11 tips for supply chain AI potency

AI is fast becoming mission critical for supply chain transformation. But if you look beyond the success stories, you'll find examples of stalled pilots, siloed experiments, and disappointing returns. To avoid this happening in your business, you must ensure your supply chain AI efforts aren't rolled out in isolation with no clear business purpose, no long-term plan for scale, and no foundation for trust.

For supply chain leaders, the opportunity is real — but so are the risks. To ensure positive, high-impact outcomes, supply chain AI must be applied with intent, connected across business functions, and aligned to measurable goals from day one.

Here are 11 key principles for getting it right in supply chain planning.

1. Focus on business value and ROI opportunities

With so much hype and excitement around AI and its capabilities, you might be tempted to experiment too hurriedly and without careful planning. Every AI initiative should be planned with purpose and your focus should always remain on measurable business outcomes. Prioritize supply chain planning use cases that directly tie to ROI outcomes, such as demand planning, forecasting, optimized inventory, and improved sourcing.

Decision point: Does your initiative clearly return value? If the answer is no, skip it.

2. Choose use cases strategically

The most successful supply chain AI programs start with scalable, high-value use cases in a contained business segment. This allows for visible wins, faster iteration, and lessons that can scale. Avoid overreaching in phase one. Instead, pick a well-scoped use case where AI can augment human decisions, then build momentum from there.

Decision point: Does your contained experiment promise quick wins? If the answer is no, redirect your efforts.

3. Treat data as an asset

AI is only as smart as the data behind it. Strong governance, high-quality data, and integration across silos are non-negotiables. Consider a “data product owner” model, where business leaders — not just IT — are accountable for data outcomes and governance. You must also design solutions with reusability in mind: when datasets and logic can be adapted and replicated across teams, scaling AI becomes far easier.

Decision point: Is your proposed solution reusable and composable? If the answer is no, rework it.

4. Enable cross-functional collaboration

Your supply chain doesn’t operate in a vacuum, and neither should your AI strategy. Real value comes when finance, IT, and operations align around common data, definitions, and goals. To break down silos, ensure sponsorship extends beyond supply chain and tech leaders to senior business leaders.

Decision point: Do your business users take ownership of the data and models they rely on every day? If the answer is no, there's work to do.

5. Design for scalability from the start

Too many supply chain AI projects stall after a promising pilot because they weren't designed with growth in mind. To ensure scalability, develop architectures that are modular, composable, and integrated with broader planning and decision-making workflows — not bolted on as isolated tools. Build with the bigger picture in mind: cross-functional orchestration that spans geographies and functions.

Decision point: Are you able to scale your AI project? If the answer is no, take a longer-term view.

6. Redefine roles in a human + machine paradigm

AI isn’t here to replace humans — it’s here to augment their decision-making skills. Let machines do what they’re best at: repetitive, analytical tasks such as spotting patterns, forecasting, and simulating scenarios. Let humans focus on critical and judgment-based decisions. The future is about elevating human roles, not eliminating them.

Decision point: Are humans still doing what machines could do faster and more accurately? If the answer is yes, redirect their efforts to more strategic tasks.

7. Implement robust governance

Without governance, AI can create more confusion than clarity. Define clear ownership for every dataset and model. Or, to put it another way, "You need to have a physical nose to touch, shoulder to pat, or throat to choke within the organization for each data set and identified domain owners," as Shetty Abhijeet, managing director and partner at Boston Consulting Group (BCG) said at a recent roundtable about the role of AI in supply chains hosted by Anaplan. You should standardize definitions, create a single source of truth, and ensure transparency in how models are built, maintained, and used across the business.

Decision point: Do you have identifiable owners for each data set? If the answer is no, allocate ownership.

8. Accelerate decision cycles

Traditional supply chain planning cycles are too slow for today’s volatility. AI changes that. With the right ecosystem, you can reduce cycle times from 12 weeks to three to four weeks, which enables continuous planning, simulation, and decision-making. Agent-based AI models can evaluate trade-offs in real time and surface proactive recommendations. Amazon, for example, has automated forecasting and purchase orders for 99% of SKUs — with zero human touch — thanks to a mature, interconnected AI ecosystem.

Decision point: Are your planning cycles closer to three months than three weeks? If the answer is yes, consider agentic AI and goal-seeking agents.

9. Balance innovation with change management

Most organizations and the people within them are not yet ready for full autonomy. AI adoption is as much about mindset shift and cultural change as it is about model accuracy. To reimagine your planning function means changing how teams operate and make decisions. Start with AI-assisted decisions to build trust and engage users early. Provide the training and tools your people need to move from gut instinct to data-driven confidence.

Decision point: Do your people fully trust AI? If the answer is no, develop training and education to shift the mindset.

10. Demand clarity and transparency

Up-rank supply chain AI that provides clear, traceable insights into how and why decisions are made — empowering you with full transparency across forecasts, recommendations, models, and scenarios. This will instill greater trust in the recommendations that intelligent planning models provide.

Decision point: Can your AI explain itself with traceability into how it generates insights? If the answer is no, seek solutions that will foster confidence in your decisions.

11. Think systems, not tools

AI tools alone won’t transform your supply chain. What you need is a systematic view of your supply chain with AI-powered orchestration across forecasting, planning, risk, and execution. Leading organizations are moving toward AI orchestration, where multiple intelligent agents act across domains to deliver goal-aligned, coordinated decisions at scale. This isn’t theory — it’s already happening in supply chains like Amazon’s.

Decision point: Is your AI strategy a set of disconnected planning tools or a coordinated system? If it’s the former, rethink your architecture.

Take the next step with Anaplan

AI-driven planning is here. The next generation of supply chain planning will be intelligent, connected, and continuous. Is your organization ready to seize the opportunities and reap the benefits?

Supply chain AI success requires strategic focus, trusted data, strong governance, and the right ecosystem to bring it all together. Anaplan Intelligence is built for exactly this moment — empowering supply chain leaders to turn AI possibility into business advantage.