The Unseen Costs of Ignorance: Why Understanding "As-Is" Processes is Critical to Technology Transformation Success

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Digital transformation has become an imperative for organizations striving to remain competitive and efficient in a rapidly evolving global landscape. Yet, despite significant investments and ambitious objectives, a striking majority of these initiatives fail to deliver their anticipated value. This report delves into the core reasons behind these pervasive failures, spotlighting a critical blind spot: the profound lack of understanding of current operational processes and the actual activities users perform to complete their jobs. This fundamental oversight creates a cascade of downstream impacts, leading to strategic misalignment, poor user adoption, extensive rework, and ultimately, the derailment of costly projects. Drawing upon reputable industry research and compelling statistics, this analysis underscores the urgent need for a data-driven approach to "as-is" process understanding, a foundational element for building resilient and effective transformations.

Executive Summary

Digital transformation initiatives, while essential for modern enterprises, are frequently undermined by a fundamental flaw: an insufficient grasp of existing operational processes and the day-to-day activities of their workforce. This report highlights how this "current state" blind spot leads to significant downstream consequences, including project misalignment, widespread user resistance, and costly rework, ultimately resulting in the failure of a staggering number of transformation programs.

The problem is rooted in traditional approaches that often neglect detailed "as-is" analysis, leading to the design of solutions that fail to address real pain points or integrate effectively with actual user workflows. This oversight is a primary driver of the high failure rates and immense financial waste observed globally. Automated process discovery, exemplified by advanced solutions, offers a powerful antidote. By objectively tracking user activity, these tools generate accurate process flows and requirements, providing the foundational understanding necessary to design effective future states, ensure strategic alignment, foster robust adoption, and mitigate the inherent risks of large-scale technological change.

I. The Pervasive Challenge of Digital Transformation Failures

The pursuit of digital transformation is a strategic necessity for organizations navigating today's complex business environment. However, the journey is fraught with peril, as evidenced by consistently high failure rates and substantial financial losses.

A. Alarming Statistics and Financial Implications of Failed Initiatives

The landscape of digital transformation is marred by a stark reality: a significant majority of initiatives do not achieve their desired outcomes. Research indicates that over 70% of digital transformations fail to deliver positive results.¹ Some analyses even suggest that more than 80% of digital transformation initiatives ultimately fail.³ This pervasive lack of success translates into staggering financial waste. Globally, an estimated $2.3 trillion has been squandered on unsuccessful digital transformation programs.² With global spending on digital transformations projected to reach $3.4 trillion by 2026, and a continued 70% failure rate, the financial implications are set to escalate further.²

Further substantiating this trend, PwC research, which examined over 10,500 projects, revealed that only a mere 2.5% of companies successfully completed 100% of their projects.⁴ The overwhelming majority faced unachieved goals, budget overruns, or significant delays. In the United States alone, problems arising during IT development are costing American companies between $50 billion and $150 billion.⁴ These consistent and alarming statistics, reported by various reputable sources such as McKinsey, BCG, Taylor & Francis, and Third Stage Consulting, underscore that these failures are not isolated incidents or minor technical glitches. Instead, they point to deep, systemic issues within how organizations conceive, plan, and execute large-scale technological change. The problem extends beyond simply selecting the wrong technology; it reflects a widespread immaturity in strategic planning, change management, and the fundamental integration of technology with human processes. This necessitates a profound shift in methodology, moving beyond a superficial embrace of digital tools to a more holistic and grounded approach.

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Exhibit 1: Digital Transformation Failure Statistics - Failure Rate, Impact, Reason

• Beltcourse: 70% Failure Rate; Reason: Unrealistic Expectations, Internal Misalignment, Resistance To Change
• Third Stage Consulting: 80% Failure Rate; Reason: Poor Planning, Lack of Alignment, Unrealistic Expectations, Imbalanced Budget Allocation
• PwC Research: 97.5% Did Not Complete 100% Of Projects; $50-$150B in US Losses Alone; Reason: Unachieved Goals, Budget/Time Overrun, Project Rework

