Samuel Holcman: Thank you for tuning in to Real Talk. Be sure to join your host Sam Holcman again for another edition of our program. We'll have more Real Topics of discussion then. How do you sort out the so-called jargon from real-world practices that work? Do the members of your organization find some business or technology advice utterly confusing? Welcome to Real Talk with Sam Holcman. In this program, we set the record straight and in terms that business people and technology people can understand. Now, here is your host, Sam Holcman. Your AI problem is not data debt. It is executive relevance. You're listening to Real Talk with Sam Holcman. I'm Sam Holcman, where we cut through the buzzwords and get to what actually works in enterprises today. There's a lot of noise right now about artificial intelligence and artificial intelligence strategy. And some of that noise is more the noise. And one phrase you've probably heard is this. your AI strategy will fail if you do not fix your data debt. Now, of course there's truth to that. Years of fragmented systems and consistent definitions and patchwork reports really are catching up with us. AI will expose every weakness you've had buried in your data. But if you take that message into the C-suite or the boardroom and you lead with data debt or process standardization, you will lose the audience in a matter of minutes. Today I want to reframe the issue. Your biggest AI problem is not actually data debt. Your biggest AI problem first is executive relevance. This is the real issue. Multiple versions of the truth. Let us start with what executives actually see. In most organizations, different functions use different definitions for the most basic terms. Customer means one thing to sales, something else to finance, something different again to service. Revenue doesn't reconcile across product, region and channels. Risk is calculated and reported differently in every silo. You do not need a data catalog to see this. Just bring three reports to the next leadership meeting that claim to answer the same question and don't match. That is not a data... governance problem slide, that is a board level risk slide. Because when there are multiple versions of the truth, three things happened. Decisions slow down because no one trusts the numbers. Performance gets misread because each function optimizes its own definition. And when you add AI on top of this, it does not fix the confusion, it amplifies it. by the way, at machine speeds. So yes, AI exposes data debt, but more fundamentally, AI exposes definition debt. And definition debt is not a technical problem, it is a strategic problem. Data is the asset, process is one use. Here's the next misconception. We need to standardize our processes before we can succeed with AI. Now, process standardization of course has its place, but processes change all the time. New products, new markets, new regulations, new organization structures, every one of these shifts your processes. What does not change nearly as often is the meaning, the definition of your core business concepts. and then the data that represents them. Data is the enduring asset. Data is the enduring asset. Process is just one way you use that asset. If you start conversations with process design, you'll end up with something tailored to today's organization chart and today's systems that virtually guarantees a short-term tactical and ultimately fragile solution. If instead you start by treating data as the enterprise asset, you ask very different questions. What truths about our business must be consistent everywhere? What are the core concepts that drive our financials, our risk in our operations? What is the agreed meaning of customer, product, revenue, risk, and so on? Once you do that, processes and technologies plug into a shared backbone of meaning, instead of embedding their own private definitions. This is how you avoid yet another expensive in-quotes transformation that expires with the next reorganization. The first real use case is a common vocabulary. There's a lot of enthusiasm about use cases in AI. Summarizing documents, automating tasks, building co-pilots, predicting churn, predicting demand. All of these are great things. But none of those is or should be the first use case. Your first and most critical use case is this, a common vocabulary for the business. Before you approve another AI platform, another pilot, another data-driven initiative, ask one simple question. Do we as an enterprise agree on what this data actually means? If the answer is no, then every dashboard, every analytic model, and every AI application will carry that ambiguity into production. A common vocabulary is not unnice to have. It is control mechanism. It creates one version of the truth across the enterprise. It exposes where financial, customer, and operational metrics do not line up. And it gives you a stable foundation, a stable foundation, where AI can operate without embarrassing you in front of customers, regulators, or the market. So, the first visible value from a serious data and AI effort is not a chatbot. It is a shared language for your executives that they can trust. Now, why don't boards buy process-first programs? Let's talk about sponsorship. You need senior management and board-level backing. But here is why so many data programs don't get it. Most boards and C-level suites already have lived through, here we go, the data warehouse that never quite delivered. the master data initiative that turned into an endless integration project, the governance committee that produced policies and not outcomes, the process automation activities that really didn't improve any outcomes. So when they hear we need to fix our data and standardized processes before we can do AI, here's what they really hear. Here comes another multi-year, high-cost, low-visibility effort. They're not wrong to be skeptical. Executives don't fund process and plumbing. They fund business relevance. So we need to talk to them in their language. For example, we cannot produce a single reconciled view of revenue by product, region, and channel. We are making risk decisions on conflicting definitions from different systems. We cannot trace the numbers in your board pack back to auditable, consistent sources. If we automate on top of this, AI will fail in ways that are visible to customers and regulators. Some people would say those are uncomfortable discussions. Well, they are. But what we need to do is data recognized as not being an IT cleanup exercise, it becomes a direct driver of financial, operational, and compliance risk. That's when the board starts to pay attention. That's when the executives start paying attention. That's when the program managers start paying attention. What executive sponsorships now should really do based on those conversations. Let me suggest three tangible moves. Number one. declare data as a managed asset. And of course, declarations without substance aren't going to go anywhere, and we know that. Treat critical data, customers, product, revenue, risk, etc., with the same discipline that the board and management applies to financial assets or physical infrastructure. Mandate one version of the truth and measure that mandate. require enterprise level definitions for core business concepts with clear ownership and insist that reports and AI models align with those definitions. Number three, tie funding to meaning, not just tools. Approve investments where it produces visible business outcomes, reconciled metrics, trusted dashboards, consistent definitions used across business units, not just a new platform. When executives take these steps, governance and process discipline stops looking like a technical overhead. They become the way you protect and grow the value of the data asset. So let me leave you with this. You can buy any AI technology you like. You can hire the best data scientists in the world. But if your organization does not agree on what data means, every model you build is sitting on shifting sand. This is the first step, not an AI use case. The first step is shared meaning, common vocabulary, one version of the truth. and then the technology. This has been Real Talk with Sam Holcman. If today's message hits a little close to home, that's good. That means you are seeing the real work that has to happen. Stick with us for more practical conversations about how to build enterprises that continuously improve and not just look good on a presentation slide. Until next time, I'm Sam Holcman. Thanks for listening. 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