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As organizations expand workflow automation and introduce AI into more areas of the enterprise, the consistency of the processes behind that technology becomes increasingly important. Before automating more work, leaders need to understand how work is actually being performed—and whether the variations across the enterprise are intentional, understood, and governed. AI Changes the Consequences of Process Variation Organizations have always had process variation. Different business units, locations, markets, customers, and regulatory environments often require work to be performed differently. That variation is not inherently a problem. The challenge is that many organizations do not fully understand where those differences exist or why. Over time, documented processes are supplemented by local practices, manual workarounds, spreadsheets, email handoffs, institutional knowledge, and exceptions that become part of how work actually gets done. For years, people have compensated for this complexity. Experienced employees know which exception applies, who needs to approve something, where to find missing information, or how to navigate a process when reality does not match the documented workflow. AI changes that equation. As organizations introduce AI into business processes, the ability to rely on human judgment to continuously reconcile inconsistencies becomes less sustainable. AI increasingly depends on clear business rules, reliable operational context, and workflows that behave predictably enough for technology to act with confidence. This does not mean every process must be standardized. AI doesn’t require every process to be the same. It requires the differences to be understood, intentional, and governed. And that creates a different question for executives considering AI: Do we understand how work is actually being performed across the enterprise well enough to automate or augment it? Not All Process Variation Is a Problem Process consistency is often interpreted as standardization: one process, performed the same way, everywhere. In a complex enterprise, that is rarely practical—and it may not even be desirable. A manufacturing facility may operate differently because of local regulatory requirements. A business unit may serve customers with unique contractual obligations. Different markets, products, suppliers, or operating models may legitimately require different workflows. These variations can be an important part of how the business operates effectively. The greater concern is operational drift—variation that develops without deliberate design or governance. A workaround introduced to solve a temporary problem becomes permanent. An approval step is interpreted differently across locations. A spreadsheet is created because an existing system does not support a particular need. Employees develop their own ways of handling exceptions that eventually become embedded in daily operations. Eventually, the organization may have multiple versions of the same process without clearly understanding why they differ. That distinction becomes increasingly important as AI is introduced into workflows. Before deciding what should be standardized, automated, or AI-enabled, organizations need to distinguish between variation that serves a legitimate business purpose and variation that simply accumulated over time. The problem isn’t process variation. The problem is variation the organization doesn’t understand or govern. When the Documented Process Isn’t the Actual Process One of the greatest challenges for enterprise leaders is that the process they believe exists may not be the process employees actually follow. A documented workflow might show a straightforward sequence of activities, approvals, and decisions. In practice, employees may rely on spreadsheets to track information, email to obtain approvals, manual data entry between systems, or institutional knowledge to determine what happens next. Exceptions that appear uncommon on paper may actually be part of everyday execution. These workarounds often exist for good reasons. Employees create them to keep work moving when systems, processes, or business rules do not fully reflect operational reality. The problem arises when those workarounds become embedded in execution without being visible or governed. This creates a particular challenge for workflow automation and AI. Technology operates against the processes, data, and rules available to it. It may not recognize the unwritten knowledge employees use to resolve an exception or understand why a particular step is routinely performed differently. Automating the documented workflow without understanding the actual workflow can therefore create a false sense of improvement. The process may move faster, but the underlying inconsistencies remain—or become more difficult to manage at scale. Before organizations automate more of their work, they need visibility into how that work is actually being performed. Why AI Raises the Stakes Process inconsistency is not new. Organizations have managed it for decades because people are remarkably effective at filling the gaps between systems, processes, and business rules. AI changes the consequences of those inconsistencies. As AI becomes more involved in operational decisions and workflow execution, it increasingly depends on clear rules, reliable data, consistent context, and defined decision authority. When those conditions are unclear, AI does not automatically resolve the ambiguity. It operates within it. Consider a process where different business units apply the same approval policy differently. Employees may understand those differences because they know the customer, location, history, or unwritten rules behind the decision. An AI-supported workflow needs that context to be explicit enough to determine which rule applies and when an exception is appropriate. The same issue extends to data. When processes are performed differently, they often create, validate, and maintain information differently as well. What begins as workflow inconsistency can quickly become a data-quality problem—and ultimately an AI reliability problem. This is why workflow consistency is an AI Readiness issue, not simply a process optimization issue. Research Insight McKinsey’s global research on AI adoption reinforces the connection between workflow design and business value. Among 25 organizational attributes studied, redesigning workflows had the greatest impact on an organization’s ability to realize EBIT value from generative AI. Yet only 21% of respondents whose organizations were using generative AI reported that they had fundamentally redesigned at least some workflows. Source: McKinsey & Company, “The State of AI: How Organizations Are Rewiring to Capture Value,” March 2025. Organizations do not need to eliminate every variation before adopting AI. But they do need enough visibility and governance to ensure that AI is operating within processes the organization understands and business rules it

