Tag: SAP

  • NemoClaw Gives Enterprise AI Agents The Security Layer They’ve Been Missing

    NemoClaw Gives Enterprise AI Agents The Security Layer They’ve Been Missing

    The NVIDIA GTC announcement of the NemoClaw stack addresses one of the real reasons enterprises haven’t deployed AI agents at scale — and one vendor’s alternative to address those concerns.

    OpenClaw became the fastest-growing open-source project in history while enterprise buyers watched from the sidelines.

    Not because the technology wasn’t interesting. It is. Not because employees weren’t already using it. They were — quietly, on personal machines, sometimes on corporate devices. Enterprises held back because an autonomous AI agent that can access file systems, execute code, log into corporate systems, and communicate externally is not something you hand to 10,000 employees without a security framework underneath it.

    NVIDIA ‘s NemoClaw announcement at GTC 2026 offers one solution to address that gap directly. It is not a competitor to OpenClaw. It is a reference stack that adds the infrastructure layer OpenClaw was missing: policy-based security guardrails, a privacy router, a sandboxed runtime called OpenShell, and integration with the security tools enterprises already use, installed in a single command.

    Jensen Huang put it plainly in his GTC keynote:

    “Agentic systems in the corporate network can have access to sensitive information. They can execute code and communicate externally. You could access employee information, access supply chain, access finance information, and send it out. Obviously, this can’t possibly be allowed.”

    For enterprise buyers, this is the right framing. The question was never whether AI agents would be useful. The question was whether they could be deployed safely inside an enterprise environment. NemoClaw is NVIDIA’s answer to that question for OpenClaw.

    Why OpenClaw Alone Wasn’t Enterprise-Ready

    Before discussing what NemoClaw adds, it is worth clarifying the enterprise IT and security leader’s concerns.

    OpenClaw is designed to act like a digital assistant sitting at your computer. It can view your screen, control your browser, open files, execute commands, and log in to websites. If an employee grants OpenClaw access, it operates the computer the same way the employee would. That means if the employee can access corporate email, CRM systems, internal dashboards, customer data, or finance systems, the tool can access them too.

    The issue is not the AI itself. The issue is how much access the software has and where the data goes afterward. Specific risks enterprises were managing before NemoClaw include:

    • Credential exposure. If OpenClaw interacts with browser sessions or system logins, it can gain access to saved passwords, session tokens, and authentication cookies. This could allow the tool — or anything that compromises it — to impersonate the employee in corporate systems.
    • Data leaving the organization. AI automation tools often send information to external services for processing. That can include screenshots, text from documents, browser content, and system commands. If sensitive enterprise data is transmitted outside approved systems, it may violate GDPR, HIPAA, PCI, or data-residency requirements across multiple jurisdictions.
    • Autonomous actions without audit trails. AI agents can operate autonomously, executing workflows, interacting with websites, and performing transactions without logging. If poorly configured, an agent could send emails, download files, update records, or trigger business workflows without IT or compliance teams having visibility.
    • Shadow IT at scale. Meta instructed employees not to use OpenClaw on work computers due to security concerns. Many companies followed suit. Even so, adoption continued. Prohibition alone does not work when the tool is genuinely useful. Enterprises need a sanctioned, controlled alternative or safeguards— not just a policy.

    What NemoClaw Actually Does

    NemoClaw is not a product. It is a reference stack — an open-source configuration of existing and new NVIDIA tools that installs on top of OpenClaw to add the enterprise infrastructure layer. Here is what each component does:

