoffs because no one established where accountable review actually sits. Efficiency analysis cannot identify these as candidates for elimination because it does not ask what a process is worth, only what it costs to run and how fast it can run faster. The question of value never enters the frame.
Economic process modeling: The complete picture
The knowledge exists inside every organization. It particularly surfaces in interviews with the people who actually do the work. There is a gap between what managers believe is happening and what their teams experience daily. “Even managers don’t really have a good grasp of where their people feel like they’re wasting their time,” Hornback observes.
What conventional process analysis treats as noise, a concept called economic process modeling (EPM) treats as signal. EPM decomposes processes into their constituent components and evaluates each across five economic dimensions:
- Revenue contribution: Which process steps directly influence customer retention, expansion or acquisition
- Cost and friction: Where resources are consumed relative to the value being generated
- Risk exposure: Which components create liability, compliance exposure or operational vulnerability
- Option value: Which steps preserve or foreclose future strategic choices
- Information value: What decision-relevant data assets the process generates, degrades or destroys
The output is a map of which components and subcomponents of a process generate economic value, which consume it and which destroy assets (relationship capital, information yield, decision quality) that operational metrics never surface. More than a map, EPM aggregates these economic signals up and down throughout a process. And transformation programs built in this way tend to pursue a different and broader objective than programs built on a process inventory scored only by effort and volume.
The asset nobody’s counting
The revenue contribution dimension of EPM alone tends to reorder transformation priorities significantly. Activities that appear administratively unremarkable often carry direct influence over whether customers renew, expand or defect and the economic consequence of improving them dwarfs the savings available from automating the most labor-intensive steps in the portfolio. A mid-market account review process that is technically efficient but experientially perfunctory may contribute to churn at a rate that costs the organization far more annually than the entire efficiency program is designed to recover, though the two numbers are rarely placed next to each other. Fixing the efficiency metrics of that process while leaving its economic function unexamined is the organizational equivalent of polishing a car with a failing engine.
The information value dimension surfaces a different category of opportunity altogether, one that treats data as an asset, not a byproduct. EPM also identifies process components that could generate decision-relevant or monetizable data assets but do not. Organizations routinely automate data-generating steps in ways that improve throughput while destroying signal fidelity. This is a tradeoff that rarely appears in the metrics used to declare the transformation a success, and that compounds across every subsequent decision that depended on the signal.
The economic case, made up front
EPM also changes how transformation programs compete internally for resources. Teams that enter capital allocation reviews with an initiative grounded in economic modeling (i.e., cost basis, throughput projections, ROI and information value contributions mapped to specific process components) don’t merely argue more credibly for budget. They come to the table with analyses that overshadow efficiency-only business cases. This kind of rounded economic business case can accelerate the investment decision, not just the argument for it.
Still, cost reduction is a legitimate objective, and processes that are both economically valuable and operationally wasteful are obvious candidates for improvement on both dimensions. The argument is merely against efficiency as the primary frame for transformation decisions because the frame determines what gets measured, what gets prioritized and what counts as success. Hornback observes that organizations have been comfortable with efficiency metrics precisely because efficiency is the low-hanging fruit that doesn’t force any accountability for driving up value. As a result, businesses that have spent decades optimizing process mechanics while leaving full-flavored process economics unexamined have been working with the most popular tools, but not the sharpest. EPM, on the other hand, cuts across multiple value dimensions in a way that business transformation programs have long needed.
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