Data Integration Challenges

However, the path to leveraging ERP data in cloud analytics platforms like Databricks or Snowflake is often complicated by several prevalent issues that can hinder the realization of advanced financial analytics.

A primary obstacle is the frequent lack of comprehensive, out-of-the-box connectors within Databricks or Snowflake for a wide array of specific ERP systems, particularly for their detailed financial modules. This often necessitates custom scripting and API management, increasing the burden on IT and ERP support teams.

Additionally, financial intelligence rarely resides solely within the ERP. It typically requires a consolidated view, incorporating data from Customer Relationship Management (CRM) systems like Salesforce, specialized accounting software like QuickBooks, and various proprietary databases. The disparate nature of these systems and their data models makes achieving a unified financial perspective complex.

The dynamic nature of financial markets and business operations also necessitates access to current data. FP&A teams require information that reflects the latest transactions and financial state to ensure the relevance and accuracy of their analysis. Delivering this from traditional ERP architectures to cloud platforms in near real-time poses a considerable technical challenge.

The example of a financial firm, while focused on Microsoft Fabric, illustrates a relevant principle: effectively integrating siloed financial data significantly enhances analytical capabilities and business agility. This underscores the importance of overcoming data integration hurdles.

Addressing ERP Integration with CData

Data integration solutions from providers like CData are emerging to address these challenges, offering a streamlined approach to connect ERP systems with platforms such as Databricks and Snowflake. Its capabilities include:

• Broad system connectivity: CData provides an extensive suite of connectors designed for direct, real-time access to a multitude of data sources, including a wide range of ERPs, CRMs, and accounting systems. These tools facilitate easier access to specific financial data structures required by FP&A within Databricks or Snowflake.
• Change data capture (CDC) for timeliness: CData incorporates CDC capabilities that allow changes in source systems, such as an ERP, to be efficiently identified and replicated to Databricks or Snowflake, enabling FP&A to work with up-to-date information.
• Semantic data harmonization: Beyond data movement, these solutions can aid in correlating data from diverse financial systems, enabling a more unified and coherent view for analysis within the target analytics platform.
• Emphasis on security and governance: Recognizing the sensitivity of financial information, CData’s solutions incorporate enterprise-grade security and governance features that protect data during transit and storage.
• Predictable pricing models: To assist with budget management, CData utilizes a predictable pricing structure, which can help organizations avoid unexpected costs often associated with data integration and transfer.

Technical Architecture Overview

CData solutions can be integrated into Databricks or Snowflake architectures in several ways.

Tools such as CData Sync or CData Arc are designed for replicating data from ERPs and other sources into Databricks Delta Lake or Snowflake tables. This method supports the development of centralized financial data warehouses or lakehouses optimized for analytics. Connection setup typically involves selecting the source and destination and configuring secure data transfer schedules.

For scenarios requiring direct queries to source systems without data replication, CData’s JDBC drivers enable Databricks Spark engine or Snowflake compute resources to interact with ERP data in real-time, offering flexibility for specific analytical tasks.

Finally, by addressing the data integration bottleneck, organizations can better empower their FP&A teams, potentially leading to improved insights, faster decision-making, and a more robust financial strategy.

What This Means for ERP Insiders

Enhanced operational efficiency for ERP teams. Modern integration tools like CData’s solutions significantly boost efficiency by providing pre-built, standardized connectors for numerous ERP and financial applications. This approach reduces the need for extensive, time-consuming custom coding and scripting traditionally associated with data integration projects. Consequently, ERP teams can deploy data pipelines faster, allocate resources more effectively, and reduce the ongoing maintenance burden.

Improved data utility and decision-making for FP&A. Supplying finance teams with near real-time, consolidated data from the ERP and interconnected systems is essential for high-quality financial analysis. This comprehensive and timely data allows FP&A professionals to move beyond static reporting to perform more accurate forecasting, dynamic resource allocation, proactive risk identification and mitigation, and develop more precise revenue projections, ultimately leading to more informed and agile strategic business decisions.

Upholding stringent security and compliance standards. The integration of sensitive financial data from ERP systems into analytics platforms necessitates a robust security framework. This involves implementing comprehensive security measures such as end-to-end encryption, granular access controls based on user roles and permissions, and detailed audit trails to track data access and modifications. Adherence to these protocols ensures compliance with industry regulations and mitigates risks associated with data breaches or non-compliance.

The post How Advanced Data Integration Bridges ERP Systems with Modern Analytics appeared first on ERP Today.

