It’s not just a tech refresh—it’s a bid to future-proof the company against industry upheaval and redefine what enterprise IT can deliver across a 6,000-user, 140-location footprint.

Semler’s legacy infrastructure included 45-year-old systems built on mainframes, Lotus Notes, and custom code. Reliable? Sure. Agile and scalable? Not so much.

“The system worked, but our customers demanded more,” says Christensen. “Volkswagen Group, our key OEM partner, expects digital innovation. Internally, our teams needed smarter workflows and real-time data. We couldn’t deliver that with old tech.”

Enter: a cloud-based ERP overhaul using Microsoft Dynamics 365, enhanced with Icelandic ISV Anetta’s automotive layer. With rollout scheduled for September 2026, Semler is staging a “big bang” go-live across 4,000 users in a single day. That means not just migrating data—but transforming processes, retraining staff, and building an entirely new operating model.—

Embracing Generative AI and Azure Innovation

Semler didn’t stop at Dynamics. The company has embedded Azure OpenAI into its ecosystem with a custom-built language model called “Semler GPT.” Originally a proof-of-concept Power App in 2023, the assistant has matured into a full enterprise AI companion.

“One use case is for our used-car sales advisors,” Christensen says. “They enter a registration number, and the AI pulls data from factory systems and our databases to generate a listing. It writes the ad with the right tone of voice—Audi luxury feels different than a Volkswagen Golf.”

Even more impressive? The tool also estimates optimal pricing by analyzing market dynamics and historical sales data.

But Christensen is clear: this isn’t about cutting jobs. “It’s about quality. Freeing up people’s time for more meaningful tasks. We’re not reducing FTEs—we’re raising the bar.”

Digital Adoption: Where Whatfix Fits In

Semler’s definition of transformation success isn’t just about deployment—it’s about adoption. “We talk about the J-curve,” Christensen explains. “Productivity drops right after go-live. But the faster we get our people climbing the curve again, the faster we unlock business value.”

That’s where Whatfix, a digital adoption platform, comes into play. “We’re integrating Whatfix with Semler GPT. So when users ask how to do something—like ‘create a sales order for a new car’—they’re not just shown a document. The AI starts the process for them. Eventually, agents may do the entire task.”

This “one interface to rule them all” vision is foundational to Semler’s user experience strategy. From booking time off to checking lunch menus, the enterprise interface is being simplified and reimagined through AI.

Data Readiness and Integration: Not Just a Checkbox

With AI, everything hinges on data readiness—and Christensen knows it.

“GDPR forced us to start cleaning customer data back in 2018,” he says. “Since then, every automation project has pushed us to mature our data governance.”

Even so, some use cases—especially customer-facing AI—are still out of reach. “We have high internal usage, like call-center assistants. But until data quality improves, exposing these tools externally isn’t feasible.”

To unify its systems, Semler built an integration platform in Azure. Over 200 integrations—ranging from OEM factory systems to tire partners—run through this layer. It’s not a traditional enterprise service bus, but rather a loosely coupled microservices model. “It gives us full control, auditability, and scalability,” Christensen says.

Industry Matters

Why Microsoft? The availability of a complete platform—ERP, cloud, AI, Power Platform, and Office 365—played a role, but Christensen says the ISV offering was the clincher.

“We needed a full-scale automotive solution,” he notes. “When we found out Anetta was building that on Dynamics, and that we already used Microsoft heavily, it made sense. That said, CE and Finance and Operations aren’t fully integrated. It’s not quite plug-and-play.”

Semler’s also helping shape the Anetta solution, providing requirements and helping the ISV meet Volkswagen certification standards. “It’s a co-build, not just an implementation,” Christensen says.

Looking Ahead: From AI to In-Car Commerce

Christensen sees the future filled with agentic AI, embedded services, and predictive analytics.

“We’re starting to support in-car commerce—apps people can buy directly from their car’s interface. That has to connect back to our ERP,” he explains. “We’re also investing in predictive inventory management. When you hold a car, you’re tying up a lot of capital. Forecasting demand more precisely will be crucial.”

As compute power increases and models evolve, Christensen expects more business decisions to be AI-assisted. But first? “Still a lot of data governance to do,” he admits with a smile.

What this means for ERP Insiders

ERP is now an ecosystem, not just a system. Semler’s transformation illustrates a broader market shift: ERP is no longer a monolith. It’s a foundation that’s only valuable when paired with cloud-native integration, AI augmentation, and continuous user enablement. Microsoft’s platform strategy—combining Dynamics 365 with Azure, Power Platform, and copilots—lets companies design modular, adaptive ecosystems. ERP leaders should not only focus on core functionality, but also on how AI agents, digital assistants, and self-healing data architectures amplify value across the entire process landscape.

AI is real, but readiness is everything. Semler GPT shows that even mid-sized companies can build custom AI assistants if they have the right cloud infrastructure, data models, and culture of experimentation. But most ERP customers are still in “data denial”—overestimating the cleanliness and usability of their information. Tech leaders must prioritize metadata enrichment, master data consolidation, and data governance policies now, or risk failing to operationalize AI even when tools like Whatfix or Microsoft Copilot become turnkey.

Digital adoption platforms could be the missing link. The biggest ROI blocker for ERP investments isn’t architecture—it’s behavior. Semler’s pairing of Whatfix with AI assistants provides a compelling blueprint for how to accelerate user adoption and shorten the post-go-live productivity dip. By embedding training, guidance, and automated workflows directly into the user interface, companies can shift from “train the user” to “empower the user.” For Microsoft Dynamics customers especially, combining Whatfix’s contextual guidance with Azure-native AI could be a game-changer for long-term ERP success.

The post From Mainframe to Microsoft: Semler’s Epic ERP Overhaul to Drive a Data-Powered, AI-Enabled Automotive Empire appeared first on ERP Today.

The collaboration brings Red Hat Enterprise Linux and Red Hat OpenShift support to a wider array of deployment options on Oracle Cloud Infrastructure (OCI), encompassing government, sovereign, and distributed cloud services. This expansion is particularly pertinent as organizations increasingly seek flexible and secure cloud solutions to meet diverse operational requirements.

