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Why Should Organizations Assess AI Readiness Before Scaling? Organizations are investing in artificial intelligence with expectations of greater efficiency, improved decision-making, and measurable business value. Yet many are moving forward without fully understanding whether their existing operations can support those expectations. AI doesn’t correct inconsistent workflows, unreliable data, unclear governance, or gaps in organizational capability. It operates within those conditions—and can amplify their consequences as adoption expands. An AI Readiness Assessment helps executives identify these weaknesses before they become costly implementation challenges. Rather than focusing solely on technology, it evaluates the operational foundations necessary for AI to deliver reliable, sustainable outcomes. More importantly, the assessment establishes where improvements should begin. Strengthening data quality, simplifying workflows, and clarifying governance can generate meaningful business value even before AI is deployed. AI readiness is a diagnostic discipline, not a technology selection exercise. What Six Areas Should Every AI Readiness Assessment Evaluate? AI readiness extends well beyond technology. An effective assessment examines six interconnected areas that determine whether an organization can adopt, govern, and scale AI successfully. 1. Governance, Risk & Compliance Are policies, accountability structures, risk controls, and oversight mechanisms established and consistently followed? Without clear governance, organizations risk introducing AI into environments where responsibilities, decisions, and controls are inconsistently defined or applied. 2. Data Quality Is enterprise data accurate, complete, consistent, and trustworthy? AI depends on the information it receives. Poor data quality can undermine decisions, increase operational risk, and limit confidence in AI-generated outcomes. 3. Process & Workflow Readiness Are business processes documented, understood, and executed consistently? AI can accelerate workflows, but it cannot independently resolve unnecessary complexity, undocumented exceptions, or inconsistent execution. 4. Technology & Integration Can existing systems reliably exchange information and support AI-enabled workflows? Readiness requires understanding integration limitations, system dependencies, and whether the current technology environment can support scalable adoption. 5. People, Skills & Operating Model Do employees and leaders have the skills, understanding, and organizational support necessary to adopt AI effectively? Even technically sound initiatives can struggle when roles, expectations, and change readiness are overlooked. 6. Business Strategy & Value Alignment Are AI initiatives connected to clearly defined business priorities, measurable outcomes, and executive accountability? Readiness requires more than identifying potential applications; it requires understanding where AI can create meaningful value. These six areas should be evaluated collectively. A significant weakness in any one area can limit the effectiveness of the others, regardless of an organization’s overall readiness. Understanding these foundations is the starting point. The next step is recognizing how they may affect your own organization. What Questions Should Be Included in an AI Readiness Checklist? A practical AI readiness checklist helps executives look beyond technology and consider whether their organization has the foundations necessary to support successful AI adoption. The following 12 questions are intended to encourage executive reflection and highlight areas that may warrant closer examination. They are not a substitute for a structured assessment, but they can help identify where deeper evaluation may be valuable. Governance, Risk & Compliance Are clear policies, accountability structures, and oversight mechanisms established for AI-related decisions and activities? Are risks, regulatory requirements, and compliance obligations consistently evaluated and monitored across the organization? Data Quality Is the data required for AI applications accurate, complete, consistent, and sufficiently reliable? Are data ownership, validation, and governance practices established to maintain data quality at its source? Process & Workflow Readiness Are critical business processes documented, standardized where appropriate, and consistently executed? Are process exceptions, manual workarounds, and dependencies understood and actively managed? Technology & Integration Can existing systems reliably exchange the information needed to support AI-enabled processes? Are technology limitations, integration dependencies, and security requirements understood before introducing AI solutions? People, Skills & Operating Model Do employees and leaders understand how AI may affect their responsibilities, workflows, and decision-making? Are training, communication, and organizational change practices in place to support effective AI adoption? Business Strategy & Value Alignment Are proposed AI initiatives aligned with clearly defined business priorities and measurable outcomes? Is there executive ownership and a structured approach for evaluating, prioritizing, and scaling AI opportunities? Executives should consider where answers are clear and supported by established practices—and where they depend on assumptions, informal workarounds, or inconsistent execution. Uncertainty in any area may signal the need for a more structured assessment. The objective isn’t to answer every question perfectly. It’s to recognize where organizational readiness may be limiting the ability to achieve reliable, measurable AI outcomes. How Should Organizations Identify and Prioritize AI Readiness Gaps? Reviewing an AI readiness checklist is only the beginning. The real value comes from understanding what potential weaknesses reveal and determining which areas require closer attention. Organizations should evaluate readiness across all six areas rather than relying on an overall impression. Strong technology capabilities, for example, do not compensate for unreliable data, inconsistent workflows, or inadequate governance. Not every gap carries the same business risk or requires immediate action. Executives