Most conference recaps open with the word shift. Jacksonville earned it. The WERC 2026 Annual Conference made one thing clear: distribution and warehousing operations are no longer debating whether to adopt AI. The organizations that showed up this year are past that. They are working through the harder questions: what their data infrastructure can actually support, which decisions AI should touch, and how to get frontline teams to trust the outputs. For those of us working at the intersection of ERP, operations, and analytics, the practical implications are significant.
AI as the Decision Layer in Distribution
What stood out across every session and peer discussion was a consistent framing: AI is becoming the decision layer, not just the automation layer. The distinction matters. Automation executes a defined task. A decision layer surfaces what needs attention, recommends an action, and helps the person responsible act on it faster and with more confidence. Distribution centers are under real pressure to get there, and the challenge is not simply adding more technology. It is configuring existing systems to do something they were rarely designed for: tell operators what to do next, not just what happened.
That shift from reactive to predictive is what we see playing out on the ground every day with Dynamics 365 Business Central clients. The ERP is in place. The data is moving. The gap is almost always in the layer between the data and the decision.
Key Sessions and Takeaways
1. Readiness: Data and Process Discipline
The opening session on AI readiness covered ground that will feel familiar to anyone who has been through an ERP implementation: organizations consistently underestimate how much the outcome depends on the quality and structure of the data going in. Fragmented systems, unstandardized workflows, and years of accumulated data hygiene debt do not stop companies from deploying AI tools. They just guarantee that the outputs will not be trusted, which means they will not be used. As distribution industry research confirms, AI systems are only as reliable as the transactional data, product attributes, and supplier performance signals feeding them.
The practical implication is that AI readiness and ERP readiness are the same conversation. Companies that have not yet achieved solid alignment between their ERP and WMS, or that are still managing inventory through a patchwork of spreadsheets and workarounds, are not ready to add an AI layer on top of that. They need to solve the foundation first. Our post on what to know before migrating to Microsoft Dynamics 365 Business Central gets into what that foundation actually requires.
2. People-Centric Adoption
One of the most practically useful sessions focused on what separates AI implementations that stick from those that quietly get abandoned. The answer was not the sophistication of the tooling. It was whether the people expected to use the outputs actually trusted them, and whether the workflow around the tool was redesigned to make acting on those outputs the path of least resistance.
The organizations seeing the most traction are treating AI adoption the same way they treat any major process change: with deliberate change management, clear ownership, and feedback loops that let frontline teams flag when something does not look right. AI outputs that are explainable get used; outputs that feel like a black box get ignored. This is exactly what we see with Copilot adoption inside Business Central, and it is why our post on real-world Copilot and Power Platform use cases in Dynamics 365 Business Central focuses less on features and more on how those capabilities fit into actual daily workflows.
3. Rationalizing the 2026 Warehouse Tech Stack
There was broad agreement in the room that most warehouse technology stacks have gotten too complicated, and that the complexity itself is now a liability. Leaders are consolidating platforms, cutting tools that were never properly adopted, and prioritizing systems that actually share data with each other rather than requiring manual reconciliation between them. The emerging pattern points toward three tiers: ERP and WMS as the system of record, analytics and data platforms as the intelligence layer, and AI as the orchestration layer on top of that.
For most distributors, the most immediate question is not where AI fits in that stack. It is whether their current ERP and analytics setup is producing data clean enough to build on. That is where Microsoft Power Platform does a lot of the connecting work, bridging the system of record to the reporting and automation that operations teams actually need in day-to-day decisions.
4. Advanced Analytics: Focus on Action
The analytics discussion at WERC this year was notably less about dashboards and more about what happens after the data is surfaced. The problem most operations teams describe is not a lack of visibility; it is a lack of prioritization. A warehouse manager looking at a screen full of metrics still has to decide which one to act on. The value of advanced analytics is in reducing that cognitive load, pointing to the exceptions that actually matter and filtering out the noise.
That is where AI earns its place operationally: not by generating more reports, but by narrowing the field to the three things the shift supervisor should care about right now. The Business Central Copilot and agentic features now available in Dynamics 365 are a practical example of this, moving the ERP from a system that records what happened to one that surfaces what to do about it.
5. Rescuing Underperforming WMS Solutions
One session took on a problem many organizations are reluctant to name publicly: they spent significantly on a WMS, and it is not delivering what they expected. The case studies were instructive not because the technology failed, but because the pattern was always the same. The system was misaligned with actual workflows from the start, data quality was not addressed before go-live, and user adoption was treated as something that would happen on its own once the system was live. It did not.
Successful turnarounds shared a common thread: the organizations that recovered went back to process discipline first, data governance second, and technology configuration third. They stopped trying to fix the software and started fixing the way the software was being used. The parallel to ERP projects is direct, which is also why choosing the right Business Central partner comes down to more than certifications. A partner who will challenge the process assumptions before the build is worth considerably more than one who will simply configure what you ask for.
Economic Outlook: Growth Sectors Driving Investment
The economic keynote, delivered by top-ranked economist Jason Schenker, offered a realistic read on where capital is flowing despite the macro headwinds. Geopolitical tension, inflation, and trade uncertainty are real, but they are not evenly distributed. Certain sectors are drawing significant investment precisely because of those pressures, and distribution operations tied to those sectors are feeling the demand.
Energy and power infrastructure is absorbing capital at a pace the grid was not built to handle, driven in part by AI data center demand. Defense and dual-use technologies are seeing elevated spending as geopolitical risk reshapes procurement priorities. High-tech and data infrastructure investment continues to accelerate as AI adoption expands. And across warehousing and manufacturing broadly, automation investment is holding firm as a response to persistent labor constraints, a point that WERC’s conference agenda reflected across multiple sessions.
For distributors in industrial supplies, industrial machinery and equipment, and medical and hospital equipment, the customers operating in these high-growth sectors are raising expectations for delivery speed, inventory accuracy, and operational visibility. That pressure moves upstream.
From Systems to Intelligent Operations
The clearest takeaway from Jacksonville is that the warehouse of the future is not just more automated. Automation has been the story for years. What is different now is the intelligence layer on top of it: systems that interpret operational data rather than just collect it, analytics that narrow down what needs attention rather than just display everything, and ERP platforms that are starting to recommend actions rather than simply record outcomes.
The distributors who separate from the pack over the next two years will not be the ones with the most sophisticated AI tools. They will be the ones who got their data right, aligned their people around the outputs, and built the operational discipline to act on what the system is telling them. The technology is largely available. The gap is almost always in execution.
For organizations working through where to start, the sequence that came up most consistently at WERC was the same one we recommend: data and process foundation first, technology rationalization second, and AI embedded into workflows as the third step, not the first. Teams that try to shortcut that order tend to end up back at the beginning.
If your organization is in the middle of working out what that sequence looks like for your specific environment, the ACE Micro distribution solutions page is a practical starting point, or contact our team to talk through where you are in the process.