Skills
About
I am a product and AI strategy leader who has spent my career helping organizations turn complex technologies into systems that actually work. I have partnered with founders, executive teams, and boards at critical moments from early validation to scale, where decisions about AI are no longer theoretical. My focus is not on tools, but on structure Incentives, Workflows, Governance, and the Human systems that determine whether AI creates value or erodes trust.
Su Belagodu
Published content
expert panel
For many workers, learning artificial intelligence tools has quietly become “a second job”—one layered onto already full workloads, unclear expectations and rising anxiety about job security. Instead of freeing time and cognitive energy, AI initiatives often increase pressure, leaving employees feeling overworked or even disposable. A 2024 McKinsey report on generative AI adoption found that employees are more likely to experience burnout when AI tools are introduced without role redesign or workload reduction, even as productivity expectations rise. Similarly, a recent study from The Upwork Research Institute reveals that while 96% of execs expect AI to improve worker productivity, 77% of employees feel it’s only increased their workload (with an alarming 1 in 3 employees saying they will quit their jobs within the next six months due to burnout). Members of the Senior Executive AI Think Tank—a curated group of leaders in machine learning, generative AI and enterprise AI applications—note that this growing problem is not necessarily due to employee resistance or lack of technical ability, but how organizations sequence AI adoption, structure learning and communicate intent. Below, Think Tank members offer a clear roadmap for introducing AI as a system-level change—not an extracurricular obligation—to help ensure this technology empowers people rather than exhausts them.