Most articles about AI Centers of Excellence (CoEs) focus heavily on organizational structures, steering committees and high-level governance models.  They explain why enterprises need an AI CoE, but they rarely address the far more difficult challenge of how successful organizations operationalize AI at enterprise scale.  In practice, many of these discussions remain theoretical, emphasizing aspirational maturity frameworks without addressing the operational complexities organizations encounter once AI systems move into production.

This article takes a different approach by grounding the discussion in real-world enterprise implementation experience.  Rather than relying on abstract models, it draws from operational lessons learned while deploying production AI systems across industries.  The guidance is informed by governance practices that have successfully passed security and compliance reviews, operational realities associated with managing large language models (LLMs) and AI agents after deployment, and practical implementation patterns observed across enterprises scaling AI initiatives beyond experimentation.

Instead of presenting an idealized roadmap, the article focuses on the foundational capabilities consistently implemented by organizations that have successfully operationalized AI at scale.  These enterprises are not simply experimenting with isolated AI pilots; they are deploying enterprise-grade AI agents, Retrieval-Augmented Generation (RAG) systems, copilot platforms, multi-agent orchestration frameworks and comprehensive AI governance models.  Equally important, they are establishing disciplined AI application lifecycle management processes that ensure AI solutions remain secure, observable, maintainable and aligned to measurable business objectives over time.

At the center of this article is a key thesis: the most successful AI Centers of Excellence do not begin with innovation labs or experimentation theater.  They start by establishing the operational foundations required to scale AI responsibly across the enterprise.  These foundations include operational governance, enforceable security controls, standardized approaches to data grounding, rigorous evaluation disciplines and mature LLMOps and observability capabilities.  Together, these disciplines form what can best be described as the enterprise “AI operating system”, a repeatable operational framework that enables organizations to deploy AI securely, govern it consistently and scale it sustainably across the business.

Why traditional AI CoEs fail

Traditional AI Centers of Excellence (CoEs) often fail because they become innovation-focused organizations that lack operational accountability.  In many enterprises, the CoE evolves into a disconnected strategy function that produces prototypes, frameworks and vision documents without establishing the operational foundations required to scale AI responsibly.  These organizations frequently lack ownership of production deployments, standardized implementation practices, observability frameworks, security enforcement mechanisms, and measurable business outcomes.  As a result, AI initiatives remain experimental rather than becoming integrated, governed capabilities that deliver sustained enterprise value.

Another major failure pattern is the rapid proliferation of shadow AI across the organization.  Without centralized governance and architectural oversight, business units begin deploying isolated copilots and standalone AI solutions independently.  This fragmentation creates inconsistent user experiences, duplicate investments and increased operational costs as multiple teams unknowingly build similar capabilities.  More critically, the absence of standardized governance introduces significant security and compliance risks, including sensitive enterprise data leaking into prompts, uncontrolled model usage and expanding regulatory exposure.  Over time, the organization accumulates uncontrolled AI sprawl that becomes difficult to secure, monitor or optimize.

Many organizations also become trapped in what is commonly referred to as “pilot purgatory,” where AI initiatives never progress beyond experimentation into scalable production solutions.  This typically occurs because no formal evaluation framework exists to measure success, no ownership model is defined between business and IT teams, and security approval processes remain unclear or inconsistent.  Compounding the problem, AI architectures are often developed independently across teams without standardized patterns or governance controls.  Without clearly defined business KPIs tied to measurable outcomes, leadership struggles to justify broader investment or operationalization.  The result is an organization with numerous AI pilots but little enterprise-wide adoption, governance or measurable business impact.

A reference model

This perspective is informed by a recent field engagement to design an AI and agentic Center of Excellence (CoE) for a global enterprise software organization, and reflects patterns consistently observed across AI readiness assessments, data maturity evaluations, executive workshops and production-scale deployments.

While no two AI Centers of Excellence are identical, the underlying drivers behind them are strikingly consistent.  Each organization faces its own combination of competitive pressure, cultural dynamics, leadership ambition and legacy technology constraints.  These factors ultimately shape not only the need for a CoE, but also how it must operate to succeed.

The approach outlined here reflects a structured, repeatable model for establishing an AI and agentic CoE, from initial discovery through to a fully defined operating model, executive narrative and measurable value framework.  Although tailored in execution, the model has proven broadly applicable across industries, offering leaders a pragmatic path to scale AI beyond experimentation into sustained business impact.