should prioritize findings based on three considerations: Business Impact: How significantly could the weakness affect operational performance, customer outcomes, or the expected value of AI initiatives? Risk and Exposure: Could the gap introduce compliance concerns, unreliable decisions, security vulnerabilities, or operational disruption? Dependencies: Does resolving the weakness establish a foundation needed for other improvements or AI initiatives to succeed? For example, improving data validation at the point of entry may deserve greater priority than introducing another AI application that depends on the same unreliable information. A structured assessment helps organizations move beyond initial observations by evaluating the severity, implications, and interdependencies of readiness gaps. The objective is not simply to improve assessment scores. It is to identify the foundational improvements that reduce risk, strengthen enterprise operations, and create the conditions for AI to deliver measurable value. How Do You Turn an AI Readiness Assessment Into an Actionable Roadmap? An AI readiness assessment should lead to more than a list of findings. It should provide a clear path for addressing weaknesses, strengthening operational foundations, and preparing the organization for successful AI adoption. A practical roadmap translates

Most enterprises do not have a shortage of technology. They have ERP systems, CRM platforms, workflow tools, data platforms, specialized applications, and increasingly, AI. Each may perform its intended function well. The challenge is what happens between them. Business processes routinely cross system, functional, and organizational boundaries. When those connections are not managed effectively, employees often become the integration layer—moving information, reconciling data, tracking approvals, resolving exceptions, and determining what should happen next. A process orchestration platform helps coordinate that execution across workflows, systems, data, and teams. What Does Process Orchestration Mean? Process orchestration is the coordination of activities, decisions, data, and systems required to execute an end-to-end business process. Unlike automation focused on an individual task, orchestration considers how the entire process operates across the enterprise. A customer onboarding process, for example, may involve sales, finance, compliance, operations, master data, and multiple technology platforms. Each system may support part of the process, but no individual system necessarily manages the complete journey. Orchestration provides a way to connect those activities without requiring every underlying system to be replaced. Why Existing Enterprise Systems Are Often Not Enough ERP and other core platforms remain essential enterprise systems. But many were designed primarily to manage transactions within defined functional boundaries. Business execution rarely stays within those boundaries. As organizations grow, acquire businesses, add applications, or operate multiple ERP environments, processes can become increasingly fragmented. Employees compensate by moving between systems, using spreadsheets, sending emails, tracking approvals manually, and relying on institutional knowledge to keep work moving. The problem is not necessarily that the underlying systems are failing. The missing capability may be the execution layer between them. What a Process Orchestration Platform Does A process orchestration platform can provide a common layer for coordinating how work moves across the enterprise. Depending on the process and technology environment, that can include: Connecting workflows across multiple enterprise systems.  Applying consistent business rules and governance.  Validating required data before it enters downstream systems.  Routing tasks and decisions to the appropriate people.  Managing approvals and exceptions.  Providing visibility into process status and bottlenecks.  Maintaining an auditable record of decisions and actions.  Rather than forcing one system to perform every function, orchestration helps existing systems work together as part of a governed end-to-end process. Orchestration Is More Than Automation Automation and orchestration are related, but they solve different problems. Automation typically asks: How can we perform this task faster or with less manual effort? Orchestration asks a broader question: How should this entire process execute across people, systems, data, and decisions? That distinction becomes increasingly important as enterprises introduce AI. AI can accelerate individual activities and decisions, but it still depends on reliable context, trusted data, clear rules, and connected workflows. Adding AI to a fragmented process does not eliminate the fragmentation. When Should Organizations Consider Process Orchestration? Process orchestration becomes particularly relevant when organizations experience recurring challenges across system or functional boundaries. Common examples include multi-ERP environments, customer and supplier onboarding, master data processes, M&A integration, approvals, exception management, and workflows that depend heavily on spreadsheets, email, or manual reconciliation. These are often signs that the organization does not need another system of record. It needs a better way to coordinate execution across the systems it already has. A process orchestration platform can provide that governed execution layer—connecting workflows, systems, data, and people while allowing existing enterprise technology investments to continue doing what they were designed to do. Thinking differently precedes doing differently. Mark KruckebergMark Kruckeberg is the Managing Partner of Soltec and a recognized leader in enterprise transformation, operational excellence, governance, and AI readiness. With more than 26 years of experience leading large-scale business, operational, and technology initiatives, Mark has helped organizations improve execution, strengthen governance, optimize workflows, and build trusted data foundations. Today, he works with enterprise leaders to improve operational performance, organizational readiness, and long-term business outcomes while preparing their organizations for successful AI adoption and future innovation.

