AI investment is surging, and so is the pressure on supply chain leaders to turn potential into profit.
In the next 18 months, over 90% of executives plan to invest in AI, building on the $252.3 billion already invested globally in 2024. Yet 85% of AI initiatives deliver close to zero measurable value. And of the 88% of organizations that report using AI in at least one operational function, only a third eventually scale.
Where things often go wrong is when businesses treat AI as a technology decision rather than a readiness one. An organization’s level of maturity determines what AI capabilities can currently deliver and, critically, where investment should go next.
The companies that consistently convert AI spending into returns are the ones that understand their current state clearly enough to plot a deliberate path toward compounding, measurable value.
Mark your place on the AI maturity map
Marking where your organization sits on the maturity curve is easiest when you have a map.
There are four distinct stages of AI adoption and company readiness. Each stage is a step-change in competency and progress, capable of delivering greater, more consistent growth.
Stage 1: Rigid rule-based system
In this initial stage, planning happens despite the system rather than because of it. It depends heavily on pre-defined rules and considerable manual effort by planners reliant on spreadsheets updated through rudimentary data input processes. Data is dispersed, walled off inside impermeable ERPs and rigid legacy platforms.
At the same time, crucial institutional knowledge stays locked away in planners’ minds. This siloed infrastructure provides zero visibility across functions, leading to discrepancies and contradictory decision-making. With limited automation, planners must also often respond to volatility on their own.
Stage 2: Foundational, specialized AI
At this stage, companies implement the technological foundation that’s essential to later-stage evolution. It’s also where meaningful ROI is seen for the first time. Those static, inflexible rules are replaced by ML-driven forecasting, which adapts in real-time, learns from incoming data, and improves how it deals with challenges after each cycle.
Companies in Stage 2 advance to a variety of sophisticated capabilities. Diagnostics allows them to pinpoint the root causes of inventory issues. Predictive analytics let them anticipate what’s likely to happen based on past and current data, with tools like scenario testing and demand forecasting. Finally, prescriptive tools make recommendations on the most optimal responses to emerging scenarios.
Here, technology provides insight but also prompts action. The planner still spearheads decision-making, but the system tells them the best move to make.
Real results from leading organizations
Blount Fine Foods (fresh prepared foods manufacturer, US)
Managing 1,500 SKUs across 10 distribution centers on spreadsheets and a legacy ERP, Blount Fine Foods connected ML-driven demand planning from RELEX to production and went live on day one.
The results included:
- 50% reduction in forecasting errors
- 35% less waste
- 20%+ compound annual growth
Stage 3: Assistive & agentic AI
AI at this stage moves from running unnoticed behind the scenes to playing a central role in planning decisions. This includes employing purpose-built AI agents to carry out specific, high-value supply chain tasks, such as analyzing forecasts and performing root cause analysis along the value chain.
What does that look like on the ground?
Imagine a key supplier reports a sudden large shipment delay. The AI agent immediately identifies which SKUs will be affected, calculates the availability risk, stress-tests alternative sourcing options, and then suggests the optimal response, all before the planner has even loaded up the dashboard.
GenAI assistants, steeped in domain-specific intelligence, augment planning workflows armed with an understanding of supply chain-specific contexts that generic AI tools simply don’t have. Natural language interaction levels the playing field entirely, putting the work of strategic analysis in the hands of everyone on the team. Through plain and simple dialogue, they’re free to probe forecast anomalies, scrutinize scenario outcomes, and review inventory positions, without depending on specialist teams to do so.
Real results from leading organizations
KICKS (beauty retailer, Nordics)
KICKS was losing sales to inexplicable stockouts with limited visibility into why. Manual analysis was consuming time without producing answers. They deployed RELEX Diagnostics, which autonomously identified DC scarcity and late deliveries as the main culprits, guiding the team toward corrective action at source.
Improvements included:
- 34% reduction in lost sales value
- Late deliveries reduced from 5.2% to 3.4%
- Supplier processes redesigned based on data-driven evidence
Stage 4: Multi-agent orchestration
The most transformation-ready organizations reach this point, morphing into an AI-native, naturally autonomous operation. They employ AI capabilities from third parties but also develop and deploy their own on an extensible platform. This allows domain experts to create custom agents, plugins, and automated skills designed according to the specifications of their business needs.
