Now, the countdown is on. By June 7, all 27 EU member states are expected to adopt the directive into national law, marking what many HR leaders are calling 鈥淒ay One鈥� of a new era in workplace transparency.  

But while the deadline is fast-approaching, many organizations are still far from operationally ready. Even though employers will be required to share pay information with both employees and candidates during the recruiting process, current practices suggest a significant gap. For example, across Europe, salary disclosure in job postings remains inconsistent and often limited, according to .

For HR leaders, the challenge is no longer understanding the directive鈥攊t鈥檚 executing on it with confidence.

The barrier to execution 

For many organizations, the challenge often starts with the state of their HR and compensation data. In multinational organizations, compensation data often spans multiple systems, payroll providers, spreadsheets, and local processes. Job classifications vary across countries, salary bands are not consistently defined, and workforce data remains siloed across regions. 

As a result, many organizations struggle to produce consistent pay comparisons, define standardized salary ranges, explain compensation decisions clearly, and generate accurate reporting across multiple countries.听

Without a connected and reliable workforce data foundation, pay transparency becomes difficult to operationalize at scale. 

Building a foundation for continuous transparency听听

The organizations making the most progress are focusing first on data consistency, workforce visibility, and connected HR processes. 

This is where technology is becoming critical. AI can help organizations move beyond manual reporting by identifying pay anomalies, surfacing unexplained pay variance, and accelerating workforce equity analysis across large, complex data sets. 

With pay transparency insights (generally available on June 5),  a capability within the  package in , organizations can unify compensation and workforce data across systems while applying AI-assisted analysis to help identify inconsistencies, support explainable pay decisions, and improve reporting readiness. 

Instead of relying on fragmented systems and disconnected reporting processes, organizations can move toward a more consistent and scalable approach to transparency. 

Three areas HR teams need to execute now 

With the right data foundation in place, organizations are better positioned to address the directive鈥檚 three major operational requirements. 

1. Enabling employee pay transparency 

Under the directive, employees have the right to request information about average pay levels by gender for comparable work. For many organizations, this听immediately exposes data consistency issues. Comparable roles may be classified differently across countries or听business units, while compensation data听often lives in disconnected systems that were never designed to work together.听

听helps organizations provide employees with pay transparency statements through听听while supporting more consistent comparisons across worker groups.听These statements can give clear insight into the employee鈥檚 annual pay and the average pay of the same worker听category听broken down by gender.听听

2. Preparing for candidate pay transparency 

The directive also requires employers to disclose salary ranges in job postings or before interviews and prohibits asking candidates about salary history. While this may sound straightforward, many organizations are discovering they lack standardized pay ranges, consistent job architecture, or alignment between recruiting and compensation systems. 

 allows organizations to display salary ranges directly within job postings and support more transparent hiring experiences. AI-driven recommendations can also help organizations establish more consistent pay ranges aligned to internal equity, external benchmarks, and evolving workforce needs. 

3. Meeting gender pay gap reporting obligations 

Mandatory gender pay gap reporting represents one of the directive鈥檚 most operationally demanding requirements. Annual reporting obligations begin in 2027 based on 2026 workforce data, meaning organizations need to prepare now.  

For many HR teams, the challenge is turning complex, multi-country workforce data into accurate and defensible reporting. With听, organizations can use AI-assisted analysis to听identify听potential drivers behind pay gaps, surface workforce equity insights more quickly, and support more proactive decision-making听before reporting deadlines arrive.听听

What HR should do now 

The EU Pay Transparency Directive is not just introducing a new compliance requirement. It鈥檚 accelerating a broader shift toward continuous transparency in how organizations manage compensation, hiring, and workforce equity. 

The organizations best prepared for this shift are taking action now to: 

• Unify workforce and compensation data听
• Standardize job and pay structures听
• Improve reporting readiness听
• Build more consistent, explainable compensation processes across the business

As transparency expectations continue to grow among employees, candidates, regulators, and business leaders, pay equity can no longer operate as a periodic reporting exercise. It is becoming an ongoing operational capability. 

Watch the  to learn how to move from policy to execution and prepare your organization for EU Pay Transparency requirements at scale.  

Maryann Abbajay is chief revenue officer for 麻豆原创 SuccessFactors.

Market volatility, liquidity pressures, and currency fluctuations are exposing the limits of traditional planning models built around fixed timelines and after-the-fact analysis. To keep pace, finance teams need the ability to continuously sense change, understand its impact, and steer performance with confidence.

The challenge is that many organizations are still planning with processes designed for a different era. Siloed data, manual workflows, and episodic planning cycles make real-time decision-making difficult, limiting visibility across the entire business. reinforces the urgency: 72% of organizations still find financial planning, budgeting, and forecasting too time-consuming.* In a volatile environment, that lag translates directly into slower responses to risk, missed opportunities, and diminished confidence in the decisions that shape performance.

This is why finance needs a new operating model, one that moves beyond periodic exercises and toward continuous steering. At 麻豆原创 Sapphire, we are introducing 麻豆原创 Enterprise Planning, a new flagship offering designed to close the gap between insight and action, enabling planning to continuously drive business performance.

The shift from periodic planning to continuous steering

Traditional financial planning has always provided structure, but too often that structure comes at the expense of agility. Planning occurs in fixed windows. Teams work from historical snapshots, static assumptions, and fragmented inputs. By the time a variance is understood or a scenario is modeled, the business may already be operating in a fundamentally different environment.

麻豆原创 Enterprise Planning is designed to move organizations beyond these constraints through a continuous approach to planning and execution built on speed, confidence, and control. Finance teams gain the ability to detect signals as they emerge, evaluate constraints in real time, and connect plans directly to execution.

This Sense-Reason-Act model represents a fundamental shift in how planning operates. Rather than waiting for a planning cycle to surface issues, agents continuously monitor for material changes and respond through guided, explainable decisions embedded in everyday processes. At the same time, 麻豆原创 Analytics Cloud continues to support the iterative Plan-Do-Check-Act cycles that finance teams rely on for strategic and tactical planning across mid- to long-term horizons, including model creation, forecasting, variance analysis, and scenario simulation. Together, these two approaches create a planning ecosystem that is both responsive in the moment and disciplined over time.