    1. OpenShell. This is the core of the security layer. OpenShell runs OpenClaw agents in an isolated sandbox that limits what files they can access and restricts network connectivity. Enterprises can write policy rules in YAML to define exactly which systems an agent can access, which data it can process, and which actions require human approval. YAML Ain’t Markup Language (YAML) is a human-friendly, plain-text format used to store data, configure software, or move data between systems. Some rules are hot-swappable, allowing them to be changed without restarting the agent.
    2. Privacy Router. When agents need to call cloud-based frontier models, the Privacy Router ensures sensitive enterprise data is not transmitted to those external models. This is the mechanism that makes hybrid local-cloud architectures viable without creating data residency exposure.
    3. Nemotron Models. NVIDIA’s open model family can run locally on dedicated hardware, including NVIDIA RTX PCs and DGX Station. Running inference locally means sensitive enterprise data stays on-premises for tasks that require it. This also reduces cloud inference costs. NVIDIA reports that its hybrid architecture — using Nemotron for research tasks and frontier models for orchestration — can cut query costs by more than 50 percent.
    4. Single-command installation. This matters more than it sounds. One practical barrier to enterprise AI deployment is configuration complexity. A stack that requires days of professional services to stand up will not achieve broad adoption. NemoClaw installs the entire reference configuration in one command.

    Kari Briski, NVIDIA’s VP of Generative AI Software, described it this way at GTC: OpenShell “provides the missing infrastructure layer beneath claws to give them the access they need to be productive, while enforcing policy-based security, network, and privacy guardrails.”

    That framing is accurate. The productivity value of OpenClaw was never in question. What was missing was the control layer.

    The SaaS Ecosystem Is Already Moving

    For enterprise buyers, the breadth of the partner ecosystem matters as much as the technology itself. You are not just evaluating a security sandbox. You are evaluating whether the tools your teams already use will work within that sandbox.

    The list of software companies integrating with NVIDIA Agent Toolkit and OpenShell includes Adobe, Atlassian, Box, Cisco, CrowdStrike, Red Hat, Salesforce, SAP, ServiceNow, and Siemens. A few specific integrations are worth noting:

    • Cisco‘s AI Defense will provide AI security protection for OpenShell, adding controls and guardrails to govern agent actions. For enterprises already running Cisco security infrastructure, this is a direct integration path.
    • CrowdStrike unveiled a Secure-by-Design AI Blueprint that embeds Falcon platform protection directly into NVIDIA AI agent architectures. Enterprises with CrowdStrike endpoint protection can extend that coverage to AI agents.
    • Salesforce is working with Agent Toolkit to enable customers to build and deploy AI agents through Agentforce for service, sales, and marketing tasks, using Slack as the conversational interface and orchestration layer. This matters because Salesforce and Slack are already inside most enterprises.
    • ServiceNow ’s Autonomous Workforce of AI Specialists is built on Agent Toolkit and includes NVIDIA Nemotron models. ServiceNow is the workflow backbone for IT operations in many large enterprises, which means agentic AI is now moving into ITSM and operational workflows directly.
    • SAP is using Agent Toolkit to enable AI agents through Joule Studio on SAP Business Technology Platform, allowing customers to design agents tailored to specific business processes. For enterprises running SAP as their ERP backbone, this is a direct integration with operational data.

    The pattern here is important. These are not experimental integrations. Salesforce, ServiceNow, and SAP together represent the core of enterprise application infrastructure. When your existing SaaS platforms build their agentic strategies on the same security and identity stack, it significantly reduces the interoperability problem. You are not assembling a security architecture from scratch for every new AI agent. You are extending an existing framework.

    It is also worth noting that NVIDIA is collaborating with Microsoft Security, Google , CrowdStrike, and TrendAI to build OpenShell compatibility with their security tools. The intent is to make OpenShell work within security stacks enterprises already have, not replace them.

    What This Means for CIOs and Enterprise Buyers

    NemoClaw does not eliminate the work required to deploy AI agents safely. It reduces the barrier significantly. Before evaluating whether NemoClaw belongs in your environment, three questions are worth resolving:

    Are employees already using OpenClaw? If the answer is yes — even informally — then the risk exists today. NemoClaw gives you a sanctioned path to bring that activity under enterprise controls rather than competing with shadow IT through prohibition.

    Is your identity framework ready for AI agents? NemoClaw provides the guardrails, but agents still need registered identities and role-based access controls. Most enterprise identity frameworks were designed for people. Before deploying agents at scale, confirm that your identity infrastructure can assign, manage, and revoke agent credentials separately from human credentials.

    Do you have the observability to know if agents are working correctly? OpenShell provides audit logging and network guardrails. That is a starting point, not a complete observability stack. You need monitoring in place to detect when agents are drifting, making errors, or accessing systems they should not.