B. Beyond Technical Glitches: The Deeper Roots of Failure

The common perception that technology transformation failures are primarily due to technical breakdowns is largely a misconception. Instead, these technical issues are often symptoms of deeper strategic problems.³ Analysis reveals recurring themes such as unrealistic expectations, internal misalignment, and entrenched resistance to change.¹ These underlying issues often stem from a lack of clear vision for the future state of the organization, excessive delegation to project teams without adequate direction or governance, and an over-reliance on technical implementers, which inadvertently diminishes internal ownership and acceptance of the change.¹

A critical observation across numerous failed initiatives is the consistent emphasis on the human element. Brian Harkin, a recognized expert in digital transformation, emphatically states that "The most significant factors that lead to a lack of success in transformation programs revolve around people".² He further elaborates, "Some organizations appear to have forgotten that it’s the people within the company, their relationships, and how they are led and managed that drive transformational change".² This perspective underscores a crucial understanding: the effectiveness of any new technology is inherently limited by the capabilities and willingness of the individuals using it, and the efficiency of the processes it is designed to support. When organizations neglect the human and procedural context, they inadvertently constrain the technology's potential, leading to suboptimal outcomes. This points to a fundamental truth: successful transformation is not merely an IT-driven endeavor but a comprehensive organizational change management challenge. It demands a holistic approach that prioritizes understanding and adapting human behavior and existing processes, rather than simply deploying new software in isolation.

II. The Critical Blind Spot: Lack of Current State Understanding

A pervasive and often overlooked factor contributing to transformation failures is the insufficient understanding of the "as-is" state—how work is currently performed and the actual activities users undertake. This blind spot creates a fragile foundation for any change initiative.

A. Why "As-Is" Processes and User Activities Remain Undocumented

A common pitfall in transformation initiatives is the eagerness of leaders to bypass the crucial step of documenting the "current state" and instead jump directly to designing the "future state".⁶ This inclination often stems from a perception that current state analysis is a painful, time-consuming, and costly waste of resources.⁶ The desire is to expedite the process and immediately move to solution design.

Compounding this issue is the misconception that if a process is not formally documented or technologically enabled, it somehow does not constitute a "current state." However, as the research clarifies, "Lack of technology does not indicate lack of current state, simply that current state is manual".⁶ This highlights a fundamental misunderstanding of process existence. Even if work is performed manually, informally, or through a series of ad-hoc steps, it still represents the "as-is" reality. The perception that "there is no current state" often merely indicates unaddressed gaps within an existing process that the new future state is intended to resolve.⁶ This short-sighted approach, driven by a desire for quick wins, inadvertently introduces foundational flaws into transformation programs. By failing to thoroughly understand the existing operational reality, organizations are essentially designing solutions in a vacuum. This detachment from the actual workflow inevitably leads to misalignment, unforeseen complications, and the need for extensive rework later in the project lifecycle, ultimately incurring far greater costs than the initial investment in a comprehensive "as-is" analysis.

B. The Foundational Importance of Comprehensive Current State Analysis

A clear and comprehensive understanding of current systems and processes is critical to effectively building the future state.⁶ The "current state" serves as the "beginning of the roadmap" that guides the entire transition through the complexities of change to the desired future state.⁶ A core principle often overlooked is that "You cannot transform something if you don’t understand the details of what needs to be transformed".⁶ This statement succinctly captures the essence of why detailed "as-is" analysis is indispensable.

The granular details of the current state process are essential to designing a future state that genuinely meets the practical needs of the employees performing the process.⁶ Current state analysis is the mechanism through which existing gaps are identified and subsequently documented as requirements for the future state design. If these gaps remain unaddressed and undocumented in the initial analysis, they will inevitably persist as deficiencies in the future state, undermining the effectiveness of the transformation.⁶ As W. Edwards Deming famously stated, "If you can't describe what you are doing as a process, you don't know what you're doing".⁷ This highlights the fundamental link between a clear articulation of processes and genuine operational knowledge.

