In a revealing, interview with ERP Today’s Mark Vigoroso during SAP Sapphire 2025, 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.”

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.

Listen in to learn more about SAP’s latest AI-driven strategy and how SAP’s go-to-market approach prioritizes controlled experimentation.

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The Dynamic Duo: AI and IoT

The study found that 29% of manufacturers are already witnessing artificial intelligence and machine learning (AI/ML) integrated into their ERP systems, with the Internet of Things (IoT) integration following closely at 19%. Moreover, nearly 30% and 20% of these businesses see AI and IoT being baked into ERPs specifically for efficiency gains.

SYSPRO describes AI not as a job-stealing robot but as a tireless, insightful co-pilot that can help monitor workflows, flag anomalies before they become disasters, and automate complex decision-making processes. The power provided by AI helps organizations move from reactive firefighting to proactive problem-solving. Utilizing these technologies enhances data analytics, optimizes maintenance schedules, and drastically minimizes downtime.

The Supporting Cast of ERP Innovations

While AI and IoT might be grabbing the spotlight, other crucial ERP advancements are quietly revolutionizing complex manufacturing, the study shows:

• Real-time data analytics (16% adoption): The ability to tap into real-time operational data provides the visibility needed to sidestep disruptions and make informed decisions quickly. For instance, if there is a sudden spike in demand for a particular product, an organization can view that rise via its ERP dashboard. It can then immediately adjust production schedules, check raw material availability, and coordinate with logistics in real time, preventing stockouts and capitalizing on the opportunity.
• Cloud-based ERP solutions (13% adoption): Cloud ERPs offer the scalability to grow with the business, enhanced security, seamless data integration from various sources, and easier adoption of advanced technologies like AI.
• Mobile accessibility and remote management (13% adoption): The factory floor and the entire supply chain are no longer tethered to a desktop. ERP functionality accessible via mobile devices empowers everyone from quality inspectors to on-the-go sales teams who need instant inventory updates.
• Enhanced cybersecurity features (10% adoption): With legacy systems being replaced, and data becoming more centralized, robust cybersecurity isn’t just an IT concern; it’s a business continuity imperative. This is underscored by the fact that over 50% of industry experts in some regions view cybersecurity as a critical concern.

The Smart Factory and The Human Element

These evolving ERP capabilities are the building blocks of the smart factory — a highly connected, intelligent, and responsive manufacturing environment. Future-focused factories seamlessly integrate Industrial IoT (IIoT), AI, digital twin simulations, and, at their core, modernized ERP systems. This integration enables businesses to prevent disruptions before they happen and continuously optimize operations.

However, the path to achieving this goal isn’t always smooth. The report highlights some very real barriers:

• High upfront costs (cited by 25% of respondents)
• Resistance to change (22%)
• Lack of technical expertise (19%)
• Challenges with integrating new tech with existing systems (12%)
• Technical difficulties with complex integrations (43%)

These figures represent real-world headaches for operations managers, IT departments, and ERP professionals. Overcoming them requires technological savvy and a focus on the human side of change. This includes clear communication, robust training, and showcasing how these tools empower employees to do their jobs better, faster, and with less frustration.

What This Means for ERP Insiders

Embrace AI & IoT as your efficiency power-ups. ERP professionals are pivotal in deploying these technologies. SYSPRO, for instance, leverages its ERP to integrate with IoT devices for real-time data and machine-to-machine communication, driving tangible gains like predictive maintenance and optimized production. ERP professionals’ role is to translate this potential into reality on the shop floor to create truly digital manufacturing facilities.

Champion data and cloud for agility and scalability. Real-time analytics and cloud ERP enable proactive decision-making and scalable infrastructure. ERP professionals should guide this transition. For example, SYSPRO customer Merle Norman Cosmetics saved $6 million in inventory costs through real-time data, and Aspire Pharmaceuticals achieved 98% inventory accuracy. Moving to platforms like SYSPRO Cloud ERP also paves the way for easier adoption of AI, which is often “built into the heart of such solutions.

Drive modernization and fortify security strategically. While high upfront costs and complex integration are significant hurdles to becoming a smart factory, the strategic need is undeniable. With over 50% of industry experts in some regions citing cybersecurity as a critical concern, ERP professionals must prioritize secure, robust systems. Your expertise is crucial in navigating these challenges, advocating for the necessary investments, and ensuring the ERP backbone is future-proof and resilient against emerging threats.

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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.

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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.