    The Caveats That Curb My Enthusiasm

    The broader context here matters. Jensen Huang framed the OpenClaw moment as equivalent to the arrival of Linux, HTML, and Kubernetes — foundational infrastructure that reorganized entire industries. That may or may not prove accurate.

    What is accurate is that agentic AI as a category, not just OpenClaw, will fundamentally change enterprise workflow platforms. The companies that define their governance architecture now will be better positioned than those that build it under pressure later.

    It is also worth being realistic about what NemoClaw does not solve. Cross-vendor agent orchestration remains complex. An agent working in Salesforce does not automatically collaborate with an agent in SAP without significant integration work. Governance frameworks for multi-agent systems are still nascent. The tools are improving faster than the enterprise readiness in most organizations.

    Other things to consider. NemoClaw and OpenShell are open source. Anyone can download them. However, to run Nemotron models locally (the privacy-preserving option that keeps data on-premises), you need NVIDIA GPU hardware — an RTX PC, DGX Spark, or DGX Station.

    YAML policy configuration is an IT burden. Enterprises customize OpenShell by writing YAML rules. That’s a developer-friendly approach, not an enterprise admin-friendly one. Large organizations with thousands of use cases will need tooling and staffing to manage that policy layer. This is not point-and-click governance.

    The partner ecosystem is announced, not proven. The list of SaaS partners — Salesforce, SAP, ServiceNow, etc. — represents intent and roadmap, not certified integrations. Most of these are “working with NVIDIA” statements. Before betting enterprise deployments on them, buyers should ask specifically what is shipping, when, and what certification or testing has been completed.

    Start with Governance, Not the Agent

    NemoClaw is genuinely useful for enterprise buyers. It addresses the right problem in the right way — by adding enterprise security controls to an open-source agent platform that employees are already adopting, rather than building a competing proprietary stack.

    The partner ecosystem — particularly Salesforce, ServiceNow, SAP, Cisco, and CrowdStrike — means NemoClaw is not a greenfield deployment for most enterprises. It fits into the existing infrastructure.

    But the technology being ready does not mean you are ready. Before deploying agents in any production environment, define what they are allowed to do, what data they can access, and how you will know when something goes wrong. That work is not optional — and no reference stack does it for you.

    Governance first. Agents second. That is the sequence that works.

    Subscribe to my AI with Maribel Lopez podcast on your channel of choice here.

  • AI Search Helps SAP Improve Customer Support With Coveo

    AI Search Helps SAP Improve Customer Support With Coveo

    Most companies struggle to prove AI ROI, but enterprise software provider SAP found an approach that works


    The artificial intelligence (AI) market is expanding rapidly. Enterprise AI spending surged eight-fold in 2024 according to the Menlo Ventures. This year is expected to be a banner year for AI adoption. However, many AI initiatives are not delivering measurable returns.


    Recent AI studies paint a stark picture. MIT reports that 95% of generative AI pilots fail to reach production, while RAND found that 46% of companies scrapped their AI projects before launch. Perhaps most telling, Boston Consulting Group discovered that only 24% of firms have developed the capabilities needed to move AI beyond proof-of-concepts into tangible business value. While we could debate what the actual percentages are, one thing is clear. Companies are spending money on AI but aren’t getting the returns they expected.

    What separates the AI strategy winners from the also-rans? It appears the key to AI success is selecting a set of narrowly scoped use cases with a quantifiable cost that will benefit from using AI. As Best Buy said, we don’t have AI projects, we have projects that use AI. SAP’s journey offers one success story with crucial lessons for any organization struggling to prove AI’s worth.


    Some of the best AI success stories start by addressing a simple problem. For example, Cisco discussed how leveraging AI to detect network configuration issues solved a key customer support challenge.

    The Million-Case Support Crisis That Sparked SAP’s Internal AI Journey


    For SAP, the path to AI success began with a simple but expensive problem: customer support for its over 300 million cloud subscribers. Like many companies, the German enterprise software vendor was experiencing significant outflow of money on avoidable service requests. The goal was not to remove employees, but to eliminate unnecessary inquiries.