Red Hat OpenShift is now certified to run on OCI Roving Edge Infrastructure, Oracle Compute Cloud@Customer, and Oracle Private Cloud Appliance. This certification enables customers to deploy workloads on Red Hat OpenShift across various OCI environments with confidence, knowing they are tested, certified, and supported by both Oracle and Red Hat.

Recognizing the growing importance of AI in enterprise operations, the collaboration extends support for Red Hat OpenShift AI on OCI. This includes validation on OCI’s NVIDIA A100 and H100 GPU shapes, facilitating high-performance model training and inference for demanding AI and machine learning workloads.

Oracle is actively validating key applications and software, such as Oracle WebLogic Server, to run seamlessly on Red Hat OpenShift containers. This initiative delivers a jointly supported, premium customer experience, empowering users to leverage the full potential of Oracle software on the flexibility and scalability of Red Hat OpenShift, whether on-premises or in the cloud.

Furthermore, the Oracle Cloud Scale Monetization portfolio has been validated on Red Hat OpenShift, offering communications service providers enhanced infrastructure flexibility for comprehensive, unified business support systems.

The collaboration maintains the certification of Oracle Database, including Oracle Real Application Clusters (RAC), on Red Hat Enterprise Linux. Additionally, Red Hat has initiated a validation effort for Red Hat OpenShift Virtualization to better support Oracle Database customers on this evolving platform.

What this means for ERP Insiders

Strategic hybrid cloud adoption. For tech leaders, this collaboration underscores the importance of adopting a strategic hybrid cloud approach. By leveraging the combined strengths of Red Hat and Oracle, organizations can achieve greater flexibility and scalability in their IT operations. The certification of Red Hat OpenShift on various OCI environments ensures that enterprises can deploy applications across different infrastructures with consistent performance and support.

Accelerated AI integration. The enhanced support for AI workloads, including validation on NVIDIA GPU shapes, provides enterprises with the necessary tools to accelerate AI integration into their operations. This development is crucial for organizations aiming to harness AI for improved decision-making and operational efficiency. By utilizing Red Hat OpenShift AI on OCI, businesses can streamline the development and deployment of AI models, leading to faster time-to-value.

Optimized enterprise applications. The validation of Oracle applications on Red Hat OpenShift offers tech leaders the opportunity to optimize their enterprise applications for cloud-native environments. This move facilitates the modernization of legacy systems, enabling smoother transitions to the cloud and improved application performance. By adopting this integrated approach, organizations can enhance their agility and responsiveness to market changes.

The post Red Hat and Oracle Expand Collaboration to Accelerate Hybrid Cloud Transformation appeared first on ERP Today.

Thomas Saueressig, SAP Board Member for Product Engineering, opened the keynote by reminding the crowd that “the pace of change has never been this fast—and yet it will never be this slow again.” He emphasized that transformation today is the foundation for tomorrow’s resilience. “We shipped, we delivered, but we cannot stop here,” he stated, citing customers’ demand for “faster time-to-value, innovation cycles, and cost control.”

But this keynote was different. Less monologue, more dialogue. Throughout, live audience polling was used to surface concerns, with one striking result: 40% of the world’s GDP is now touched by customers using Rise with SAP. SAP Sapphire 2025 showcased a confident, customer-aligned SAP—one that understands its own complexity and is willing to rewire itself alongside its users. “The best is yet to come,” promised SAP’s new Chief Customer Officer. But for enterprise leaders, the time is now. Transform not because it’s trendy—but because today’s decisions, like SAP says, “will shape tomorrow’s resilience.”

From Legacy to Lift-Off: Mercedes-Benz, Mars, Phoenix Global

Mercedes-Benz, a symbol of precision engineering and innovation, shared how it’s tackling complexity head-on. With over 10,000 enterprise applications—1,200 of them SAP-related—CIO Cathey Lehman turned to Rise with SAP to rationalize and modernize the company’s digital core. “We grouped and tackled systems using the TIME method—transform, invest, migrate, eliminate,” she explained. More than just cloud migration, this strategy underpins Mercedes’ AI ambition, from engineering (via GitHub Copilot) to a natural-language interface for shop-floor problem solving.

“We’re leveraging AI across our entire value chain,” Lehman emphasized. “We’ve deployed our internal ChatGPT-like solution, and we’re excited to expand with SAP’s Joule and Business AI stack.”

At Phoenix Global, a metals and mining company, the transformation story was existential. After explosive growth led them into bankruptcy, new CIO Jeff Salentros spearheaded a radical reboot: eight sites replatformed in 8 months using SAP Public Cloud ERP. “We ripped out everything—paper and pen, fragmented systems—and replaced it with best-practice SAP,” said Salentros. “We’ve already returned millions through better equipment maintenance, utilization, and embedded AI.” It’s a textbook example of cloud ERP enabling not just operational agility, but financial recovery.

Mars Inc. highlighted the scaling power of SAP’s three-tier ERP strategy. The confectionery giant runs flagship brands like M\&M’s and Snickers—but it’s their newer acquisitions (True Fruit, Kind Snacks, Nature’s Bakery) that demanded a flexible ERP backbone. CIO Bill discussed how Mars deploys SAP Public Cloud for agile startups, a Rise instance for retail, and a customized S/4HANA template for its core operations. “It allows small businesses to plug in fast and scale,” he said. VP Praveen Muthuraj added, “With model-driven architecture and embedded analytics, we’ve seen huge productivity gains—especially from Joule and real-time automation.”

The Age of Agentic ERP: SAP’s Response to AI Uncertainty

Amid enthusiasm for AI, the audience revealed a common concern: data remains the biggest barrier to value realization. SAP responded with a clear direction—business-ready AI, embedded natively, governed responsibly.

With the SAP AI Foundation and Joule, SAP aims to democratize AI across finance, HR, procurement, and operations. Embedded copilots will become ubiquitous, from quality issue detection on factory floors to onboarding via natural language prompts. “This isn’t just AI—it’s agentic AI,” said Thomas, referring to SAP’s vision of interconnected, autonomous agents coordinating tasks, governance, and outcomes across the enterprise.

SAP also unveiled a new partnership with AWS, offering a “dedicated AI co-innovation fund” to accelerate industry-specific AI applications built on SAP BTP using AWS models and infrastructure. This move signals SAP’s commitment to unlocking verticalized, customer-defined AI without the technical sprawl or cost typically associated with custom builds.

