Satish Natarajan is CEO and Co-Founder of DispatchTrack, a global leader in last mile delivery technology and customer experience. This article originally appeared for the Forbes Technology Council here.
When it comes to the supply chain, you can bet that if Walmart is doing it, there’s a good reason. Which is why this quote from a recent interview with Indira Uppuluri really struck me: “What’s important to us is not singling out one application but looking at how AI is helping us build a stronger, more resilient supply chain overall.” For all the talk about cutting-edge AI techniques like retrieval augmented generation and multilayer perceptrons within the supply chain, this focus on holistic supply chain resilience is a welcome shift—especially now that productivity is becoming more of a focal point for many logistics operators. Most decision makers intuitively grasp that AI can be transformative. But actually getting the most out of AI deployments requires a shift in mentality toward empowering your people and prioritizing productivity.
AI Productivity Gains
The modern pace of technological change isn’t slowing, which means that shiny new AI use cases are cropping up constantly. At the same time, the practical impacts of existing use cases are coming into view—with real proof points.
I’ve seen logistics organizations decrease inbound call volumes from customers by 75% or more by leveraging a streamlined AI-powered chat agent. By the same token, I’ve worked with companies that have utilized AI-powered voice briefings to give drivers contextual intelligence for upcoming stops, to the tune of one extra delivery per driver per day.
These productivity gains add up quickly. And crucially, they aren’t dependent on finding the most cutting-edge software or deploying the most complicated AI systems.
The most sophisticated AI agent in the world—if you can keep its failure rate down—will boost productivity more than a comparatively simple one. But a simple deployment will gain significant benefits with minimal risk, positioning you to improve your technology incrementally from there.
An AI Deployment Strategy Around Productivity
Building a more resilient, more productive supply chain using AI starts with laying a strong foundation and keeping your focus on operational efficiency—not technology for its own sake. Every little percentage point helps, signifying that your initial deployments will help boost productivity even if they don’t represent the end of your roadmap. Here are a few ways to deploy AI with a focus on boosting productivity and operational efficiency:
Don’t go hunting for problems.
You already know the areas where you have the most room to boost productivity (if you don’t, that’s the first problem you need to tackle), and by addressing those areas head-on, you can right-size your deployments and ensure alignment with your operational priorities. Keep your focus on the classics to start with: improving driver productivity, decreasing manual paperwork and streamlining customer experience.
Map out the interactions between humans and AI.
AI needs to be positioned in such a way that it augments the work that your team is doing, which means avoiding the kind of gray areas where no human is taking ownership over a process. When deployments fail to yield results and productivity stagnates, it’s often because the handover from AI to human isn’t well defined.
Choose your partners wisely.
Let’s face it, we’re not all Wal-Mart. Most supply chain businesses have a limit to how technologically innovative they can be in-house, and it’s the job of software providers and other technology organizations to set these projects up for success. And not just success, but ongoing iteration and improvement as AI continues to improve.
Deploy with an eye toward scale.
These will be the most impactful areas for productivity gains that you’re targeting, so it’s critical to define the procedure for scaling your initial deployment to meet the scope of the challenge. Set out some clear KPIs for success and have a change management playbook you can rely on.
Focus on UX for your teams.
Empowering your internal users to get the most out of a solution is all about making the use of AI tools as painless as possible. If you’re offering AI to drivers, it shouldn’t require them to adjust their normal workflows—it should just help without distracting. If you’re leveraging AI for dispatching, it should be practically impossible for them to misuse it or get confused. The more effectively you hit the target here, the less friction you’ll have between your people and your productivity goals.
Smarter Supply Chain AI—Putting People First
Walmart isn’t the only logistics leader that’s fitting its AI deployments to the holistic needs of its overall supply chain. Unilever is using AI to boost consistency in its food supply chain capabilities. And McKinsey & Company found that 95% of distributors were considering using AI.
Connectivity and visibility are crucial for these kinds of companies: they’re the bedrock of effective productivity strategies and intelligent AI deployments. For businesses that lack a baseline level of visibility into their logistics operations, those are some of the most pressing matters to target with technology.
If your logistics operations aren’t standardized, you can bet that there’s someone in your organization already ringing the alarm bells.
People-first AI deployments help empower those people to better diagnose the obstacles to productivity and take steps to address them quickly and at scale. As AI for reporting becomes more sophisticated, the ability to utilize an agent for quick answers to logistics questions will help bridge the gap between productivity goals and properly-scoped and scaled technology deployments.
Gartner’s Hype Cycle may put us at the “Peak of Inflated Expectations,” but from this vantage point, it seems like reasonable expectations are finally coming into view—and they center around boosting logistics productivity in smart, streamlined and scalable ways.