The solution embeds Joule Agents directly into the planning process, helping connect strategy to operations in real time. Agents can interpret internal and external data signals, model their impact on KPIs, simulate scenarios, recommend actions, and orchestrate planning workflows with built-in governance and explainability. Planning shifts from a single point in time to continuous workflows. When decisions are made, Joule Agents can update plans to support downstream execution. General availability is planned for Q3 2026.

Built on 麻豆原创 Analytics Cloud and 麻豆原创 Business Data Cloud, these capabilities form a more connected, intelligent planning ecosystem that enables organizations to act decisively and with full transparency.

Why governed data and connected planning matter

Continuous planning is only as reliable as the data it is built on. Without a unified data foundation, even the most advanced analytics cannot produce trustworthy outcomes. As automation increases, this challenge becomes more acute: decisions execute faster, but errors can scale just as quickly.

That is why our approach is not AI in isolation. 麻豆原创 Enterprise Planning is built using 麻豆原创 Business Data Cloud data products and the 麻豆原创 Analytics Cloud solution. 麻豆原创 Analytics Cloud remains the foundation for strategic and tactical planning cycles, while 麻豆原创 Business Data Cloud provides the governed data foundation underpinning the entire ecosystem. This helps ensure compliance, auditability, and enterprise-wide trust, which becomes even more critical as AI-driven automation expands.

Continuous planning in practice

What makes this vision tangible is how it shows up in real financial workflows. By continuously monitoring market signals and financial positions, these solutions help organizations reduce the lag between insight and action, improving both speed and decision quality. This is the Sense-Reason-Act model at work: sensing shifts in currency markets, reasoning through the impact on cash positions, and acting through guided decisions that keep the business aligned with its financial objectives.

More broadly, the Autonomous Finance domain brings together Joule Assistants and Joule Agents to provide CFOs and finance organizations with more insight, control, and support across their operations. Beyond planning, specialized Joule Assistants coordinate multiple agents to support key finance processes including financial closing, billing, governance, and tax and compliance. The result is a finance function where intelligence is embedded across the full operational scope, not confined to a single workflow.

Because these agents are delivered within 麻豆原创鈥檚 planning and finance solutions, they carry a native understanding of enterprise data, planning semantics, and mission-critical business processes. The goal is not to replace finance expertise, but to augment it. This gives teams the foresight needed to navigate complexity with greater confidence.

The Autonomous Finance capabilities run across our cloud ERP application portfolio, including 麻豆原创 Cloud ERP Private, for end-to-end coverage across business processes and systems.

The future of finance is continuous

The future of finance will be defined by the ability to connect data, processes, and decisions across the enterprise in a continuous loop. Organizations that can sense change as it happens, reason through its impact using trusted and governed data, and act by connecting plans back to execution will be best positioned to navigate volatility with the agility and discipline that modern finance demands.

With 麻豆原创 Enterprise Planning, organizations can move beyond static planning cycles and toward a more intelligent, continuous approach to steering performance.

For more details, refer to the and the .

Lawrence Martin is chief product officer and head of Public Cloud Engineering at 麻豆原创.
David Imbert is head of Finance Product Marketing at 麻豆原创.

*IDC Spotlight, sponsored by 麻豆原创, The Rise of Dynamic Planning in the Agentic AI Era, #US54493826, April 2026

In his kickoff keynote at 麻豆原创 Sapphire Orlando, Florida, Klein and other 麻豆原创 Board members detailed how 麻豆原创 plans to bring agentic AI to the world’s most critical business workflows so that humans and AI can meet the accelerating demands of global business profitably, strategically, and safely.

鈥淭oday I鈥檓 super proud to launch our new 麻豆原创 Business AI Platform, which forms the basis for our vision of the future of business: the Autonomous Enterprise, where agents run the business and you can focus on what truly matters,鈥� Klein said.

Enterprise AI is at an inflection point, Klein told his 30,000-strong in-person and virtual keynote audience, and 麻豆原创 is in a unique position to deliver what customers need to turn their businesses into autonomous enterprises.

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The business AI imperative

Across industries, organizations are investing heavily in artificial intelligence, yet many still struggle to translate that investment into meaningful business value. At 麻豆原创 Sapphire, the message was clear: This isn鈥檛 a technology problem; it鈥檚 a context and execution problem.

While 80% accuracy may be sufficient for consumer AI applications, Klein said, 鈥淓ighty percent is just not good enough when you run the world鈥檚 most business-critical businesses. They [LLMs] should not guess; they should deliver accurate, compliant, and secure outcomes.鈥� 

Klein acknowledged that while adoption of AI has become near-universal, tangible business value remains elusive. Citing a recent Stanford AI survey, he noted that almost every company is now using AI, but seeing only limited return.

The reason, he argued, lies in a structural gap. Above the waterline of enterprise AI, LLMs continue to improve at tasks trained on publicly available data, while below it lies what enterprises truly need: AI that understands mission-critical business data, end-to-end processes, and operates within security, compliance, and governance frameworks.

ERP as the foundation for business AI

麻豆原创鈥檚 answer to this challenge begins with what Klein described as 鈥渢he brain of every company: its ERP system.鈥� For over 50 years, 麻豆原创 has had solutions with incredibly deep process and data domain know-how alongside the governance requirements, compliance controls, and company-specific configurations that define how businesses actually run.

Now, as part of the company鈥檚 new vision, 麻豆原创 plans to infuse this institutional knowledge into AI agents, enabling them to navigate thousands of business processes, select from more than 7 million data fields, and verify identity and access authorizations before returning any output.

鈥淲e鈥檙e bringing together LLMs with 50 years of business know-how stored in our ERP. But to do this, we had to do nothing less than completely reinvent our company,鈥� he told the audience. 鈥淭oday we are very excited to show you the new 麻豆原创 and our vision for the Autonomous Enterprise.鈥�

麻豆原创 Business AI Platform

To bring this vision to life, 麻豆原创 executives on stage announced a series of important innovations, beginning with the launch of the new 麻豆原创 Business AI Platform, a unified architecture bringing together 麻豆原创 Business Technology Platform, 麻豆原创 Business Data Cloud, and AI Foundation under a single roof.