Complexity: The Silent Killer of Productivity

A striking revelation came during a poll about enterprise application growth. In 2000, the average $10B company ran just 10 core applications. In 2025? Over 600. The cloud has enabled rapid adoption—but at the cost of fragmentation.

Christian Klein’s message was clear: “More technology doesn’t equal better business outcomes. In fact, global productivity growth has declined—despite SaaS explosion.” From 1995–2005, productivity grew 2.6%. From 2005–2025? Just 1.5%. SAP’s proposed antidote: an integrated, harmonized enterprise foundation, powered by Business Technology Platform (BTP), unified data models, and embedded AI.

Saueressig reinforced the point with use cases:

• BHP created $500M in value by using Signavio to orchestrate process redesign and shared services reinvention.
• Nestlé reclaimed 105M work hours by leveraging SAP Enable Now across 200+ applications.
• First Abu Dhabi Bank reduced its application landscape by 25%—streamlining cost and complexity.

What this means for ERP Insiders

Reinvent your core with fit-to-suite, not fit-to-standard. SAP’s evolution of Rise with SAP—from “fit to standard” to “fit to suite”—is more than marketing lingo. It reflects a new model of cloud ERP implementation: one that embraces tailored lifecycle orchestration, custom dashboards, and embedded AI for transformation readiness. CIOs should evaluate SAP’s new transformation prep services and explore toolchains that cut project costs by 30%—critical at a time when many SAP S/4HANA programs still run in the $100M+ range. For example, Mercedes-Benz’s use of AI-guided migration is a leading model worth emulating.

Use AI as a business lever, not a side project. SAP’s embedded Joule copilot and BTP-based agentic AI framework are designed to operationalize AI at scale, from procurement to HR. But successful adoption depends on unified data models and strong governance. Mars’ model-driven architecture and tiered ERP rollout show how to scale AI across M&A-heavy portfolios. Tech leaders should build AI competency centers and partner with SAP on use-case co-creation—particularly via the new AWS/SAP AI fund. Start with low-lift, high-impact scenarios like maintenance scheduling or finance forecasting to prove value.

Tame application sprawl—focus on integrated outcomes. The statistic that productivity has dropped despite a 60x increase in application count is a wake-up call. Instead of expanding toolsets, CIOs should consolidate workflows around unified platforms. SAP’s BTP, Signavio, and Business Data Cloud (BDC) provide foundational capabilities to align business processes, enable reuse, and simplify integration. Start by auditing your application portfolio and building a business capability map to drive rationalization. As Phoenix Global’s turnaround showed, even a complete rip-and-replace can be profitable if guided by simplicity and standardization.

The post SAP Sapphire 2025 Customer Keynote: “Bring Out Your Best” and Reinvent the Future of Enterprise ERP appeared first on ERP Today.

There’s a lot to unpack here. First, let’s get the obvious out of the way and point out how times have changed between the two German enterprises. Founded in 2011, Celonis began as a spin-off from the Technical University of Munich, the brainchild of Alex Rinke, Bastian Nominacher and Martin Klenk. By 2012, Celonis joined the SAP Startup Focus program. Come 2015, Celonis became the first company from the program to sign a global reseller agreement with SAP, with the former’s process mining tools integrated into SAP environments. According to an archived webpage from the Celonis website, the relationship resulted in over 220 joint implementations across more than 30 countries, including high-profile customers such as 3M, Airbus, and Coca-Cola. On its own, Celonis boasts a prestigious client base, becoming the go-to name in process mining for the likes of PepsiCo, Lufthansa, Jaguar Land Rover, IKEA/Ingka and more.

The shift in affairs arguably occurred in 2021 when SAP acquired Signavio, a Berlin-based business process modeling (BPM) company. The enterprise shares a similar university incubator backstory with Celonis, but with Signavio designed for end users to collaboratively model and redesign processes. The added twist came later when Signavio Process Intelligence was added to the suite, a tool which analyzes logs to uncover process behavior. But with the offering more of a complementary addition, we have to track SAP Signavio’s developments in the last year or so to understand how it may have become a business process kingmaker of sorts between SAP and other vendors in the market.

The Evolution of SAP Signavio

Since SAP’s 2021 acquisition, news on SAP Signavio’s developments in the SAP suite had been somewhat understated. This isn’t remarkable in itself  –  one can make a comparison now in the developments of LeanIX and WalkMe, more recent additions to the SAP stable which require integration at the right pace. But perhaps the key difference is LeanIX and WalkMe have had the spotlight remain on them, with recent announcements such as those of SAP SuccessFactors/WalkMe integration, and LeanIX winning various awards last year, alongside availability through the UK Government G-Cloud.

SAP Signavio news had been a little muted post-acquisition, especially when comparing LeanIX and WalkMe’s first years as parts of the SAP family. But the irony perhaps is that SAP Signavio’s slow and steady development has led to a little disharmony in Germany  –  and, more significantly, put an emphasis on the AI-fueled dominion of data currently being pursued by vendors.

The changing market positioning and customer base for SAP Signavio are two such examples. Its recent availability on platforms like AWS Marketplace no doubt augmented Signavio’s visibility and market reach, while the enhanced accessibility and functionality enabled by AI-driven updates aims for a broader customer base, with the potential to encroach on markets traditionally served by competitors. That said, Celonis arguably stands stronger on the AI front since the release of its AgentC agentic AI tools in 2024, to the extent that when thinking of AI in process management, you’re perhaps more likely to picture Celonis than its rivals. Celonis also boasts EMS with AI at its core, using machine learning in its analysis engine (e.g. to cluster variants, predict delays) and in its Action Engine for recommendations.

Going back to the SAP corporate family, we can just take one look at LeanIX’s role with regards to Signavio’s broadening reach, in which AI is not a paramount factor. By combining LeanIX and Signavio, SAP aims to give organizations an all-encompassing view into their processes alongside their IT landscape, the latter courtesy of LeanIX.