During SAP Sapphire 2025 in Orlando, Christensen spoke with ERP Today’s Mark Vigoroso on why in an enterprise world where 20 or more instances are not uncommon, Jabil stands out with successfully running a majority of its operations on just two SAP instances across its 135 global sites.

“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.”

Listen in to learn more.

The post Chase Christensen on Why Jabil’s Relationship with SAP is More Than a Legacy appeared first on ERP Today.

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.

The post AI, IoT and Smart Factories: The Game-Changers for Complex Manufacturing 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.

Unit4 is a major player in this arena, offering a purpose-built platform that empowers public organizations to reimagine how they operate, serve, and adapt in an increasingly complex world. Its platform integrates core capabilities such as Financials, Budgeting and Financial Planning & Analysis, Fixed Assets, Procurement, Human Capital Management (HCM), Payroll, Travel Expenses, and Project Lifecycle Management. This comprehensive suite is delivered through a single, unified system—eliminating siloed tools and rationalizing the back-office technology landscape. 

By consolidating functions on one platform, organizations can reduce the total cost of ownership, increase operational transparency, and improve service reliability. The consistent user experience and centralized data architecture enable faster decision-making, easier compliance with statutory requirements, and better cross-departmental collaboration. 

Unit4 supports digital transformation by automating routine transactions and streamlining back-office functions, allowing organizations to shift their focus toward higher-value, citizen-facing activities.  

“The migration has reduced the time spent on manual routine work, making finance teams more productive, and offering an enhanced user experience,” said Duncan Squires, Finance Systems Manager at Berkshire Healthcare NHS Foundation Trust, which deployed Unit4 ERP in 2020. 

Crucially, Unit4’s flexible architecture supports interoperability with other systems, enabling seamless data integration and simplifying statutory reporting. Its open approach ensures that public sector organizations can evolve their technology stack without disrupting critical operations—an essential requirement in today’s fast-changing environment. 

Modernizing internal systems is only part of the equation. Unit4 also helps public sector organizations build a more engaged, digitally empowered workforce. With embedded tools for talent management, training, and recruitment, the platform supports workforce development and retention initiatives while providing employees with easy-to-use tools that improve productivity and morale. Built-in predictive analytics and contextual insights help decision-makers understand operational dependencies, allocate resources effectively, and adapt to evolving demands. 

“A manager can see that they have spent 60% of their budget, right down to individual cost line items and expenses. They can look at the labor by employee, by team or function. Armed with these insights, they can better plan how to allocate the balance of the budget, ensuring they don’t overspend,” said Tyson Ganske, Finance Planning Manager at the City of Port Moody, British Columbia, Canada, which deployed Unit4 in 2021. 

At the citizen level, Unit4’s platform enables rapid reconfiguration of services to meet changing needs, enhancing agility in areas like healthcare, education, infrastructure, and housing. By automating administrative processes, organizations can redeploy staff and resources to improve front-line services. Importantly, the platform ensures that sensitive data is managed securely and in compliance with relevant legal and regulatory frameworks. 

What this means for ERP Insiders 

Unifying operations on a single platform reduces complexity, improves control, and lowers costs. Many public sector organizations struggle with fragmented systems and siloed processes across finance, HR, procurement, and planning. Unit4’s integrated platform consolidates these functions into a single, cloud-based solution, reducing the administrative burden and IT overhead associated with managing multiple systems. This streamlined architecture enables better data visibility, enhances auditability, and supports more consistent decision-making—while also lowering total cost of ownership through simplified support and maintenance. 

Automation and integration free up resources to improve citizen-facing services. By automating time-consuming back-office processes—such as payroll, budgeting workflows, expense tracking, and compliance reporting—Unit4 allows public institutions to shift focus from transactional tasks to mission-critical outcomes. The platform’s ability to integrate with existing systems and technology stacks further ensures smooth data exchange and reduces manual intervention. As a result, teams can reallocate capacity toward areas like public health, education, and infrastructure, enhancing the quality and responsiveness of citizen services. 

Modern tools enable agile planning, scenario modeling, and long-term resilience. Public sector leaders need tools that support proactive planning and rapid adaptation. Unit4 delivers capabilities such as predictive analytics, in-context reporting, and what-if scenario modeling, allowing organizations to simulate budgetary outcomes, manage financial risk, and forecast resource needs. These features help decision-makers build more resilient operations, ensure accountability, and stay aligned with evolving policy and community demands. 

The post Transforming Public Sector Operations with Unit4 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.