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Self-Healing IT: Automating resolution of IT issues, reducing downtime, and enhancing reliability.

Autonomous Operations: Streamlining workflows and processes for increased efficiency and productivity.

“The launch of the Inetum ServiceNow AI Centre of Excellence marks a pivotal moment in our journey towards digital excellence,” said Hemant Lamba, CEO of Inetum Solutions. “This, coupled with the valuable recognition as an ISG Rising Star, underscores the strong momentum in our ServiceNow practice. Our work here is directed towards developing AI solutions that can support the future of business operations and equip organizations with tools for the digital environment.”
“Inetum’s collaboration with ServiceNow is more than a partnership; it’s a convergence of visions to redefine the technology landscape,” said Marin Marinov, SVP ServiceNow Global Practice at Inetum. “Our Agentic AI offering is a testament to this shared vision, where we not only anticipate the needs of IT operations but proactively address them to ensure seamless and efficient service delivery.”

Inetum is a European leader in digital services. Inetum’s team of 27,000 consultants and specialists strive every day to make a digital impact for businesses, public sector entities and society. Inetum’s solutions aim at contributing to its clients’ performance and innovation as well as the common good. Present in 19 countries with a dense network of sites, Inetum partners with major software publishers to meet the challenges of digital transformation with proximity and flexibility. Driven by its ambition for growth and scale, Inetum generated sales of 2.4 billion euros in 2024. For more information: www.inetum.com

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The company received the “Rising Star” designation in the 2025 ISG Provider Lens ServiceNow Ecosystem Partners report for Europe, underscoring its growing expertise. Another pivotal development is the March 2025 inauguration of its Center of Excellence for ServiceNow Agentic AI. Inetum is among the initial ten partners chosen by ServiceNow for Agentic AI development, co-creating solutions like “CI Smart Recommendations,” an add-on that leverages ServiceNow’s GenAI applications to promote self-healing IT and autonomous operations.

While these achievements directly enhance ServiceNow’s capabilities, the underlying technological shifts and strategic focuses carry clear parallels and learning points for ERP professionals and the systems they manage.

Inetum’s advancements within the ServiceNow sphere are a significant marker for the direction of enterprise technology. For ERP sector professionals, these developments underscore the accelerating integration of intelligent, proactive, and increasingly autonomous capabilities. Adapting to and leveraging these changes will drive future innovation and efficiency in ERP-managed operations.

What This Means for ERP Insiders

Advanced AI integration is becoming standard for enterprise platforms. Inetum’s focused investment in Agentic AI for ServiceNow indicates a broader trend: sophisticated AI is transitioning from an auxiliary feature to an integral component of enterprise software. This suggests a future beyond current analytics or basic automation for ERP systems. The expectation will be that AI agents are capable of predictive analysis and proactive intervention in core processes such as inventory management, production scheduling, or financial reconciliation. ERP professionals should anticipate AI that identifies issues and recommends or even initiates solutions based on continuous learning. This necessitates strategically evaluating how AI can be embedded to redefine operational efficiency within ERP environments.

Self-healing and autonomous operations are extending to ERP domains. The concepts of self-healing IT and autonomous operations, central to Inetum’s new AI offering, directly apply to ERP functionalities. This could manifest as systems that autonomously detect and resolve discrepancies in period-end financial closing processes, flagging only critical exceptions for human review. Similarly, supply chain modules could gain the ability to autonomously adjust logistics based on real-time disruption data, learning from events to mitigate future risks. This evolution does not point to replacing ERP personnel, but to augmenting their roles, enabling a shift from reactive problem-solving to strategic oversight and process optimization.

Evolving partner ecosystems and the value of specialized AI solutions. The collaboration between Inetum and ServiceNow to develop targeted AI solutions like CI Smart Recommendations highlights the increasing importance of specialized expertise in the evolving technology ecosystem. While general AI tools provide a foundation, maximum value in the enterprise space, particularly for complex ERP systems, will be derived from AI solutions tailored to specific business functions and industry verticals. ERP professionals should, therefore, identify partners and solutions that combine AI proficiency with deep knowledge of their specific ERP platform and operational intricacies. As stated by Hemant Lamba, CEO of Inetum Solutions, their focus is on developing AI solutions that can support the future of business operations — a future that integrally involves ERP systems.

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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.