“The synergy between process transparency – enabled by SAP Signavio – and application landscape visibility – powered by SAP LeanIX – offers organizations a clear, end-to-end path from strategy to execution,” according to Johannes Strasser, Global SAP Signavio lead for Westernacher Consulting (pictured). He tells ERP Today that for SAP-centric customers, this can translate into faster insights, stronger alignment between business and IT, and reduced dependence on fragmented third-party tools.

“While some customers still leverage third-party process mining tools for specific use cases, we’re increasingly seeing a shift toward consolidation within the SAP ecosystem for holistic, end-to-end transformation.”

The LeanIX compatibility further bolsters the status of one of Signavio’s tools in SAP Signavio Process Transformation Suite. While originally built around process modeling, the years since the SAP buyout has seen the suite expand to include a process intelligence tool for mining and visualizing real process flows, collaborative BPMN-based design management, a customer experience journey modeler, and process insights for real-time SAP S/4HANA analysis with benchmarks. As a quick comparison, Celonis Process Management also supports BPMN 2.0 (with extended BPMN e.g. requirements, risks, RASCI, auto-layout, etc.) alongside a “Customer Journey” object in its process repository.

According to Strasser, Signavio’s developments reflect how companies today are facing increasingly complex challenges and rapid change across all areas.

“We’re no longer talking about static process models where polished diagrams are seen as mere hygiene factors,” he tells ERP Today. “Instead, it’s about gaining fact-based insights into the actual performance of business processes – insights that support decision-making, enable scenario simulation, and drive effective change even in uncertain times. SAP Signavio is now deeply invested in end-to-end business transformation management, offering capabilities that support holistic process excellence [and at] Westernacher, we’re seeing strong interest in [the solution].”

In Strasser’s view, this interest is often driven by goals such as operational excellence (OPEX), SAP S/4HANA migration, supply chain optimization, or process integration in the context of mergers and acquisitions. But still, Celonis remains arguably hard to beat when it comes to the nitty gritty, such as handling very large event logs and complex object relations, thanks to its object-centric process mining abilities and a proprietary PQL that is well-regarded for custom analyses. Celonis claims 15+ million automations run daily via its engine, and as a whole Celonis is generally viewed as more mature for end‑to‑end automation orchestration, drilling down into end-to-end cost, quality and sustainability metrics.

It’s also worth remembering that another plus point in Celonis’ favor comes without the SAP “baggage”, designed as it is with a modular approach and available with a Free Plan for the not-necessarily SAP-beholden mid-market to trial.

The Data Question

With what SAP is offering on the table, the Celonis lawsuit can be summed up in its claim that customers are being pushed towards SAP Signavio by what it deems to be anti-competitive behavior. For example, limited APIs or obfuscated data structures that, in the Celonis view, may make it difficult for SAP’s competitors to operate effectively, especially in the cloud.

Celonis itself stands strong as ever in the market; recently it has gained Magic Quadrant plaudits in process mining, while its partnership with Microsoft has expanded to include its platform as a workload in the analytics platform of Microsoft Fabric, bringing Celonis ever closer into the lucrative Teams sphere.

But what’s really at the heart of the SAP/Celonis dispute is more AI than the underlying base of process management. More specifically it’s about data control, with regards to who can access business process data inside SAP systems, under what terms, and with what technical and commercial freedoms. As AI becomes increasingly central to enterprise strategy, the ability to access and act on operational data is now a competitive differentiator. The outcome of the lawsuit could help define whether AI innovation in the enterprise remains open and plural or consolidates around a few data gatekeepers.

What this means for ERP Insiders

Celonis vs. SAP: Antitrust Allegations and Ecosystem Tensions. Celonis has filed a lawsuit against SAP, accusing the ERP giant of leveraging unfair practices to promote its own business process platform, SAP Signavio, at the expense of third-party tools. The dispute centers on claims that SAP restricts data access through limited APIs and complex data structures, making it harder for external process mining vendors to operate within SAP environments. The outcome of this case could reshape competitive dynamics in the BPM and process mining space, particularly around data accessibility.

SAP Signavio’s Strategic Growth and Integration. Since acquiring Signavio in 2021, SAP has methodically built it into a comprehensive process transformation suite. Combined with LeanIX, SAP now offers customers a unified view of their business and IT landscapes, and recent advancements have strengthened Signavio’s role in end-to-end digital transformation, positioning it as a central pillar within SAP’s broader enterprise solutions ecosystem.

The Battle for AI-Driven Process Control and Data Ownership. At the heart of the Celonis-SAP conflict is a broader question of who controls access to enterprise data, an increasingly vital asset in AI-powered process optimization. With both companies doubling down on AI and automation, the ability to harness clean, real-time operational data is a key strategic advantage. Celonis continues to grow through alliances like its deepening partnership with Microsoft, while SAP is consolidating its own ecosystem around Signavio and LeanIX. The resolution of this legal battle could determine whether the future of AI in business process management remains open and competitive or shifts toward a model dominated by integrated platform providers.

The post Unsung Signavio: Business process kingmaker? appeared first on ERP Today.

In a revealing, freewheeling interview with ERP Today, Kask peeled back the curtain on how SAP is building, scaling, and operationalizing AI at enterprise scale—not just as a technology trend, but as a competitive moat. “Even I have trouble keeping track of all the announcements and use cases,” he admitted with a laugh. “But make no mistake—this has been years in the making.”

In a landscape flooded with AI hype, SAP’s strategy stands out for its depth, rigor, and customer-first pragmatism. As Kask summed it up: “You want assets that are hard to replicate, and an organization that can exploit them. That’s how you lead—not just this year, but for the next ten.”

Building a Platform, Not Just a Product

Kask’s 13-year journey with SAP began in cloud transformation and matured through the company’s AI evolution, particularly since joining SAP’s machine learning and AI unit eight years ago. Today, he reports directly under CEO Christian Klein as part of a growth area that behaves more like a startup-within-a-giant.

SAP’s approach to AI is intentionally pluralistic, avoiding the common trap of model monoculture. “We realized early that no ERP company is going to sink $200 million into training foundational models at the pace hyperscalers do,” Kask explained. “These models commoditize fast, and performance improves every few weeks. So our strategy is to partner broadly with curated models—OpenAI, Google Gemini, AWS Nova, Meta, Mistral, NVIDIA, even niche ERP-specialized models like Zora.”

