Manufacturing has not been slow to adopt AI because the industry is behind. On the shop floor, the cost of getting it wrong shows up before anyone can catch it.
Season 1 of AI Afterhours, Signals & Noise, is about how organizations cut through noise, find the signals that matter, and turn them into better decisions. In Episode 4, Manufacturing with Dovient, I sat down with Shashank from Dovient and Harsha, who was back after missing Episode 3. Varun was away completing his executive MBA. Get it done, Varun.
Shashank opened with the frame that carried the whole conversation. Before AI, the shop floor runs on delayed visibility, static maintenance schedules, and coordination that happens by phone call. Teams react to what already happened. With AI, the same teams can finally act before the failure arrives. Not because the machine got smarter. Because the decision layer now has context.
Harsha made the invisible visible. The core problem is not the signal. It is that 80% of what governs the outcome never makes it into any system. It lives in shift logs, maintenance notes, and the memory of the technician who fixed this same machine three years ago and is not on shift tonight. Dovient’s approach is to digitize that unstructured knowledge, build the graph that connects events across time, and let background agents surface the pattern before the failure compounds.
Shashank’s closing thought is the one to take with you. Pick the one KPI where static planning is already failing. Start there. Measure it. Then expand.
Next up is Episode 5, still with Shashank and the Dovient team. Capacity decisions, lead time management, inventory positioning, and working capital trade-offs.
Shashank, the before and after frame you opened with is the clearest version of this problem I have heard explained. Hard to top that one. Harsha, good to have you back.
Thank you for tuning in. See you next episode.
