Delivering at Scale: Navigating the Realities of Large Generative AI Programs

By Karun Sarabhai, Senior Director for Agile Program Management, Publicis Sapient

As Generative AI (GenAI) reshapes how organizations engage customers, create content and unlock new avenues for innovation, the question is no longer “if” but “how”, and more importantly, “how fast and how well?”

Traditional program management thrives on predictability—defined milestones, fixed scope, and linear progress. GenAI flips this paradigm. It replaces deterministic logic with probabilistic creativity, where outcomes emerge through experimentation. Program managers must, therefore, shift from rigid roadmaps to dynamic hypothesis backlogs, treating each sprint as a learning loop rather than a delivery checkpoint.

Building the GenAI Operating Model: From Strategy to Execution
In today’s AI-era, cross-functional collaboration is essential. GenAI teams blend data scientists, prompt engineers, legal advisors, and product strategists. Roles blur: product managers must grasp prompt tuning, while data scientists must consider UX. This convergence demands leadership that cultivates psychological safety, encourages experimentation ...