Phase 1: Starting with questions, not answers

An AI CoE cannot be designed correctly without first understanding what the organization is already doing, where it is breaking down and what specific outcomes leadership needs to be able to defect.  This discovery is organized around six core areas

• Executive narrative: Why how? Establish why an AI CoE is necessary at this moment.  What credibility risks would the company face if it proceeded without one?  What would change the day the AI CoE launched?  This framing becomes the foundation for every subsequent conversation with the CIO and senior leadership
• Mission, Scope and Decision Rights. What is the AI CoE responsible for?  Is it accountable for all AI and agentic solutions (no-code, low-code and pro-code), including both employee-facing and customer-facing use cases?  Where does its authority begin and end?  Anything that will be sold as a product is typically excluded from the charter, keeping internal AI development clearly in scope and commercial product development out.  These boundaries prevent scope creep and protect the AI CoE’s credibility before it launches.
• Portfolio, intake and demand management: The “front door.” A consistent theme in discovery is the need for visibility into incoming AI demand.  Multiple teams are typically pursuing AI initiatives without coordination, making it impossible to prioritize, allocate resources or avoid duplication.  Discovery establishes the need for a formal intake mechanism, a structured “front door” that every AI use case passes through before technology decisions are made.
• Technology strategy. Are the foundational prerequisites in place?  Landing zones, identity and access patterns, data environments and security frameworks for agentic development.  These questions surface gaps that must be addressed as part of, or prior to, CoE buildout.
• Platform strategy: The three lanes. Organizations have teams with varying levels of AI capability, and a single platform strategy will not serve all of them.  Discovery defines three lanes: no-code (for citizen developers and business users), low-code (for analysts and domain experts), and full-code (for engineers and architects).  Each lane carries different governance rules, promotion criteria and risk tolerances.  Defining the lanes in specific organizational terms, not as generic archetypes, is essential to making the strategy real.

Phase 2: Listening

After asking the questions, the most important step is listening.  In the reference engagement, discovery revealed multiple siloed technology teams. Each team was building AI solutions independently and was often using different platforms to solve the same class of problem. The result was duplicative investment, inconsistent quality and no shared institutional knowledge

Leadership recognized several compounding pressures:

• Duplicative technologies: Different business units selecting different AI tooling for identical use cases, increasing cost and creating fragmentation.
• Speed gaps: Teams spending significant time on undifferentiated work (environment setup, security review, access provisioning) that a CoE could handle once, centrally.
• Expertise concentration: Deep AI knowledge existing in pockets, with no mechanism to share it across the organization.
• ∫ No single owner of AI demand, prioritization or outcomes measurement.

These were not abstract concerns; they were named, specific pain points raised by the people who would need to operate the AI CoE.  That specificity shaped every structural decision that followed.

Phase 3: Design the structure — A lifecycle, not an org chart

What emerged was not organized around headcount or hierarchy. It was organized around the lifecycle pictured below:

This lifecycle framing is deliberate.  An AI CoE that focuses only on “Deliver” without investing in “Enable,” “Measure,” and “Learn” will plateau quickly.  The full lifecycle ensures the CoE creates compounding organizational capability over time, not just a project pipeline.

Each pillar in the lifecycle describes where the CoE will consistently drive outcomes: recurring improvement areas observed across discovery sessions, readiness assessments and production deployments.

The starting “Enable” pillar sets out to provide enterprise-wide enablement for AI, explicitly not owned by any individual business unit.  This independence is essential for credibility.  A CoE housed within one business unit will always be perceived, correctly, as serving that unit’s interests first.  Centralized ownership reduces friction between business and technical teams and ensures risk and governance considerations are addressed early, not retroactively

The Enablement Pillar needs to consistently drive:

• Skilling. Structured learning roadmaps that guide progress from foundational to advanced levels (including certifications, progress tracking and practical project applications), made available and easy for staff to consume.
• Communities of practice. Cross-functional forums that surface patterns, reusable assets and lessons learned across business units, so expertise does not remain concentrated in pockets.