As organizations move from experimenting with AI to deploying it across the enterprise, governance becomes increasingly important. The conversation often begins with AI policies, security, privacy, compliance, and responsible-use guidelines. Those are essential considerations. But enterprise AI governance extends beyond governing the technology itself. Organizations also need to govern the data, processes, decisions, and accountability surrounding how AI is used. That is where AI governance consulting can play an important role. What Is AI Governance Consulting? AI governance consulting helps organizations establish the structures, responsibilities, controls, and operating practices needed to deploy AI responsibly and consistently. That can include areas such as: Governance and decision rights.  Risk and compliance requirements.  Data quality and access.  Security and privacy.  Human oversight and escalation.  Monitoring and auditability.  Process ownership.  Organizational adoption.  The objective is not simply to create more AI policies. It is to establish an environment in which AI can be implemented with appropriate control while still delivering measurable business value. Why AI Governance Goes Beyond AI Policy An organization can have a strong responsible-AI policy and still struggle to scale AI successfully. Why? Because AI operates inside existing business processes. If those processes rely on inconsistent data, unclear ownership, manual workarounds, or decisions that vary by individual or location, AI inherits those conditions. For years, people have often compensated for these inconsistencies. Experienced employees know which information to question, which exception matters, whom to contact, and when a process needs to deviate from the documented path. AI does not automatically possess that organizational context. As AI becomes more embedded in enterprise workflows, assumptions that once lived in people’s heads must increasingly become explicit through data standards, business rules, governance, and defined processes. The Connection Between AI Governance and AI Readiness AI governance and AI readiness are closely connected, but they are not the same thing. Governance establishes how AI and the environment around it will be controlled, monitored, and held accountable. Readiness asks whether the organization has the broader foundation necessary to use AI effectively in the first place. That foundation includes more than technology. It includes trusted data, standardized processes, governance, integration, organizational capability, and clear business outcomes. An organization may therefore have appropriate AI policies while still not being operationally ready to scale AI. Governance Should Begin Before AI Scales Governance is much easier to establish before AI becomes deeply embedded across dozens of processes and use cases. Organizations should be able to answer fundamental questions such as: What business outcome is this AI use case expected to produce?  Who owns that outcome?  What data does the AI depend on, and can that data be trusted?  Which decisions can AI make or support?  Where is human judgment required?  How are exceptions handled?  How will performance, risk, and outcomes be monitored?  What happens when the process or underlying data changes?  These are not simply technology questions. They are enterprise execution questions. From AI Experimentation to Governed Adoption Successful enterprise AI adoption requires organizations to move beyond isolated experimentation. That does not mean every AI initiative needs a large governance structure. Governance should be appropriate to the use case, its business impact, and its level of risk. But as AI moves into core workflows and begins influencing decisions, organizations need greater consistency in how use cases are selected, implemented, measured, and governed. The organizations best positioned to scale AI will not simply be those with the most advanced technology. They will be those that create the operational discipline necessary for AI to work reliably within the enterprise. AI cannot fix poor data or inconsistent execution. Thinking differently precedes doing differently. Mark KruckebergMark Kruckeberg is the Managing Partner of Soltec and a recognized leader in enterprise transformation, operational excellence, governance, and AI readiness. With more than 26 years of experience leading large-scale business, operational, and technology initiatives, Mark has helped organizations improve execution, strengthen governance, optimize workflows, and build trusted data foundations. Today, he works with enterprise leaders to improve operational performance, organizational readiness, and long-term business outcomes while preparing their organizations for successful AI adoption and future innovation.