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.

The post Inetum Earns ISG “Rising Star,” Launches ServiceNow Agentic AI Center appeared first on ERP Today.

Leading this evolution is Sharat Prakash, Senior Manager of ERP and Analytics, whose vision for operational excellence in a low-margin, high-volume industry like horticulture blends technology, education, and empathy. “This industry never makes the tech headlines,” Prakash said. “But precision farming needs precision systems.”

Metrolina had been running Sage MAS 500 for over two decades—but without fully using it as the source of truth. Production data was fragmented. Financials were isolated. Most decision-making happened in spreadsheets. So when Metrolina decided to adopt Sage X3 and Practical Software’s grower vertical, it wasn’t just about software. It was about shifting mindsets.

“In most ERP projects, you turn one system off and another on. But here, the goal was to embed a new way of thinking into a workforce that spans over 1,000 seasonal and full-time employees,” Prakash explained.

Instead of starting with finance, Metrolina went live first with production, inventory, and purchasing to match the seasonality of the business. But implementing ERP in a greenhouse isn’t like a factory. It requires intuitive workflows, mobile-first access, and deep integrations. “We had to build custom apps on top of Sage X3 to make it usable for our floor teams. You can’t expect someone on a production line to use a desktop interface designed for back-office finance.”

The result: an ERP core that supports everything from raw material purchasing to finished plant inventory, with real-time visibility across the greenhouse and dock. “We ship over 200 trucks a day in peak season,” said Prakash. “You can’t run that on spreadsheets.”

The real challenge wasn’t just technology. It was adoption. In an industry where many workers are seasonal, part-time, and bilingual, ERP usability matters. “You can’t force systems on people who don’t see the value. You have to prove that entering data isn’t an extra step—it’s the work itself,” Prakash said.

To solve this, Metrolina partnered with Practical Software and internal developers to create tablet-friendly UIs, lightweight mobile apps, and simplified transactions tailored to end users. They also rolled out SOPs and embedded training into the seasonal onboarding process.

“We’re not just implementing ERP. We’re creating a digital workforce from the ground up,” Prakash noted. And while finance and sales modules are still rolling out, production adoption is already yielding better forecasting and inventory accuracy.

Looking ahead, Metrolina sees massive opportunity to bring AI, machine learning, and computer vision into the greenhouse. “Precision farming means knowing the health of your crops in real time. Cameras and image recognition can spot disease, count inventory, and guide irrigation—but only if the data backbone is there,” said Prakash.

The global precision farming market is projected to grow from $10.5 billion in 2023 to over $21.9 billion by 2028, driven by increasing demand for crop yield optimization, sustainability mandates, and operational cost control. Specific technologies such as AI-powered vision systems, smart irrigation sensors, and GPS-guided equipment are becoming increasingly accessible to mid-sized growers.

Yet, challenges persist. A 2024 report from McKinsey highlights that fewer than 30% of agriculture businesses currently leverage end-to-end digital operations platforms. For precision technologies to scale, farms and growers must first modernize their ERP and data foundations—a process Metrolina has already begun.

While cost remains a barrier, especially in an industry with razor-thin margins, Prakash is optimistic: “We’ve seen quotes for $10M imaging systems. That’s not realistic for most growers. But as adoption grows, costs will fall.”

Sage X3, with its modularity and deep partner ecosystem, gives Metrolina a path forward. While the AI wave is still on the horizon, the foundation for intelligent automation is now in place.

What this means for ERP Insiders

Verticalization drives relevance and adoption. Generic ERP doesn’t work in specialized industries. Sage X3, combined with Practical Software’s grower vertical, shows how sector-specific extensions drive faster value realization and user buy-in. ERP leaders must prioritize industry depth over breadth to win adoption in complex verticals like agriculture.

Success requires a human-centered approach to tech. For Metrolina, the challenge wasn’t just data—it was behavior. Embedding ERP into a multilingual, transient workforce required rethinking interfaces, training, and even the definition of work. Tech leaders should apply the same user empathy in any low-tech legacy environment.

Build now for the AI future of agriculture. Precision farming will be AI-powered—but only if the ERP layer is digitized, integrated, and extensible. With Sage X3 and Practical Software’s vertical, Metrolina is building the right foundation. CIOs in agriculture and manufacturing should follow suit to enable scalable innovation down the line.

The post From Greenhouse to Datahouse: How Metrolina Is Cultivating a Modern ERP Mindset with Sage X3 appeared first on ERP Today.