But SAP doesn’t just “plug and play” with LLMs. The company layers a sophisticated AI foundation atop its Business Technology Platform (BTP), using tools like retrieval augmented generation (RAG), knowledge graphs, output validation, and a new “Prompt Optimizer” developed with a Silicon Valley startup. This optimizer automatically rewrites prompts when switching models—cutting what used to be weeks of effort down to near zero.

“It’s behind-the-scenes magic,” said Kask, “but it’s the kind of infrastructure that allows us to deliver AI that’s not just impressive in the lab, but reliable and explainable in a real-world enterprise.”

Embedded, Ethical, and Explainable

For all its technical firepower, SAP’s AI strategy begins with ethics. “We design for human empowerment, not displacement,” said Kask, referencing SAP’s published AI Ethics Policy—one of only six companies to earn a perfect score in the World Benchmarking Alliance’s ethics evaluation.

This philosophy translates into every product decision. Before AI takes an action in a system, it requires human verification. Outputs are transparent and traceable, showing what data was used, where it was pulled from, and what the system “thought” step-by-step. “Think of our agents like very fast interns,” Kask quipped. “They’re helpful, but you still want to double-check their work.”

Even SAP’s go-to-market approach prioritizes controlled experimentation. Early adopter programs—like the rollout of “Joule” to thousands of internal and partner consultants—enable rigorous feedback loops before mass deployment. And yes, Kask said, adoption is accelerating. “It reminds me of the early days of cloud. There’s a learning curve for legal, compliance, and security teams. But once the first use case lands, the floodgates open.”

Joule, Agentic AI, and the Rise of the Autonomous ERP

Nowhere is this more evident than in Joule, SAP’s AI assistant, which recently surpassed 1,600 “skills” and is moving from task automation toward what Kask calls “native agenticness.”

“Most people think of agents as standalone bots you have to manage. With Joule, it’s different. The system itself becomes the agent,” he said. “If you ask it to ‘give a spot award to my five top-performing employees and send a personalized email,’ it can plan and execute that multi-step process autonomously, because those skills are already natively embedded.”

That’s a far cry from a glorified chatbot—and it’s underpinned by some serious architectural muscle. SAP has constructed a proprietary knowledge graph across its massive ERP landscape—452,000 ABAP tables and over 7 million fields—giving context that generic LLMs can’t hope to replicate. “When you extract ERP data into hyperscaler data lakes, you lose context,” said Kask. “You can try to rebuild it, but you’ll never match the depth we have.”

This knowledge graph now enables foundation models on tabular data—distinct from LLMs—to predict regressions, match invoices, and improve accuracy on narrow AI tasks. It’s a clear example of SAP’s strategy: use general-purpose models where appropriate, but rely on proprietary assets for true differentiation.

Commercializing AI, Carefully

With AI capabilities embedded into its product suite—and often activated via subscription—SAP is seeing strong commercial traction. “Christian [Klein] has said that 40–50% of new deals now have AI attached,” Kask noted. “There’s a commercial model, and yes, the models can be expensive to run. But customers see the value.”

SAP now mandates AI discovery workshops post-sale and tracks adoption closely. “Our success metric is not just building it, but getting it live. That’s why we published the AI feature catalog. That’s why we have dashboards. It’s all about real use.”

Still, the company isn’t inflating its numbers for Wall Street. “Some vendors are touting their AI revenue, but we’re skeptical. There’s a lot of fuzzy math—direct vs. indirect. We’re playing the long game.”

Owning the Integration Layer

As the AI arms race heats up, so too does the question of ecosystem positioning. Where does SAP fit in a world where hyperscalers, competitors like ServiceNow, and even niche ERP players are all building agents and automation?

“We’re not trying to be everything,” Kask clarified. “But we are laser-focused on integration. Our agents don’t just wrap around processes—they live inside them. With tools like Signavio, LeanIX, and WalkMe, we can see, map, and optimize entire process flows across applications.”

That includes embedding AI into transformation tools like enterprise architecture (via LeanIX) and UI-level interactions (via WalkMe). “We want to own the transformation stack. Not just the database. Not just the UI. The intelligence in between.”

What this means for ERP Insiders

AI adoption is the new differentiator—but requires intentional design. CIOs and COOs should treat AI adoption not as a standalone innovation track but as a core KPI. SAP’s Joule, for example, has demonstrated measurable time savings—up to 1.5 hours per day per consultant. By embedding AI in business processes via tools like BTP, enterprises can reduce friction, accelerate time-to-value, and gain strategic advantage. Begin with AI discovery workshops, leverage SAP’s AI feature catalog, and use internal pilot programs to refine before scaling.

Proprietary context beats generic models—invest in knowledge graphs. One of SAP’s most defensible innovations is its domain-specific knowledge graph. For SAP customers, this means far lower hallucination risk, more accurate agent outputs, and better integration between structured and unstructured data. Tech leaders should evaluate vendors on their ability to preserve context—especially for agent-based use cases—and consider building their own knowledge layers on top of vendor platforms to enable AI-powered autonomy with precision.

Ethics, transparency, and governance will define winners. AI success isn’t just about speed or scale—it’s about trust. SAP’s top-tier ethics score from the World Benchmarking Alliance underscores a crucial shift: regulatory scrutiny is coming, and enterprises that can demonstrate auditable, human-in-the-loop processes will be favored. ERP buyers should demand full transparency from vendors: where models run, what data they access, how decisions are made, and what governance layers exist. Design for explainability from day one.

The post From SAP Sapphire 2025: Sean Kask, Chief AI Strategy Officer, Opens SAP’s AI Playbook, Covers Agentic Intelligence, Strategic Differentiation, and the Race to Real Adoption appeared first on ERP Today.

Built natively into Salesforce Financial Services Cloud, Agentforce represents a strategic bet on “digital labor”—an intelligent layer of AI automation embedded directly into customer-facing workflows. Whether it’s preparing an investment review, reversing a fee, guiding a borrower through loan discovery, or escalating complex cases, Agentforce operates in the background to ensure that humans can focus on what matters: meaningful client engagement.

