Where do you see AI changing manufacturing in 12 months?
Premkumar: Over the next 12 months, we’ ll see a variety of things take place. We’ ll see AI move from pilots to scaled deployment; greater adoption of closed loop optimisation, not just insights. Tighter integration between IT, OT and engineering workflows will take place, and there will be an increased focus on governance, safety certification and lifecycle management. The conversation will shift from“ Can AI work?” to“ How fast can we scale it responsibly?”
The most meaningful impact will come from practical Physical AI deployments in wellbounded parts of the manufacturing system, rather than broad, autonomous factories.
We’ ll see AI move closer to the physical process – embedded at the edge, integrated with machines, sensors, robotics and control systems – to:
• Enable closed loop optimisation in specific operations such as quality inspection, intralogistics, equipment health and energy management
• Make automation more adaptive, able to handle variability in materials, operating conditions and demand
• Improve operator effectiveness, with AI acting as a real-time copilot rather than a replacement
Critically, these systems will operate within defined constraints, grounded in physics, rules and operating envelopes. This is Physical AI that is engineered for reliability, not generalised autonomy.
Yuriy: Over the next 12 months, we will see a rapid shift from isolated AI features to what I call the“ Agentic Transformation”. Right now, the industry is heavily focused on disconnected point solutions, like using AI solely for predictive maintenance alerts or building a“ digital twin”. In the next year, we will move into“ Agentic Fusion” – organically integrating predictive maintenance, digital twins and AI co-pilots into a single, continuous human-machine integrated workflow.
We are moving toward ecosystems of AI agents that actively collaborate to orchestrate work. For example, a Maintenance Co-Pilot Agent won’ t just throw a warning light to tell an operator what is wrong. It will proactively query the machine’ s digital twin and previous maintenance records to diagnose the root cause, check enterprise systems for replacement part inventory, autodraft the procurement request and then safely and visually guide the frontline technician through the physical repair process using voice interaction, allowing the technician to keep both hands free.
78 May 2026