    AI has the ability to create capacity for the existing workforce to build new customers service experiences. Customers were struggling to find answers , leading to unnecessary support tickets that customers could have avoided through self-service.
    “We weren’t hitting our self-service targets,” explains Michelle Lewis-Miller, the Head of Digital Experience and Voice of Customer for Customer Support at SAP. “We couldn’t see what was happening and couldn’t measure anything.”

    Visibility is a key issue across businesses of all sizes. Many companies deploy AI solutions without clear metrics or understanding of where the technology can create the most impact. SAP started with a measurable business problem and worked backward to find the right AI solution.

    The Foundation: Getting Knowledge Quality Right First


    Before implementing any search solution, SAP had already invested heavily in knowledge quality. Lewis-Miller uses a culinary metaphor to explain: “If you’re serving dinner with poor ingredients, it doesn’t matter how great the chef is.”

    Too many organizations rush to implement AI technology without first ensuring their underlying data and content are ready to support it. Knowledge wasn’t the company’s problem. SAP had over 10 million knowledge assets—spanning help documents, SAP Knowledge Base Articles, community posts, and videos- as a foundation for its search efforts. “SAP had already made massive investments to get the knowledge into good shape, and introduced AI-driven Incident Solution Matching recommender for knowledge. The problem was an inferior knowledge discovery experience via search,” Lewis-Miller notes.

    30% Case Reduction: The Pilot Results Nobody Expected


    Rather than launching a company-wide AI initiative, SAP began with a targeted pilot in their Concur travel and expense division. The Concur division was already using Coveo’s AI-powered search platform, making it an ideal testing ground for generative AI capabilities.


    The Coveo AI pilot results exceeded all expectations. Within six months, SAP Concur experienced a 30% drop in support case volume, translating to €8 million in annual cost avoidance. For many customers, this was their first encounter with generative AI—and it delivered immediate, tangible value.


    “When we piloted Relevance Generative Answering in Concur, it went so well it was shocking,” Lewis-Miller recalls. “We saw such a huge decrease in case submission that helped us tremendously in keeping up with business growth.”


    The Coveo-SAP Concur pilot’s success illustrates a key principle: AI ROI comes from solving specific, measurable problems rather than implementing technology for technology’s sake. The SAP Concur team had clear metrics (case volume reduction), a defined user base, and a focused use case.


    The Cultural Shift That Enabled Bold Decisions


    The cultural shift at SAP proved crucial to the initiative’s success. In a discussion with Lewis-Miller, she emphasizes that executive support went beyond budget approval: “Our executives came in and made a very clear statement. They said, You’re not going to be punished for trying.” This created an environment where teams could be decisive rather than seeking consensus from dozens of stakeholders.


    She refers to this as the “tip of the spear approach,” noting that “at an organization of any size you need to find the people who are willing to take risks, and you need to have the executive leadership being unequivocal about supporting that person who’s willing to take the risk.” Without this top-down support for calculated risk-taking, the project likely could have stalled in committee discussions and consensus-building exercises that ultimately may have prevented any meaningful progress.


    Scaling Success: The SAP for Me Challenge


    Emboldened by the Concur results, SAP faced a much larger challenge: implementing AI-powered search across SAP for Me, the central portal serving millions of customer interactions every month. Unlike the focused SAP Concur deployment, this required integrating 14 to 20 different knowledge bases while serving vastly different user types—from certified engineers with 30 years of SAP experience to small business owners with limited technical background.


    The complexity initially threw the team. When they deployed the same generative AI technology on SAP for Me, the results looked worse at first glance—higher click rates and more case submissions seemed to indicate the system wasn’t working.
    “We expected it to address the low-hanging fruit. It wasn’t at all like what we saw with Concur. We asked Coveo if something was broken in their backend,” Lewis-Miller admits.


    The breakthrough came when SAP’s analysts realized they were measuring the wrong things. While simple questions decreased on the Concur platform, SAP for Me users were asking much more complex queries. The AI wasn’t avoiding low-value cases—it was helping resolve sophisticated problems that would have required expensive expert support.


