Why AI Means Process Change

I recently listened to another HBR Ideacast interview (link below) with Professor Tsedal Neeley, the founding Chair of the HBS AI Academy. Based on her research, AI is an opportunity for leaders to rethink how work gets done across their organizations. As Professor Neeley says, "You need to innovate in your processes. You cannot cut and paste your old processes onto the new platform… or the new strategy that’s AI-driven.”

Why process change is required

• AI changes inputs and outputs. Models need consistent, high-quality data; legacy data and silos must be addressed to ensure good results.

• AI changes timing and scale. Tasks that once took hours now happen in minutes or continuously, so upstream and downstream steps must be rebalanced.

• AI enables new decision patterns. Automated predictions and agentic workflows shift decision-making, so roles and approvals must be redesigned.

• AI multiplies failure modes. Small process gaps can cascade quickly at AI speed and scale; tighter exception handling and monitoring are essential.

• AI creates new cross-functional dependencies. Data, product, ops, legal and customer teams must coordinate in real time rather than via serial handoffs.

Evidence and examples

• Moderna used a technology-first organizational design and integrated processes to outpace much larger incumbents during COVID. This required redesigned cross-functional workflows that operated as a cohesive system.

• TikTok-driven beauty sales required unified workflows across marketing, inventory, and fulfillment, so that spiky influencer-driven demand could be met. Copying legacy handoffs onto that platform would have failed.

• Studies of AI adopters show technical platforms alone don’t drive performance; process and organizational redesign are the dominant success factors based on HBS research.

What to do next

• Map end-to-end workflows and data flows: don’t assume existing handoffs still make sense.

• Revisit roles and responsibilities: clarify decision rights where AI provides outputs.

• Keep humans in the loop: add quality gates and KPI monitoring at AI touchpoints.

• Identify and address bottlenecks: look for where work is piling up and redesign or rebalance the process accordingly.

• Pilot, measure, iterate: treat process redesign like product development; ask team members for feedback and ideas.

  
  

In the HBR podcast, Professor Neeley provided case studies to illustrate the impact of organizational redesign for AI capability. To compete in today’s environment, your processes need to be clearly documented and aligned across the value chain. If your team needs help with process innovation, please reach out. Visit my booking page to find a time to chat!