Agentic AI is emerging as one of the most transformative technologies shaping the future of business. Its promise is especially powerful in supply chain planning, an area where complexity, volatility, and interdependencies demand faster, more adaptive decision-making.
Analysts predict that by 2030, half of all cross-functional supply chain management (SCM) solutions will use intelligent agents to autonomously execute decisions across the ecosystem. The shift is underway, and supply chain leaders who move early will be positioned to lead with greater resilience and agility.
Unlike traditional automation, agentic AI goes beyond following predefined workflows. Its strength lies in its ability to reason, learn, adjust, and re-plan when conditions shift, all in near real time. This unlocks new levels of responsiveness and efficiency across the end-to-end supply chain. But to fully capitalize on this wave of innovation, organizations must adopt the right mindset and foundational practices.
Here are five essential tips to help supply chain planning leaders successfully embrace agentic AI.
1. Understand What AI Agents Are – and What They Are Not
Given the rapid rise of AI technologies, there is a lot of confusion around the difference between AI agents, chatbots, and digital assistants.
What is an AI agent?
An AI agent is a software component that has the agency to act on behalf of a user or a system to perform tasks. AI agents are capable of achieving goals by independently executing complex, multi-step tasks. They can reason, plan, utilize tools such as APIs, learn from feedback, and act proactively, without requiring constant human intervention. They’re powered by large language models (LLMs) but go further by determining the actions needed to reach a desired outcome, rather than simply generating responses based on prompts.
These agents are much more advanced than chatbots and assistants. Chatbots are reactive; they wait for an input and return an answer, while assistants can perform tasks like drafting emails or summarizing meeting notes. But agents elevate the game with their ability to interact with their environment, make decisions, adapt to new information, and orchestrate actions across systems. They are also goal-driven and come in multiple forms (policy-based, utility-based, event-driven, and more) each designed to pursue objectives rather than simply respond to instructions.
Understanding these differences allows planning leaders to identify when an agentic AI approach is appropriate and to set the right expectations for value and autonomy.
2. Focus on the Problem You Need to Solve
One of the most common missteps organizations make is approaching AI initiatives from a technology-first perspective rather than a problem-first perspective. Without well-defined priorities or use cases, teams often end up applying the wrong AI techniques or deploying solutions that deliver little or no meaningful business value.
The key is to begin with clarity. Identify the operational challenges that slow down planning, drive inefficiencies, or require excessive manual work. Define what needs to be automated, what decisions require deeper insight, and where predictive or prescriptive intelligence could improve performance. When the business problem is well understood, the path to the right AI approach becomes clearer, and the likelihood of adoption and ROI grows substantially.
A focused, problem-driven mindset ensures that agentic AI is applied purposefully and aligned with strategic objectives.
3. Don’t Let Data Become a Roadblock. But Don’t Ignore It Either
Data quality remains one of the biggest concerns for organizations considering AI initiatives. It’s true that agentic AI relies on high-quality, contextualized data to deliver optimal results. But it’s equally important not to let imperfect data stop progress.
Organizations should invest in processes and tools that cleanse, contextualize, and connect both internal and external data across systems. Advanced supply chain planning solutions can help by providing built-in data quality scoring, hierarchical inference, and automated tracking. This gives teams the confidence to move forward even when data isn’t perfect.
Agentic AI can also enhance data quality by harmonizing and transforming information across sources to create richer insights.
The most important mindset is momentum: imperfect data should be an area of improvement, not a barrier to innovation. With the right systems and governance, companies can elevate data quality while simultaneously advancing AI adoption.
4. Build Organizational Readiness
Technology alone won’t transform your supply chain. True progress requires an organization prepared to embrace and operationalize AI in supply chain.
This includes developing skills, improving data literacy, empowering teams to collaborate with AI, and fostering a culture where innovation is encouraged rather than resisted. Many companies are hiring AI-focused roles; others are upskilling existing teams. What matters is ensuring the talent and culture are ready for AI-enabled operations.
A clear vision for how AI fits into your organization’s future is essential to guiding investments and accelerating adoption.
5. Adopt a Test-and-Adapt Mindset
Agentic AI’s value will become evident much sooner than many organizations expect, and it will thrive in environments where experimentation is encouraged. Be ready to experiment, measure outcomes, and refine its applications based on performance.
Collaborate with your software provider to start experimenting with early use cases, learn from results, iterate, and scale. Begin with a manageable scope, train your agent, and expand as it demonstrates value and reliability.
John Galt Solutions’ Atlas Planning Platform is leading this evolution, bringing agentic AI directly into end-to-end supply chain planning. With explainable AI capabilities built into its core, Atlas provides the transparency and guidance needed to support this iterative learning process, enabling supply chain teams to adopt agentic AI at a pace that aligns with their readiness and goals, and to engage with AI in an intuitive way.
Agentic AI is designed to grow with your business. Start small, build trust, and scale as the benefits compound. Let’s talk about it!