鈥淭he heart of this new platform is the rich context layer,鈥� said Klein. 鈥淗ere, we infuse the deep ERP business domain know-how into the AI agents. Through our knowledge graphs, our AI agents have now a compass, a map, to find the right process and data in your ERP universe. And to provide the agents even more context, we are also introducing our new 麻豆原创 Domain Models. They have been trained on 麻豆原创’s code to even better understand the business logic of your company.鈥�

But, he said, 麻豆原创 is going further: 鈥淏ecause you run your business not only with 麻豆原创 solutions, our AI agents have to also understand non-麻豆原创 data. That’s why we included our 麻豆原创 Business Data Cloud in the context layer to build a single semantical data layer across 麻豆原创 and non-麻豆原创. No more silos, no spaghetti data sprawl鈥攂ecause no AI agent can compensate for a broken data model.鈥�

Echoing Klein, 麻豆原创 CTO Philipp Herzig, who presented the platform in detail, said it has been designed to close the agent adoption gap in the enterprise by delivering outcome, speed, enterprise-readiness, and context. 鈥淚t’s the place where you build, contextualize, reason, and govern AI,鈥� he said.

Herzig explained that the platform is structured around three layers: the context layer which Klein referenced, the build layer, and the governance layer. 鈥淎gents are only as powerful as the context they operate on,鈥� he said. 鈥淟acking context is the number one reason why enterprise AI projects fail to deliver value.鈥�

Within the build layer of the new platform, the new Joule Studio is designed to understand a company鈥檚 business challenges and enables the building of new AI agents quickly and easily.

The third tier is the governance layer, anchored by the new 麻豆原创 AI Agent Hub built on 麻豆原创 LeanIX. This provides a single command center to discover, manage, and govern all AI agents鈥斅槎乖� and non-麻豆原创. It will be generally available in Q3 and included in 麻豆原创 Business AI Platform at no additional charge.

Underscoring the changing AI marketplace, Herzig was joined on stage by KPMG Global Head of Advisory Rob Fisher, who told the audience: 鈥淲hat I鈥檓 hearing from clients is a clear shift; they鈥檙e moving from AI pilots to embedding integrated AI and agents into how work gets done. Where we see leaders really separating from the pack is in the execution and the organizational adaptability.鈥�

麻豆原创 Autonomous Suite

Building on the platform, 麻豆原创 Executive Board Member Muhammad Alam, 麻豆原创 Product & Engineering, announced the transformation of 麻豆原创鈥檚 SaaS application portfolio into the 麻豆原创 Autonomous Suite, described as the most significant evolution of 麻豆原创鈥檚 applications business in the company鈥檚 history.

The suite spans five domains: Autonomous Finance, Autonomous Spend, Autonomous Supply Chain Management, Autonomous HCM, and Autonomous CX, with more than 200 agents and over 50 assistants available in the coming months. Each assistant is mapped to core business roles and carries defined KPIs tracked through 麻豆原创 AI Agent Hub.

鈥溌槎乖� Autonomous Suite brings together the depth of our process expertise, semantically rich data, and built-in governance and compliance,鈥� said Alam. 鈥淭hese agents are designed with outcomes as a core objective. Each assistant has a defined set of ROI KPIs that you can expect it to deliver.鈥� 

鈥淯nderpinning the autonomous suite are out-of-the-box agents鈥攈undreds of agents cutting across all core business processes,鈥� he shared. 鈥淭hese agents come together into what we call assistants, or Joule Assistants. We’ve mapped these assistants to roles across the core processes of an organization, because we know that the first step 
in realizing value from AI is to empower your people to do more, do it better, or do things that just weren’t possible to be done before.鈥�

Turning to Joule itself, Muhammad said 麻豆原创 is fundamentally reimagining how users will interact with 麻豆原创 applications in the future.

鈥淲e call this Joule spaces and along with the familiar Joule conversations experience and Joule Studio 2.0, it is now part of what we call Joule Work,鈥� he explained.

鈥淛oule Work represents a massive step forward in super-charging the capabilities of Joule as we know it today,鈥� Alam said. 鈥淲ith Joule Work, we’re bringing a claw-based agentic harness to Joule along with computer and file access, better support for open standards such as MCP and A2A, access to a more complete knowledge base, and, of course, amazing visualizations on the fly.鈥�

Industry AI: H&M and Sector-Specific Transformation

During the keynote, 麻豆原创 Chief Operating Officer Sebastian Steinhaeuser introduced the Industry AI initiative, delivering AI-powered solutions built on decades of sector-specific expertise across 26 industries. In life sciences, he highlighted how 麻豆原创 customer Takeda is achieving up to 10% productivity gains, up to 25% reduction in revenue loss from stock-outs, and up to five percent reduction in safety stock through Autonomous Regulated Manufacturing.

He was also joined on stage by H&M Group CDIO Ellen Svanstr枚m, who discussed how the fashion retailer is embedding AI across its value chain. Built on RISE with 麻豆原创, 麻豆原创 Business Data Cloud, 麻豆原创 Commerce Cloud, and 麻豆原创 SuccessFactors solutions, H&M has developed a Store Intelligence Agent that processes real-time signals to generate actionable recommendations for store managers. Svanstrom also demonstrated the AI-powered InStore Concierge, a customer-facing agent that bridges digital and physical retail through personalized outfit recommendations and real-time availability.

RISE with 麻豆原创 and 麻豆原创 GROW: Path to the Autonomous Enterprise

Returning to the keynote stage, Klein emphasized that technology adoption alone does not create business value. Simply plugging AI agents into your system landscape will drive zero value, he said. 鈥淢oving to the Autonomous Enterprise requires serious change management. Adoption of AI goes hand-in-hand with business process change and end user enablement.鈥�

To support customers on this journey, 麻豆原创 announced a comprehensive reset of its RISE with 麻豆原创 and 麻豆原创 GROW offerings. RISE with 麻豆原创 customers will receive contractual commitment to activate three Joule Assistants within the first year, with the Max Success Plan extending adoption across the full enterprise.  

麻豆原创 GROW customers will receive more than 20 AI assistants from day one, with an AI-enabled toolchain designed to support go-live in weeks. New partnerships with Palantir and Accenture will support the most complex migration scenarios.

Closing: The Autonomous Enterprise

Klein closed the keynote by asking Joule to summarize the key takeaways and noting that 麻豆原创 is evolving from being a software company to becoming a business AI company.

