Why AI Doesn't Scale (And How to Change It) with Jack Shay

AI initiatives often stall at the pilot stage, not because of technical limitations, but because they are not designed to fit how organizations actually work.Matt Egol speaks with Jack Shay, founder of End Product, about how leaders can operationalize AI in ways that connect to real workflows, build trust with teams, and deliver measurable business outcomes.A central theme is the gap between experimentation and capability. Many organizations invest in promising pilots, but fail to integrate them into broader systems of work, leaving value unrealized and trust diminished.The conversation highlights the importance of starting with customer needs rather than tools. By grounding AI efforts in clearly defined problems and applying principles from design thinking and product development, organizations can avoid over-engineering solutions that do not translate into impact.Trust and pride emerge as critical factors in adoption. Employees, particularly in craft-oriented roles, evaluate new tools not only on efficiency but on whether those tools enhance or diminish the quality of their work.The discussion also reframes the role of AI beyond efficiency gains. While cost reduction and speed are common entry points, the more significant opportunity lies in improving effectiveness through better decision-making, personalized experiences, and stronger feedback loops.Additional insights explore how organizations can balance centralized governance with citizen-led innovation, curate the right data context for AI systems, and create co-pilot experiences that elevate both employee and customer outcomes.The episode reinforces a consistent message: technology alone does not drive transformation. Leadership behaviors, cultural alignment, and product thinking determine whether AI becomes a scalable capability or remains an isolated experiment.Listen on Spotify.Follow for new episodes.Save episodes to revisit leadership frameworks.