As organizations expand their use of AI, the conversation often turns to data: how much is available, where it resides, and how AI can access more of it. But becoming AI-ready is not simply about giving AI access to more enterprise data. It is about ensuring the information AI relies on is accurate, complete, consistent, accessible, governed, and understood in context. The more important question for executives is not “Do we have enough data for AI?” It is “Can AI trust the data our enterprise creates?” AI Inherits the Data Environment It Enters Organizations have operated with imperfect data for decades. In many cases, employees have learned how to compensate for it. They recognize duplicate records. They know which system is usually more reliable. They understand that the same customer, product, supplier, or business term may be defined differently across functions. When information is incomplete or conflicting, experienced employees know who to call, where else to look, or how to reconcile the difference before making a decision. Those human interventions can make underlying data problems less visible than they really are. AI changes that equation. As AI becomes more involved in analysis, recommendations, decisions, and workflow execution, organizations are increasingly asking technology to act on enterprise information with less human interpretation between the data and the outcome. AI does not automatically know which duplicate record is correct, which system should be trusted, why two definitions differ, or whether missing information represents an error or a legitimate exception. Unless that context is available and governed, AI inherits the same uncertainty the organization has learned to work around. That is why AI-ready data is not simply about volume or access. It is about whether the enterprise can provide information with enough quality, context, consistency, and governance for AI to use it reliably. AI doesn’t fix an organization’s data environment. It operates within it. Clean Data and Trusted Data Are Not the Same Thing When organizations discuss data quality, the conversation often focuses on the condition of the data itself. Are records complete? Are duplicates being removed? Are formats consistent? Are errors being corrected? Those efforts matter. Data cleansing, normalization, deduplication, and consolidation can all improve the quality of enterprise information. But clean data and trusted data are not necessarily the same thing. A record can appear complete and accurate without providing confidence in how the information was created, validated, or maintained. Different systems may contain equally plausible values. Business units may define the same data differently. Information may be technically correct but lack the operational context needed to determine when or how it should be used. For AI, those distinctions matter. Trusted enterprise data requires organizations to understand more than what information they have. They need to understand where critical data originates, who owns it, which business rules govern it, how it is validated, when it changes, and whether its meaning remains consistent as it moves across processes and systems. This shifts the data-quality conversation from correcting information after problems occur to understanding the conditions that create and maintain reliable information in the first place. Clean data describes the condition of information. Trusted data reflects confidence in how that information was created and maintained. Data Quality Begins Where Data Is Created Many data-quality problems do not begin in databases. They begin in the business processes that create and maintain the data. Every day, employees, customers, suppliers, systems, and applications create or modify enterprise information as work is performed. Customer records are established, suppliers are onboarded, materials are created, orders are entered, approvals are completed, and transactions move between functions and systems. The quality of that information is often determined at the moment it enters the enterprise. If different workflows capture different information, employees interpret requirements differently, validation rules are inconsistent, or manual workarounds bypass established controls, unreliable data can become part of the operational environment before a traditional data-quality process ever encounters it. Organizations may then invest significant effort downstream cleansing records, resolving duplicates, reconciling systems, and correcting information. Those activities can address the symptoms, but they do not necessarily address the conditions that continue creating the problems. If the enterprise continually creates unreliable data, cleansing it after the fact becomes an endless cycle. Creating trusted enterprise data therefore requires looking upstream at the processes and business rules responsible for creating and changing it. Where is information first captured? What validates it? Who determines whether it is correct? What happens when an exception occurs? Can employees bypass required information or controls to keep work moving? These are operational questions as much as they are data questions. For AI readiness, that distinction is critical. Improving data quality for AI is not simply an exercise in preparing existing datasets. It requires strengthening the way important information is created, validated, governed, and maintained through everyday execution. Trusted data begins at the source—not with the cleanup that happens afterward. Governance Turns Data Quality Into an Operating Discipline Creating trusted data at the source requires more than validation rules and technology. It requires clarity about who is responsible for the information and how it should be managed throughout its lifecycle. In many enterprises, data crosses organizational boundaries constantly. Customer information may originate in one function and be used by several others. Supplier or product data may move through procurement, operations, finance, logistics, and multiple enterprise systems. Each interaction creates an opportunity for information to be interpreted, changed, supplemented, or duplicated. Without clear governance, responsibility can become fragmented. Different functions may apply different definitions, validation requirements, or business rules to the same information. Employees may know how their part of the process works without understanding how decisions made upstream affect the people and systems that depend on the data downstream. Governance helps establish that clarity. Who owns critical information? What does a particular data element mean? Which business rules determine whether it is valid? Who has authority to change it? How are exceptions handled? What happens when information moves between systems or business functions? For AI, these

