In this article, learn about:
Practical do's and don'ts for implementing AI in supply chain operations
Why data quality and connected networks determine AI success
How emerging technologies like agentic AI fit into a responsible AI strategy
In supply chain management, AI is already everywhere. From forecasting to inventory planning, fulfillment to returns, nearly every challenge has an “AI-powered” solution attached to it. And while that excitement is understandable, a little skepticism is not only healthy, but also necessary. Not all AI is created equal, and not every promise translates into real-world operational impact.
The real question is not if AI belongs in the supply chain. That ship has already sailed. Instead, the question is how to use AI responsibly. When implemented well, AI can help teams make better decisions at scale. When implemented poorly, it can magnify data gaps, inefficiencies, and organizational misalignment.
This article is meant to be a practical set of do’s and don’ts to help decision-makers evaluate AI solutions with clear eyes, avoid costly missteps, and focus on what actually drives long-term value.
The Do’s of Leveraging AI in the Supply Chain
Start With a Clear Business Problem
The fastest way an AI initiative goes off the rails is when it starts with the technology instead of the problem. Before evaluating vendors or models, supply chain leaders should be clear on what outcome they’re trying to improve. Strong AI projects are anchored to measurable results: better forecast accuracy, higher OTIF performance, healthier inventory turns, or more efficient use of labor.
Implementing a sophisticated AI tool without a clear business objective often leads to impressive demos, with subsequently disappointing results. If success can’t be clearly defined (or even measured), it’s a sign the project isn’t ready.
AI tends to perform best in areas where there’s high data volume, repeatable patterns, and clear feedback loops. AI is far less effective when problems are driven by inconsistent processes, one-off decisions, or missing data. In those cases, fixing the underlying process will deliver more value than adding another layer of technology.
Related Reading: A Checklist for Teams Considering AI or Lot Code Tools for EDI
Invest in a Strong, Connected Data-Sharing Network
For AI to deliver consistent value, data needs to be clean, standardized, and timely, not just within a single organization, but across the entire network of trading partners. Late, incomplete, or inconsistent data both reduce accuracy and create blind spots that AI can’t reason around.
This is where network-based data becomes critical. When information flows through a connected network, it’s validated, normalized, and continuously updated across partners. That creates a far more reliable input layer for AI models, enabling better predictions and more trustworthy recommendations.
Solutions like SPS Commerce provide the underlying data consistency and connectivity that successful AI initiatives depend on. Without that foundation in place, even the most sophisticated AI tools struggle to move from promise to performance.
Pilot Before You Scale
AI works best when introduced slowly, not rolled out all at once based on the strengths of a single sales demo. Controlled pilots and proof-of-concepts give supply chain leaders a low-risk way to validate assumptions and see how an AI solution performs under real-world operating conditions. Gartner found that, on average, only 48% of AI projects make it into production, highlighting the challenges organizations face when moving from pilot programs to operational deployment.
The key is setting realistic success criteria from the start. Early AI pilots should focus on narrow, measurable goals: improving forecast accuracy in a specific category, reducing manual effort in a single workflow, or identifying exceptions faster in one region or partner group. Clear benchmarks make it easier to evaluate results objectively and decide whether scaling makes sense.
Testing first, learning quickly, and scaling deliberately turns AI from a risky experiment into a strategic investment.
Keep Humans in the Loop
In supply chain management, context and relationships matter, and not every decision can be automated. AI is best when it’s used to support human decision-making rather than replace it.
Human judgment remains essential in areas like managing exceptions, navigating supplier and customer relationships, and handling negotiations or trade-offs that don’t fit neatly into a model. AI can surface patterns, highlight risks, and prioritize actions, but people provide the nuance and accountability that technology alone can’t.
Keeping humans in the loop also plays a critical role in technological adoption. Transparency, training, and clear guardrails help turn AI from a “black box” into a trusted part of everyday work, one that strengthens teams instead of sidelining them.
Understand What Agentic AI Can and Cannot Do
Most supply chain teams are beginning to explore agentic AI; a newer category of AI that can act rather than simply generate recommendations. Instead of answering a question or producing an analysis, an AI agent can monitor conditions, make decisions within defined parameters, and trigger workflows across systems.
For example, an agent might identify a potential inventory shortage, gather relevant data from multiple sources, recommend a response, and initiate the next step in a replenishment workflow. In more advanced environments, multiple agents may work together to coordinate forecasting, procurement, transportation, and exception management activities.
While the potential is significant, agentic AI is not a substitute for strong governance. Organizations should establish clear decision boundaries, approval workflows, and accountability before allowing agents to take action autonomously. The most successful implementations start with narrow, low-risk use cases and expand gradually as trust, visibility, and performance improve.
Like any AI initiative, agentic systems are only as effective as the data, processes, and business rules that guide them.
Evaluate Vendors for Transparency and Practicality
Not all AI vendors are equally forthcoming, and supply chain leaders should be prepared to ask direct questions about how a solution works, what data it relies on, and how its recommendations can be trusted. If a vendor can’t clearly explain their models, data inputs, or limitations in plain language, that’s a red flag.
The strongest partners focus less on buzzwords and more on operational impact. Practicality, explainability, and alignment with real-world operations are far better indicators of long-term value than the most hype-generating AI claims.