    “We were handling a more valuable subset of cases,” Lewis-Miller explains. “When we changed our perspective and looked at the overall numbers, we saw that submissions had actually gone down.”


    Here’s where many AI projects fail: teams panic when initial metrics don’t match expectations and abandon promising initiatives. SAP took a different approach, partnering with their technology vendor to dig deeper into the analytics. Successful AI implementations require a company to deploy, monitor, analyze and iterate. SAP’s story highlights how theory rarely matches reality.


    The Results


    Since launching in 2023, the AI-powered search has enabled intelligent scaling of the support function to drive business growth. SAP’s AI strategy ensures that support costs grow slower than revenue, thereby expanding profit margins, and allowing its business to absorb significant market expansion. As a result, customer self-service success from search is now at over 80%, and the technology spans 47 different sources and indexes 11.2 million documents across the SAP ecosystem.


    The Technical Foundation That Makes It Work


    SAP’s success stems from choosing the right technical approach for their specific needs. Rather than building custom AI models from scratch, which is labor and time intensive, SAP leveraged a “hybrid search” system that combines multiple AI techniques:


    • Lexical search (traditional keyword matching) finds documents that contain the exact words you type—like searching for “invoice processing” and getting results that include those specific terms.
    • Semantic search (context-aware search) understands what you actually mean, not just the words you use. It recognizes that “payment issues” and “billing problems” refer to similar concepts, even when the exact words don’t match.
    • Behavioral machine learning (learning from user patterns) tracks which search results people actually click and use, then automatically surfaces the most helpful content for similar future searches.
    • Generative AI (AI-powered answer creation) provides direct responses to questions in plain English while showing exactly which company documents or sources the information came from, similar to how ChatGPT works but trained on your organization’s specific knowledge base.


    This hybrid approach addresses common search frustrations: lexical systems miss relevant results when you don’t use the exact right keywords, while pure generative AI can sometimes provide inaccurate information. The combination delivers both precision and intelligence.


    Advice For Building ROI with AI


    SAP’s re-invested cost avoidance didn’t happen by accident. Behind those impressive numbers lies a methodical approach that any organization can follow—if they’re willing to resist the allure of flashy AI demos and focus on fundamentals. Key steps that every organization can take include:


    • Start with measurable problems rather than exciting technology possibilities
    • Ensure your knowledge foundation is solid before implementing AI search solutions—garbage in, garbage out applies to AI just as much as any other technology
    • Pilot in controlled environments where you can isolate variables and measure impact
    • Secure executive support for risk-taking and empower decision-makers to act without requiring consensus from dozens of stakeholders
    • Document rigorously for compliance and architecture
    • Evaluate enterprise-ready AI solutions instead of jumping first into extensive custom AI development
    • Invest in proper analytics and measurement to understand true impact versus surface-level metrics


    While all of these points are important, Lopez Research has seen many companies skip the governance and compliance step. Lewis-Miller identifies meticulous documentation as a critical but often overlooked success factor.

    “We invested a lot of time in finding employees who had the soft skills necessary to interpret the technical detail for the legal people and the legal detail back to the technical detail.” This documentation became essential reference material that prevented the project from stalling at multiple compliance checkpoints. At enterprise scale, having detailed outlines for every architectural and legal decision proves invaluable.


    Beyond Cost Avoidance: Proactive Customer Experience


    SAP isn’t stopping at self-service enablement. The company is now using behavioral analytics to intervene before customers encounter problems. Lewis-Miller describes the vision: “As a user is engaging in one of our support journeys, our goal is to intervene in real time to get ahead of issues before they actually emerge.”


    The approach involves using customer behavior patterns to identify when someone is struggling to find information, then proactively routing them to chat support before frustration sets in. “We’re not trying to give everybody a white glove experience. We’re specifically intervening only in those moments where we can see it’s going down the wrong path,” Lewis-Miller explains.


    This includes predicting system issues before they occur and routing insights to product teams to fix confusing features before they generate support cases. By analyzing search patterns and support interactions, SAP can identify friction points in their products and address them proactively—turning support data into product improvement fuel.