At this point, the AI operates with much higher levels of autonomy. Agents are deployed to the full potential to autonomously undertake a wide range of tasks, whether that’s anticipating changes, automatically analyzing new data, or taking independent action to adapt to market conditions as they emerge. They can do so individually, or within an interconnected multi-agent system where several work in tandem, each dedicated to, and governing, a particular domain within the operation.
Humans now focus solely on strategic oversight, out-of-the-box creativity, resolving edge cases, and imparting their contextual experience. The nature of their roles has evolved around human-in-the-loop functionality: overseeing agent goals or supervising autonomous processes. Humans are also largely responsible for maintaining accountability, setting clear governance frameworks for autonomous technology.
What does this look like in practice?
Consider a situation where a manufacturer’s retail partner significantly increases their order volume with less than a week’s notice. Instead of scrambling with escalation emails or emergency meetings, a network of agents spanning demand, production, inventory, and logistics springs to life.
Without human input, they model the incoming uplift against current capacity, re-sequence production to expedite the order, and uncover raw material constraints before they become shortages. Finished goods inventory is reallocated between DCs to ensure revised fulfilment timelines are met.
Assessment: Is your organization ready to be AI-native?
Knowing where your organization currently sits on the maturity curve is only part of the challenge. You must also understand what’s holding it back from advancing to the next stage. Once you identify which areas of the business are the bottleneck to progress, you can make the right decisions to move forward. Your organization may have implemented best-in-class ML forecasting and AI assistants, but without proper governance established, the ROI promised by Stage 3 may never materialize.
Below is an assessment that scores your organization across five key dimensions that together determine AI readiness in supply chain planning. Rate each dimension from 1 to 5: 1 being if your organization still operates with legacy systems and ways of working, 3 if you’re making notable progress toward AI-powered systems, and 5 if you’re well ahead of the AI adoption curve. Your lowest score is your transformation bottleneck, and your starting point for change.
Data foundation: How ready is your data?
1 (laggard): Your data is dispersed across ERPs, spreadsheets, and legacy systems. Your sales history is patchy and unreliable, making confident forecasting close to impossible. Each operational function has its own version of truth.
3 (reformer): You’ve consolidated essential data streams, but with only partial system integration. You can see where the quality issues are and are working to resolve them.
5 (leader): You have a unified architecture of data that connects all planning functions. The data itself is clean, and you have access to a well-structured, complete history of performance. External signals that impact market conditions are incorporated into the model and factored into forecasts.
Planning processes: Are your workflows up to scratch?
1 (laggard): Your planning is labor-intensive and largely organized around unwieldy spreadsheets. Forecasts are rebuilt from scratch every cycle, leaving little time for strategy development. In times of volatility, responses are often too slow and uncoordinated.
3 (reformer): Automation has been incorporated into some processes, but manual overrides happen regularly. You have an S&OP cadence in place but haven’t yet achieved cross-functional alignment or installed constructive methods of collaboration. AI exists within pockets of your planning function, but its impact on how things get done is negligible.
5 (leader): Your planning is AI-driven and exception-based. Touchless forecasting drives the vast majority of decisions across your portfolio, with planners able to fully focus on where technology is not an appropriate substitute for their judgment and experience. Every function — demand, supply, and production — is aligned to your S&OP process, and all pulling in the same direction.
Technology platforms: Has your tech stack set you up for success?
1 (laggard): The platforms your operation relies on are brittle and rigid, incapable of communicating with other systems and consequently resistant to change. When new capabilities are needed, you struggle to meet the moment, beholden to vendor release cycles and costly custom development.
3 (reformer): You have a modern platform in place, but extensibility is still limited. While you can set business rules, tweak workflows, and connect with a defined number of approved integrations, you’re still unable to deploy custom capabilities or connect your broader tech stack to external systems without constraint. What you can and cannot do in terms of transformation still largely depends on what vendors are willing to accommodate.