Leveraging Platform-Based Strategy

With an ambitious yet focused approach, Inetum is leveraging its platform-based strategy, deep client intimacy, and differentiated delivery model to create meaningful business outcomes across Europe and growing markets. Quashie emphasizes, “From public sector modernization to AI-enabled service innovation, Inetum is positioning itself as a scalable and trusted partner.

“Digital” is a term that’s lost much of its punch through overuse. For Inetum, however, digital transformation isn’t a nebulous aspiration — it’s a defined, data-driven strategy anchored in three principles: cloud-first, data-first, and AI-first.

“When we speak of transformation, it’s about harmonizing technology with business—not just implementing software,” explains Lamba. “We take a platform-centric view, integrating best-in-class solutions like ServiceNow, SAP, Salesforce and Microsoft, and across the value chain. That’s where real impact begins.”

This strategy emphasizes seamless integration—from customer acquisition and internal employee experience to backend supply chain and compliance. What sets Inetum apart is its dedication not only to the mid-market and public sector but also to verticalization. We develop industry-specific use cases in areas such as e-health, Industry 4.0, utilities, smart cities, retail, and telecom. This approach ensures that transformation initiatives are not only technical but intimately aligned with industry needs.

Embedding GenAI and Agentic AI

With GenAI and Agentic AI reshaping enterprise value propositions, Inetum has taken a decisive step toward embedding these technologies into its core offerings.

“Innovation is in our DNA,” says Lamba, who also oversees the group’s innovation labs. “We don’t just experiment with GenAI — we’ve built an internal GenAI Hub that is LLM-agnostic and designed to scale. From training to deployment, we help our clients be AI-ready, not just AI-curious.”

The company’s “AI readiness” model begins with robust data foundations, followed by cloud optimization and platform alignment. It’s a pragmatic model that rejects “proof-of-concept fatigue” — a trap many organizations fall into when AI projects stall after initial tests.

“When we speak of transformation, it’s about harmonizing technology with business—not just implementing software. We take a platform-centric view, integrating best-in-class solutions across the value chain.” – Hemant Lamba, CEO of Inetum Solutions

Inetum’s enterprise AI solutions range from predictive analytics and workflow automation to Agentic AI that enables autonomous decision-making within defined business constraints. Its early selection by ServiceNow as one of the top 10 partners to pilot its Agentic AI use cases further underscores Inetum’s credentials.

Focused Market Strategy

Inetum focuses squarely on markets where it can build depth, not just breadth. Nearly 100% of its business is concentrated in Europe, particularly in mid-market enterprises and the public sector.

“We’ve worked closely with regional governments and public institutions,” Quashie notes. “We speak the languages, understand the regulations, and build intimacy at a regional level.”

This intimacy fuels trust—an asset Inetum cultivates deliberately. Its best-shore strategy, a hybrid of nearshore, offshore, and on-site delivery, reflects this client-centric ethos. Whether it’s leveraging SAP expertise from Portugal or deploying ServiceNow talent from Bulgaria and India, the company offers flexibility without compromising cultural relevance or quality.

Culture as a Competitive Differentiator

To Quashie and Lamba, culture is more than a corporate cliché—it’s a competitive differentiator. Both have gathered leadership across geographies to co-create trust-focused team norms, emphasizing trust, accountability, and excellence.

“In a high-trust environment, people are empowered to innovate and take responsibility,” Lamba explains. “Culture isn’t taught—it’s experienced.”

Quashie echoes this sentiment, pointing to Inetum’s strategic bet on the UK and Ireland (UKI) as a proving ground for cultural and operational cohesion. With Inetum’s acquisition of ServiceNow specialist Unifii, the UKI market has become a launchpad for its broader strategy of organic growth in the growing markets.

Disciplined Expansion

Despite operating in a competitive landscape, Inetum’s growth strategy avoids the pitfalls of undisciplined expansion. Instead, it targets high-potential regions like the UK and Ireland, combining local acquisitions with innovation labs and deep partner ecosystems.

Their success in public sector IT has been bolstered by the UK government’s BOS2 framework (RM6285), G-Cloud 14 and DOS 6, giving Inetum direct access to public sector procurement for cloud and hybrid SaaS solutions. These strategic footholds are part of a deliberate plan to replicate their proven European model in newer markets.

“Focus is our superpower,” Quashie notes. “We’re not trying to be everything to everyone. We’re solving real problems in sectors we understand deeply.”