    AI is doing more for internal innovation: For the cases that do reach a support engineer, SAP focuses on agent augmentation. AI-based case summarization enables faster handoffs, and intelligent routing connects a case to the correct expert instantly.
    The AI revolution is about implementing the right technology to solve real business challenges. SAP found their formula. Now it’s time for other enterprises to find theirs.

  • SAP Modernization Gains Momentum: Customer Success Stories From Sapphire

    SAP Modernization Gains Momentum: Customer Success Stories From Sapphire

    For nearly two decades, Lopez Research has tracked SAP customers’ struggles when transitioning to standardized, cloud-based solutions. The reality of enterprise IT deployments is undeniable: SAP powers critical business operations—from finance and human resources to sales, marketing, and supply chain management—making any transition extraordinarily complex.

    Lopez Research has consistently heard these concerns from organizations across industries. However, we’re witnessing a significant shift as customers finally embrace modernization. Two key factors drive the modernization momentum: substantially improved SAP offerings and an urgent desire to capitalize on artificial intelligence capabilities that require modern infrastructure.

    Migrating business software from on-premises to cloud environments is like changing a car’s tires while driving at full speed. This challenge gets amplified by years of customizations that make simple drop-in replacements to modern cloud offerings impossible. The path to a new business suite is inevitably costly, complex, and time-consuming.

    Simplifying Business with SAP

    At Sapphire 2025, SAP’s message was unmistakable: they’re committed to simplifying how customers engage with their products—whether through faster, more cost-effective modernization of existing solutions, embracing AI innovations, or building solutions with partners. This comprehensive transformation has been years in the making.

    On day one, Christian Klein introduced a new transition guidance tool to accelerate migrations with fewer resources. SAP is streamlining AI deployment by embedding intelligence directly into its products. The company expects to deliver more than 400 embedded AI scenarios by year-end, with Joule functioning as a CoPilot interface across the entire suite. Multi-agent frameworks are this year’s dominant theme, and SAP has embraced agentic AI capabilities. A key objective is providing tools that reduce custom application and AI development costs throughout the entire lifecycle. SAP is enhancing customer experience through several key initiatives:

    • Line-of-business specific solution packages tailored to different buying centers (finance, HR, supply chain, sales, procurement, services)
    • Simplified commercial terms and contracts
    • Out-of-the-box AI packages with managed integration
    • Pre-packaged, SAP-managed industry-specific content
    • Inclusion of SAP Build to extend applications while maintaining a clean core

    With these changes, SAP aims to create solutions that deliver a “best of suite as a service” approach, significantly reducing complexity and implementation costs for companies adopting SAP’s business suite.

    At Sapphire 2025, SAP also announced new partnerships with AWS and Perplexity.ai. The Perplexity answer engine is being embedded directly into SAP Joule. It allows companies to integrate accurate, trusted, real-time answers on public web data with their SAP data for enhanced decision-making. The AWS collaboration establishes a dedicated AI co-innovation program and fund to support the development and deployment of generative AI applications on the SAP Business Technology Platform for cloud ERP workloads.

    SAP Responds to Customer Challenges

    On day two of SAP’s Sapphire conference, Thomas Saueressig acknowledged these customer challenges and highlighted the company’s progress over the past year. At last year’s Sapphire, SAP introduced its simplified engagement model, the RISE with SAP offering, and an integrated tool chain to streamline transformation processes. This year, Saueressig revealed that “customers using the RISE with SAP methodology and the integrated tool chain saved up to 30% of the cost for their transformations.”

    The RISE with SAP offering helps businesses transition their SAP ERP systems from on-premises to the cloud. Rather than a new standalone product, it’s a bundled package of software, platform, infrastructure, and services, marketed as “Business Transformation as a Service.” The RISE with SAP methodology encompasses SAP S/4HANA Cloud and the SAP Business Suite, enabling customers to progress from fit-to-standard to fit-to-suite implementations.

    SAP also offers GROW with SAP, a program tailored for small to medium-sized businesses seeking rapid cloud ERP adoption. It delivers a comprehensive package of solutions, best practices, adoption services, community access, and learning resources for smooth cloud implementation. The company now supports 8,500 RISE with SAP customers and manages over 146,000 systems with an impressive five-nines (99.999%) availability service level agreement.

