What if the real competitive edge with AI isn’t adopting it first—but knowing exactly where it creates value? In this episode of Scouting for Growth, Sabine VdL sits down with Frank Mendoza, founder of Catalytics and one of the most respected voices in data analytics and AI strategy, to demystify artificial intelligence and turn it into something leaders can actually act on. This is not a conversation about chasing the latest AI tools—it’s about making disciplined, value-led decisions in a space crowded with hype. Frank has spent more than two decades helping Fortune 500 companies—including Nike, Kellogg’s, Keurig Dr Pepper, and Jim Beam—translate data into measurable business outcomes. Over the past eight years, he’s been introducing organisations to machine learning, analytics, and conversational AI long before generative AI dominated headlines. What’s changed recently isn’t just the technology—it’s the pressure to prove impact fast. A central theme of the episode is focus. Every organisation, regardless of size, faces the same constraint: time. Large enterprises may have budget, but they often dilute impact by spreading AI efforts too thin. Smaller companies lack that margin for error and must be even more precise. Frank’s message is consistent: start with the business problem, not the tool. Without a direct line to value, AI initiatives quickly descend into analysis paralysis. The conversation also tackles one of the most critical—and often underestimated—issues in AI adoption: data stewardship. Customers entrust organisations with highly personal information. How that data is used, shared, and exposed to AI models matters. Frank cautions that tools like ChatGPT can retrain on inputs, making it essential for organisations to define clear guardrails around what data can be entered, by whom, and for what purpose. Bias and human judgment play a crucial role as well. Generative AI models are trained on publicly available data, which reflects the world as it is—with all its imperfections. Biases, gaps, and harmful patterns still exist. Frank is unequivocal: AI must operate with humans in the loop, not as an unchecked decision-maker. Responsible AI isn’t slower—it’s smarter. One of the most thought-provoking insights in the episode is Frank’s observation that we’re shifting from inventing with tools to tools inventing. AI is now capable of creating outputs that didn’t previously exist. That power is transformative—but only if organisations are prepared to govern it with intent, ethics, and clarity. Underlying everything is data quality. AI execution, Frank argues, is only as strong as the information that feeds it. Data enablement—improving data quality, accessibility, and governance—is the unglamorous but essential foundation of success. The goal isn’t to boil the ocean, but to identify where focus, energy, and effort will deliver the greatest return. This episode is essential listening for executives, data leaders, and innovation teams navigating AI under real-world constraints. It’s a reminder that growth doesn’t come from adopting more technology—but from using the right technology, in the right place, with purpose. Because in the AI era, speed alone won’t win. Focus, judgment, and disciplined execution will.