With over 7,000 experts in its Solutions unit and a goal to double its team size in the coming years, Inetum places massive emphasis on capability building. Its 2025 certification strategy aims to ensure 100% of consultants are certified across its four core platforms— ServiceNow, SAP, Salesforce and Microsoft.

Centers of Excellence

Regional hubs such as Bulgaria (ServiceNow), Portugal (SAP), and Belgium (Microsoft) are not only delivery centers but also Centers of Excellence. These hubs enable Inetum to execute at scale while retaining regional specificity, a balance few competitors manage well.

“Focus is our superpower. We’re not trying to be everything to everyone. We’re solving real problems in sectors we understand deeply.” – Kathy Quashie, EVP and CEO of Inetum Growing Markets

Moreover, each hub operates with internal academies and innovation labs. We have seven GenAI Hubs across Europe to accelerate learning and product development in tandem.

Overcoming AI Fatigue

Lamba acknowledges the industry-wide fatigue around AI. “There’s too much ‘death by POC,’” he quips. “Everyone is experimenting, but few are scaling. Our approach is grounded—we ask: What is the business case? Are you AI-ready? If not, we get you there.”

Inetum’s GenAI Factory is a cornerstone of this effort, offering enterprises a structured way to develop, deploy, and scale generative and agentic AI solutions across their platforms. This includes industry-specific applications already live in ServiceNow’s marketplace.

Enterprise-First AI Approach

Lamba stresses, “Crucially, Inetum’s AI approach is enterprise-first. Inetum helps clients bridge structured and unstructured data environments, a key hurdle in most GenAI implementations. Whether dealing with legacy databases or real-time cloud services, the goal is to unify and extract value seamlessly”. Security is also paramount. As AI expands, so do the risks. Inetum’s AI roadmap includes a strong focus on zero-trust frameworks and cybersecurity, integrating safety protocols from day one.

Bold Ambitions and Repeatable Playbook

Inetum’s ambitions are bold: to be the uncontested leader in digital transformation for Europe’s upper mid-market and public sector. Yet, it’s the clarity of execution that’s winning clients. Its playbook is repeatable: start with client intimacy, build on trusted platforms, layer in AI and data, and deliver through a blended nearshore/onshore model. It’s a strategy grounded in execution, not hype.

As Lamba puts it, “We’re not interested in being the loudest voice—we want to be the most dependable one.”

Inetum’s story is not one of radical reinvention but one of focused excellence. For C-level leaders navigating a complex landscape of AI promises, hybrid cloud realities, and digital fatigue, Inetum offers a model worth emulating: pragmatic innovation, deeply local engagement, and an uncompromising focus on results. At a time when many digital transformation narratives are fraying under scrutiny, Inetum’s is just beginning to unfold—with clarity, credibility, and a roadmap built not just for growth, but for trust.

What This Means for ERP Insiders

Focus on industry-specific solutions and deep platform integration. Inetum’s competitive advantage lies in its verticalization strategy (developing industry-specific use cases) and its platform-centric approach, integrating best-in-class solutions like ServiceNow, SAP, Salesforce and Microsoft. The company focuses on understanding the unique needs of specific industries like smart cities, e-health or utilities, for example, and ensuring seamless integration of ERP systems within a broader digital ecosystem. As Quashie aptly summarized, “Thus, seeking partners, like Inetum, who not only implement ERP but also possess deep industry knowledge and a proven track record for delivering tangible business outcomes.”

Emphasize client intimacy and a trust-focused culture for true success. Deep client intimacy, particularly within the European mid-market and public sector, leveraging regional understanding and a blended delivery model are some of the hallmarks that have made Inetum successful. As Quashie and Lamba point out, their internal culture emphasizes trust, accountability, and excellence. This underscores the value of prioritizing strong, long-term relationships, understanding regional context and regulations, and fostering a collaborative and trustworthy engagement. All these factors can result in more tailored ERP implementations and ongoing support that aligns with an organization’s specific needs and cultural nuances.

Focus on AI readiness and scalability for pragmatic innovation. Inetum’s approach to innovation, particularly with AI, emphasizes practical application and scalability rather than just experimentation. Their “AI readiness” model, starting with data foundations and cloud optimization, aims to avoid “proof-of-concept fatigue.” This highlights the fact that when organizations seek implementation partners, they must look for one that can guide them through a structured AI adoption journey, ensuring their data and infrastructure are prepared for AI integration. The partner should help the organization focus on solutions that can be scaled for real business impact within their ERP landscape and beyond. This is what Inetum’s mission is all about – to help our customers transform potential into performance.

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