October is here, and that means Back to the Future Day is just around the corner. In Back to the Future II, Marty McFly and Doc Brown set the DeLorean’s time circuits to October 21, 2015, to explore the world thirty years ahead of their present. Can you believe it’s been 10 years since that cinematic future?!
When Marty landed in the future, audiences were dazzled by hoverboards, flying cars, and self-lacing sneakers. The year 2015 felt lightyears away. But when the date finally arrived, reality looked a little different. We didn’t get flying cars or highways in the sky, but the film was surprisingly accurate in many ways. Video calls and smart glasses are now part of daily life. Biometric devices for identity verification are common today through fingerprints and facial recognition. Wall-mounted flat-screen TVs, wearable tech, drones delivering news, and even hands-free video games all became real.
The lesson? The future always shows up sooner than you expect! (with a slightly different shape than Hollywood imagined). Now, a decade after Marty’s “future,” agentic AI is the next leap forward.
In our previous blog, we explored the evolution of LLM-based AI solutions and how agentic systems go far beyond chatbots and assistants. Unlike chatbots that just provide responses, agents can interact with their environment, adapt to new information, deliver recommendations, and orchestrate actions to deliver results. If that sounds futuristic, it is—but just like Marty’s 2015, it’s also much closer than you think.
The “Future” Isn’t Far Off
According to Gartner, about 32% of respondents still believe usable AI agents are far from reality. Well, if you’re in that group, in the words of Biff, you should “Make like a tree… and get out of here” as you’re already behind.
The same polling shows 15% are already using agentic AI today. Which tells us this wave of innovation is already here.
Much like Back to the Future’s vision of biometric scanners, drones, and automated services—many of which are commonplace now—agentic AI is rapidly moving from science fiction to daily business reality. It’s shaping up to be our own version of the future, and it’s happening faster than many expect.
What Makes Agentic AI Different
Here’s what separates agentic AI from the AI you may be already familiar with:
- Goal-Driven, Multi-Step Ability: Instead of a sole task focus like answering a question or running a command, agents can execute multi-step actions to pursue an outcome.
- Adaptive and Interactive: Agentic AI can be highly context-aware – it can continuously evaluate its environment and adjust to new data and scenarios. Agents can respond to changing inputs, navigate incomplete data, and seek additional information. They can also have a memory, meaning they’re able to learn and improve.
- Explainable Action: Agents can make decisions and execute actions with clear why and how guidelines, while humans define the goal and provide oversight. This makes agentic AI especially powerful for enterprise applications. As businesses generate increasingly complex data environments, agents can streamline planning, coordination, and execution in ways that static systems cannot.
But adoption of Agentic AI depends on trust and confidence in recommended actions and the process used to reach them. Agents can leverage those explainable AI capabilities, so decision-makers understand the “why” behind actions and recommendations. This helps organizations build supply chain processes that leverage AI for faster and smarter decision-making.
IDC’s Eric Thompson underscores this point in the report, IDC Perspective, The Emerging Role of Agentic AI in Supply Chain Planning, #US53080225: “Agentic AI is fast emerging as a powerful step forward in supply chain optimization, insight gathering, and automation.” While the technology’s full potential is yet to be explored, “the value proposition is high enough that it’s worth understanding developments and building strategies for adoption.”
3 Tips to Get Started with Agentic AI in Supply Chain
Agentic AI is gaining speed. How can companies gear up and ensure they’re ready when the DeLorean hits 88 mph? Here are three recommendations:
- Focus on the Problem Before the Solution. Many companies look to deploy AI without clearly defined priorities or use cases. This often leads to using the wrong AI approach, poor adoption, and poor return on investment. Instead, clearly articulate the pain points, and what can be automated, analyzed, or explained. Be like Doc – he didn’t start by building the kind of time machine others had imagined, like boxes or phone booths. He focused on the problem to solve: How to travel through time? Then, he chose the solution that fit the goal, and added some style. “If you’re going to build a time machine out of a car, why not do it with some style?”.
- Focus on Data But Don’t Let it be a Roadblock. AI relies on quality and relevancy of data. Ensure your organization has the right processes and tools for cleansing, contextualizing, and connecting data both internal and external, across systems. Using an advanced supply chain planning solution with built-in tools for data quality scoring, tracking, and hierarchical inference, providing the confidence to move forward, even when data isn’t perfect. AI can also provide richer data by harmonizing and transforming data across a wide range of sources. Much like Marty escaping Biff, he did not let being chased by bullies in a car stop him. He turned an old scooter into a skateboard and avoided the manure (of bad data).
- Develop Your Organizational Readiness: Technology alone won’t get you to 88 mph. It helps to build the skills and empower supply chain teams to work with AI to make the most of the latest innovations. Are you ready to embrace ownership of AI in your supply chain or do you need to engage consultants or hire new talent? Recent research shows that companies are recruiting more employees for AI-related roles, and increasingly investing in AI and data-centric platforms to better navigate disruption. If you aren’t recruiting AI-related roles, is your organization cultivating a culture that adopts innovation and usage of AI? Much like Doc needed his vision for a flux capacitor to build a time machine, you need to have a view of what you want to address with AI and build the right time machine for you.
Get Ready for Agentic AI
The Atlas Planning Platform is at the forefront of embedding agentic AI directly into end-to-end supply chain planning. By integrating explainable AI capabilities at its core, Atlas helps companies like yours anticipate and mitigate disruptions, gain actionable recommendations and guidance, and enable supply chain teams to engage with AI in an intuitive and approachable way.
The powerful value of agentic AI – as with Marty’s 2015 – will become clear much sooner than you think.
Ten years after Marty’s leap into the future, we’re in our own pivotal decade. The flux capacitor is pointed straight at the enterprise supply chain — and the road ahead doesn’t need to exist for us to get there.
So, fasten your seat belt. It’s time to gear up. Let’s have a chat to get you started!