The timing is no accident. According to McKinsey, the financial services industry is facing a looming talent shortfall, with over 100,000 financial advisor roles projected to go unfilled by 2034 and half of the insurance workforce expected to retire within 15 years. Meanwhile, customer expectations are surging: Salesforce’s own research found that only 21% of consumers are satisfied with personalization from their financial providers, while 35% say they feel treated “like a number.”

That’s the friction Agentforce is designed to remove.

“AI shouldn’t replace the human connection—it should scale it,” said Eran Agrios, SVP and GM of Financial Services at Salesforce. “With Agentforce, financial institutions can tap into digital labor built on a deeply unified platform to help their human teams boost productivity, efficiency, and revenue while still delivering the trusted, personalized experiences their clients expect.”

Agentforce comes with pre-built templates for various financial services roles:

• Financial Advisor & Banker Agents automate meeting prep and post-call wrap-ups by surfacing portfolio insights, life event triggers, and action items—all drawn from unified CRM, transactional, and behavioral data.
• Banking and Insurance Service Agents handle high-volume service tasks like fee reversals, card cancellations, and insurance coverage queries. These agents streamline the repetitive and free up human reps for complex resolution.
• Digital Loan Officer Agents guide borrowers through loan discovery 24/7, analyzing income and credit score data to suggest relevant loan products—all while escalating exceptions to human officers for final review.

Each agent comes with embedded compliance guardrails: audit trails, approval workflows, and legal disclosures are baked into every digital interaction. No code is required to deploy, customize, or extend agents—opening the door for rapid configuration across unique workflows and jurisdictions.

Leading institutions are already validating the model. CaixaBank, Spain’s largest bank, plans to use Agentforce to personalize banking at scale for over 20 million customers. “Agentforce will enable us to provide faster and more accurate responses to customer inquiries,” the bank shared. “It will also enhance the employee experience by providing personalized advice.”

Other early adopters have also seen measurable gains.

• Nexo reports a 20% increase in chat deflection and 10,000 additional chats resolved autonomously in Q1.
• Cumberland Mutual is expanding Agentforce to claims and procurement.
• Absa Relationship Banking achieved an 88% faster resolution rate on fraud inquiries.
• Groupe Hueber Assurances is scaling personalized client support while cutting administrative overhead.

Together, these results show how digital labor isn’t just a technology play—it’s a strategic move to restore trust and efficiency to the frontlines of financial services.

What this means for ERP Insiders

Rethink workforce planning with embedded digital labor. Enterprise tech leaders must anticipate that digital labor will fill the gap left by retiring professionals and surging service demands. With Agentforce, Salesforce delivers AI agents embedded directly in financial service workflows—not as bolt-ons, but as native team extensions. For ERP strategists, this is a signal: augmenting the human workforce with AI is no longer aspirational—it’s operational.

Integrate AI without compromising compliance. Compliance is non-negotiable in regulated industries. Agentforce ensures every digital task follows built-in regulatory logic—approvals, audit trails, and disclosures included. Unlike general-purpose copilots, Salesforce’s compliance-first architecture makes it safe to scale AI across finance, HR, and customer-facing use cases. This embedded compliance model will become the gold standard as AI pervades ERP stacks.

Drive measurable ROI through pre-built extensibility. Agentforce proves that pre-built AI doesn’t have to mean rigid. With Salesforce’s no-code configurability, firms can customize agents to reflect proprietary processes while still launching fast. ERP leaders should prioritize platforms that offer both speed-to-deployment and extensibility—because in a market demanding agility and precision, speed without control is just risk repackaged.

The post Digital Labor, Real Trust: Salesforce Launches Agentforce to Reinvent Front-Office Financial Services appeared first on ERP Today.

At the heart of that journey: SAP. But for Crounse, this wasn’t a routine ERP upgrade. “This was not just about SAP—it was a digital transformation and simplification mandate,” he says. “We needed a platform that could orchestrate our global supply chain and help us deliver massive $300 million projects on time, or face serious financial penalties.”

Fluence doesn’t manufacture batteries—it orchestrates them. Its business model revolves around designing, delivering, and maintaining utility-scale energy storage systems that stabilize power grids around the world. That requires real-time visibility into a vast, globally distributed supply chain and contract manufacturing network—something Crounse says only SAP could deliver at scale.

After assessing their legacy platforms like NetSuite, Workday, and Salesforce, Fluence determined that they wouldn’t scale to $10 billion in revenue. The team initiated a 13-month SAP implementation covering finance, supply chain, analytics, and sustainability. The result? A 99.7% clean core SAP deployment on Google Cloud via RISE with SAP. “We couldn’t afford custom processes,” Crounse said. “Clean core and standardization were essential to scale at this velocity.”

Speed, in fact, has been a strategic mantra for Crounse. “Speed over perfection,” he says. “In hypergrowth, an MVP mindset is critical. You can’t wait for perfect—you iterate, pivot, and move.”

That mindset also shaped how Fluence approached its implementation partners. While Deloitte handled the front end, Crounse emphasized the importance of transition planning. “I didn’t want to be on the consultant drug forever,” he joked. “We deliberately brought in a boutique partner and built a global delivery model to move from lights-on support to sustainable internal operations.”

Fluence’s SAP footprint is large: it spans S/4HANA, SAP Analytics Cloud (SAC), Integrated Business Planning (IBP), Sustainability Control Tower, and Signavio. “This wasn’t a finance-only project,” Crounse emphasized. “It was about end-to-end orchestration—from project inception to delivery and long-term asset maintenance.”

With installations across 24 countries and a growing physical presence in strategic markets like India, Germany, and the U.S., Fluence’s localization strategy relies heavily on SAP’s global capabilities. “Made-in-America is part of our strategy, but so is ‘sold-by-Germans-in-Germany’—it’s about being present where sustainability matters most,” Crounse said.

SAP’s role at Fluence also extends into sustainability and AI. As a green energy company, Fluence is exploring SAP Green Ledger and other emerging tools. “We were one of the first to sign up for RISE,” says Crounse. “But I’ve told SAP: I don’t want to be half-in. We need end-to-end cloud-native capabilities, especially as we lean into AI and agent-based operations.”

While Crounse doesn’t foresee AI fundamentally reshaping Fluence’s core engineering work in the short term, he does see operational use cases exploding. “Can I cut my development team by 20% with GenAI? Can we automate testing or generate telemetry insights from our battery systems? These are the questions I’m asking today,” he said.