鈥淲e showed how to turn the promise of business AI into reality with 麻豆原创 Business AI Platform, which provides the data processes and governance AI need to deliver accurate and secure outcomes at scale; we introduced the 麻豆原创 Autonomous Suite, where applications reason, decide, and act for you; and we showed how to manage change management with RISE with 麻豆原创. Together with customers and partners, we showed how 麻豆原创 is helping companies realize the vision of the Autonomous Enterprise.鈥�

鈥淲e鈥檝e been reinventing how businesses run for over 50 years, and now by infusing 麻豆原创鈥檚 ERP brain into the new 麻豆原创 Business AI Platform, we鈥檙e solving one of the biggest challenges businesses are facing today: how to turn AI into business value,鈥� he said. 鈥淚t鈥檚 the end of long negotiations, supply chain disruptions, financial blind spots, and the beginning of better: Welcome to the Autonomous Enterprise.鈥�

Terms of the deal were not disclosed. The transaction is still pending regulatory approval.

Most enterprise AI projects fail to deliver value not because of the AI itself, but because the underlying data is fragmented, locked in proprietary formats and stripped of the business context that makes it meaningful. The result is a familiar and costly pattern: pilots that cannot scale, slow integration of new data sources, duplicated engineering work and compliance risk when organizations cannot explain how an AI-driven decision was reached. Dremio helps eliminate that data fragmentation and integration friction. The acquisition will complement the 麻豆原创 Business Data Cloud and 麻豆原创 HANA Cloud offerings to ensure seamless data integration across 麻豆原创 and non-麻豆原创 data with high performance and low cost to accelerate AI-ready context and time-to-value for AI.

“Enterprise AI doesn鈥檛 stall because the models aren鈥檛 good enough; it stalls because the data isn鈥檛 ready for AI agents,” said Philipp Herzig, CTO, 麻豆原创 SE. ” Dremio eliminates that bottleneck. Combined with 麻豆原创 Business Data Cloud, we can now take customers from raw, fragmented data to governed, AI-ready intelligence on a single open platform.”

With Dremio, 麻豆原创 Business Data Cloud will become an Apache Iceberg-native enterprise lakehouse that unifies 麻豆原创 and non-麻豆原创 data to power agentic AI at enterprise scale. Apache Iceberg is the industry-standard open table format, and 麻豆原创 Business Data Cloud will natively support it as its foundation. This means no data movement or format conversion will be necessary. 麻豆原创 and non-麻豆原创 data can coexist on the same open foundation, with federated analytical reach across every enterprise data source, combined with 麻豆原创 HANA Cloud鈥檚 in-memory engine for real-time transactions and operational performance.

The Dremio lakehouse platform is set to vastly improve the economics of enterprise analytics. It is serverless and elastic, scaling up automatically when demand spikes and scaling back down when it subsides, meaning no fixed capacity to provision and no performance ceiling when it matters most.

With Dremio, 麻豆原创 will deliver a universal, open catalog built on Apache Polaris and the open Apache Iceberg REST Catalog API. It serves as both the discovery and semantic layer of 麻豆原创 Business Data Cloud, giving every connected engine 鈥� 麻豆原创 or non-麻豆原创 鈥� a single point of access to unified business context: meaning, relationships, access rights and data lineage. This catalog will form the foundation of the 麻豆原创 Knowledge Graph, embedding business relationships, organizational hierarchies, regulatory classifications and cross-system lineage as native properties.

Dremio has been a leading steward of open-source projects at the heart of its platform: Apache Iceberg, Apache Polaris and Apache Arrow, and 麻豆原创 is fully committed to continuing to invest in and prioritize these contributions.

The transaction is expected to close in Q3 of 2026, subject to customary closing conditions, including regulatory approvals.

Visit the . Get 麻豆原创 news via  and .

About Dremio

Dremio is the Agentic Lakehouse: the only Iceberg-native data platform built for agents and managed by agents. Every knowledge worker and AI agent gets instant, governed access to enterprise data through any LLM or tool of their choice. Federated queries reach any source without ETL pipelines. An AI Semantic layer adds business context so every agent draws from the same source of truth. The lakehouse manages itself, running clustering, optimization, and compaction autonomously. The result: trusted insights that drive better business outcomes, without the infrastructure complexity or overhead. A lead contributor to Apache Iceberg and co-creator of Apache Arrow and Apache Polaris. Trusted by Shell, TD Bank, Michelin, and thousands of organizations worldwide.

About 麻豆原创

As鈥痑 global leader in enterprise applications and business AI, 麻豆原创 (NYSE:麻豆原创)鈥痵tands at the鈥痭exus鈥痮f business and technology. For over 50 years, organizations have trusted 麻豆原创鈥痶o bring out their best by uniting business-critical鈥痮perations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit鈥�.

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With the听听1H鈥�2026 release,听we鈥檙e听deepening those connections across the HR lifecycle. This release focuses on听four听core priorities:听connected,听suite-wide听AI; unified experiences that adapt to how organizations work;听processes designed for clarity, accuracy, and compliance; and stronger foundations for skills and long-term growth.听Together, these innovations help organizations听anticipate听needs earlier, reduce friction in daily work, and move forward with greater confidence.听

Connected AI that works across HCM

AI in HR delivers the greatest impact when it works continuously across the entire workforce lifecycle鈥攏ot as isolated features, but as connected capabilities that share context and insight.

The 1H鈥�2026 release expands听suite-wide听agentic AI听across 麻豆原创 SuccessFactors solutions, helping听employees听get clearer answers, act sooner, and keep听work moving听across roles and responsibilities.听A connected network of听听now supports areas such as recruiting, workforce administration, payroll, learning, performance, and talent development鈥攚orking together behind the scenes to help听anticipate听next steps and surface relevant guidance.

This release also introduces a growing听workforce knowledge network, bringing trusted external expertise and research directly into the flow of work through Joule.听Teams can now access expert-backed global employment guidance and听research-driven听insights without leaving their workflows鈥攕upporting听faster, more听confident decisions.

