Why I believe the organizations that thrive in the AI era won’t collect more data—they’ll make better decisions with the data they already have.
Every executive meeting eventually arrives at the same conversation. Whether the topic is revenue growth, pipeline generation, customer retention, or AI strategy, someone inevitably concludes that the organization needs more data. More leads. More accounts. More customer intelligence. More signals. The assumption is understandable. If we can gather enough information, we’ll naturally make better business decisions.
After spending nearly twenty years building products in commercial data, customer intelligence, data quality, and AI, I’ve come to believe that assumption is incomplete.
Most organizations don’t have a shortage of data. They have a shortage of confidence.
They’re trying to determine which opportunities deserve investment, which customers are most likely to buy, whether forecasts accurately represent reality, and increasingly, whether AI-generated recommendations are reliable enough to act upon. Those challenges are often described as data problems, but I don’t think they are. They’re decision problems. Data is simply one of the inputs that helps organizations make those decisions with greater confidence.
Better data isn’t the goal
One of the most important lessons I learned while working in commercial data came from watching customers use the same information in completely different ways. Many organizations believed they were purchasing a better database. They expected larger prospect lists, more complete contact information, and additional company intelligence to produce more revenue. Sometimes it did. More often, however, the organizations that realized extraordinary value weren’t using commercial data to build larger lists. They were using it to build a more complete understanding of their customers.
Commercial reference data became one layer within a much broader decision system. These leaders combined it with CRM history, marketing engagement, buying intent, customer interactions, opportunity data, and the operational knowledge unique to their business. Every source contributed to another piece of context. Every additional signal reduced uncertainty. Over time, I realized that the commercial data itself wasn’t creating value. The value came from the decisions people made because they finally had enough context to act with confidence.
Customer data is infrastructure
I saw the same pattern emerge while leading products focused on data quality. Organizations frequently began those projects believing they simply needed to clean their CRM. Duplicate records had accumulated, reports no longer matched reality, and confidence in customer information had begun to erode. The expectation was that once the data was cleaned, the problem would be solved.
Experience taught me otherwise.
Customer data isn’t static. People change jobs. Companies merge, relocate, expand into new markets, and introduce new products. Buying signals appear and disappear almost daily. By the time one improvement project is complete, parts of the data have already changed again. The organizations that consistently outperformed their competitors understood something many businesses still overlook today: customer data isn’t a project that reaches completion. It’s an operational capability that requires continuous investment because the business itself never stops changing.
That realization fundamentally changed how I think about customer data. I no longer view it as an asset that organizations accumulate. I think of it as infrastructure that supports every important decision a business makes. Sales depends on trusted information to prioritize accounts. Marketing relies on it to reach the right audiences. Customer success uses it to identify emerging risk. Product teams use it to recognize market opportunities. Executive teams use it to allocate resources, forecast growth, and determine strategic direction. Every one of those decisions improves when the underlying information becomes more complete, more accurate, and more trustworthy.
AI amplifies decisions
I believe this becomes even more important as organizations adopt AI.
Much of today’s conversation focuses on automation. Can AI generate content? Can it write software? Can it answer customer questions? Can it replace work previously performed by analysts? Those are worthwhile discussions, but I don’t think they’re the questions business leaders should spend the most time asking.
The better question is whether AI helps organizations make better decisions.
Artificial intelligence is remarkably good at identifying patterns, summarizing information, and generating recommendations. What it cannot do is determine whether the information it receives accurately represents the real world. AI doesn’t eliminate the need for trusted customer data. It amplifies it. Poor information no longer produces an inaccurate report that someone notices during a quarterly review. Instead, it produces recommendations that appear intelligent, arrive almost instantly, and can influence meaningful business decisions before anyone has an opportunity to challenge the underlying assumptions. The familiar principle of “garbage in, garbage out” hasn’t become less relevant in the AI era. If anything, it has become significantly more important.
Throughout my career, one observation has remained remarkably consistent despite constant advances in technology. The organizations that achieve lasting success rarely depend on technology alone. They invest in capable people who understand their customers and exercise sound judgment. They establish disciplined processes that transform information into consistent action. Then they use technology to scale those capabilities across the organization. Every meaningful innovation I’ve worked on—whether in commercial data, customer intelligence, data quality, or AI—has reinforced the same lesson. Technology accelerates good decisions, but it doesn’t create them.
I’ve never really been in the data business
Looking back, I’ve realized that I’ve never really been working in the data business.
I’ve been working in the decision business.
Everything I’ve built throughout my career has ultimately been focused on helping organizations improve the quality of the decisions they make every day. Sometimes that meant improving customer data. Sometimes it meant enriching records with trusted commercial reference data. Today it increasingly means helping AI operate from a more reliable foundation. The technology continues to evolve, but the objective has remained remarkably consistent.
Five years from now, I don’t believe the organizations leading their industries will simply have larger data platforms or more sophisticated AI implementations. Those technologies will become increasingly accessible, and many of their capabilities will inevitably become commodities. The organizations that separate themselves from their competitors will be the ones that consistently make better decisions because they invest in trusted information, capable people, disciplined processes, and technology that strengthen all three.
Information is becoming abundant. Artificial intelligence is becoming ubiquitous.
Better decisions will remain remarkably rare. And I believe they’ll become the most sustainable competitive advantage any organization can build.