Fluence’s systems already collect data from thousands of IoT-connected devices, calling home to report temperature changes, performance degradation, or potential safety risks. “Our Battery Management System (BMS) sends alerts like: ‘I’m overheating—turn me down or I’ll shorten my lifespan,’” said Crounse. “This telemetry is where GenAI and SAP tools like AI Core and the Sustainability Control Tower can have real impact.”

The value isn’t just internal. Fluence’s long-term strategy revolves around recurring revenue from post-installation service and maintenance—an area that increasingly relies on smart analytics, AI, and predictive maintenance driven by SAP’s integrated platform capabilities.

For those embarking on similar journeys, Crounse offers straightforward advice: “Keep moving. You won’t hire perfect candidates, you won’t scope perfect processes—but the faster you move, the faster you learn, pivot, and scale. That’s how we’re building Fluence for the long run.”

What this means for ERP insiders

Think clean core for real scale. Fluence’s 99.7% clean core SAP deployment allowed it to grow from $900 million to $4 billion in two years—without tripping over customization bloat. For CIOs facing hypergrowth, clean core isn’t just a buzzword; it’s a requirement. SAP S/4HANA’s standardized processes and modular extensions (like SAP BTP and Signavio) let companies move fast while retaining governance and future-proofing their enterprise stack.

Make your supply chain predictive, not reactive. Fluence treats its global supply chain as a dynamic network, not a linear chain. SAP IBP and SAC provide forward-looking insights that help avoid penalties for late delivery on $300M projects. Tech leaders should invest in predictive analytics and supply chain orchestration platforms that turn data into timely action—especially in capital-intensive industries like energy, aerospace, and automotive.

Lean into sustainability and AI – but don’t get stuck in pilots. From Green Ledger to AI-generated code, Fluence is moving beyond buzzwords. CIOs should evaluate how SAP’s AI Core, Joule, and Sustainability Control Tower integrate with real business scenarios. Don’t pursue AI as a science project—pursue it to reduce headcount where possible, automate processes, and deliver smarter service to customers already demanding accountability and uptime.

The post From SAP Sapphire 2025: Fluence Commits to ‘Speed Over Perfection’ on Hypergrowth Trajectory with SAP appeared first on ERP Today.

“We needed a platform that could handle global scale, localization, and compliance right out of the box,” said Christensen. SAP provided that foundation early on, enabling Jabil to achieve SOX compliance while supporting lean, highly standardized operations. Over time, SAP’s role has expanded, powering everything from supply chain management to finance and regulatory compliance.

Today, Jabil runs the majority of its operations on just two SAP instances across 135 global sites. That’s an anomaly in the enterprise world, where 20 or more instances are not uncommon. “We’re consolidating even further,” Christensen noted. “And that simplicity is a strategic asset—it’s what gives us the ability to respond to tariffs, chip shortages, or pandemics in real time.”

That ability to respond swiftly and intelligently underpins Jabil’s definition of resilience. SAP, acting as the company’s single system of record for supply chain data, enables real-time analytics and scenario modeling across operations. During the COVID-19 pandemic and subsequent supply chain turbulence, this data backbone allowed Jabil to make instant adjustments and reconfigure supply models—without waiting 12 hours for an analyst to re-run reports.

“It may sound simple, but having the right data at the right time changed how we survived disruption,” Christensen said. “That’s what resilience looks like.”

Jabil’s move from ECC to S/4HANA is another example of its pragmatic innovation. Initially, the company piloted a Greenfield implementation. But after assessing organizational impact, they pivoted to a Brownfield approach. That decision, supported by SAP MaxAttention and advisory services, enabled Jabil to migrate 250 accounts to SAP S/4HANA within 18 months—at enterprise scale.

SAP’s ongoing innovation in AI has caught Christensen’s attention as well. Jabil was an early adopter of AI and machine learning, using them to improve forecasting and automate processes through RPA and NLP. Now, with the rise of generative and agentic AI, Jabil is evaluating SAP Joule and other emerging tools—but with a healthy dose of skepticism.

“We exist in a hybrid environment—on-premise, cloud, Rise with SAP—and we’re not going to rewrite everything overnight. We need to understand how tools like Joule complement our current stack and where the ROI really is,” he said.

That demand for value realization is a recurring theme. Christensen emphasized the importance of holding partners accountable—not just for delivery, but for outcomes. “I pay SAP a lot,” he said, half-joking. “I want net-new value from what I’m already paying, not just more licenses. That means tangible ROI from the tools we’ve already bought.”

This pragmatic mindset extends to Jabil’s entire innovation strategy. “We don’t invest in the shiniest object,” Christensen said. “We invest in what moves a metric. If we can’t see a business case in a few weeks, we shut it down.” That fail-fast, scale-fast mentality allows Jabil to stay competitive without becoming chaotic.

Jabil also leverages SAP Business Technology Platform (BTP), SAP Integration Suite, and Signavio to drive business architecture maturity, connect legacy systems, and manage compliance across diverse regulatory environments. They’ve prioritized integration layers, document management, and GTM alignment tools to future-proof their SAP investment.

That investment is also about people. As AI agents evolve, Christensen sees the opportunity not as replacing jobs but as augmenting employees—especially in manufacturing and supply chain roles. “Yes, we’re rebalancing the workforce,” he said. “But it’s about removing the menial tasks so employees can do higher-value work. That’s the real power of AI.”

He also believes that workforce transformation will be driven by generation shifts. “More digital natives are entering the workforce, and they expect systems to work like their phones. We have to design for that.”

Ultimately, Jabil’s vision is one of controlled transformation—moving quickly, but never recklessly. “We have a North Star,” Christensen concluded. “And every technology decision—whether it’s SAP, AWS, or GenAI—is about getting us closer to that vision without losing what makes us operationally excellent.”

What this means for ERP Insiders

Prioritize simplicity in global ERP strategy. Jabil runs its global operations with just two SAP instances across 135 sites. For ERP leaders, that’s a compelling argument for consolidation and standardization. By minimizing ERP sprawl, organizations can unlock agility, reduce risk, and improve response times during disruption. SAP customers should evaluate the feasibility of single-instance strategies using SAP S/4HANA, Integration Suite, and BTP to centralize operations.