5 (leader): Your platform is both extensible and composable, meaning it is built from separate, modular components. This includes planning functions, connectors and plugins, adaptive business rules, and task-driven AI agents, which can be incorporated without overhauling the whole system. Your supply chain planners can build AI capabilities and set skills for automation as and when the business needs them.
People and change leadership: Will your culture embrace transformation?
1 (laggard): New AI initiatives are launched without a champion in senior leadership to drive momentum, while roles do not adapt to the new environment and its need for innovation and rapid iteration. There is a company-wide attitude that the implementation of AI sits purely with IT, rather than being a business-wide priority. Nobody owns the outcome or any project’s performance, making AI a hot potato in the organization.
3 (reformer): Internal sponsors are sometimes assigned to new launches, and many departments have discovered the role they play in fueling progress. Some roles have already adapted to the new AI-assisted planning environment. Yet the bigger picture of the transition is uneven. While there are AI-curious team members who participate in the ways that they can, others resist the change. Ownership of outcomes is clearer than it has been, but a question mark sits over accountability as well as organization-wide readiness.
5 (leader): Every function involved in AI initiatives in your business has a clearly defined area of ownership and accountability for outcomes. Initiatives never launch unless there is a confirmed senior leader with real skin in the game, personally accountable for AI performance and the promised ROI. Roles have been radically changed. Your planners no longer build forecasts, but manage exceptions, train AI models, and perform strategic analysis. KPIs have been revised, and your people are widely upskilled to match.
Governance: Do you have the appropriate guardrails and procedures in place?
1 (laggard): Your organization has no formal governance framework for AI initiatives. Decisions are made in the spur of the moment, untraceable and inconsistently applied. You have no established process for reviewing AI outputs or challenging system reasoning. Often, your teams launch AI capabilities on intuition alone. Trust in AI-generated recommendations is in short supply, with planners overriding the system or lobbying to drop it entirely.
3 (reformer): Although your organization has a governance framework, it’s applied haphazardly, allowing for knee-jerk reactions to anomalies or edge cases. Reviews of AI outputs are ad hoc, occurring only when something goes wrong rather than as part of a structured routine. KPIs are being tracked, but there is no consensus yet on how or when they should be acted on. The degree of independence your AI systems are permitted to exercise was initially defined but has not been revisited much since.
5 (leader): Your organization maintains systematic governance reviews that include reviewing outputs, continuous assessment of KPIs against pre-AI performance, and routing decisions that fall outside AI’s ability to make judgments to the right people. You are comfortable with AI systems being highly autonomous, as there are sufficient guardrails, and you know exactly how to rein it in if necessary. Every decision that AI makes is auditable, explainable, and reversible. Governance is not something that requires enforcement, but simply how innovation is done.
The three most common failure patterns (and the companies doing things right)
Having completed the maturity assessment, you may now have a better idea of where your organization sits on the path to becoming AI-native. Equally important, though, is understanding what can stymie your progress between stages. The most debilitating obstacles are almost always behavioral, and they follow recognizable patterns. There are three that stand out above the rest.
1. Starting with the technology before defining the problem
Caught in the allure of a shiny new AI capability, supply chain companies fall into the trap of deploying before they have even defined what they are trying to solve. Not enough effort is put into articulating the actual business problem — one that, if resolved, would make a measurable difference to the P&L.
Every AI initiative should start with the highest-value, most feasible use case: one where the business impact is clear, and feedback loops are fast enough to prove it quickly.
Doing it right — Rastelli Foods: Defining the problem, then deploying the tech
When Rastelli adopted ML-driven forecasting from RELEX, they had a specific use case in mind. Before comparing vendors, they identified that their forecasting process was inadequate, and every technology deployment discussion revolved around solving it. They focused on deploying something to help them overcome this problem — highly accurate forecasting that could be used as a single source of truth for production and inventory teams. Rastelli’s teams now meet weekly and base every planning discussion on AI-driven forecasts. The impact on business and operational performance was soon evident:
- $3.5M saved in the first year from inventory visibility alone
- 85% forecast accuracy achieved
2. Bypassing the foundation in pursuit of the cutting edge
Like opening a commercial kitchen and hiring a world-class chef before buying the essential ingredients, some supply chain leaders try leaping straight to the latest technology before laying down the specialized AI those capabilities depend on. Dazzled by the boundless potential of AI agents and autonomous planning, they disregard the unglamorous work. But consolidating data into a unified platform and replacing static rules with adaptive, learning-based models is precisely what makes those more advanced capabilities so impressive in the first place.
