A Step-by-Step Path Shared by Blue Ridge Product Leaders
AI is transforming supply chain planning. From demand forecasting to inventory optimization, today’s supply chain leaders are using AI to make smarter, faster, and more accurate decisions. But knowing where to begin can often feel overwhelming.
In a recent Blue Ridge webinar, product leaders Steve Park, Product Manager and Austin Li, VP of Product Development didn’t just define AI, they broke down how supply chain teams can adopt it safely and effectively, without disrupting operations or overcommitting to unproven tools.
“Gartner has shared a statement that by 2027, AI will be involved in 50% of business decisions,” Steve noted. If you’re wondering how to implement AI in your supply chain, or how to choose the right software, this guide breaks down the steps to get started using AI-powered planning.
Step 1: Identify High-Impact Areas
“Start by identifying your high-impact areas,” said Austin. “That’s the easiest way to begin.”
Look for processes that are highly manual, repetitive, or prone to delays, these are ideal for automation or augmentation. Common examples include:
- Reviewing demand forecasts
- Generating purchase orders
- Managing service levels across locations
Steve offered a framework for thinking about where AI can be applied in the supply chain:
Traditional AI
Focuses on improving forecast accuracy by analyzing large volumes of structured data
Generative AI
Allows planners and buyers to interact with data using natural language (e.g., “Why did this SKU spike?”)
Agentic AI
Automates actions based on data, goals, and real-time conditions
These categories help teams match use cases with the right level of AI capability. Many teams start with AI for demand forecasting, replenishment, or exception management. These are areas where intelligent planning tools can drive fast ROI and eliminate guesswork from supply chain decisions.
Step 2: Evaluate Your Data Readiness
“AI is only as good as the data it’s trained on,” Austin emphasized.
Before launching any initiative, assess your data quality and completeness:
- Is your historical sales data clean?
- Can you access promotional calendars, weather history, or external event data?
- Are systems connected in a way that supports centralized analysis?
Steve shared how far modern forecasting has come compared to old-school time series models:
“For a long time, we relied on simple methods like moving averages. But those models are one-dimensional, they only look at past sales. Now we can bring in holiday data, weather data, social sentiment… just about anything that influences whether a customer decides to buy.”
This ability to use contextual data is what separates legacy forecasting software from modern AI-powered supply chain platforms.
Step 3: Choose the Right Platform to Power AI
Before you scale, you need the right foundation. “You can’t pilot AI if your system doesn’t support it,” Austin hinted.
Not all supply chain software is built for AI, or even ready to support modern forecasting methods. Many ERP systems still rely on static rules, manual overrides, or simple historical averages.
To fully benefit from traditional, generative, and agentic AI, your team needs a supply chain planning software platform that’s purpose-built for AI, not just an ERP module or static forecasting tool.
Look for a platform that:
- Ingests and leverages external data sources
- Supports machine learning forecasting and GenAI
- Offers explainability for user trust
- Enables plain language requests and automated recommendations
Blue Ridge’s AI-powered supply chain planning software does exactly that, helping distributors, manufacturers, and retailers unlock faster, smarter decisions through AI.
Our platform is designed to guide you through all three levels of AI, from improved forecast accuracy to conversational GenAI to agentic automation. Whether you’re piloting AI for the first time or looking to expand your capabilities, Blue Ridge gives you the tools (and the trust) to move forward confidently.
Step 4: Pilot, Measure, Improve
Once you’ve chosen a strong use case and validated your data, start small. “Capture the results. Identify areas of improvement. Fine-tune prompts or model settings,” said Austin.
The goal of the pilot isn’t just functionality, it’s trust. Both Steve and Austin emphasized the importance of forecast explainability, especially for planners:
“The ML forecast process can’t be a black box,” Steve said. “We need to build trust with the people using this data.” “Once users see the value, they’ll adopt it. But they must understand it first,” Austin added.
In practical terms, that means pilots should:
- Show before/after forecast performance
- Surface why a recommendation changed
- Give users opportunities to challenge or confirm results
Whether you’re testing AI-generated forecasts or exploring GenAI features like “Why is this SKU out of stock?”, the right software makes piloting safe, measurable, and easy to scale.
Step 5: Scale to More Products and Locations
If your pilot delivers clear ROI, such as improved accuracy, fewer stockouts, or faster ordering, it’s time to scale.
“Once you have one successful implementation under your belt,” Austin said, “tackle the next priority that was identified earlier on.”
Expansion might mean:
- Adding more product categories
- Including additional distribution centers or store locations
- Enabling more team members to use AI-powered tools
As this happens, your organization may move through the three levels of human involvement that Steve described:
- In the loop: Human uses the AI’s data analysis output to make decisions (Traditional AI)
- On the loop: AI suggests actions; human selects the best one (Generative AI)
- Out of the loop: AI makes decisions; human reviews or approves (Agentic AI)
In the Loop
Human uses the AI’s data analysis output to make decisions
(Traditional AI)
On the Loop
AI suggests actions; human selects the best one
(Generative AI)
Out of the Loop
AI makes decisions; human reviews or approves
(Agentic AI)
Understanding where you are, and where you’re comfortable going, is key.
Step 6: Train, Promote, and Drive Adoption
“Trust isn’t built overnight,” Austin reminded listeners. “Keep the human in the loop.”
Even with high-performing AI, adoption hinges on user experience and communication. Make sure you:
- Train users on how the system works
- Provide clear, explainable output
- Promote AI as a companion, not a replacement
“In traditional AI, a planner researches and chooses option A. With generative AI, the system presents options A, B, and C, and the planner selects. With agentic AI, the system acts, and the planner either agrees or provides feedback,” Steve said.
This isn’t about removing people. It’s about giving them smarter tools to make better, faster decisions.
“Everyone comes to work trying to do their best,” Austin said. “Your AI solution should act as a companion to increase everyone’s effectiveness.”
With the right roadmap, starting small, validating results, selecting the right platform, and focusing on trust, your supply chain team can unlock the power of AI today.
Want to Hear the Full Discussion?
Want to see how supply chain teams are using AI today?
Watch our on-demand webinar to learn how Traditional AI, Generative AI, and Agentic AI are transforming forecasting, planning, and inventory management, and how platforms like Blue Ridge make it easy to get started.
FAQs
What is the best way to start using AI in supply chain planning?
Start small. Focus on high-impact areas like demand forecasting, replenishment, or service level management. Run a pilot using clean, structured data and measure the results. Once you’ve proven value, scale.
Do I need special software to use AI in supply chain?
Yes. Most ERP systems aren’t built to support modern AI. To take full advantage of Traditional, Generative, and Agentic AI, you need a supply chain planning platform that’s purpose-built for AI, like Blue Ridge. Look for features like machine learning forecasting, external data integration, explainability, and natural language interaction.
What types of AI are used in supply chain planning?
Three levels of AI in supply chain:
- Traditional AI: Uses machine learning to improve forecast accuracy
- Generative AI: Allows planners to ask questions using natural language (e.g., “Why is this SKU out of stock?”)
- Agentic AI: Automates actions based on real-time data, without needing human input
How can I make sure my team trusts the AI?
Start with explainability. Your platform should show users why a forecast changed and let them validate or challenge the recommendation. Building trust leads to adoption.
What are common mistakes when adopting AI in supply chain?
- Starting too broad
- Using poor-quality data
- Skipping pilot phases
- Failing to train end users
- Not following IT governance
