By Priya Prasad, IG Head – Retail, CPG & Logistics, Happiest Minds Technologies
Supply chains entered 2025 facing a landscape of compounding disruptions that legacy planning models were never created to survive. Geopolitical fragmentation has escalated beyond isolated flashpoints into a persistent operating condition – 82 percent of supply chain leaders expect geopolitical risks to increase, yet only 25 percent feel prepared to manage them.
The median revenue loss from disruptions has reached five percent, with one-third of enterprises suffering even greater financial impact. In today’s export-driven manufacturing and retail sectors, this translates directly to compressed margins and eroded customer trust. Concurrently, inflation, tariff volatility, climate disruptions, and shifting demand have transformed supply chain management from predictable planning into continuous firefighting.
The “control tower plus dashboards plus manual escalation” paradigm has reached its limits. While visibility platforms improve awareness, they require human planners to interpret signals, convene teams, and make sequential decisions taking hours or days. When port congestion collides with supplier quality issues and demand spikes, the human-cantered model cannot keep pace, resulting in missed deliveries, excess safety stock, and expedited freight costs.
Agentic AI represents the critical evolution beyond visibility and predictive analytics. These systems perceive their environment, reason through trade-offs, collaborate with other AI agents, and trigger actions directly in ERP, WMS, TMS, and planning platforms with minimal human intervention (2). Unlike traditional automation following rigid rules, agentic AI operates with autonomy and adaptability, compressing decision cycles from days to minutes (3).
What “Agentic AI” Actually Means in Supply Chains
Agentic AI differs from previous supply chain technology in three fundamental ways. First, contrasted with rules-based automation, agents dynamically determine actions based on real-time context rather than executing predefined workflows. A rules-based system triggers replenishment at fixed thresholds; an agentic system continuously balances service levels, working capital, lead time variability, and transportation costs to optimize inventory across the network.
Second, compared to classical optimization engines solving point-in-time problems, agentic AI operates continuously and autonomously. Traditional optimization requires analysts to define problems, gather data, run models, and implement results. Agents maintain persistent awareness, detect when conditions warrant re-optimization, and execute decisions without human initiation.
Third, moving beyond generative AI that produces content and recommendations, agentic AI acts. While generative AI might draft reports suggesting route changes, agentic systems directly re-route shipments in TMS, update order promise dates in OMS, and alert customer service teams, all while learning.
Autonomous agents are replacing manual planner decisions by using real-time data to automate inventory rebalancing and supplier risk mitigation. In logistics and shop floor operations, they instantly reroute shipments and re-optimize production schedules to adapt to shifting constraints. This transition from slow, manual cycles to autonomous execution ensures continuous agility and service levels across the entire value chain.
What It Takes to Get There: A Pragmatic Roadmap
Step 1: Data and Process Readiness
Identify one to two high-value decision journeys where data quality is sufficient and latency impacts performance. Ideal candidates include order promising, logistics exception handling, or short-term inventory rebalancing. Map current processes end-to-end, identifying data sources, system interfaces, and handoff points.
Step 2: Governance and Guardrails First
Establish AI governance frameworks before deploying agents. Define autonomy levels, risk thresholds, and compliance boundaries. Align with enterprise AI policies covering data privacy, cybersecurity, and ethical AI. Create audit trails for all agent decisions, ensuring transparency and regulatory compliance.
Step 3: Design the Agent Operating Model
Specify which agents to deploy, their autonomy levels, which systems they invoke, and how humans oversee them. Design multi-agent collaboration protocols. Define performance metrics: forecast accuracy improvements, order fulfilment cycle time reduction, inventory turn improvements, and planner productivity gains. Target 10-15 percent improvement in forecast accuracy and 20-30 percent reduction in manual interventions.
Step 4: Pilot, Measure, and Scale
Start with bounded pilots in one product category or geographic region. Run agents in shadow mode, making recommendations without executing, to validate logic and build trust. Measure KPIs rigorously, comparing agent performance to human baselines. Scale gradually, adding product lines, decision types, and autonomy levels as performance data accumulates. Expect six to nine months from pilot to scaled deployment.
Step 5: Change Management and Capability Building
Upskill planners and operations managers in AI fundamentals, understanding what agents can and cannot do. Create AI literacy programs. Align incentives to reward supervising agent networks and improving algorithms rather than manual firefighting. Foster a culture that views agents as augmenting human judgment rather than replacing it.
The Decisive Moment for Supply Chain Leaders
The next 12–24 months are critical as Agentic AI moves from experimental pilots to scaled, governed deployments. By replacing manual delays with real-time responses, leaders will gain an irreversible advantage in cost, agility, and resilience. Embracing this evolution is the only path to thrive in an era of permanent volatility.