    While companies recognize data as their strategic asset, unlocking its value has remained challenging. In February 2025, SAP announced the SAP Business Data Cloud (BDC)—a next-generation, fully managed Software-as-a-Service platform that unifies, governs, and analyzes business data across SAP and third-party sources. This solution represents the evolution of SAP’s data and analytics offerings, integrating capabilities from SAP BW, SAP Datasphere, SAP Analytics Cloud, and new AI features enabled through a strategic partnership with Databricks.

    Real-World Customer Success Stories

    The critical question remains: Will SAP’s efforts yield meaningful results? This year’s Sapphire showcased customers sharing their modernization journeys and the value they’re creating through SAP solutions and artificial intelligence.

    Mercedes-Benz: Navigating Complex Transformation

    Mercedes-Benz decided to adopt RISE with SAP in 2024. Katrin Lehmann, CIO of Mercedes-Benz Group, explained, “I took on that role just a year ago, and the first thing you do is assess what you have. I have over 10,000 applications in the system landscape, and 1,200 of those are SAP applications. Most of them (SAP apps) are still on ECC. How did we tackle that topic? We bought LeanIX.”

    SAP’s LeanIX software provides comprehensive visibility into an organization’s entire software estate—whether purchased, planned, or custom-built. By offering detailed insights into the current IT landscape and all interdependencies, SAP LeanIX helps companies plan and navigate both technical and business aspects of ERP transformation.

    Lehmann continued, “We implemented LeanIX and sorted the systems by importance. We used the time methodology, transform, invest, migrate, eliminate, to plan where we need to go. We took the most important systems, and we’re migrating those to RISE right now.”

    She emphasized that “Moving to RISE also means that we can leverage innovation, and AI is very important to us. But, AI is not new to us at all.” Mercedes-Benz engineers have been using GitHub Copilot for several years throughout their supply chain and value chain. The company has developed its own internal ChatGPT called MRS GPT, which is used across finance, HR, and manufacturing plants. When quality issues arise, workers can use natural language to ask MRS GPT for resolution assistance. The company is now looking to incorporate Joule and other SAP AI capabilities.

    Metal Services: Transformation During Crisis

    Jeff Suellentrop, CITO of Metal Services LLC, shared how his company grew too quickly and needed to reinvent its business during bankruptcy, which followed rapid growth. “We wanted to reimagine how we do business. We selected GROW with SAP, and an all-best practice implementation to replace every system in the entire company. Now there are other subsystems, but we got rid of the paper and pencil. And not only do we use the best practices for simplification, but also to level up. The (SAP) best practices were better than many existing processes. It was a few huge lifts, and we went live within eight months of initiating that process.”

    He added, “We’ve built out our full data model, which is one of the big unlocks (of value). We have the ability with a standardized, out-of-the-box core product to deploy to multiple sites, and then we can continuously innovate on top of that. We use SAP maintenance to maintain all our equipment, and we’ve returned millions and millions of dollars back into the business. Our profitability and utilization are up. We have some other AI models as well, and we’ve just scratched the surface.”

    Mars: Multi-Business Unit Strategy

    Large multinational companies face unique challenges when designing systems that can’t follow a one-size-fits-all approach. Will Beery, Global CIO of Mars Snacking, outlined the company’s digital strategy to create an architecture that is “global, standardized, scalable, and has the ability to make it easier to plug in acquisitions.”

    “We’re using the public cloud to house True Fruit, Nature’s Bakery, and now KIND. For my retail business, we’ve deployed a retail-specific RISE instance that will run M&M stores but also be fit-for-purpose (solution) for Hotel Chocolat. For Mars Wrigley, we’re developing a more customized, complex template for the rest of Mars Incorporated. The three-tier model allows us the agility to support smaller businesses like KIND. It allows us interoperability across all businesses, to drive scale and efficiency where it makes sense. It’s also going to enable a massive integration if we were to acquire a sizable snacking business in the future. SAP and RISE are a massive partner, helping us do that fast and at a lower total cost of ownership.”