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A Big Opportunity for SMBs

According to Sage, this development is a direct response to the evolving needs of SMBs. The company’s “Small Business, Big Opportunity” report indicates that 85% of SMBs now view technology as critical to operational success. The upgraded Sage 50 aims to meet this demand by offering:

• Faster reporting via natural language AI searches
• AI-powered data extraction to automate purchase transactions and reduce errors
• Enhanced security features, including AI-driven fraud detection

Access to these new cloud-connected features, including Sage Copilot, will be managed through the unified Sage account login, promising a more secure and seamless user experience.

“Sage 50 has been the backbone of small business accounting for decades, and these new enhancements ensure it will remain a trusted tool for years to come,” said Neal Watkins, EVP, Accounting & HR, Sage. “We know many of our customers rely on it, and we’re committed to making it even better by integrating AI and automation to save them time, reduce manual work, and improve cash flow.”

Reaping Benefits with AI

The AI-powered Sage Copilot will proactively monitor financial data, offering actionable recommendations. For SMB users, this translates into tangible benefits like:

• Generating custom reports instantly
• Identifying trends
• Spotting anomalies
• Reducing administrative overhead

Commenting on the practical implications of this enhanced solution, David Harvey, Co-Founder of Seabass Vinyl and an early adopter, said: “We’re really excited about what Sage Copilot can do, especially in saving time on admin that takes us away from bigger priorities. Having those proactive insights will be a huge help as we scale.”

What This Means for ERP Insiders

Sage is doubling down on its focus on UK-based SMBs. The AI integration in Sage 50 augments a familiar system that UK-based businesses use. Key use cases directly address SMB pain points: automating tedious purchase data entry, simplifying report generation (a common bottleneck), and bolstering security without requiring deep IT expertise. For ERP consultants serving this segment, it highlights a trend of embedding AI to solve particular, practical challenges rather than offering broad, undefined AI capabilities. This approach eases adoption for traditionally less tech-forward SMBs.

AI democratization and SMB tech reliance. AI is no longer an enterprise-only play. Sage’s move exemplifies the democratization of AI, making sophisticated tools accessible within existing, trusted software. For ERP professionals, this means even smaller clients will increasingly expect AI-driven efficiencies in any new or upgraded system. The market demands intelligent automation that delivers clear ROI, particularly regarding time savings and error reduction.

With Sage 50, users are evolving with a known quantity. This approach minimizes disruption and leverages existing customer trust. This strategy suggests that for established software vendors, incrementally adding AI value to core products can be more successful than a rip-and-replace approach, especially when dealing with change-averse SMBs. The focus should be on how AI assists and augments human capabilities, making processes faster and decisions smarter, thus freeing up valuable owner/operator time for strategic tasks rather than administrative churn.

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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.

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What sets AI Foundation apart is its promise to reduce the cost and complexity of enterprise AI. Features like prompt optimization, which automates the generation of high-quality prompts for LLMs, and the tabular AI engine that turns structured data into real-time predictions, democratize advanced analytics across SAP’s suite. Companies can now go from data to insight to action in minutes instead of months.

For developers, the low-code and no-code capabilities in Joule Studio enable the creation of AI agents grounded in enterprise data. Meanwhile, the AI Hub, leveraging SAP LeanIX, ensures governance by mapping AI agents to business capabilities across the enterprise. These innovations are designed not just to build smarter tools, but to give organizations a systematic way to scale AI across every line of business.

What this means for ERP Insiders

Democratize enterprise AI without fragmentation. CIOs and CDOs can leverage SAP AI Foundation to empower both technical and business teams to build AI-driven applications without compromising governance. The inclusion of Joule Studio and the Knowledge Graph ensures that enterprise-specific data semantics are preserved and exploited for smarter automation. Customers like Jabil and Microsoft have already seen 30% productivity gains from embedded AI copilots.

Reduce TCO by centralizing AI development. AI Foundation consolidates AI tools into a single platform, reducing reliance on costly integrations and disconnected data pipelines. A recent market study found that organizations using centralized AI platforms saw a 38% faster time to deployment. With Joule, SAP provides out-of-the-box capabilities alongside customizable extensions, helping finance and supply chain teams launch AI agents in weeks, not quarters.

Accelerate innovation with built-In governance and interoperability. SAP’s use of LeanIX to inventory and manage AI agents helps IT leaders mitigate risk, track usage, and ensure agentic AI aligns with business priorities. This will be critical as agent-based automation expands into core processes like dispute resolution, supply chain disruption management, and employee performance reviews—all of which are now supported by SAP Joule agents with reasoning capabilities.

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