Documenting the current state provides numerous benefits across the organization. It offers a clear picture of what is changing by pinpointing existing gaps and defining precise requirements for the future state.⁶ This comprehensive documentation ensures that every element necessary for the business process has been thoroughly considered and accounted for in the future state, effectively getting everyone on the same page—from leadership and the project team to stakeholders and technology vendors.⁶ Furthermore, it helps identify weaknesses and opportunities for improvement even before implementation begins.⁶ For organizational change management, understanding the current state allows for an accurate assessment of the change's impact (its size, complexity, and scope), enabling the development of targeted strategies to help users navigate the change curve and quickly adopt new systems and processes.⁶ The interconnectedness between "as-is" analysis and successful "to-be" design is paramount. Without a detailed understanding of the current state, future solutions will inevitably be flawed because they will not address real gaps or user needs. This means that the quality of the "to-be" state is directly proportional to the depth of understanding of the "as-is" state. Investing in this foundational phase is not merely an expense, but a critical risk mitigation strategy and a prerequisite for achieving desired outcomes and a positive return on investment.

III. The Cascade of Consequences: Downstream Impacts of Ignorance

The failure to deeply understand the current state of operations and user activities creates a ripple effect, leading to significant and costly problems throughout the transformation lifecycle.

A. Project Misalignment: Disconnected Strategies and Objectives

A primary consequence of neglecting current state analysis is widespread project misalignment. Companies frequently embark on technology adoption without a clear understanding of the specific problems they intend to solve.⁸ This absence of clarity results in a fundamental misalignment between the chosen technology and the organization's actual business needs.⁸ Without a coherent strategy, organizations often find themselves grappling with fragmented efforts, misaligned priorities, and considerable resource wastage.⁵ Indeed, a significant 57% of executives report that their organizations lack a coherent digital strategy ⁵, highlighting a pervasive strategic void.

This issue is exacerbated by an overemphasis on technology, often at the expense of adequately considering the people and processes it impacts.⁸ This imbalance is a major contributor to failure. As a key principle states, "Focusing solely on the technology aspect of transformation, without giving equal attention to people and processes, is a major reason for failure".⁸ The reality is that "The technology is only as good as the people using it and the processes supporting it".⁸ New technologies, such as AI, machine learning, and cloud computing, are merely tools; their success hinges on "how well these tools are integrated into the organization’s operations and how they enhance workflows".⁹ Historically, a disproportionate 80-90% of digital transformation budgets are allocated to technology, leaving insufficient funds for crucial aspects like organizational change management, business process optimization, and strategic planning.³ A useful benchmark suggests that if more than 50% of the budget is spent on technology, the strategy is likely off-balance.³ This consistent pattern, where organizations prioritize the allure of new technology over strategic rigor and human-centric considerations, often leads to a "shiny object syndrome." This misallocation of resources, rooted in flawed strategic prioritization, means that many transformations are driven by technological capability rather than genuine business need or a deep understanding of actual work performance. The result is solutions that, while technically sound, are functionally misaligned, failing to deliver real value because they do not address the right problems for the right people.

B. Poor User Adoption: Resistance, Frustration, and Underutilization

A significant downstream impact of inadequate current state understanding is widespread poor user adoption, manifesting as resistance, frustration, and underutilization of new systems. Resistance to change is a major hurdle, often stemming from employees' fear of the unknown, concerns about job security, or a lack of understanding regarding the benefits of the transformation.⁵ A striking 70% of digital transformation initiatives fail, with cultural resistance identified as a leading factor.⁵ The issue is compounded by insufficient user involvement; only 52% of employees feel involved in their company's digital transformation journey.⁵ Conversely, organizations that actively involve employees in the planning stages report a 25% higher adoption rate of new technologies.⁵

Insufficient training is another pervasive problem. Traditional, one-time training sessions, often lasting only a day or two, are frequently inadequate for achieving comprehensive proficiency with complex enterprise platforms.¹¹ This leaves many employees to navigate the new technology independently, leading to underutilization and an increase in IT support requests.¹⁰ The value of investing in employees is clear: 94% of employees would remain longer at companies that invest in their career development.⁵ Furthermore, organizations allocating 10-15% of their transformation budgets to training initiatives report a 25% increase in workforce productivity post-implementation.⁵

When digital tools are perceived as too complex or lack a user-friendly interface, employees find them difficult to learn and use effectively, leading to disengagement.¹¹ This is reflected in the fact that 69% of employees report frustration with workplace technology.¹⁰ This negative experience has a tangible impact on morale, with 55% of employees stating that negative technology experiences affect their mood and morale.¹⁰

The consequences of poor user adoption are severe and far-reaching:

• Reduced Return on Investment (ROI): Organizations with low user adoption rates consistently fail to realize the full ROI on their technology investments. The full potential of the new technology remains untapped, resulting in inefficiencies and wasted resources.¹²
• Decreased Productivity: Employees waste significant time attempting to understand and use new software, leading to inefficiencies and errors. Frustration often causes them to revert to outdated, less efficient methods, further hindering productivity.¹²
• Higher Support and Maintenance Costs: Low adoption rates correlate with significantly higher IT support costs, including increased helpdesk requests and additional training needs to address issues arising from improper tool usage.¹²
• Competitive Disadvantage: Companies with poor user adoption risk falling behind competitors who more effectively leverage technology. A Deloitte study highlighted that companies with high technology adoption rates are 2.5 times more likely to be market leaders ¹², underscoring the critical link between adoption and market leadership.

This creates a detrimental cycle: a lack of user involvement and insufficient training directly contribute to resistance and difficulty with new technology, which in turn leads to poor utilization and employee frustration. The ultimate outcome is a reduced ROI, decreased productivity, and higher support costs. This negative feedback loop perpetuates wasted investment and discourages future investment in proper change management, trapping organizations in a cycle of underperformance. User adoption is not a post-implementation afterthought; it is a critical success factor that must be integrated into the core planning and design phases of any transformation. Neglecting the "people" aspect directly undermines financial and strategic objectives, turning technology investments into liabilities rather than assets.

C. Costly Rework and Project Delays: The Cycle of Correction

Perhaps the most direct and quantifiable consequence of a lack of current state understanding is the pervasive issue of costly rework and project delays. Requirement shifts frequently originate from vague early analysis, leading to a continuous accumulation of corrections later in the project lifecycle.¹³ When the client and development team fail to precisely define project details at the outset, unforeseen issues and surprises become inevitable, almost guaranteeing the need for adjustments down the line.¹³

Common challenges in requirements gathering include communication problems, constantly changing requirements, poorly defined requirements, stakeholders altering their minds, incomplete requirements, insufficient user involvement, and undocumented processes.¹⁴ As one expert notes, "Vague, poorly understood requirements lead to overly optimistic estimates, which come back to haunt us when the inevitable overruns occur".¹⁵ Furthermore, "Insufficient user involvement leads to late-breaking requirements that delay project completion" ¹⁵, and "Ambiguous requirements result in wasted time when developers implement a solution for the wrong problem".¹⁵

The financial burden of rework is substantial. Rework, defined as redoing something that was thought to be completed, is a major consequence of requirements problems.¹⁵ It can consume 30-50% of the total development cost, with requirements errors alone accounting for 70-85% of that rework cost.¹⁵ Critically, correcting a defect found late in the project costs significantly more than fixing it early on.¹⁵ This demonstrates that the longer a misunderstanding of the current state or user activity persists, the exponentially more expensive it becomes to rectify.

The impact extends beyond direct development costs. In the U.S. construction industry, for example, 48% of all rework is attributed to poor data and miscommunication, costing over $31 billion annually.¹⁶ Globally, this figure reached an estimated $280 billion in 2018.¹⁶ These issues also severely impact project timelines: large projects frequently take 20% longer than planned and run up to 80% over budget, partly due to misaligned information and slow decision cycles.¹⁶ This data reveals a direct causal link between initial requirements deficiencies and massive downstream financial penalties. The initial "pain" of thorough current state analysis is a preventative measure against much greater future pain. Neglecting current state analysis and robust requirements gathering is not a cost-saving measure; it is a significant financial risk. The investment in understanding "as-is" processes and user activities is essentially an investment in cost avoidance and project success, directly impacting the bottom line and project viability.

Exhibit 2: Financial Impact of Requirements Problems and Rework

• Rework Cost (Software Development): 30-50% of total development cost; contributing factors: requirements errors (70-85% of rework costs)
• Project Delays: 20% longer than planned; contributing factors: misaligned information, slow decision cycles
• Budget Overruns: Up to 80% over budget; contributing factors: misaligned information, slow decision cycles

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IV. Illustrative Case Studies of Transformation Failures

Examining real-world examples provides concrete evidence of how a lack of current state understanding, coupled with strategic and cultural missteps, can derail even the most ambitious transformation efforts. These cases demonstrate that failures are often interconnected, stemming from a complex interplay of process, people, and strategy.

