Enterprise partnerships are evolving as AI becomes more operational, supply chains face new disruption, and customers demand faster time to value. That shift is showing up in Blue Yonder’s product direction, with agentic AI expanding across manufacturing, transportation, warehousing, and retail, alongside deeper Microsoft Teams integration and more mobile execution capabilities.
In this conversation with ERP Today, Blue Yonder’s Corporate Vice President and General Manager of Global Alliances Kelley Lear discusses how partner strategies are evolving across hyperscalers, ERP providers, and consulting firms; why AI is raising the bar for ecosystem collaboration; and what it takes to build partnerships that deliver measurable outcomes rather than just market positioning.
Q: You have worked in ERP tech for more than 20 years. What has changed the most in the market?
KL: The biggest shift has been the move from on-premises systems to cloud and platform-based models. ERP vendors always wanted to be platforms, but a lot of them were held back by acquisitions, tech debt, and hand-built integrations. In many cases, the integrations were manual or heavily customized.
What’s changed is the ability to rearchitect those platforms so they’re more integrated, with shared data models and less redundancy. That reduces overhead and makes it easier to extend the application without as much coding and customization. I don’t think the market is all the way there, but it’s a lot closer than it used to be.
Q: You have led alliances across major enterprise vendors and consultancies. How is Blue Yonder aligning its partner strategy with growing adoption of generative and agentic AI?
KL: The partner ecosystem is critical for us, both on the technology side and the consulting side. On the technology side, partners like Microsoft and Snowflake are part of our core stack; we’re not just integrating with them, we’re innovating with them.
That means close alignment between engineering teams, go-to-market teams, and partner teams so customers can use the technology in a practical and optimized way. On top of that, we bring in consulting partners that may already know the customer’s business deeply, maybe through a long SAP or ERP transformation or migration. When the platform, the domain expertise, and the customer context are combined, that’s where the real value starts to show up.
AI is accelerating that dynamic, but we’re trying to stay pragmatic. We’re not interested in doing AI for AI’s sake. We’re looking at where the use case is real, where the value is clear, and where it lines up with customer priorities.
Q: Has AI changed what makes a partnership valuable?
KL: Yes, because the focus is getting sharper. Customers are under pressure to move faster, but they also want proof that something will work. That changes what matters in a partnership.
We’re spending more time on hands-on innovation sessions with customers and partners to show where the value is already being delivered, what the ROI looks like, and how the use case actually works in practice. That helps move the conversation away from abstract AI ambition and toward outcomes.
The criteria are less about broad messaging and more about whether a partner can help deliver something specific, whether that’s technical depth, deployment capability, industry expertise, or speed to value.
Q: How do you make sure partnerships do not stagnate as the market moves so quickly?
KL: You need structure and regular review. We have alliance leads assigned to key partners, and those teams meet frequently—in some cases weekly. We also run quarterly business reviews, innovation reviews, and 360-degree reviews with larger partners where we’re simultaneously a customer, a partner, and a go-to-market collaborator. That rhythm helps us stay aligned on product direction, customer needs, and where the partnership is creating value. It also helps surface issues early, before they become delivery or customer problems.
Q: Many vendors talk about co-innovation with hyperscalers, but often it stops at the announcement. What does real co-innovation look like in practice?
KL: Real co-innovation has to show up in both product and go-to-market execution. With technology partners like Microsoft, it means we’re working together on the stack, on engineering alignment, AI Innovation, and on how customers will consume the joint solution. Co-innovation is real when the customer can see the use case, the value, and the path to execution.
It also means building together in a way that helps customers get value faster. We’re launching ideation and innovation sessions with Microsoft that are built around customer pain points, business outcomes, and practical examples. That’s different from just saying two companies are aligned. It’s about showing where the technology works today and where it can go next.
You can already see that in areas like warehouse operations and transportation, where Blue Yonder has been expanding AI agents that surface live operational briefs, identify disruptions, and recommend actions inside the flow of work.
Q: Blue Yonder works with SAP as well. How do you describe that relationship to ERP Today readers?
KL: We integrate with SAP and other major ERPs in order to provide the best-in-class AI supply chain platform integrated with the key data and functions from the ERP. That integration is much easier now than it used to be. SAP has a platform, we have a platform, and we both have new more seamless, secure, scalable, and cost-effective ways to integrate or share data.
