In this game-changing episode of the Cognixia Podcast, we unpack one of the most misunderstood concepts in today’s tech-driven business world - the difference between “Doing AI” and “Using AI.” It’s a distinction that’s shaping boardroom debates, technology budgets, and competitive landscapes across every industry. Get it wrong, and you risk pouring millions into projects that drain resources without delivering results. Get it right, and you position your organization for lasting success in the AI-powered economy.

“Doing AI” means building artificial intelligence from the ground up - designing neural networks, training models, and pushing the boundaries of what AI can do. It’s the domain of research labs, tech giants, and companies whose competitive advantage depends on proprietary capabilities. “Using AI,” on the other hand, means leveraging existing AI platforms, tools, and APIs to solve business problems quickly and cost-effectively - without reinventing the wheel.

Through real-world examples from healthcare, finance, manufacturing, retail, and tech, we show how this choice plays out in practice. A pharmaceutical company developing unique drug discovery algorithms may need to do AI. A hospital optimizing patient scheduling can thrive by using AI tools already available. An investment bank may require custom trading algorithms, while a community bank gains more value from proven fraud detection solutions.

We explore the radically different investment profiles, timelines, and talent needs of each approach, as well as the risks of getting them wrong. You’ll learn how AI-as-a-Service is leveling the playing field for companies to access world-class capabilities, and why the next 2-3 years will be critical for aligning your strategy.

Whether you’re a startup founder, CIO, business leader, or just curious about how AI is shaping the future, this episode will equip you with the clarity to decide whether your organization should be building AI - or buying it. The answer could determine your competitive future.