The “Intake” pillar ensures all AI use cases are well-defined, comparable and strategically aligned before any technology decision is made.  In practice, business unit leads present use cases in a structured format, discuss ROI and business goals, review budget parameters and receive a prioritization decision from a cross-functional group.  The intake process is the CoE’s most visible mechanism for demonstrating value.  It is where the organization first experiences the CoE as a partner, not a bureaucracy.  Within this pillar, the CoE consistently drives:

• Use case qualification: Application of frameworks such as design thinking and the BXT framework (Business; eXperience; Technology) to design journey maps and personas for qualification, prioritization and business alignment, with example scenarios that help teams identify workflow stages where agents can add value.
• Realistic estimates of outcomes: An up-front assessment of how realistic the goals of each AI project are before code is deployed, rather than discovering after the fact that the goals were unrealistic.  This requires a clear approach to measure performance, adoption and impact.

As you move along through to “Delivery”, there needs to be a technology strategy that determines the right approach: build, buy or extend.  It owns solution architecture decisions, ensures foundational infrastructure (data access, identity, dev/test environments) is in place, and applies the three-lane platform model to route work to the appropriate development capability.  This pillar also carries responsibility for democratizing AI development, enabling broad adoption through governed citizen development while maintaining the guardrails that keep the organization compliant and secure.  Within this pillar, the CoE consistently drives:

• Data foundation. In collaboration with a data practice team, building a unified, durable data culture and grounding strategy to fuel every agent with high-quality enterprise context.
• Well-architected AI workloads. Incorporation of Well-Architected Framework (WAF) and Cloud Adoption Framework (CAF) into the CoE’s advice frameworks, with periodic assessments of deployed architectures as both architectures and workloads evolve.
• GenAIOps processes. Appropriate GenAIOps processes implemented throughout each AI workload’s lifecycle.
• Deployment discipline. Automated deployment pipelines with versioning and the ability to rapidly roll back if a new release’s results or performance do not meet expectations.
• Monitoring and optimization. Organizational best practices around what workload elements to monitor, how monitoring is performed and how telemetry data is collected, stored and reviewed, so that significant time is not lost trying to reconstruct what caused an issue.
• Infrastructure. Where appropriate, direct management of infrastructure components: network design, VM operating system and SKU configuration, container repositories and base images and subscription configuration.

Moving from “Delivery” to “Operate”, Evaluation and LLMOps Are Non-Negotiable. One of the most common mistakes organizations make is assuming that traditional software quality assurance practices can be directly applied to AI systems.  They cannot.  Conventional applications are deterministic; given the same input, they produce the same output every time.  Large language models, by contrast, are probabilistic systems whose behavior can vary based on model updates, prompt changes, retrieval context, grounding data and evolving user interactions.  As a result, enterprise AI requires an entirely different operational discipline.  A mature AI Center of Excellence must establish evaluation frameworks, golden datasets, red-team testing, drift monitoring, A/B testing, acceptance thresholds, observability capabilities, feedback loops and end-to-end traceability through correlation identifiers.  These capabilities transform AI deployment from an experimental exercise into an engineered, measurable and governable business capability.

The organizations that successfully scale AI recognize that deployment is not the finish line; it is the beginning of a continuous optimization cycle.  They treat AI systems as living platforms rather than static applications.  Model behavior is continuously monitored, prompt performance is versioned and measured over time, outputs are continuously tested against expected outcomes, and drift detection mechanisms automatically identify degradation in quality, accuracy or relevance.  Equally important, they establish rollback procedures that allow teams to quickly revert prompts, agents, retrieval pipelines or models when issues arise.  This operational rigor enables enterprises to innovate aggressively while maintaining the reliability and trust required for business-critical workloads.

What is emerging today with AgentOps, LLMOps and AI observability engineering is remarkably similar to what occurred with DevOps more than a decade ago.  Organizations eventually learned that software delivery could not scale through manual processes, disconnected tools and siloed teams.  The same reality now applies to AI.  As enterprises move from isolated proofs of concept to fleets of agents, copilots and intelligent applications, they require automated processes for monitoring, evaluation, governance, deployment and lifecycle management.  LLMOps is rapidly becoming the operational foundation that enables AI systems to scale safely, reliably and efficiently across the enterprise.