Embed real-time decision support in supply chain operations. Christensen highlighted that Jabil’s ability to re-run supply models in minutes was critical to their pandemic and tariff resilience. SAP customers should focus on embedding real-time analytics using SAP HANA Cloud and SAP Analytics Cloud to enable instant scenario modeling. Resilience is about responsiveness—and that starts with trustworthy, accessible data.

Invest in AI use cases that augment, not replace. Jabil’s approach to GenAI is rooted in augmentation—freeing up supply chain professionals and analysts to focus on what matters most. SAP customers can replicate this with Joule and SAP AI Core, targeting use cases like invoice reconciliation, contract analysis, and demand forecasting. When AI removes low-value tasks, humans can focus on strategic impact.

The post From SAP Sapphire 2025: How SAP Helps Jabil Achieve Resilience at the Speed of Industry appeared first on ERP Today.

SAP and Google Cloud are working across every layer of enterprise technology—data integration, AI agents, infrastructure, and security—to help customers reduce project risk, accelerate innovation, and tap into the power of their existing SAP systems. This isn’t AI for AI’s sake. This is AI for outcomes.

At the core of this partnership is a shared focus on generative AI and agent-based architectures. SAP’s Joule is now enhanced with Google Cloud’s Gemini models via the SAP Generative AI Hub on SAP BTP. These AI agents can now act across SAP applications with intelligence and autonomy—providing everything from invoice resolution and marketing content generation to natural language customer service.

Google Agentspace further empowers this vision, offering a unified interface for users to access enterprise agents and SAP data from wherever they work. Whether it’s a CRM, IT ticketing tool, or data analytics suite, employees can now invoke AI with full SAP context. Agent2Agent (A2A) protocols allow SAP and Vertex AI agents to talk to each other—cutting out brittle, manual integrations and replacing them with standards-based intelligence orchestration.

Companies like AMD are already seeing results. Using Vertex AI and the Cortex Framework, AMD built finance and customer operations chatbots that fetch real-time SAP data and respond in natural language—reducing service time and accelerating forecasting insights.

For decades, SAP data has been siloed, locked behind complex integrations and analytics workarounds. Now, BigQuery and SAP Business Data Cloud are changing that. With prebuilt connections and models from Google Cloud’s Cortex Framework, customers can activate their SAP data in real time for ML, analytics, and operational workflows.

Brazilian paper giant Suzano is a standout example. Using Cortex and gen AI, the company created a natural language interface that allows employees to query SAP data via simple questions—cutting data retrieval time by 90% and democratizing enterprise insights across teams.

But AI means nothing if it’s not supported by the right infrastructure and security. That’s why Google Cloud has rolled out new SAP-optimized VM families like M4 and C4D, offering 127% performance gains at 36% lower TCO. With single-instance 99.95% uptime SLAs, even the most demanding S/4HANA environments now run reliably and securely under RISE with SAP.

Security is equally robust. Google Unified Security and Assured Workloads provide multilayered defense for SAP workloads, supporting ITAR and public-sector compliance mandates. These tools offer real-time monitoring, access control, and threat mitigation built into the cloud fabric—enabling SAP customers to operate with resilience and confidence.

What this means for ERP Insiders

Activate your AI strategy with agents, not experiments. Enterprise leaders should prioritize AI initiatives that are embedded into business workflows—like Gemini-powered SAP Joule agents or Google Agentspace integrations. AMD’s chatbot success shows that AI, when paired with real-time SAP data, can enhance operations without requiring a total replatform. Start with finance, service, or procurement to see fast wins.

Make your SAP data actionable now. Instead of warehousing data for dashboards, consider using Cortex and BigQuery to make SAP data active, connected, and ready for ML. As Suzano proved, giving teams self-service access to SAP insights via natural language can transform decision-making and accelerate ROI across the board.

Future-proof your ERP infrastructure for scale and security. Don’t wait for legacy systems to fail before upgrading. Google Cloud’s new SAP-optimized infrastructure ensures better performance and lower costs, with built-in security and compliance. For RISE with SAP customers, this means peace of mind with always-on uptime, ITAR-ready workloads, and real-time observability.

The post From SAP Sapphire 2025: How SAP and Google Cloud Are Accelerating Real-World Impact from AI appeared first on ERP Today.

The new Joule is powered by SAP’s AI Foundation and is designed to live inside workflows, not outside them. That means finance users can request margin forecasts, HR leaders can identify attrition risk, and supply chain managers can simulate disruption scenarios – all within their existing SAP UI, using conversational prompts.

This isn’t a point solution. Joule is integrated into core SAP analytics and apps, enriched by the SAP Graph, and governed through LeanIX. It understands context, permissions, and process state—a leap forward from generic chatbots.

Joule’s impact is already visible. A global consumer goods company is using Joule to automate 60% of its HR service requests. A European auto parts supplier has cut time-to-resolve for customer disputes in SAP S/4HANA by 40%. And SAP itself has deployed over 100 Joule use cases internally, generating measurable cost savings and productivity improvements.

What this means for ERP Insiders

Shift from process automation to decision co-piloting. With Joule embedded in every SAP app, enterprises can move beyond workflow automation and into intelligent co-piloting. Joule understands business rules, adapts to user behavior, and surfaces recommendations in real time. This supports faster, more confident decisions—especially in cross-functional ERP environments where latency kills momentum.

Eliminate friction in daily operations. Joule’s omnipresence means users no longer need to toggle between systems or rely on technical teams for insights. For example, a procurement manager using Ariba can use Joule to summarize supplier risk, generate RFQs, and initiate approvals—all within a single pane of glass. This embedded intelligence reduces cognitive load and accelerates time-to-outcome.

Operationalize AI at scale through embedded governance. Joule is not just smart—it’s secure. Its actions are governed through SAP LeanIX, ensuring agent activity is visible, auditable, and aligned with enterprise goals. As SAP expands Joule agents across functions, ERP leaders gain a federated, controlled way to scale generative AI without introducing risk or fragmentation.

The post From SAP Sapphire 2025: Joule Everywhere – How SAP Is Making Business AI Omnipresent and Proactive appeared first on ERP Today.