    Praveen Moturu, Global Vice President – Digital Platforms at Mars Information Services, elaborated, “We took a model-driven approach from our business capabilities, business processes, data, applications, and technology, and put those models in the SAP Business Suite. It has helped us to build a cleaner platform for the future, which is resilient, and that’s one of the key cornerstones of our transformation.”

    Starting with what SAP calls a “clean core” is challenging but potentially rewarding. Moturu explained, “The productivity from the business suite is enormous for us. We started with leveraging the automation that’s already available in the (SAP) Business Suite to reduce manual interactions, human errors, and give our associates time back. Real-time data and embedded analytics helped us to drive productivity and make accurate and timely decisions that help us to drive quality. And last, but not least, the AI components that the business we introduced with Joule have increased productivity across the company. We have deployed it in our public and private cloud, and it helped us onboard new users, change management, and learning.”

    NBCUniversal: From Project to Platform Mindset

    How you start the transformation journey will shape how resilient and flexible your company becomes as market dynamics change. Abhinav Gupta, SVP of Enterprise Product at NBCUniversal, shared how his company approached transformation and offered advice for business leaders.

    “The fundamental premise of our strategy is to consolidate disparate technology platforms supporting similar processes onto a common technology platform with a standardized set of processes. It sounds easy, right? This is hard to do because this is not about the technology. It requires driving a significant mindset shift within the company. The way I like to verbalize this mindset shift is this concept of moving from a project mindset to a product or platform mindset.”

    Gupta explained how NBCUniversal structured its governance to drive this mindset shift: “One of the ways we are driving this mindset shift within NBC Universal is in the way that we have structured our governance model. The steering committee for our finance transformation effort has representation across the entire breadth of the company. We have the four CFOs who represent each of the four divisions. We have our chief accounting officer, our corporate Financial Planning and Analysis leader, our global CIO, our CTO, and our head of AI on the steering committee as well. We also have representation from EY, our primary Systems Integrator in this effort, and SAP.”

    He praised SAP’s collaborative approach, noting that SAP “brought its top product and engineering folks that have collaborated with our teams, and collectively, we have overcome some significant problem statements.”

    Despite the successes, NBCUniversal acknowledged the ongoing challenges of transformation. Gupta emphasized, “In spite of the incredible wins that I’ve shared with all of you today, we still have a long way ahead of us. The one key takeaway for the audience is this. It’s important to bake in incremental wins across your long transformation journeys. This is helpful to keep your organization and people energized, which is crucial to your success.”

    Expanding Success Stories

    Saueressig highlighted additional customer achievements, including PHP’s creation of $500 million in value using Signavio for business process transformation, enabling the company to reinvent its shared service center model. SAP Signavio is a comprehensive suite for business process transformation that helps organizations design, analyze, improve, manage, and monitor process changes.

    Nestlé saved over 5 million hours using solutions like SAP’s WalkMe—a digital adoption platform providing in-app guidance, automation, and analytics—to drive enablement and digital adoption for 270,000 people across more than 200 applications. First Abu Dhabi Bank achieved a 25% reduction in application count.

    Jan Gilg, Chief Revenue Officer for SAP Americas and SAP Business Suite, and Member of the SAP Extended Board, emphasized, “An integrated, harmonized enterprise foundation is the key, and that’s the reimagined SAP Business Suite. It is data-driven, AI-powered, and can democratize access to business AI. I’m convinced that this will then truly unlock the next wave of productivity, and the time is now.”

    Lopez Research Perspective

    Lopez Research believes we’re witnessing a critical inflection point in enterprise software modernization. After years of hesitation, SAP customers are accelerating their transformation journeys, driven by more flexible migration options and the compelling business case for AI-powered operations. While the path remains challenging, the potential benefits in operational efficiency, data-driven decision making, and competitive differentiation are becoming too significant to ignore.

    SAP customers have long viewed their relationship with the software provider as a partnership rather than a typical vendor arrangement. They expect SAP to innovate on their behalf but adopt these innovations cautiously, recognizing the potential business disruption of hasty implementation. This balanced approach to modernization—combining thoughtful planning with strategic ambition—appears to be gaining traction as organizations seek the agility needed to thrive in increasingly digital markets.