For CIOs, the implication is clear: responsible AI is impossible without operational visibility.  If an organization cannot explain why a particular AI response was generated, identify which model produced it, determine what grounding data influenced the outcome, or detect when quality has deteriorated over time, then it is not operating enterprise AI at scale with the level of discipline required.  Trustworthy AI is not simply a function of model selection.  It is the result of rigorous evaluation, comprehensive observability and continuous operational governance embedded throughout the AI lifecycle.  In the age of enterprise AI, LLMOps is no longer optional infrastructure; it is a core competency.

The last pillar I am going to cover in depth is “Measure”.  Setting KPIs and measuring against them is pivotal to gauging effectiveness.  Regular assessment allows the CoE to track progress, identify trends and foster a culture of continual improvement.  Collecting the data is not enough.  It must be visible (both good and bad), so that issues, changes required and decisions are based on evidence rather than anecdotes.

High-maturity customers do not track AI accuracy alone. They consistently measure across five dimensions:

Dimension	What’s Measured
Productivity	Time saved, cycle-time reduction, hours returned to employees.
Operations	Cost, downtime, automation rate, throughput.
Quality	Accuracy, forecast reliability, first-time-right rate.
People	Adoption, burnout reduction, satisfaction, capabilities.
Trust	Governance posture, human-override rate, policy adherence.

However, sitting across all the pillars, AI risk and governance is engaged throughout the lifecycle, not as a gate at the end, but as a continuous participant.  This positions the CoE as a responsible innovator, not a shadow-IT function that moves fast and asks forgiveness later.  Within this pillar, the CoE consistently drives:

• Security controls and guardrails. Guidelines and compliance support that work with existing security and workload teams so that security considerations are embedded into every AI-related process and aligned with organizational security policies.
• Compliance. Mechanisms to assess whether workloads are compliant against relevant standards. It remains the responsibility of AI workload teams to configure their workloads to meet regulatory requirements.
• Cost management (FinOps). Processes and tools to monitor, forecast and optimize spending, ensuring that models and resources are efficiently utilized.  FinOps principles drive collaboration between finance, engineering and business teams so that financial considerations are integrated into every stage of AI solution development and deployment.

Emerging trends shaping AI CoEs in 2026 and beyond

As AI adoption accelerates, the mandate of the AI Center of Excellence is expanding well beyond model selection and governance.  The next generation of AI CoEs will be responsible for addressing emerging challenges such as agentic AI governance, multi-agent orchestration standards, AI cost governance and token economics, memory and context management, and the oversight of increasingly diverse open-source and proprietary model ecosystems.  At the same time, enterprise model marketplaces are emerging as a mechanism for standardizing the discovery, approval, deployment and lifecycle management of AI assets across the organization.  Together, these trends signal a fundamental shift: the AI CoE of the future will operate not only as a governance body, but as the enterprise institution responsible for managing the full operational, economic, security and regulatory lifecycle of AI at scale.

Conclusion

The organizations achieving the greatest success with enterprise AI are not necessarily those with the largest or latest models or innovation budgets.  They are the organizations that established governance, evaluation, observability, security and organizational readiness early in their AI transformation journey.  These foundational capabilities enabled them to move beyond experimentation and scale AI responsibly across the enterprise.

As AI adoption accelerates, the AI Center of Excellence is evolving from a strategic advisory group into a mission-critical operational function.  Modern AI CoEs are increasingly responsible for standardization, risk management, security enforcement, lifecycle governance and operational scalability across AI platforms and agents.

Ultimately, the next generation of AI leaders will not be measured by how many Proofs-of-Concept or AI pilots they launched, but by how securely, responsibly and repeatably they operationalized AI to deliver measurable business value at enterprise scale.

Artificial intelligence has quickly become one of the biggest priorities in the executive suite. Organizations are investing heavily in new capabilities, employees are experimenting with AI every day, and technology leaders are under pressure to identify opportunities that improve productivity and reduce costs.

In many organizations, the first question is, “What can we automate?”

It sounds like the right place to start, but I believe it is the wrong question.

Too often, organizations use AI to automate workflows that were designed years ago for a very different business environment. Those workflows have accumulated unnecessary approvals, duplicate activities, manual handoffs and outdated policies over time. AI may execute those processes faster, but it does nothing to address the underlying complexity.

This challenge is not unique to my experience. In its article, The secret to successful AI-driven process redesign, Harvard Business Review explains that organizations create the greatest value when they rethink business processes before applying AI, rather than simply layering technology onto existing ways of working. Likewise, MIT Sloan’s article, How AI is reshaping workflows and redefining jobs, argues that AI delivers its biggest impact when organizations redesign how work flows across the enterprise instead of focusing only on automating individual tasks.