A. Operational Inefficiencies and Data Management Failures

• Procter & Gamble (P&G): P&G's digital overhaul, despite significant investment, struggled because it operated on the flawed assumption that technology alone could resolve deep-rooted operational inefficiencies.¹⁷ The company failed to seamlessly integrate digital initiatives into its existing traditional marketing and sales strategies, resulting in a disjointed approach.¹⁷ This case clearly illustrates that operational inefficiencies are often deeply embedded in how work is actually done and cannot be resolved by simply layering new technology on top. A lack of understanding of the actual workflows meant the new technology did not fit the existing operational reality, leading to friction and failure.
• Nike’s Supply Chain Misstep: Nike's implementation of i2 software for its global supply chain proved disastrous. The system was configured for ideal scenarios, completely lacking the flexibility required for Nike's complex, real-world supply chain variations.¹⁷ This fundamental misunderstanding of the actual variability and complexity of their current processes led to inaccurate demand forecasting, severe inventory issues (overstocking and understocking), and ultimately, $100 million in losses.¹⁷ This is a direct example of how an "ideal scenario" assumption, born from insufficient detailed current state analysis, directly caused massive financial loss and operational disruption. It underscores that real-world processes are rarely linear or perfectly predictable.
• Target’s Failed Canadian Expansion: Target's foray into the Canadian market failed significantly due to its inability to adapt digital and supply chain strategies to local nuances. The company attempted to replicate its U.S. success without a deep understanding of the Canadian market's specific consumer behaviors and operational differences.¹⁷ Their data management and IT systems, which functioned adequately in the U.S., could not handle the complexity of a new international market, leading to critical stock issues and unsatisfactory customer experiences.¹⁷ This illustrates a failure to understand not just internal processes, but how those processes interact with external market realities and user behaviors in a new context. The absence of a detailed "as-is" analysis of the Canadian market's operational nuances and consumer habits led to a catastrophic misstep.

B. Lack of Market and Consumer Behavior Adaptation

• Nokia’s Struggle with Digital Innovation: Once a dominant force, Nokia failed to adapt to the smartphone revolution despite possessing advanced technological capabilities. This was primarily due to an internal culture that resisted change and lacked the agility to pivot quickly in response to evolving market trends.¹⁷ This cultural inertia stifled innovation and delayed Nokia's entry into the smartphone market, allowing competitors to seize dominance.¹⁷ This highlights that understanding "how work is done" extends beyond internal processes to encompass external market dynamics and evolving consumer behaviors. Nokia's failure to adapt its internal "way of working" (processes and culture) to external shifts in user preferences proved fatal.
• Blockbuster’s Digital Ignorance: Blockbuster, a former giant in video rentals, clung resolutely to its physical rental model, fundamentally underestimating the disruptive potential of online streaming services like Netflix.¹⁷ This demonstrated a profound lack of understanding of evolving user activities and preferences, as consumers were rapidly shifting towards digital consumption of media. Their internal processes remained rigidly tied to a brick-and-mortar model, failing to adapt to the new digital landscape, ultimately leading to bankruptcy.¹⁷ This is a classic example of a company failing to understand evolving user activities and preferences outside their traditional operational model, leading to irrelevance.