To听further听support learning in the flow of work,听intelligent Q&A in听听now helps employees find information more easily. AI听can deliver instant,听context-aware听responses drawn directly from an organization鈥檚 learning content,听along with relevant links and resources,听so employees can get answers quickly without searching through courses or documentation.听

Unified experiences that adapt to how work gets done听

As HR听tasks听become more embedded in听day-to-day work, experiences need to feel intuitive, connected, and responsive听wherever work happens. In the 1H 2026 release,听麻豆原创 SuccessFactors solutions continue to unify experiences across the suite, giving employees, managers, and HR teams what they need听in听the moment.听

• Connected recruiting and onboarding:听Native integration between听 solutions, , and听听can bring AI-enabled听recruiting, core HR, and onboarding together into a single, continuous experience, helping hiring teams move faster while听maintaining听consistency from candidate through new hire.听

• Tailored experiences,听built faster:听The new听extensibility wizard听can provide guided, step-by-step support for creating custom extensions on听听(麻豆原创 BTP) directly within听麻豆原创听SuccessFactors solutions, making it easier to adapt experiences to unique business needs while preserving governance.听
• Simpler, clearer employee moments:听A redesigned, configurable 401(k) experience听in听听for U.S.听employees helps simplify enrollment and management by clearly explaining employer contributions and guiding deferrals and beneficiary setup, helping employees make informed decisions with confidence.听

Processes designed for clarity, accuracy, and compliance听

In the 1H鈥�2026 release, 麻豆原创 SuccessFactors introduces new capabilities that help organizations bring greater clarity and rigor to pay practices.

With听pay听transparency insights听in the , organizations can analyze compensation patterns and potential pay gaps, supporting transparent, data-driven pay practices in-line with evolving regulatory expectations,听including in the EU.听

Skills governance听for sustainable growth听

Preparing for听what鈥檚听next requires trusted, consistent skills data that organizations can rely on across HR, talent, and workforce planning.

In the 1H鈥�2026 release,听we are听strengthening听the听听with enhanced听skills governance, providing a centralized interface to help manage skills, apply governance standards, and ensure alignment across 麻豆原创 SuccessFactors solutions and partner applications. This helps organizations improve听skills听data quality, maintain consistency at scale, and make more confident,听skills-based听decisions.听

A connected foundation for the future 

This听release听reinforces听麻豆原创鈥檚 continued focus on an intelligent, connected HCM听foundation鈥攐ne designed to evolve with your organization and support confident decisions at every stage of work. By bringing together data, AI, and experiences across the HR lifecycle, these听enhancements help organizations reduce friction today while听laying听the groundwork for听tomorrow.

To explore what鈥檚 included in this release, check out the or watch the overview .

Bianka Woelke is group vice president and head of Application Product Management for 麻豆原创 SuccessFactors.

鈥淲hen we give, we gain鈥� is this year鈥檚 , and in the workplace, giving can take many forms: mentoring, advocacy, visibility, resources, and transparency.

For organizations, pay transparency is one of the most tangible ways to 鈥済ive鈥� in service of gender equality. When employees better understand their compensation, historically underrepresented groups gain clarity and fairness. And when organizations commit to equitable practices, the benefits ripple across the business鈥攆rom greater trust and engagement to stronger talent outcomes and overall performance.

Transparency starts with accountability

Many organizations are still early听in听their pay transparency journey.听At 麻豆原创,听this has been a听multi-year听effort听grounded in data, accountability, and action.听Each year, we conduct global internal pay equity analyses听comparing听employees in comparable roles, levels, and听geographies听to ensure compensation is fair, market-aligned, and internally听consistent. When outliers are听identified, centrally funded adjustments bring听pay听in-line.

This reflects 麻豆原创鈥檚 fair pay philosophy: equitable compensation that is transparent and free from bias, forming the foundation for performance-based differentiation.

Technology is central to this approach. 麻豆原创 operationalizes fair pay through听听and听, embedding听pay analysis, job architecture, and range guidance, so managers can consistently apply structured, explainable decisions during hiring and annual cycles.

Today,听over 99% of 麻豆原创 employees worldwide have transparency into their pay听range through听a compensation assistant听tool built on (麻豆原创 BTP). This tool听integrates 麻豆原创 SuccessFactors data to display salary ranges across career levels, which听can replace听guesswork with confidence and can give employees clear insight into their value, career progression, and how pay decisions are made.

From compliance to strategic intelligence: the EU Pay Transparency Directive

The shift from voluntary transparency to regulatory mandate is already underway. For European Union member states, the EU Pay Transparency Directive is driving change by requiring salary range disclosures in advance of the first interview, employee access to pay information, and gender pay gap reporting, with corrective action mandated when unexplained gaps exceed 5%. As implementation timelines approach, HR, legal, and finance teams across the EU are racing to operationalize new transparency requirements, making pay governance a board-level issue for many organizations.

鈥淥ne of the most meaningful shifts introduced by the EU Pay Transparency Directive is giving employees clearer tools to understand their own compensation. With capabilities like individual pay transparency reports generated through 麻豆原创 SuccessFactors Employee Central, employees now have a self-service way to see how their pay compares within their role and organization. That level of visibility is a major step forward for pay equity because it brings clarity to something that historically has been difficult for employees to question or address.鈥�

Anita Lettink, Future of Work and Pay Expert

Compliance is just the starting point. Organizations that embed transparency into everyday HR processes ensure pay decisions are consistent, equitable, and aligned with skills, performance, and business priorities. 麻豆原创 is already preparing customers for this shift, with tools designed to help meet these new requirements confidently.

With EU Pay Transparency Insights, a new capability within the , organizations can:

• Identify structural pay gaps and outliers before they become systemic issues.
• Connect compensation data to job architecture, skills, and performance to inform decisions and governance.
• Generate directive-aligned, ready-to-use reports without heavy manual effort.
• Turn transparency into action, guiding adjustments, equitable promotions, and workforce planning at scale.

These insights complement established fair pay practices鈥攕uch as structured job architecture, peer-based analysis, and centrally funded adjustments鈥攅nabling customers to implement transparent, equitable pay practices while meeting regulatory requirements.

Giving to gain: the leadership opportunity

This International Women鈥檚 Day, transparency should be treated as a strategic priority, not a compliance task. Clear, consistent pay practices help employees understand their value and help leaders make smarter, data-driven workforce decisions.

Pay transparency is accelerating, and organizations that act now will be the ones that lead. Don鈥檛 miss our upcoming webinar, EU Pay Transparency: Turning Fair and Equitable Pay into Your Strategic Advantage, where Future of Work and Pay expert Anita Lettink will break down the latest regulatory expectations and share best practices for building fair, equitable, and motivating compensation structures. .