Those findings reinforce an important lesson for leaders. Before asking where AI belongs, ask whether the workflow itself still makes sense.

Every workflow reflects yesterday’s decisions

Most business processes were never designed from beginning to end. They evolved over many years as organizations expanded into new markets, acquired businesses, introduced new systems, responded to audits or adapted to changing regulations.

Each change made sense at the time. Collectively, they often create unnecessary complexity.

Consider a purchasing process that requires six approvals before an order can be placed. One approval may have been added after an audit. Another may have resulted from an acquisition. A third may have been introduced because one business unit wanted additional oversight. Eventually, those approvals simply become “the way we do things.”

Artificial intelligence can summarize purchase requests, route approvals automatically, notify managers and even recommend decisions. What it cannot determine on its own is whether six approvals are still necessary.

That requires leadership.

The same pattern exists throughout finance, manufacturing, supply chain, human resources, customer service and countless other business functions. Organizations often focus on making individual activities faster while overlooking opportunities to eliminate activities altogether.

This is where workflow redesign becomes essential. Instead of asking how AI can automate each step, leaders should ask which steps continue to create value, and which exist simply because they have always been part of the process.

Sometimes the greatest improvement comes from eliminating work rather than automating it.

Redesign first, automate second

The organizations creating the most business value from AI tend to approach the problem differently. Rather than starting with technology, they begin with the business outcome they want to achieve.

That outcome might be reducing order cycle time, improving forecast accuracy, increasing manufacturing throughput, accelerating product development or improving customer responsiveness. A clearly defined objective creates a much stronger foundation than simply looking for places to use AI.

Once the outcome is clear, the next step is understanding the entire workflow. Many delays occur not because individual tasks are inefficient, but because work passes through too many people, too many systems or too many approval points. Mapping the complete process often reveals unnecessary handoffs and redundant activities that can be removed before automation is introduced.

Deloitte has reached a similar conclusion in its ongoing research on enterprise AI adoption. Its latest State of Generative AI in the Enterprise report highlights that organizations generating the greatest business value are redesigning how work is performed rather than simply automating existing tasks. In other words, they view AI as an opportunity to change how work gets done instead of accelerating yesterday’s approach.

Leaders should also distinguish between administrative work and human judgment.

AI is exceptionally good at gathering information, organizing data, preparing summaries and performing repetitive tasks. People continue to provide the greatest value when decisions require experience, context, creativity, negotiation or ethical judgment.

The objective should not be to replace people. It should be to remove low-value administrative work so employees can spend more time applying their expertise where it matters most.

Standardization is equally important. When every business unit performs the same work differently, AI solutions become more difficult to implement, maintain and scale. Simplifying and standardizing workflows before introducing AI creates a stronger foundation for enterprise adoption while producing more consistent business results.

Finally, organizations should measure business outcomes instead of technology activity.

The number of AI assistants deployed or prompts submitted may indicate adoption, but they do not demonstrate business value. Leaders should instead measure improvements in cycle time, quality, customer satisfaction, operating cost, revenue growth and employee productivity. Those are the outcomes executives ultimately care about.

A simple framework for AI-enabled workflow redesign

Over the past several years, I have found it helpful to think about workflow redesign as a simple four-step sequence.

• Simplify. Remove unnecessary work, approvals, reports and handoffs before introducing technology.
• Standardize. Create a consistent way of working across the organization so improvements can be repeated and scaled.
• Redesign. Build the workflow around the desired business outcome instead of existing organizational structures or legacy systems.
• Automate. Apply AI only after the process has been simplified and redesigned.

Organizations often reverse these steps. They automate first and hope efficiency follows. In reality, automation should be the final step, not the first.

Following this sequence helps ensure AI is solving the right problem rather than making an outdated process run faster.

AI should improve work, not preserve it

One of the most valuable questions leaders can ask is surprisingly simple.

If we were designing this process today, would we build it the same way?

That question changes the conversation. It encourages people to challenge assumptions, eliminate unnecessary complexity and rethink how work should flow before technology enters the discussion.