C. Strategic and Cultural Missteps

• General Electric (GE): GE's ambitious digital transformation failed due to a pervasive lack of cohesive vision, communication, and collaboration across all levels.¹⁸ Their deeply ingrained traditional "Six Sigma" product development process, which emphasizes perfection and long cycles for physical products, fundamentally clashed with the iterative and rapidly evolving nature of software development. This required a new mindset and approach that GE struggled to adopt.¹⁸ This illustrates a cultural resistance to adopting new "ways of working" (processes) that are fundamentally different from entrenched organizational habits. A lack of understanding that software development processes differ from physical product manufacturing processes led to strategic misalignment and failure.
• Ford’s Attempt to Transform into a Mobility Company: Ford's vision to transform into a mobility services provider failed due to a lack of cohesive vision and a critical oversight of actual customer needs.¹⁸ The company treated its "Smart Mobility LLC" as a separate, isolated entity rather than integrating it as an inherent part of its core business processes.¹⁸ This led to a poorly designed platform and a disconnect between the new initiative and the established business. This demonstrates a failure to integrate new strategic directions with existing operational processes. By treating the new digital initiative as an isolated entity, Ford failed to understand how it needed to seamlessly integrate with and transform its core business, leading to a disconnect between ambition and execution.
• Wells Fargo: Wells Fargo's digital transformation was hampered by a rigid, siloed operational structure and a lack of clear leadership and vision.¹⁸ While understandable for a banking concern, this deeply ingrained organizational structure, which dictates "how work is done," actively impeded digital efforts, leading to stagnation despite innovative ideas like cardless ATMs. This demonstrates how deeply ingrained organizational structures and cultures can actively impede digital transformation, even with innovative ideas. The lack of understanding of how to break down these silos and integrate processes across departments led to stagnation.

These case studies consistently demonstrate that transformation failures are not isolated to one area (e.g., just technology or just people) but are a complex interplay. Nike's operational inefficiency stemmed from a misunderstanding of process variability. Target's failure linked data management to market adaptation. Nokia's and Blockbuster's failures were rooted in cultural resistance and a lack of understanding of evolving consumer behavior. GE, Ford, and Wells Fargo highlight how strategic vision and cultural rigidity directly impact process adoption and integration. This reveals a profound truth: the lack of current state understanding is not merely a technical flaw; it is a strategic and cultural blind spot that cascades across all facets of a transformation. Successful transformation requires a holistic view, where understanding the "as-is" state of processes, user activities, organizational culture, and market dynamics is paramount. Ignoring any of these interconnected elements, particularly the foundational understanding of how work is actually done, significantly increases the likelihood of misalignment and failure.

V. Paving the Path to Success: The Role of Automated Process Discovery

Given the pervasive challenges and costly failures associated with traditional technology transformations, a new approach is clearly warranted. Automated process discovery emerges as a vital solution, directly addressing the critical blind spot of current state understanding and paving a more reliable path to success.

A. How Automated Process Discovery Illuminates Actual Workflows

Process discovery is a strategic technique designed to gain a deep understanding of an organization's workflows, enabling the visualization and analysis of processes in their "as-is" state. It serves as a foundational step in truly understanding how processes actually function in practice. Historically, process discovery was a manual, labor-intensive effort involving interviews with workers and in-person observations. While still useful, these traditional techniques are time-consuming, subjective, and highly prone to errors. Moreover, many complex processes today are deeply embedded within technology systems, making manual discovery inadequate for uncovering hidden inefficiencies or undocumented steps in digital workflows.

Mechanism: Tracking User Activity for Process Flows and Requirements

Automated business process discovery overcomes these limitations by leveraging technology to retrieve data directly from system logs and digital workflows, capturing real-time process execution. This is achieved through tools like process mining software and AI-driven analytics platforms. The mechanism typically involves:

• A lightweight, app-agnostic software client that continuously records every user touchpoint across various applications, shifts, and roles within an organization.
• The raw data, often in the form of event logs and user commentary, is then converted into structured data using sequencing and Natural Language Processing (NLP) techniques.
• Artificial Intelligence (AI) and Machine Learning (ML) models are employed to aggregate, clean, and transform this structured data, culminating in the creation of comprehensive Process Definition Documents (PDDs).
• Finally, sophisticated process visualization and granular reporting capabilities enable stakeholders to clearly understand process variations across different departments, teams, or regions, and to precisely identify operational inefficiencies. This objective, real-time data on how work is actually done provides the verifiable proof points needed to drive effective transformation.