Maryann Abbajay is chief revenue officer for 麻豆原创 SuccessFactors.

Headquartered in Nuuk and 100% owned by the Government of Greenland, Royal Greenland is modernizing its 麻豆原创 platform and moving from on premise to cloud ERP in order to future鈥憄roof core processes and unlock embedded AI across its 麻豆原创 business applications.

鈥淲e are moving from our existing setup to 麻豆原创 Cloud ERP and 麻豆原创 Business Data Cloud because we want access to the capabilities you can consume on a cloud platform,鈥� said Lars Bo Hassinggaard, CIO at Royal Greenland for more than 25 years.

The company brings high鈥憅uality wild鈥慶aught fish and shellfish from the North Atlantic and Arctic Ocean to consumers worldwide. It has been running 麻豆原创 since 1998 but is now embarking on its most significant transition to date: migrating 麻豆原创 ERP Central Component to 麻豆原创 Cloud ERP while simultaneously elevating its business intelligence (BI) landscape into 麻豆原创 Business Data Cloud and later transforming BI into 麻豆原创 Datasphere.

The project follows the structured RISE with 麻豆原创 framework, which consolidates platform transformation, operations, and the innovation cycle into one contract.

Lean, selective data transition: 90% fewer data to move

As part of the migration, Royal Greenland is reducing its data volume significantly using the 鈥淟ean Selective Data Transition鈥� method.

鈥淲e are keeping 10 years of data and cleaning up, so we avoid outdated company codes and historical data that no longer create value,鈥� Hassinggaard explained. 鈥淲e鈥檝e achieved a 90% reduction in what needs to be stored and migrated. The method combines data analysis, scoping, and standardized mapping objects in a guided process, ensuring that Royal Greenland only carries forward what is truly necessary, making the financials of the transformation more predictable and avoiding unnecessary complexity.鈥�

Technology first, innovation next

Go鈥憀ive is planned for March 1, 2027. The year 2026 is dedicated to the platform lift itself. From 2027, Royal Greenland will begin building business鈥慸riven improvements on top of the standardized core鈥攆or example, new user interfaces and process optimization using small AI agents within finance and administration.

鈥淩oyal Greenland and 麻豆原创 have worked together since 1998, and we look forward to getting started on the technical part of the platform uplift this January,鈥� Hassinggaard shared. 鈥淲e鈥檙e keeping the transformation as simple as possible for now and will use 2027 to activate the benefits, such as improved data analysis, better user experience, and more efficient work processes.鈥�

Royal Greenland is following a classic waterfall approach and has already established a 鈥済olden shell鈥� as the basis for further configuration and retrofitting.

麻豆原创 is responsible for implementing the cloud solution, which will run on Microsoft Azure, initially in Sweden, with the option to move later to a Danish data center. External advisor Spektra Analytics has supported contract validation.

From in鈥慼ouse experiments to standardized, 鈥渃onsumed鈥� AI

Although Royal Greenland has already successfully experimented with its own AI solutions, including vision鈥慴ased projects in production, the strategic direction ahead is to leverage embedded, standardized AI data products from 麻豆原创 and models built on the 麻豆原创 Business Data Cloud and its semantic data layer.

鈥淲e are a company that prefers to tap into existing AI solutions rather than invent them ourselves,鈥� Hassinggaard said. 鈥淚t鈥檚 far more efficient for us. There is no reason for us to spend resources reinventing what 麻豆原创 already provides. The initial focus will be on process optimization within administrative functions such as finance鈥攕mall AI agents that can streamline daily work.鈥�

Advice to others: Allocate more time, and understand your method

Hassinggaard is clear that the RISE with 麻豆原创 contract, methodology, and preparation work require time and organizational maturity. His advice to other companies facing a similar cloud ERP decision: 鈥淒o it thoroughly鈥攁nd allocate more time than you think. Study the methodology, pricing, and contracts. And bring a competent advisor on board.鈥�

Ellen Vig Nelausen is an integrated communications expert for 麻豆原创 Regional Communications.

To date, many employers have approached pay equity at their own pace, but a new era is beginning, one that will redefine pay practices and workplace culture for organizations across the European Union (EU). Set to take effect across EU Member States in 2026, the (EU Directive 2023/970) strengthens the principle of 鈥渆qual pay for equal work or work of equal value鈥� as enshrined in EU law, requiring employers to adopt transparent pay practices and close gender pay gaps or face potential legal and financial consequences.

can provide a single source of truth for the critical data-driven insights needed to help organizations meet EU pay transparency requirements. Today, 麻豆原创 is announcing new capabilities that can make it even easier for organizations to meet their requirements across EU countries.

Launching in 1H 2026, EU Pay Transparency Insights is a new capability within the , an intelligent, AI-powered application that can deliver ready-to-use, actionable insights to help leaders make more informed decisions. EU Pay Transparency Insights enables organizations to analyze compensation, identify outliers, and address pay gaps, helping them meet reporting requirements while unlocking the workforce and business performance benefits of achieving true pay equity.

Three requirements every employer must meet鈥攁nd how 麻豆原创 SuccessFactors HCM supports them

The EU Pay Transparency Directive represents one of the most sweeping changes to workplace compensation in decades. To meet its requirements, organizations will need to reassess their current HR data management capabilities and processes and begin planning now. As countries move toward putting the directive into law by June 2026, some obligations may already apply, with others set to take effect as soon as national laws are enacted. And because pay gap reporting deadlines vary by organization size鈥攕tarting in 2027 for employers with 150 or more employees and based on 2026 workforce data鈥攐rganizations must act now to be ready.听

麻豆原创 SuccessFactors HCM is uniquely positioned to support organizations in addressing the three key requirements outlined in the directive鈥攇ender pay gap reporting, employee pay transparency, and candidate pay transparency鈥攚ith several capabilities already available today and advanced analytics for gender pay gap reporting delivered through EU Pay Transparency Insights in 1H 2026.

1. Gender pay gap reporting

Once national laws are in place, employers will be required to disclose their gender pay gaps on a predefined schedule, and any gap of 5% or more must be explained or mitigated with a joint pay assessment. With EU Pay Transparency Insights within , organizations will be able to access rich workforce insights to analyze compensation and identify drivers behind pay gaps, giving them the information they need to generate actionable reports with a complete view of workforce equity in a single place.