Our consulting and GSI/SI’s also understand both SAP/other ERPs and Blue Yonder, which helps with delivery. Where Blue Yonder adds value is in the end-to-end supply chain footprint—from planning through execution, including warehouse management, transportation management, returns, and the supply chain network underneath it. When you combine that with SAP and then layer AI orchestration and intelligence on top, it gives customers a stronger supply chain operating model than either side could deliver in isolation.
Q: What does Blue Yonder look for when deciding whether a customer or project is actually ready for AI?
KL: Readiness matters more than excitement. We’ve seen cases where customers are very enthusiastic about AI and have strong pressure from the C-Suite or the Board, but the fundamentals are not there yet.
The biggest issue is usually data. If the data isn’t accessible, or the quality is poor, then putting AI on top of it is not going to create value. In that case, the first step is getting the data into a usable, accessible state before you start the AI work. That lines up with the broader direction of the platform, where Blue Yonder has been building on a common data model and supply chain knowledge graph to make operational data more usable for AI agents.
The other issue is priority and commitment. Some companies want the outcome but don’t want to invest in the right people, the right implementation approach, or the right level of effort. We’d rather walk away than push through a project that’s unlikely to succeed. Customers always pay for that decision later through delays, change requests, support issues, and poor outcomes. We seek to align strategically and partner with the customer to achieve real outcomes.
Q: Supply chain disruption is back at the top of the agenda. Where are customers struggling most right now?
KL: Data is still a major issue. The good news is today’s data tooling, automation, and AI make cleanup and preparation much more achievable than it was even five or ten years ago. In addition, data platforms like Snowflake have continued to advance in the tech stack to improve data sharing with lessened need for integration, zero clone data with real time access across global environments.
The other big issue is resource constraints on the customer side. Supply chain and ERP projects are large, high-impact programs. They touch every part of the business, and everyone involved still has a day job. If customers can’t get the right subject matter experts into the room, they’re going to struggle to make the right decisions.
More broadly, resilience is now central. Political shifts, economic volatility, and global supply chain disruption all affect companies much faster than they used to. That’s why I think staying still is usually the riskier decision and all the more costly. Tech debt and fragmented processes don’t get easier with time.
Q: As AI becomes more embedded in ERP and supply chain platforms, where does governance sit, and what are you doing about governance in your Global Alliances program?
KL: As AI becomes deeply embedded in ERP and supply chain platforms, governance no longer “sits” in a single place. The clear trend across recent research and enterprise practice is that governance becomes a shared operating model spanning the platform, the enterprise, and the ecosystem—rather than an overlay applied after the fact. Governance should sit in three layers, enterprise oversight, platform and product governance, and operational governance. It also has to sit across the ecosystem.
On top of that, governance has to account for security, data residency, and regulatory requirements. Those issues become even more important as customers bring more AI into operational environments and across geographies.
My team is operationalizing governance across the ecosystem as well as we engage customers and partners. We have regular partner cadences, quarterly business reviews, innovation reviews, and, for consulting partners, delivery reviews across joint projects.
We also launched an AI partner scorecard this year that pulls in customer satisfaction, implementation metrics, go-to-market performance, AI and extensibility measures, and operational data. The goal is to be more data-driven and fact-based in how we manage partnerships to achieve greater customer satisfaction and outcomes, rather than relying on subjective impressions.
Q: Looking ahead, what will distinguish the partnerships that scale AI successfully from those that struggle?
KL: The partnerships that work will be practical, outcome-focused, and grounded in real customer priorities. They won’t try to do everything at once. They’ll prioritize the right use cases, prove value, and build from there. They will be solving for greater automation and quicker time to value. Winning partnerships also will be the ones that can combine the right platform architecture, usable data, extensibility, and strong execution. AI can add a lot of value, but only if it’s sitting on a solid foundation.
My advice is to stay pragmatic. Don’t try to boil the ocean. Work with partners to decide where to start, what matters most, and what roadmap will get us to realized outcomes. That’s what builds resilience and gives companies a better chance of keeping up with how fast the market changes.
