At SAP Sapphire 2026, leaders from Gallo, Microsoft, John Deere, and Illumina shared their journeys across two panels—offering a candid look at what confident decisions, autonomous planning, and reliable execution look like in practice.

Balancing supply and demand as efficiently as possible has long been the foundational goal of supply chain management. But in a world increasingly shaped by disruption—geopolitical shocks, climate events, and rapid market shifts—that balance alone is no longer sufficient. Businesses must move faster, adapt continuously, and unlock value at every stage of the supply chain to remain competitive.

SAP Sapphire 2026 delivered a wealth of insights across the supply chain planning space. Two standout panel discussions—Decision-Centric Planning and From Strategy to Value: AI-Enabled Supply Chain Transformation —brought together customers who shared their real-world experiences and current approaches to supply chain planning. They reflected on their journeys with SAP and offered a candid perspective on the AI-driven and agentic future of supply chains.

Gallo—Breaking down the silos to reduce decision latency

Organizational silos remain one of the most persistent challenges in supply chain planning. When departments such as sales, production, and logistics operate independently, they rely on disparate data sets and pursue misaligned goals, resulting in fragmented plans, inventory imbalances, longer lead times, and higher operational costs.

Gallo, the consumer goods company, faced exactly this. Planning silos across demand, supply, production, and sourcing were creating decision latency and slowing the business down.

Nitin Murali, VP Supply Chain Excellence at Gallo, describes the nature of these silos:

"Silos are really designed silos—you have demand planning, supply planning, finance, and logistics, and these are all designed because information flows in a particular way."

To address this, Gallo chose SAP IBP as the foundation for integrated business planning, thus unifying demand, supply, inventory, and S&OP into a single connected planning process, with AI-powered decision intelligence applications built on the SAP data layer to connect cost, demand, supply, and production signals.

Nitin Murali explained Gallos’ digital transformation, saying “our AI journey is more around a decision improvement journey. We are deeply integrated into the SAP ecosystem, whether it is with S/4HANA, EWM, PPDS, IBP, etc”

Murali continued, “the whole journey that we are on right now is how do we make decisions faster, with more information but also to make sure that we are getting it to a point where we can get the right signals to make the right judgment.  So it's about value creation for us, and that's the journey that we are on with SAP”.

Nitin Murali also shared how Gallo redefines success for AI within SAP IBP, moving beyond traditional KPIs toward a fundamentally different way of measuring value.

"I always look at value creation as opposed to KPIs more, because KPIs are about the past. 'Hey, here's what happened in the past.' I'm more interested in what we need to do in the future. What do we need to do when something like this happens again? So we are starting to think in signals, not necessarily KPIs."

The distinction matters. A signal, in Nitin's view, is not just a metric, it carries context, causality, and a recommended course of action.

Nitin explained, "The difference between a signal and a KPI is a signal is able to tell you what happened, why it happened, why it matters. What happened the last time, and how confident the system is that whatever the recommendation is, it is the right one”.

Nitin said this does a few things, “it actually puts accountability in the end to the human to do something, but it also carries context and meaning about what happened in the past. So that, to me, is a clear signal. KPIs are dashboard oriented. We are decision-oriented—so it's a pretty big shift that we are making. I'm more interested in how to really drive value within our entire value chain, and that is really dependent on signals that we get."

The results speak for themselves: AI-powered decision intelligence applications built on the SAP data layer—connecting cost, demand, supply, and production signals—delivered stronger forecast accuracy and improved fill rates through tighter planning-to-execution alignment.

Microsoft’s shift to autonomous supply chain planning

Over the years, the focus in supply chain management has shifted from efficiency toward resilience, driven by a series of disruptions that exposed the fragility of traditional operating models. This shift has opened a new opportunity: to move toward decision-centric and autonomous supply chain planning, leveraging advanced AI to harness all available data and act on it at speed.