Key Advantages: Speed, Accuracy, and Comprehensive Visibility

Automated process discovery offers distinct advantages that directly address the pain points of traditional methods:

• Speed: Its automatic and continuous data collection, processing, and analysis capabilities enable rapid process discovery. Organizations can "discover processes 90% faster without disrupting the employee workflow". For instance, a multinational retailer dramatically reduced their assessment time from six months to just two weeks, resulting in millions of dollars in savings.
• Accuracy: This tool provides highly accurate, granular, and enterprise-wide process mapping, empowering organizations to make truly data-driven decisions and strategies. It allows for the discovery of every process and its variations, effectively eliminating errors and rework that are common with manual process discovery methods.
• Comprehensive Visibility: Business owners gain a complete and transparent picture of their business processes, including hidden bottlenecks, and have granular-level access to data. As the research highlights, "Because process discovery lets you see your exact workflows, it enables you to spot any differences between how you think things are working and how things truly are working". This ability to compare perceived versus actual workflows is crucial for finding hidden bottlenecks and opportunities for improvement. Process discovery also "illuminates opportunities to accelerate workflows and optimize resource allocation, empowering organizations to respond faster and adapt more readily to changing market demands".

The shift from manual, subjective, and error-prone methods to automated, data-driven process discovery represents a fundamental transformation in itself. The ability to objectively identify discrepancies between perceived and actual workflows is a direct catalyst for identifying hidden bottlenecks and inefficiencies. This moves process understanding from anecdotal evidence to empirical data, which is crucial for accurate requirements. The significant speed and cost-saving figures directly address the perception that current state analysis is "painful, time-consuming, and costly." Automated process discovery is not just a tool; it is a paradigm shift that enables organizations to overcome the inherent human biases and limitations of traditional discovery methods. It provides an objective, real-time "single source of truth" for how work is actually done, directly addressing the core problem of this report and laying the groundwork for truly data-driven transformation.

Exhibit 3: Benefits of Automated Process Discovery

1. Benefit - Speed: Rapid data collection, processing, and analysis, significantly reducing time to understand processes.
   
   1. "You can discover processes 90% faster without disrupting the employee workflow."  "A multinational retailer reduced assessment time from six months to two weeks, saving millions."
2. Benefit - Accuracy: Provides highly precise, granular, and enterprise-wide process mapping, eliminating errors and rework.
   
   1. "This tool empowers you to make data-driven decisions and strategies using highly accurate, granular, enterprise-wide process mapping. You can discover every process and its variations."
3. Benefit: Comprehensive Visibility - Offers a complete picture of business processes, including hidden bottlenecks and variations, by revealing actual workflows.
   
   1. "Because process discovery lets you see your exact workflows, it enables you to spot any differences between how you think things are working and how things truly are working."
   2. "Business owners get a complete picture of the business process, bottlenecks, and granular-level access to data."
4. Benefit: Automation Identification - Facilitates the identification of suitable candidates for automation, accelerating digital transformation.
   
   1. "Process discovery helps you identify candidates fit for automation and makes rapid continuous automation a reality."

B. Enabling Precise Requirements Gathering and Future State Design

By providing a crystal-clear, data-driven understanding of current operations, automated process discovery lays the essential groundwork for smart, high-impact business decisions. This objective view of the "as-is" state directly addresses the pervasive challenges of vague, incomplete, and constantly changing requirements that plague traditional projects.¹⁴ When organizations possess accurate process flows and detailed insights into user activities, they can eliminate the guesswork that often leads to costly rework and project delays.

The precision offered by automated process discovery ensures that requirements for the future state are accurately captured, allowing new systems and processes to be designed to appropriately address identified gaps and needs.⁶ This capability is instrumental in identifying high-value opportunities for automation, such as Robotic Process Automation (RPA), by pinpointing repetitive tasks that can be streamlined or eliminated. Automated process discovery empowers organizations to make data-driven decisions and strategies using highly accurate, granular, enterprise-wide process mapping. This transformation from subjective, interview-based requirements gathering to an objective, data-driven discipline ensures that the "to-be" state is built upon a solid, factual understanding of the "as-is," significantly de-risking the entire transformation process.

C. Driving User Adoption Through Enhanced Understanding and Training

The insights gleaned from automated process discovery play a crucial role in fostering successful user adoption. By clearly understanding actual business processes and identifying inefficiencies, organizations can implement changes that lead to a more fulfilling work environment and improved employee satisfaction. This directly counters the common "resistance to change" and "frustration with workplace technology" observed in many failed initiatives.¹⁰ When employees see that new systems genuinely improve their daily work by eliminating pain points and optimizing workflows, their desire and ability to adopt the changes naturally increase.

