Shashank .

The AI system is built on assumptions, architecture and data patterns which might eventually become outdated as the industry or business grows. In certain cases, the fundamental changes (process, business goals, hardware or regulations) in the working environment create a void as the AI’s logic does not match with the reality or intended use. Hence, even with continuous learning capability, the system would still retire or be replaced as it cannot overcome the obsolete model design, structural changes in business goals or incompatibility with new technology.
 
An example of this will be the retiring of legacy slotting optimization AI when the warehouse transitions from manual picking to autonomous mobile robot (AMR) operations. The legacy system used to recommend storage locations for SKUs in a warehouse, based on order frequency, item velocity and was designed for human pickers operations in the warehouse.  Now the AMRs introduce entirely new constraints around robot turning radii, safety zones, charging oaths and robot specific aisle rules which need to be incorporated with the current asset entitlements to churn out optimal task sequence involving AMRs and human labor. But the legacy AI system cannot incorporate the robot specific physical parameters even with continuous learning capability. To operate the AMRs efficiently a new model built on robotic kinematics and real-time fleet coordination must replace the old one.

To manage the transition, the warehouse and IT team will have to:
Capture lessons from the legacy system around what worked well and what instances operators frequently override during daily operations Test the new AI model in shadow, alongside the old one, comparing their recommendations without changing daily warehouse operations Gradually roll out the new system by introducing the new AI model in a single zone (say returns area) with a smaller set of SKUs (during the low volume window in the day) Monitor core KPIs (pick rate, travel distance etc) along with operator override instances to ensure the effectiveness of the new system After 3 – 4 months of close monitoring by the warehouse team, confirm that the new AI model successfully outperforms the legacy system while working seamlessly with the real time fleet coordination. Then the team can retire the legacy system and fully migrate to the new system.