Locus was recently acquired by Ingka Group, the parent company of IKEA Retail. The company uses agentic AI to help brands like Unilever and Nestlé make faster, smarter supply chain decisions while reducing emissions and avoiding disruptions. We spoke with Locus CEO Nishith Rastogi about how AI is changing logistics and what it means for same-day delivery.

What’s Related

Supply Chain 24/7: Thanks for joining me. What does the recent acquisition by Ingka, the parent company of IKEA, mean for Locus?

Nishith Rastogi: This partnership is an important moment for us. We’re able to continue the pursuit of our long-term vision while also gaining additional resources and a global reach we wouldn’t otherwise have access to at this stage. 

Locus will retain full operational autonomy, meaning we will continue to work with clients worldwide, across all industries. Our ability to grow with the Ingka Group, continue the service we have been providing to customers now for over 10 years, and our shared commitment to customer experience and logistics excellence made this the right choice for us.

Ingka got to know Locus through an extensive evaluation process. Our platform’s ability to combine advanced route optimization, real-time visibility, and intelligent use of vehicles and resources made us stand out. 

Ingka’s investment reinforces the advancements we’ve made in recent years and solidifies our position as a leading transportation management system for the enterprise. 

SC247: IKEA is known for rethinking how products move from the factory to the store to the customer. How do you see Locus impacting that?

NR: Locus is helping IKEA build a single global operating system for logistics across its expansive networks. This means synchronizing all fulfillment models, across home delivery, curbside pickup, and store delivery under one intelligent platform. We want to deliver a consistent experience for customers, no matter how they are shopping. Implementing predictive, AI-driven planning ensures that deliveries are both timely and sustainable. 

One specific initiative we are working towards is seamlessly coordinating tandem deliveries, which involve a large item arriving at the same time as the installation teams. This creates a more enjoyable and predictable experience for IKEA customers. 

We are also aiming to optimize fill rates on vehicles. By looking at delivery locations and areas proactively, we can see which trucks have additional capacity for nearby delivery, and reduce the number of trips a truck makes. This has been a simple sustainability initiative we have already seen make real-world impact quickly. 

SC247: For people who might not know the term, how do you explain “agentic AI” and what makes it different from other kinds of automation in logistics?

NR: Understanding agentic AI makes the most sense when broken down into a three-part framework. 

The first part is input. A traditional system would require users to learn how software defined its inputs like keyboards, clicks, or programming syntax. Agentic systems reverse that responsibility. It’s now the software’s job to learn from and understand the user’s intent, often through natural language rather than rigid commands. This makes the technology more intuitive and accessible.

The second part is the environment. Traditional SaaS systems operate best in a well-defined, rule-based environment, where they can process information, but only within tightly structured parameters. Agentic systems, however, can function autonomously in open and ambiguous environments. They’re able to make decisions independently and navigate when conditions change or datasets are incomplete. This is a crucial feature for modern logistics.

Finally, there is the output. In earlier systems, outputs meant information, whether that would be a dashboard, report or recommendation. The human would still be responsible for interpreting the information and acting on it. In agentic systems, the action is taken. It goes a step beyond telling you what the problem is, and resolves it. In logistics, this can look like automatically rerouting a delivery based on real-time traffic, instead of alerting a human of the delay and waiting for them to act on a given recommendation. 

At Locus, we are building a ‘digital supply chain officer’. These agents are capable of making autonomous, accurate decisions in a complex real-world environment like logistics. They go beyond forecasting to act and continuously learn from outcomes, allowing humans to handle the more demanding tasks. 

SC247: What does agentic AI actually do for a logistics team on a busy day? Can you walk me through a real example of how it steps in to solve a problem?

NR: Agentic AI is changing how disruptions are handled in the logistics industry. The overall goal of deploying AI agents is to reduce manual decisions, react faster, and drive better utilization. AI agents allow humans to focus on more high-level tasks instead of repetitive planning tasks. 

Let’s use routing as an example. Say you have a certain number of deliveries scheduled via truck for next Thursday. On Thursday morning, there’s traffic, or an accident, or an unforeseen road closure. Instead of a person spending time comparing alternate routes, an AI agent can immediately identify the fastest and safest option by drawing on data from past deliveries and historic traffic patterns in that area.

At scale, these micro-decisions add up, saving hours of planning time and improving delivery accuracy across entire fleets. These are the kinds of repetitive, data-driven tasks that AI agents are capable of handling in logistics, and should be tasked with. 

SC247: How are companies using predictive analytics to prevent disruptions before they happen?

NR: In a volatile supply chain environment, disruptions are a guarantee. The question is how quickly logistics teams are able to adapt. Companies still relying on forecasting are losing valuable time by waiting on an operator to make the adjustment based on the prediction. But with agentic AI, the system becomes more agile.

Tools must be able to reconfigure on the fly. For example, a new wave of tariffs may require domestic distribution routes to change. Predictive analytics allows for this reconfiguration to happen much faster, minimizing or completely preventing the disruption before it impacts the broader supply chain.

SC247: You talk about helping logistics teams “anticipate disruptions.” What kinds of disruptions are hardest to plan for, even with good data?

NR: No disruption is ‘easy’ to plan for. Each comes with its own complexities and requires fast and accurate adjustments so they don’t become a crisis. 

Geopolitical changes, such as tariffs, sanctions, and wars are very difficult to plan for. Tensions make it hard to predict when the next change will take place. Behavioral factors have always been a challenge, as it’s near impossible to accurately predict consumer behavior shifts, or the next labor shortage for delivery workers. Natural disasters, although often forecasted, can be difficult to assess impact ahead of time. What was expected to be minimally disruptive could turn out to be catastrophic, or vice versa.

Completely eliminating unpredictability isn’t possible. It’s more important to focus on creating a system that can stay agile in the midst of disruptions. Having a system that can act autonomously and not need human interaction to adapt will be crucial for suppliers and delivery partners who are looking to stay ahead of the curve. 

SC247: What’s something people tend to get wrong about AI in supply chain?

NR: One thing I see many getting wrong is that AI will be able to fully replace people. I believe the real value of AI is elevating human work by taking away repetitive, time-consuming tasks, so humans can focus on more meaningful tasks.

I think of it in regards to earlier waves of automation in manufacturing. These changes allowed for people to move out of physically demanding jobs like coal mining into healthier roles for their minds and bodies.

I see AI not as a replacement, but augmentation. It will be a voice that can help organizations reimagine how people and technology will work together. 

SC247: Looking ahead, where do you see the biggest opportunity for agentic AI to reshape global supply chains in the next few years?

NR: I feel the biggest opportunity lies in how agentic AI will change the way global supply chains are designed, not just how they respond. For decades, networks have been built on fixed routes, static planning cycles, and manual exception handling. Agentic systems are changing this. They make it possible to design dynamic networks that continuously learn and re-optimize. This shift will move supply chains from static systems to living ones.

Another angle to consider is the downstream impact. I think one of the biggest shifts we will see from AI is the rise of agentic shopping and its impact on delivery networks. As more companies like Etsy, which allow for independent merchants across the country to ship goods to consumers directly, are integrated into LLMs, we will see orders spike for these sites. As a result, there will be more pressure on local 3PLs and fragmented fulfillment networks. 

Instead of shipping through a few key delivery partners from the major retailers, which is what we have seen in years past, there will be more emphasis on these direct-to-consumer shipping options. Local networks will need to prepare for these shifts, and implementing agentic AI to get goods from point A to point B will be crucial.  

Nishith Rastogi is CEO of Locus.