It is also remarkably consistent with what leading researchers are finding. Harvard Business Review emphasizes that successful AI initiatives begin by improving the underlying process. MIT Sloan concludes that organizations achieve the greatest impact when they redesign workflows instead of automating isolated tasks. Deloitte’s research points to the same pattern, showing that the strongest business results come from treating AI as an opportunity to rethink operations rather than simply increase efficiency.

When independent research consistently reaches the same conclusion, it is worth paying attention.

Artificial intelligence is one of the most significant technologies organizations have adopted in decades. Its greatest value will not come from helping us execute yesterday’s workflows more quickly. It will come from allowing us to rethink how work should be done in the first place.

Leaders who redesign workflows before automating them will create simpler processes, better employee experiences and stronger business outcomes. Those who automate first may improve efficiency for a while, but they also risk embedding yesterday’s assumptions into tomorrow’s technology.

At Mercedes-Benz, “Digital First” has long been more than just a theoretical concept; it’s a lived strategy, as a visit to the Digital Factory Campus in Berlin demonstrated. Now, the automaker aims to take the next step in scaling artificial intelligence: Together with the German low-code specialist n8n, the company is introducing a global platform that will enable employees to develop their own AI-supported workflows and integrate them directly into operational processes.

Unicorn startup n8n offers an AI-powered, open-source platform for workflow automation. It enables companies to efficiently manage daily processes using AI agents. Since the Berlin-based company was valued at nearly $2.4 billion in 2025, n8n has further expanded its market presence through strategic partnerships, such as with Deutsche Telekom to support small and midsize enterprises (SMEs) in areas like logistics and sales. According to Deutsche Telekom, n8n is currently the most valuable German AI company, with a valuation of €5.2 billion.

Integrating AI into everyday business

The goal at Mercedes-Benz to make data usable in seconds. To this end, AI-supported automation is to become the standard across the entire group. Behind this lies the strategy of transferring the use of AI from individual pilot projects into central processes in day-to-day business.

“We give our teams at Mercedes-Benz the opportunity to translate ideas into measurable benefits along the value chain — and to actively shape how we work in the future,” says Katrin Lehmann, who will leave her position as CIO at Mercedes-Benz on Sept. 1.

The company has already developed plenty of dedicated AI use cases. These include its own LLM suite MO360LLM, the Digital Factory chatbot ecosystem, the MO360 multi-agent system, and the AI ​​Factory as an idea factory for AI tools.

Three levels of AI competence

But the company wants more. AI and automation applications are to be directly integrated into everyday work. The goal is for employees not only to passively consume AI, but to actively shape it. The automaker distinguishes between three levels of AI competence:

• Takers: Use AI tools in your daily workflow.
• Makers: Design your own automated workflows using platforms like n8n.
• Builders: As experts, they develop highly specialized software solutions.

The company-wide AI rollout received an additional boost from a hackathon. More than 1,500 employees from all business units attended the event. The goal was to independently develop ideas for AI and automation applications. Participants worked on concrete use cases to integrate AI and automation directly into their daily work.

Furthermore, the hackathon served to gather creative input directly from employees and translate it into practice. The best and most powerful concepts from the competition are to be implemented within the company. The plan is to implement AI workflows in all key business areas, namely in development, production, sales, financial services, human resources, and IT.

Modular architecture

Another characteristic of AI workflows is their connection and coordination across existing IT systems to simplify complex processes. In addition to classic automation methods, new approaches using AI agents are being specifically pursued. Last but not least, the workflows are designed to support teams in solving problems faster and making decisions based on data.

As software and AI become key competitive factors in industry, Mercedes-Benz is relying on a modular and flexible technology architecture. This is where the n8n platform comes into play as part of this architecture. It functions as a low-code platform, enabling teams worldwide to create their own AI workflows. Without requiring in-depth programming knowledge, employees can thus integrate AI directly into their operational processes.

Self-hosting on-premises

At the same time, it serves to orchestrate workflows across existing IT systems. To this end, the platform connects these systems to simplify complex processes and ensure seamless integration to ensure this. Another aspect speaks in favor of the chosen solution from Mercedes-Benz’s point of view: strengthening its own digital sovereignty.

This allows n8n to be self-hosted and operated independently of the cloud. In other words, the platform runs within a secure and governance-compliant environment within the group. This way, Mercedes-Benz retains full control over critical systems, data, and work processes.

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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