2. Employee pay transparency

In accordance with the directive, employees have the right to request information on average pay levels by gender for comparable roles. By leveraging the powerful document generation capabilities in 麻豆原创 SuccessFactors HCM, employers can provide employees with a self-service experience to display individual pay transparency statements directly from the People Profile in . These statements can give clear insight into the employee鈥檚 annual pay and the average pay of the same worker category broken down by gender.

3. Candidate pay transparency

Employers will be required to disclose pay ranges in job advertisements or before interviews. They will also be prohibited from asking applicants about salary history. 麻豆原创 SuccessFactors solutions help enhance pay transparency by enabling employers to display pay ranges directly within job postings published through 麻豆原创鈥檚 recruiting solutions, including 麻豆原创 SuccessFactors Recruiting and .

In addition to these 麻豆原创 SuccessFactors capabilities, 麻豆原创 continues to work with our vast partner ecosystem to support pay parity and pay transparency efforts.

An opportunity to lead with accountability

The EU Pay Transparency Directive sets a new baseline for accountability in how organizations manage and disclose pay. By preparing early and partnering with 麻豆原创, organizations can deliver transparent compensation insights, empowering employees with a clear view of their total rewards and career journey and supporting HR professionals in making consistent and measurable decisions with confidence. With continuous innovation and trusted localized expertise, 麻豆原创 helps organizations stay on top of changes and build a sustainable, employee-centered approach to pay equity across the EU.

Learn more about how 麻豆原创 SuccessFactors HCM can help your organization stay compliant with the EU Pay Transparency Directive in the

Dan Beck is general manager and chief product officer for 麻豆原创 SuccessFactors.

We believe this recognition reflects 麻豆原创鈥檚 continued commitment to innovation, customer success, and delivering extended planning and analysis solutions that empower organizations to thrive in today鈥檚 uncertain business environment.

The Gartner Magic Quadrant can help FP&A professionals assess which vendors are best suited for their purposes. The analyst firm defines financial planning software as solutions that enable FP&A transformation and cover planning, budgeting, forecasting, modeling, performance reporting, and agile insights.

麻豆原创 was recognized as a Leader by Gartner for its ability to execute and completeness of vision.

Unify business planning with 麻豆原创 Business Data Cloud

麻豆原创 continues to build on its market leadership by accelerating its vision for unified business planning. With the introduction of 麻豆原创 Business Data Cloud earlier in 2025, the company has brought together operational, financial, and strategic planning on a single, trusted data foundation. This benefits financial professionals and other strategic planners by eliminating silos and enabling real-time, AI-driven insights for intelligent planning across the enterprise.

麻豆原创 Analytics Cloud in 麻豆原创 Business Data Cloud provides a powerful tool to customers that drive enterprise planning. Customers benefit from the following capabilities:

• Intelligent planning: Machine learning and generative AI automate forecasting and predictive planning by simulating best, worst, and realistic 鈥渨hat-if鈥� scenarios — and providing smart recommendations. This aligns with 麻豆原创’s agentic AI vision, in which agents continuously monitor internal and external data, proactively identifying risks and opportunities for planners.
• Seamless planning: Deep integration with 麻豆原创 Datasphere enables live access to governed, semantically-rich data without replication. This supports live reporting, collaborative planning and analysis, and data-driven decision-making with one tool for data preparation, modeling, planning, and analytics.
• Extended planning and analysis: Customers can plan across all lines of business by combining transactions, analytics, and planning with 麻豆原创 S/4HANA, 麻豆原创 SuccessFactors software, 麻豆原创 Integrated Business Planning for Supply Chain, and third-party data.
• 麻豆原创 Business Planning and Consolidation (麻豆原创 BPC) modernization: 麻豆原创 BPC customers can modernize planning to a scalable, cloud-native environment with enhanced collaboration and AI capabilities.

麻豆原创鈥檚 unified approach is already delivering measurable value for customers worldwide. By leveraging 麻豆原创 Business Data Cloud and 麻豆原创 Analytics Cloud for planning, organizations are achieving faster, more accurate forecasts, streamlined planning, and greater alignment between finance and operations.

Customers successful with 麻豆原创 Analytics Cloud for planning include Blue Diamond Growers, BMW, Calleway Golf, Decathlon, Juniper Networks, Microsoft, Mondelez, Stihl, and many others across all industries.

Kemira Oyj, a global leader in sustainable chemical solutions, accelerated its digital transformation by deploying 麻豆原创 Analytics Cloud for planning. Kemira simplified over 50 percent of legacy master data and harmonized data structures to the cloud across 400 plants in 37 countries within 15 months. This transformation enables real-time financial planning for more than 1,000 users, and enhances forecast accuracy, supporting agile decision-making, sustainability goals, and future-ready business models.

Taras Podbereznyj, CIO of Kemira Oyj, said, “Data is at the heart of our transformation program. It will continue to steer the company as the foundation for innovation and insights, supporting new digital business models and levels of agility and helping us become more competitive in the market.鈥�

Looking ahead

As organizations navigate increasing uncertainty and volatility, 麻豆原创 remains dedicated to helping customers turn data into action. Moreover, the company vision is clear: to provide the most comprehensive, intelligent, and trusted planning platform, helping every business make confident plans across the enterprise and perform at their best in the face of uncertainty.

Dan Yu is chief marketing officer of 麻豆原创 Business Data Cloud.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner鈥檚 research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
GARTNER is a registered trademark and service mark of Gartner and Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.

The new capability simplifies access to semantically rich 麻豆原创 data products through bi-directional, zero-copy sharing with Microsoft Fabric, enabling enterprises to gain instant access to trusted, business-ready insights for advanced analytics and AI.