Doing it right — Bünting Group: Setting the proper foundations for automation
Bünting Group, a German family grocery retailer, had a clear goal: automating the replenishment of its fresh fruit and vegetable categories. Before landing on any automated solution, they made the conscious decision to get their data infrastructure in order first. They deployed True Inventory, a predictive inventory capability from RELEX, that detected stock discrepancies, corrected balance data, and prompted inventory checks.
Only once the data foundation was solid did Bünting Group place automated replenishment on top. But it was worth the wait. Stores went from daily manual ordering to exception-based inventory counting, with the large majority of the process now automated.
- +2% increase in sales value vs reference stores
- -43% reduction in balance errors on inventory count days
- Expansion earned to centralized ordering across all fresh categories
3. Treating AI as a technology upgrade rather than an organizational shift
This is one of the more pernicious failure patterns. Supply chain organizations can do the hard work of selecting an AI-powered capability for the right reasons before carefully and thoughtfully implementing it throughout the business. The employed technology may be performing just as it should. Yet, with all that considered, the transformation splutters to a standstill.
This stalling happens because planners have not been given the time or sufficient involvement to trust the new systems and fully incorporate them into their workflows. The misalignment between adopting tech and preparing the organization is common, as 84% of organizations have not redesigned jobs around AI. Roles, KPIs, and ways of working continue in inertia long after deployment. Processes that existed before AI arrived are just as flawed.
The organizational structure for AI adoption in the supply chain
The crux of all these failure patterns points to the same single origin: inadequate organizational structures. They are incapable of creating the conditions for AI deployments to blossom into major contributors to not just more efficient operations, but greater productivity and accelerated growth.
Research bears this out: only 20% of enterprises could attribute AI initiatives to revenue growth. The same research explains why: 84% of companies had not redesigned roles or ways of working around AI capabilities.
This calls for you to assess your organizational structure and ensure it is designed for AI. You should consider whether you have the right roles in place, and if they are, whether they’re held by people with the right drive and disposition to innovate. Enterprises that are serious about becoming AI-native rebuild teams with four key transformation-focused roles, assigned to individuals instead of whole departments.
The business owner is accountable for the outcome of the initiative in its entirety. They define the problem that needs solving, set the success criteria, identify and deploy the right AI capability, own the associated KPIs, and answers to any movement in them, positive or negative. The Business Owner is business accountability incarnate, and necessary to drive corrective action and prevent initiatives from drifting or becoming deprioritized.
The technology enabler owns everything that keeps the AI capability and the systems it runs on working reliably in production — whether that’s forecasts, generated recommendations, or autonomous agents. They are also responsible for the data infrastructure AI depends on, overseeing data streams as well as external and internal integrations. This role is essential to prevent technical failures and data quality issues from clogging up the AI initiative.
The change champion ensures the potential of adopted AI capabilities is fully realized in how the business works day to day. They are responsible for translating technical possibility into tangible process transformation: redesigning workflows, retraining planners, and cultivating a culture more receptive to new AI initiatives. On the ground, this involves stopping planners from reverting to old habits or defaulting to manual workarounds when autonomous systems are available.
The value tracker has an overarching goal of showing how and where AI investments are yielding financial returns. They take improvements in forecast accuracy, inventory reductions, and decreases in waste and link them to concrete financial results. They ensure every AI initiative is financially validated — with ROI backed by evidence — so the business case for scaling stays strong. They are the bridge between operational KPIs and P&L impact.
If any of these roles are left unassigned or absorbed into someone’s existing job description without explicit accountability, transformation will likely not succeed. These roles must be filled by the right people, with the right level and scope of accountability — each with equal amounts of skin in the game.