The objective data on user activities and process flows can inform highly targeted and contextual training programs. Instead of generic, insufficient training that often leaves employees struggling ¹⁰, insights from process discovery enable the development of training materials that address actual user pain points and specific workflow changes. This tailored approach makes training more relevant and digestible for end-users.⁶ Furthermore, a deep understanding of the "as-is" state allows for more effective change management strategies. By accurately assessing the impact of the change—its size, complexity, and specific implications for different user groups—organizations can develop precise strategies to help users navigate the change curve and quickly adopt new systems and processes.⁶ This approach shifts the focus of user adoption from a reactive "training fix" to a proactive, data-informed design process. It allows organizations to cultivate empathy for their users' current struggles, leading to the development of solutions that are intuitively adopted because they genuinely improve the daily work experience, rather than imposing abstract changes.

VI. Conclusion: Building Resilient and Effective Transformations

The evidence overwhelmingly demonstrates that traditional technology transformation programs are plagued by high failure rates and immense financial waste. This pervasive lack of success stems primarily from a critical blind spot: the absence of a deep, data-driven understanding of current operational processes and the actual activities users perform to complete their jobs. This foundational ignorance is not a minor oversight; it cascades into severe downstream impacts, including strategic misalignment, widespread user resistance and poor adoption, and exorbitant rework costs. The analysis clearly illustrates that neglecting the "as-is" state is not a cost-saving measure, but a direct path to project failure and competitive disadvantage.

Automated process discovery emerges as a vital solution to this systemic problem, offering unprecedented speed, accuracy, and comprehensive visibility into how work is truly done. By transforming subjective assumptions into objective, verifiable data, this technology enables precise requirements gathering, informed future state design, and a user-centric approach that fosters high adoption rates and maximizes return on investment. The path to resilient and effective transformations lies in prioritizing this foundational understanding, shifting from a technology-first mindset to one that places people, processes, and data-driven insights at the core of every initiative.

VII. Strategic Recommendations for Future Transformation Initiatives

To mitigate the risks and failure points endemic to traditional technology transformation programs, organizations must adopt a strategic approach that prioritizes a deep understanding of their current operational reality. The following recommendations outline a path toward more successful and sustainable transformations:

• Mandate Comprehensive "As-Is" Analysis as a Foundational Step: Implement automated process discovery tools as a mandatory "Phase Zero" planning step for all significant transformation projects. This ensures an objective, data-driven understanding of current processes and user activities before any solution design, technology selection, or future state planning commences. This proactive measure will prevent the design of solutions that fail to address real pain points or integrate effectively with existing workflows.
• Prioritize People and Processes Over Pure Technology Spend: Rebalance transformation budgets to allocate sufficient resources to organizational change management, business process optimization, and comprehensive, contextual training. Recognize that technology serves as an enabler, not the solution itself. A balanced investment ensures that the human element is prepared and empowered to leverage new tools effectively, maximizing the return on technology investment.
• Foster a Culture of Continuous Process Improvement and Adaptability: Cultivate an organizational mindset that views process understanding and optimization as an ongoing journey, rather than a one-time event. Leverage the continuous insights provided by process discovery to identify and eliminate inefficiencies incrementally, driving sustained improvements and building organizational agility to respond to evolving market demands.
• Engage Users Early and Continuously: Involve end-users and key stakeholders from the very inception of the transformation journey. Utilize the granular data from process discovery to tailor solutions and training programs to their specific needs, pain points, and actual workflows. This proactive, user-centric engagement is crucial for mitigating resistance, fostering a sense of ownership, and driving high adoption rates.
• Embrace Data-Driven Decision Making: Transition from making decisions based on assumptions or anecdotal evidence to relying on measurable, objective insights provided by automated process discovery. Utilize the granular data and visualizations to inform choices regarding future state design, identify optimal automation candidates, and strategically allocate resources, ensuring that investments are aligned with tangible business outcomes.
• Establish Robust Governance and Metrics for "Human" Factors: Implement clear, quantifiable metrics for user adoption rates, training effectiveness, and process efficiency. Track these human-centric indicators rigorously alongside traditional project Key Performance Indicators (KPIs). Hold leadership accountable not only for the technical implementation of new systems but also for the successful integration of new processes into the daily work of employees, ensuring that the transformation truly delivers value across the organization.

Find the full infographic here.

Works cited

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