Most organizations have defined processes. The bigger challenge is ensuring those processes are executed consistently across people, systems, functions, and locations. That is the role of business process execution. Business process execution is how an organization turns a defined process into repeatable action. It encompasses the workflows, responsibilities, decisions, data, controls, and technology required to move work from beginning to end. The distinction matters because a well-designed process does not automatically produce a well-executed process. Why Business Processes Break Down in Execution Enterprise processes rarely operate within a single department or system. A customer onboarding process, for example, may involve sales, finance, operations, compliance, master data, and multiple technology platforms. As complexity increases, organizations often compensate with manual intervention. Employees reconcile information between systems, track approvals through email, resolve exceptions offline, and carry knowledge that isn’t captured in the process itself. The process may work, but its success increasingly depends on people holding the pieces together. That creates variability, limits visibility, and makes improvement difficult to scale. Improving Enterprise Process Execution Improving execution starts with understanding how work should happen—not simply automating how it happens today. Organizations should first establish a clear process, defined ownership, decision points, business rules, data requirements, and measurable outcomes. Only then can technology effectively enable the process. A disciplined approach typically focuses on several fundamentals: Standardized processes that establish how work should be performed. Clear ownership and accountability across functions and decision points. Trusted data available when and where decisions are made. Governance that defines rules, controls, exceptions, and escalation. Connected workflows that allow work to move consistently across organizational and technology boundaries. Measurement that provides visibility into whether the process is producing the intended business outcome. Together, these elements turn process design into repeatable enterprise execution. Where Technology Fits Technology is an important enabler, but it should not become the starting point. ERP, CRM, workflow automation, AI, and other enterprise platforms can accelerate execution. They cannot independently resolve unclear ownership, inconsistent processes, poor-quality data, or undefined decision rules. Automating those conditions can simply make inconsistency happen faster. The objective should therefore be to establish the process and governance foundation first, then determine how technology can best support execution. From Process Improvement to Sustainable Execution Business process improvement is ultimately about more than redesigning workflows. The real measure is whether the organization can execute the improved process consistently after implementation. That requires alignment between people, processes, technology, governance, and data. When those elements work together, organizations gain greater visibility, reduce manual intervention, improve consistency, and create a stronger foundation for automation and AI. Thinking differently precedes doing differently. Mark KruckebergMark Kruckeberg is the Managing Partner of Soltec and a recognized leader in enterprise transformation, operational excellence, governance, and AI readiness. With more than 26 years of experience leading large-scale business, operational, and technology initiatives, Mark has helped organizations improve execution, strengthen governance, optimize workflows, and build trusted data foundations. Today, he works with enterprise leaders to improve operational performance, organizational readiness, and long-term business outcomes while preparing their organizations for successful AI adoption and future innovation.

As enterprise workflows become more connected and automated, an important question becomes increasingly difficult to answer: Who is responsible for ensuring the workflow actually works from beginning to end? That is where workflow governance becomes important. Workflow governance establishes the ownership, decision rights, business rules, controls, and accountability required to ensure workflows operate consistently across an organization. It turns a workflow from a sequence of activities into a governed business process. Why Workflow Governance Matters Enterprise workflows frequently cross organizational boundaries. A supplier onboarding process may begin in procurement, require information from the supplier, involve compliance and finance, create or validate master data, and ultimately interact with an ERP or other enterprise system. Each individual function may perform its responsibilities correctly while the overall process still experiences delays, inconsistent decisions, duplicate work, or data-quality problems. The issue is often not the individual task. It is what happens between the tasks. Without clear governance, ownership can become fragmented. Exceptions are handled differently. Decisions move outside the workflow. Employees develop workarounds. And leadership may have limited visibility until something goes wrong. What Effective Workflow Governance Includes Strong workflow governance does not mean adding unnecessary approval layers. It means establishing enough structure for the process to operate consistently. That typically includes: End-to-end process ownership so accountability does not stop at functional boundaries.  Defined roles and decision rights so people understand who can make which decisions.  Business rules and controls applied consistently throughout the workflow.  Exception management that defines how nonstandard situations are handled and escalated.  Data standards that establish what information is required and when it must be validated.  Performance visibility that allows the organization to identify delays, exceptions, and recurring breakdowns.  The objective is not more governance. It is better execution through appropriate governance. Governance Becomes More Important as Automation Increases Manual processes often rely on experienced employees to interpret incomplete information, recognize exceptions, and determine what should happen next. Automation changes that dynamic. Workflow automation and AI require clearer rules, better data, and more consistent decision logic because technology cannot reliably compensate for ambiguity in the same way experienced employees often do. This is why automating an inconsistent workflow rarely solves the underlying problem. Before organizations automate, they should understand how the workflow is supposed to operate, who owns it, what information it requires, how exceptions should be managed, and what outcome it is expected to produce. From Workflow Governance to Enterprise Execution Individual workflows do not exist in isolation. They interact with other processes, systems, data, and organizational functions. Effective workflow governance creates consistency at those connection points. That improves visibility, strengthens accountability, reduces reliance on manual workarounds, and creates a more reliable foundation for workflow automation, process orchestration, and AI. For organizations pursuing greater automation, the question should not simply be “Can this workflow be automated?” A better question is: “Is this workflow governed well enough to automate?” Thinking differently precedes doing differently. Mark KruckebergMark Kruckeberg is the Managing Partner of Soltec and a recognized leader in enterprise transformation, operational excellence, governance, and AI readiness. With more than 26 years of experience leading large-scale business, operational, and technology initiatives, Mark has helped organizations improve execution, strengthen governance, optimize workflows, and build trusted data foundations. Today, he works with enterprise leaders to improve operational performance, organizational readiness, and long-term business outcomes while preparing their organizations for successful AI adoption and future innovation.

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