Artificial Intelligence (AI) won’t be the differentiator—organizational readiness will. AI has become an executive priority across nearly every industry. Yet successful AI adoption depends on far more than selecting the right technology. Organizations that prepare their operational foundations as intentionally as they prepare their AI strategy are better positioned to achieve lasting business value. What Is AI Readiness? AI Readiness was never determined by the technology an organization selects. It has always been determined by the organization AI is asked to support. AI Readiness is an organization’s ability to successfully adopt, govern, and scale AI by establishing the operational foundations required for consistent, reliable, and trusted outcomes. While many organizations approach AI as a technology initiative, AI Readiness extends well beyond selecting AI platforms or deploying new tools. It reflects how prepared the enterprise is to support AI through trusted data, consistent business processes, effective governance, integrated technology, organizational capability, and a clear strategy for creating business value. Organizations that are AI-ready recognize that AI can only perform as effectively as the environment in which it operates. When workflows are inconsistent, business rules are applied differently across the enterprise, or data lacks quality and trust, AI will often amplify those weaknesses rather than overcome them. This is why AI Readiness should be viewed as a business discipline rather than simply a technology initiative. It helps executive leaders evaluate whether the enterprise is prepared to support AI successfully before making additional technology investments, reducing implementation risk while increasing the likelihood of achieving meaningful business outcomes. Why AI Readiness Has Become an Executive Priority Although the operational foundations of AI Readiness have always existed, the business environment surrounding them has changed dramatically. Artificial intelligence has become a strategic capability that is reshaping how organizations operate, make decisions, and create value.  As investment has accelerated, so have executive expectations. Organizations are no longer asking whether AI will influence their business. They are asking how quickly they can adopt it and where it can deliver the greatest value. Research Insight McKinsey’s 2025 State of AI survey found that AI adoption has become widespread across enterprises, yet organizations creating measurable business value are the ones redesigning workflows, strengthening governance, and making organizational changes alongside technology deployment. The research reinforces that successful AI adoption depends on organizational readiness—not technology implementation alone. – McKinsey & Company, The State of AI: Global Survey (2025) What has changed is not simply the technology. The game has changed. For decades, organizations could often improve business performance by implementing new enterprise applications, modernizing existing systems, or automating individual processes. AI operates differently. Rather than following predefined rules, AI depends on the quality of the operational environment it enters. It learns from enterprise information, supports business decisions, and increasingly becomes part of how work is performed. As a result, AI reflects the operational maturity of the organization itself. It cannot consistently compensate for fragmented workflows, inconsistent business rules, disconnected systems, or data that lacks quality and trust. Instead, it often exposes those challenges while accelerating their impact across the enterprise. This shift requires executives to think differently about AI investment. The first question should no longer be, “Which AI solution should we implement?” It should be, “Is our organization prepared to support AI successfully?” That question represents the beginning of AI Readiness. Why So Many AI Initiatives Fall Short Previous generations of enterprise technology could often deliver meaningful business value despite operational immaturity. Organizations successfully implemented ERP systems, CRM platforms, workflow automation, analytics, and other enterprise technologies while continuing to rely on disconnected systems, inconsistent processes, manual workarounds, spreadsheet dependencies, and data that was less than ideal. AI changes that equation. Unlike traditional enterprise applications, AI increasingly depends upon the quality of the operational environment in which it operates. It learns from enterprise information, participates in business processes, and influences decisions across the organization. As a result, weaknesses that organizations were once able to compensate for through human judgment and experience become significantly more difficult to overcome. Many organizations continue to approach AI as another technology implementation rather than recognizing it as an organizational capability. They evaluate AI platforms before evaluating operational readiness, often assuming AI will compensate for inconsistent processes, fragmented workflows, disconnected systems, or poor data quality. In reality, AI is more likely to expose those weaknesses than eliminate them. Research Insight McKinsey’s global AI research continues to show that while AI adoption is becoming nearly universal, only a minority of organizations have successfully scaled AI across the enterprise and achieved significant financial impact. The difference is increasingly tied to workflow redesign, governance, organizational change, and operational integration—not simply deploying additional AI tools. McKinsey & Company, The State of AI: Global Survey (2025) This explains why organizations with significant technology investments may still struggle to achieve meaningful business outcomes from AI. The challenge is rarely the AI technology itself. More often, organizations have not established the operational foundations required for AI to perform consistently and reliably at enterprise scale. AI does not create operational complexity. It reveals it. The Six Foundations of AI Readiness AI Readiness is not determined by a single capability or technology investment. It reflects how well an organization has established the operational foundations required to support AI consistently, reliably, and at enterprise scale. While every organization begins from a different starting point, successful AI adoption consistently depends upon six foundational capabilities. Weakness in any one area can limit the effectiveness of AI, regardless of the sophistication of the technology being implemented. Governance & Compliance Effective governance establishes the policies, business rules, accountability, and oversight that enable AI to operate responsibly and consistently. As AI becomes more deeply integrated into business operations, governance must extend beyond documentation and become embedded within operational execution, ensuring decisions are supported by trusted information and consistent business practices Data Quality AI depends upon accurate, complete, and trusted enterprise data. Organizations should understand where critical data originates, how it is maintained, and whether it can be relied upon to support business