The 30/90/12-month supply chain transformation roadmap
The maturity map has revealed what’s possible, and the assessment has shown you where your organization is. But now, you need a practical sequence of steps toward becoming an AI-native business. With three distinct phases, straightforward actions, and a checkpoint that indicates when the conditions for the next phase are met, this roadmap is your blueprint for making it happen.
| 30 days | 90 days | 12 months | |
| Phase | Assess and anchor | Prove value in production | Scale and extend |
| Focus | Establish the problem and define success. | Go live on your highest-impact use case and prove value. | Scale what capabilities work, then advance to a later stage in AI adoption. |
| Actions | – Place yourself on the AI maturity map and take the assessment at the start of this guide. – Identify 2–3 use cases with the clearest ROI. – Set a baseline for your KPIs (inventory days/ forecast accuracy/OTIF-OSA). – Assign someone as an official transformation owner with authority. – Evaluate your platform: can it support Stage 2 on the maturity map and beyond? | – Implement specialized AI for your most critical use case. – Launch as soon as you have a minimum viable data foundation. – Track KPIs weekly against the baselines you set. – Set wider business ownership and enforce a weekly operating cadence. – Build trust with planners via explainability, transparency, and human-in-the-loop workflows. | – Scale proven capabilities across sites, categories, and use cases. – Connect demand, supply, and production planning. – Introduce AI-assisted workflows with natural language systems and root cause analysis. – Explore how agentic capabilities can handle high-frequency tasks in the near future. – Revisit the maturity model to see if you’re at Stage 2 and progressing to Stage 3. |
| Checkpoint | You have chosen a transformation owner, defined your use cases, and set baselines for project KPIs. | AI is running effectively in your operational processes, ROI is tracked weekly, and planner trust is developing. | You have proved value at Stage 2 and have a clear path to Stage 3. |
Moving up the AI maturity curve is possible for any supply chain business willing to invest the time, resources, and effort into judiciously incorporating capabilities with careful, incremental intent. It is about understanding the route you are taking in your transformation, not about being the quickest or most vocal. That means starting with the problem, laying the necessary foundations, and letting results guide how you move forward.
The AI potential that lies ahead
As you follow the frameworks and practices above, working deliberately through the stages with your team, you may be wondering what an AI-native supply chain operation, with widespread networks of AI agents, looks like on the ground from function to function.
In manufacturing
S&OP: Agents pre-build scenarios, flagging trade-offs, and identify the decisions that require human judgment.
Production scheduling: Finite schedules are built around existing constraints, sequenced to minimize changeovers, and recalibrated as disruptions happen.
Inventory optimization: Safety stocks and inventory order positions are adjusted daily based on real-time demand signals, lead time changes, and capacity.
Logistics and fulfilment: Disruptions are detected immediately; shipments rerouted and warehouse allocation continuously updated.
Sourcing and procurement: Supplier risk is monitored on an ongoing basis. Exceptions are escalated to the right people automatically. Routine procurement is executed within pre-approved parameters, so teams can focus instead on relationships and negotiations.
For retail and grocery
Demand forecasting & replenishment: Touchless forecasts are generated across the vast majority of the range. Planners focus exclusively on the exceptions that require contextual experience.
Fresh and perishables: Allocation is optimized with granularity, by store and by day, and includes an array of critical variables like shelf life, local demand patterns, and weather. Waste is reduced without compromising availability.
Promotion planning: Automatically predicts promotional uplift, halo effects, and cannibalization for every campaign. Stock is allocated to precisely meet the demand for every upcoming promotion. Performance insights are then fed back before the next campaign launches.
Space and assortment: Continuous cluster-level testing for assortments. Underperformers are instantly flagged, and planogram adjusted based on sales velocity per store.
Store operations: Detects phantom stock and prioritizes inventory counts according to their impact on the business. Store teams shift focus to customer experience.
Regardless of your current readiness or maturity stage, AI innovations will inevitably reshape your organizational processes. What you need to be asking is whether you have the necessary foundations and structures to enter that AI-driven future as an innovative supply chain leader, rather than one playing constant catch-up.
Want to discuss how RELEX can help you realize the ROI from your AI initiatives?
Written by