Related Reading: How Can Retailers Use AI to Improve Supply Chain Execution
The Don’ts of Leveraging AI in Supply Chain
Don’t Assume AI Will Fix Broken Processes
When workflows are inconsistent, overly manual, or poorly defined, adding AI often magnifies those inefficiencies rather than correcting them. The result is fast, authoritative decisions built on a shaky foundation.
Layering advanced technology on top of fragmented workflows creates complexity without clarity. Before introducing AI, it’s critical to assess whether the underlying processes are stable, repeatable, and well understood by your team members. Strengthening those fundamentals first helps ensure that when AI is applied, it amplifies what’s working instead of what’s not.
Don’t Feed AI Poor-Quality Data
Along with broken processes, one of the most common reasons AI initiatives stall is poor data readiness. Incomplete records, inconsistent formats, delayed updates, and unclear ownership all create friction that AI can’t overcome on its own. When data quality issues go unaddressed, even well-designed AI models produce unreliable, and even outright false, results.
Siloed systems and inconsistent partner data make the problem worse. When information lives in disconnected networks or arrives in different formats from each trading partner, teams spend more time reconciling data than acting on it.
There’s also a hidden cost that often goes overlooked: manual data preparation. Time spent cleaning, validating, and stitching together data is time not spent improving operations. Without strong data governance and connected data flows, AI projects can shift from execution to maintenance, which limits growth.
Don’t Ignore Change Management
Even when the technology works, AI initiatives can fail if people don’t use them correctly. Adoption is often the trickiest part not because teams resist innovation, but because new tools change how decisions are made and who owns them.
Training, communication, and expectation setting are critical from the start. Teams need to understand what the AI is designed to do, what it isn’t, and how its recommendations should be used. Without that clarity, AI outputs can feel confusing, threatening, or easy to ignore.
Just as important is aligning AI outputs with how teams actually make decisions. If recommendations don’t fit existing workflows, timelines, or accountability structures, they won’t be trusted. Successful AI adoption happens when insights show up where decisions already happen, reinforcing how teams work instead of forcing them to work around the technology.
Don’t Chase Fully Autonomous Supply Chains (Yet)
The idea of a fully autonomous, “self-healing” supply chain is compelling, and may be part of the long-term future. However, that future isn’t here yet, and chasing it at this point can lead to companies overinvesting in immature solutions and underdelivering real-world value.
Near-term success comes from focusing on practical improvements, not futuristic promises. Claims of fully autonomous decision-making should raise red flags, especially in complex, multi-partner supply chains where variability, constraints, and human judgment still play a central role. In reality, most supply chains benefit far more from AI that assists, prioritizes, and recommends than from systems that attempt to operate independently.
Don’t Treat AI as a One-Time Implementation
AI isn’t something you install once and move on from. Models need to be monitored, tuned, and updated as conditions change, whether that’s shifts in demand, new trading partners, or changes in how data flows through the supply chain. Without ongoing attention, performance degrades and insights lose relevance.
That’s why successful AI initiatives depend on long-term partnerships, not one-off tools. Vendors should be invested in continuous improvement, data quality, and adaptability over time.
One of the most important realities of AI is that it tends to amplify the strengths and weaknesses already present in a supply chain. Strong data, well-defined processes, and clear governance often produce better outcomes. Weak foundations can create larger problems more quickly.
What Realistic AI Success Looks Like
The gap between how AI is marketed and how it actually delivers value is where many initiatives fail.
AI vendors often market their products as delivering a sudden transformation: a smarter, faster, almost hands-off supply chain. In reality, the wins that matter most tend to be quieter and more durable. The organizations seeing the most success today are not replacing planners, buyers, or supply chain analysts. They're helping those teams spend less time gathering information and more time acting on it.
Realistic AI success looks like incremental improvements that show up consistently over time: fewer surprises, faster response, better prioritization, and measurable gains that compound.
The most practical, high-impact AI use cases today focus on narrowing uncertainty and reducing manual effort. Think:
Demand forecasting improvements at the SKU or category level
Anomaly detection that flags inconsistencies early
Smarter exception management and returns processing
Automation of repetitive work like classification, document processing, or matching invoices and purchase orders
Behind all these outcomes is the same requirement: strong data. When AI is fed consistent, timely, standardized information across trading partners, it becomes more accurate and more useful. Network-based data helps reduce gaps and inconsistencies, speeds up visibility into what’s happening, and improves reliability over time.
Building a Supply Chain AI Strategy That Actually Delivers
AI’s capability is built on three fundamentals: reliable data, well-defined processes, and the right partners. When those pieces are in place, AI can enhance decision-making, improve responsiveness, and help teams operate with greater confidence. When they aren’t, even the most advanced tools struggle to live up to expectations.
For business leaders evaluating AI investments, the takeaways are clear. Start with real business problems, not technology trends. Be honest about data readiness and process maturity. Pilot before scaling, keep humans in the loop, and choose AI partners who prioritize transparency and operational impact over buzzwords.
Build AI on a Lasting Foundation
AI delivers results when it’s powered by reliable, connected data across your supply chain. Max by SPS Commerce helps organizations create that foundation by standardizing, validating, and connecting data across trading partners, so AI initiatives start from a place of clarity, not guesswork.
If you’re evaluating AI solutions or planning your next step, SPS Commerce can help you assess your data readiness and build the connected network your strategy depends on.
Learn more about the Intelligent Supply Chain Network from SPS Commerce today.