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For years, supply chain leaders have been advancing integrated business planning. It has created a flurry of informative dashboards and smart plans, but that hasn't necessarily translated into business outcomes.
The real problem isn't the forecast or the supply plan itself. The problem is that decisions built on that data are made in silos. A forecast is not an outcome. Someone still must decide how to meet demand, manage a shortage, prioritize an order, or balance service against cost. And right now, those decisions are made separately by finance, planning, procurement, logistics, and customer-facing teams, each optimizing for their own objectives rather than a shared one.
That fragmentation is the real challenge. These decisions touch so many different domains and stakeholders that, without a way to connect them, organizations end up solving for the function rather than the whole. Until companies bridge that gap, not just within the enterprise but across the broader partner and supplier network, they will continue to leave value on the table.
Agentic planning lets supply chains accomplish three things: sense changing conditions continuously, weigh trade-offs across every function at once, and recommend decisions based on their financial impact. These systems, which use intelligent, autonomous agents to handle chained tasks, are the key to bridging underlying systems. They can recommend decisions that connect the data to the outcomes.
Why planning excellence eventually plateaus
Kearney has been studying supply chain maturity for decades and has identified five separate eras of evolution. It began with simple transactional execution, but over time, it became more sophisticated, adding software and analytical horsepower. It moved through several stages. Functional planning improved individual supply chain processes, such as manufacturing or logistics. From there, organizations expanded into integrated planning, attempting to connect those processes for a more cohesive supply chain. Finally, connected planning used technology and data to evolve that integrated planning into real-time visibility and predictive analytics, addressing the entire end-to-end supply chain.
These were laudable advances, but they all shared the same problem. The supply chain technology stack reported what happened and what came next. But it didn't recommend which actions to take or why.
AI decisioning takes care of that problem by layering automated decision support atop comprehensive information. But it relies on agentic systems that interact across the entire supply chain and other parts of the organization.
That quality is often lacking. AI delivers limited impact when applied to narrow problem areas. The opportunity is much greater when agents coordinate decisions across functions, addressing the full decision rather than just one slice. This broader approach can drive meaningful productivity gains while creating significantly more value for the business.
"I need to have access not just to supply chain systems, but to my ERPs, to my suppliers, and to my logistics providers to understand really what that looks like," explains Neeraj Verma, a partner in Kearney's Digital and Analytics practice. "I really need a holistic cross-functional view of data with a clear understanding of the trade-offs."
Companies making the leap have seen impressive results.
An architecture built for decisions, not dashboards
This layered approach involves three separate strata to create a truly effective AI decisioning process:
- The integration layer: A connectivity fabric that connects data from different parts of the organization. Without it, the subsequent layers cannot function.
- The data layer: Agents work best when they understand the context of this data, which is where the data platform layer comes in. It defines data formats for key concepts such as customers and orders on a company-specific basis, and graphs relationships within data. Agents use this to see how supply chain events affect the business.
- The AI layer: Agents shouldn't make all decisions autonomously. This layer allows executives to decide how autonomous decisioning should be, ranging from not at all (simple machine recommendations) to fully automated with guardrails. They can balance productivity and risk, keeping humans in the loop for more impactful or complex decisions.
Where to start when everything feels like a prerequisite
Companies should begin their agentic decisioning journey with the decisions rather than the data platform, says Verma.
"Can my data be 100% accurate, clean, reliable? Never. I will always find holes in my data," he says. "They will show up as we try to make those decisions." Agents will likely help surface the gaps and fix them.
So start with a use case like distribution planning or raw material shortages, where the data is good enough and the potential wins are big. "It's a very targeted use case that every supply chain does at some point in time," he says, adding that there's plenty of room for most organizations to improve here. "To make an inventory transfer today, it takes people who are working manually about one week to three weeks. And most of the organizations try to chase all shortages."
The future of agentic decisioning in the supply chain
Fully autonomous AI-powered supply chains is a long way off, but the journey begins now. Organizations should choose autonomous areas where agents own a defined decision inside strict guardrails. That is a realistic outcome for 2030. It's already emerging in pilot projects for companies forward-thinking enough to try them.
Which of your supply chain decisions are candidates for AI automation? Connect with Kearney to build your agentic decisioning roadmap.
