Steam mechanized physical work. It took the better part of a century to reshape industry, because the world first had to build factories, rail networks and cities around it.
Computers digitized routine information work. That shift took 20 to 30 years, as companies rolled out PCs, networks and enterprise software.
Agentic AI is starting to industrialize cognitive work: analyzing, coordinating, and supporting decisions. We believe this shift could be measured in years rather than decades, possibly three to seven.
The reason is simple. Much of the enabling infrastructure already exists. Cloud, data platforms, and modern supply chain planning systems are in place. The challenge is less about building new infrastructure and more about changing how the organization works. That's the part most companies have the least time for.
The bottleneck has moved from technology to the operating model
Supply chain leaders aren't short of AI ambition.
In Genpact's Autonomy by Design research of more than 500 senior executives, about a quarter said they expected self-managing operations within three years. Only 3% reported actively implementing agentic orchestration platforms.
That gap isn't only about technology. AI agents can be configured to monitor signals, analyze data, build scenarios, and recommend actions. In our experience, pilots can stall when agents are placed on top of processes, roles and incentives designed for a world where people did every step themselves.
Look at where planning time goes today. Much of a planner's week is transactional: collecting data, checking exceptions, chasing confirmations, and correcting parameters.
As AI agents take on more of that transactional and tactical work, they can help planners spend more of their time preparing and making decisions.
Same team. Very different job.
One supply chain disruption, two very different responses
Consider a hypothetical scenario: a key supplier misses a critical delivery.
In a traditional supply chain, a familiar sequence starts. The planner checks the impact. Then they confirm the recovery date with procurement, production sequencing with the shop floor, delivery options with logistics, and customer priorities with sales. Finance works out the margin impact.
An escalation meeting then decides what to do. Each step can depend on information from the one before it. Tradeoffs may become visible late, and each function may optimize its own piece.
In an agentic supply chain, a configured orchestration agent can classify the disruption and initiate a parallel assessment. One agent checks supplier recovery and second-source options. Another checks inventory across the network. Another builds and tests scenarios in the planning platform. Others assess transport options, customer service levels. and margin impact.
Before the first meeting, the planner can have a decision package: the options, the tradeoffs, and what each one means for revenue and service.
Routine follow-ups, such as rebooking transport or resequencing production, can be carried out by agents within agreed business rules. The planner reviews the recommendation, decides or escalates, and remains accountable for committing the plan.
The planning platform doesn't disappear in this picture. It becomes more valuable, as agents work through it and extend its reach across functions and into execution.
Smarter AI agents aren't the answer. Better control is.
When AI underdelivers, the instinct is to look for a better model. But reasoning engines are increasingly available. Reliability also depends on control.
Instructions define what an agent can do. Guardrails define what it's allowed to do and when a person needs to step in.
In practice, guardrails are business thresholds that people set, and agents act only within those limits. For example:
- An agent may expedite a shipment when the extra cost stays below an agreed limit.
- It may reallocate stock when no priority customer drops below the agreed service level.
- It may adjust a production sequence within predefined limits and outside the frozen window.
Anything beyond those limits goes to the planner, along with the options and tradeoffs. That's human-in-the-loop AI in practice: agents prepare and execute defined actions, while people set the thresholds and stay accountable for material decisions.
Guardrails become the planner's steering wheel. As operational experience grows, organizations can review and adjust them.
The right level of autonomy depends on the decision:
- Routine execution (transport booking, rescheduling, replenishment): agent-executed within guardrails, with escalation controls.
- Material operational decisions (allocating scarce supply, responding to a supplier failure): agent-orchestrated, human-approved.
- Executive commitments (S&OP, capacity and network decisions): human-led, agent-prepared.
The goal isn't a fully autonomous supply chain. It's an operating model that defines where agents can act and where people remain accountable.
S&OP and S&OE stay. The work inside them changes.
None of this means abandoning S&OP or S&OE. The process backbone stays. What changes is how work gets done inside each step, and who owns what.
- The demand planner becomes a business collaboration partner who challenges signals rather than collecting them.
- The material planner becomes the owner of material availability.
- The person who consolidated short-term plans by hand becomes the decision lead who resolves escalations and commits changes.
This is also where rollouts run into trouble. People can easily hear "help us automate your process" as "help us replace you."
Many organizations still celebrate the planner who fixes a crisis at midnight, not the one whose plan prevented it. In that environment, resistance is understandable.
Adoption can be stronger when people benefit from improving the system rather than protecting the old one. That means recognizing lower workloads and better plan quality, while treating agent errors as chances to improve the system, not as personal failures.
Where to start with agentic AI in supply chain
Organizations exploring agentic AI in supply chain don't necessarily need to wait for a complete enterprise blueprint. They can start in the white spaces: the decisions that fall between functions and systems today.
- Pick one cross-functional flow with visible value, such as supply chain disruption response or forecast sharing with suppliers.
- Redesign the steps, roles, decision rights and guardrails around it.
- Test the approach, learn from it, then decide where the pattern can scale.
- Use those lessons to rethink your supply chain operating model, with a clear target state for how supply chain AI agents and supply chain planners work together.
The companies of the steam age had a generation to adapt their operating model. Today's supply chain leaders may have far less time.
The technology is advancing rapidly. The question is whether your operating model will evolve with it.
Genpact (NYSE: G) is the Agentic Operations company, where applied AI meets context-rich process intelligence. We run and transform mission-critical operations for global enterprises, grounded in decades of operating core business processes at scale. AI agents bring speed and precision, our people bring judgment, and together they deliver outcomes clients can measure.
Certain features use AI/ML models to support workflows. Outputs are subject to configuration, business rules, and human review.
Co-authors Tim Reuschlein (left) and Steffen Blunck (right)