This is precisely where SAP's product vision comes in, making complexity simple, enabling resilient, AI-first planning and decision-making, and ultimately empowering organizations to move from reactive to autonomous planning.

Microsoft is a compelling example of this transformation in practice. One of its core challenges was a heavy reliance on manual processes and spreadsheets for critical planning activities—a bottleneck that limited speed, accuracy, and scalability.

Dhaval Desai, Partner Group Engineering Manager at Microsoft, described the approach they took:

" We first looked at the process. We went through the lean approach to say, how do we linearize the processes first? So you remove the waste from the processes, you leverage AI now to automate those processes so that you are not only reducing the cycle time from a process perspective, but with the AI, you're also infusing the right degree of intelligence, and you can deliver the value at the right speed that the business expects you to do so. So that's the approach that we took."

By adopting SAP IBP (Integrated Business Planning), Microsoft replaced those manual workflows with intelligent, connected planning processes. The results were tangible:

• Optimized forecasting for intermittent-demand spare parts in data centers
• $14 million in projected savings from optimized inventory planning and smarter spare-parts transfers.

This shift from manual, spreadsheet-driven planning to AI-powered decision-making is what allows businesses to move away from rigid processes and make high-quality, timely decisions that enhance performance and adapt to disruption.

John Deere—Agentic AI for Factory Floor Execution

Jeff Thiele, Global Digital Product Manager, Manufacturing Operations at John Deere shared how a network of collaborative agents is now solving one of manufacturing's most persistent problems: missing materials on the factory floor.

" Missing materials in our factories is a problem globally; we don't always have the parts on hand that we think we might for a variety of reasons. Maybe we have had a bad inventory accuracy or a supplier might have short shipped us, we might have misplaced the parts. There's any sort of reason that that could have happened inside a factory. All the factories are trying to solve the problem in much a same way: do I have it somewhere else in the factory, and can I borrow it, can I get a partial order from my supplier and so forth”

Thistle explained that the solution is “using a collection of Agents in each of those workstreams we have automated that. So as soon as an agent becomes aware of an impending or a current material shortage—they go to work. And that automation has saved our material coordinators and material schedulers 60 to 75% in their time process. So that's being a big win for us."

Illumina—End-to-End Infrastructure First, AI Second

Leon Trevett, Senior Director, Global Integrated Planning at Illumina described a deliberate, infrastructure-led approach to AI adoption—one that prioritizes connectivity before capability.

" We've taken a slightly different approach. So rather than jumping hands long I want an agent here, and there. We are taking approach of fundamentally thinking about infrastructure and an end-to-end connected flow that we want to put in place. So we deployed a fully integrated business planning suite (IBP). And then in each of those areas we lean into the AI capabilities. We created the organizational design also to support that."

The results were measurable across multiple planning domains:

"So in demand planning as an example, we fully land into some of the AI capabilities there to just increase the forecast accuracy to give us the ability to do some quick scenario planning. Very helpful in this geopolitical environment as I need to model a scenario what the impact could be on demand. That has a flow into multi-echelon inventory optimization. When you truly think about multi-echelon inventory optimization, where I'm risk full in that inventory, is that demand variability in the field, is that supply variability from a supplier of raw material making a right investment in working capital at the right place to drive a service level and keep production running was a huge win. So I think over the period of that deployment, we have increased our turns by about 30%. So built in that infrastructure underneath, now we're stepping into what are the other AI tools we could use."

Supply Chain Planning reimagined: Confident decisions, autonomous planning, and reliable execution

At Sapphire 2026, the message was clear: SAP's vision is centered around confident decisions, autonomous planning, and reliable execution.

Reliable execution depends on synchronized operational plans—enabling a single planning environment that supports the full range of decisions businesses must make as they navigate disruption, in both directions, with seamless handovers between plans at every stage.

This is the future state of AI-enabled supply chains. Enterprises are already deploying agentic AI across their operations—and it is not a buzzword. It is a new reality.