鈥淲e are excited about continuing to strengthen our partnership with Microsoft to create more value for our customers,鈥� said Muhammad Alam, member of the Executive Board of 麻豆原创 SE, 麻豆原创 Product & Engineering. 鈥淏y bringing 麻豆原创 Business Data Cloud and Microsoft Fabric closer together, our customers can seamlessly leverage the power of data to generate real business value through AI and analytics.鈥�

鈥淥rganizations across every industry are accelerating their AI transformation by bringing together data from operations, analytics, and applications,鈥� said Scott Guthrie, executive vice president, Microsoft Cloud and AI. 鈥淲ith 麻豆原创 Business Data Cloud and Microsoft Fabric, we鈥檙e delivering a trusted foundation for analytics and AI, and helping our customers move faster, make smarter decisions, and turn insight into real business outcomes.鈥�

Unlocking the true potential of your enterprise data

麻豆原创 BDC Connect for Microsoft Fabric empowers organizations to fully harness their data and applications by delivering secure, rapid access to 麻豆原创 data products at scale鈥攚ithout the delays of data replication.

Through bi-directional, zero-copy sharing between 麻豆原创 Business Data Cloud and Microsoft Fabric, customers can realize use cases that previously required moving and managing copies of data.听麻豆原创 data products will be seamlessly integrated into Microsoft OneLake, Microsoft Fabric鈥檚 AI ready data lake, and data sets shared from Microsoft OneLake will also be available in 麻豆原创 BDC to supplement intelligent applications.

By utilizing Fabric鈥檚 data engineering, data warehousing, and Power BI capabilities, organizations can effectively integrate 麻豆原创 data with their broader ecosystem, establishing a unified foundation for their enterprise data. OneLake integration into Microsoft AI Foundry can also help customers leverage their 麻豆原创 data in building AI applications, and as OneLake is built into Microsoft 365, hundreds of millions of users can get secure access to their 麻豆原创 data through the products they use every day such as Excel and Teams.

Accelerating business insights and AI with 麻豆原创 and Microsoft Fabric

麻豆原创 BDC Connect for Microsoft Fabric enables a unified data foundation that helps organizations get insights faster and accelerate their AI strategy. Through this bi-directional integration, customers can:

• Build a semantically rich data foundation on harmonized 麻豆原创 and non-麻豆原创 data
• Perform advanced analytics and interact with enterprise data in natural language with Copilot in Microsoft Power BI.
• Develop intelligent AI applications and agents grounded in mission-critical business data using Fabric data agents, Copilot studio, and AI Foundry
• Enable multi-agent collaboration between M365 Copilot and Joule, leveraging a unified enterprise data and productivity platform to provide a seamless experience for business users

Availability

麻豆原创 Business Data Cloud Connect for Microsoft Fabric is planned to be generally available in Q3 2026.

Irfan Khan is president and chief product officer for 麻豆原创 Data and Analytics.
Arun Ulag is president of Azure Data at Microsoft.

The impact of getting people analytics right is undeniable. According to , organizations that prioritize insights models and performance processes rooted in people data are more likely to see meaningful gains across the board: 98% continuously realize stronger employee satisfaction, 90% experience a boost in workforce performance toward strategic goals, and 87% see improved employee retention.  

Yet for many organizations, the challenge isn鈥檛 recognizing the value of people data鈥攊t鈥檚 unlocking it. Data lives in silos, systems don鈥檛 connect, and insights that could drive strategy often stay buried in spreadsheets or dashboards. And let鈥檚 face it鈥攖he power of AI rests on data. Without harmonized data in one place, organizations can鈥檛 fully tap into AI鈥檚 potential to deliver proactive insights, predictive intelligence, and personalized recommendations that drive better business decisions.  

That鈥檚 why 麻豆原创 announced , now generally available, earlier this year. Built on , People Intelligence is an AI-driven application that can bring together people, skills, and business data鈥攆rom 麻豆原创 SuccessFactors solutions and beyond鈥攊nto actionable insights that help leaders make more informed people and business decisions.  

At Success Connect at 麻豆原创 Connect earlier this month, we announced in People Intelligence for recruiting, learning, succession, career development planning, and performance and goals management鈥攈elping to make it even easier for organizations to translate workforce data into measurable impact. 

Turning data into decisions 

People Intelligence combines unified data from across the enterprise with AI-driven predictions to help forecast workforce needs, anticipate labor costs and risks, and strengthen workforce planning. And with , 麻豆原创鈥檚 AI copilot, HR teams can instantly get contextual, actionable answers to critical business questions such as:  

• What鈥檚 the total count of our workforce?  
• Are employees being compensated equitably across similar roles and demographics? 
• What are the key skills gaps within my workforce, and what teams are most affected?  
• What鈥檚 the most effective way to acquire high-demand skills?  

Too often, workforce data exists in isolation from finance, supply chain, and operational metrics, which makes it nearly impossible to see how people decisions impact business results. People Intelligence can change that. By linking workforce insights to core business data in 麻豆原创 Business Data Cloud, leaders can understand how talent strategies affect productivity, compliance, profitability, and agility, and they can act in real time. 

Building a future-ready workforce 

The future of work will be defined by rapid change: accelerating AI adoption, shifting skills requirements, and evolving employee expectations. Organizations that are hamstrung by siloed data or manual analysis will always be a step behind. 

People Intelligence helps provide the foundation and insights to move forward with confidence. By harnessing people data with the same rigor as financial or customer data, organizations gain clarity on how skills, roles, and costs are evolving鈥攁nd the foresight to stay ahead of what鈥檚 next. 

to learn how data-driven insights can fuel your workforce strategy.  

Lara Albert is chief marketing officer at 麻豆原创 SuccessFactors.

We鈥檙e entering a new era, according to 麻豆原创 Executive Board Member Muhammad Alam, in charge of 麻豆原创 Product & Engineering: 鈥渢he era of agentic AI, where AI moves from being just a tool to becoming your trusted teammate.”

Joined on stage by 麻豆原创 CTO Philipp Herzig and Michael Ameling, president of 麻豆原创 Business Technology Platform, Alam set the stage during the kick-off keynote, saying: 鈥淎s developers, you are not on the sidelines of this AI revolution. You are the revolution. And we are here to supercharge you for what鈥檚 next.鈥�

Supercharging developers

Together they dismissed suggestions that AI will replace developers. 鈥淭he truth is that developers aren’t going away,鈥� Alam said. 鈥淭hey are getting supercharged. They’re becoming the architects of smart connected businesses. So, the real question isn’t if we need developers; it’s how fast can we empower them to thrive and lead in this AI-native era?鈥�

Ameling, Alam, and Herzig outlined 麻豆原创’s vision and highlighted the integration of AI, data, and intelligent agents to transform business processes and drive innovation. They also emphasized the importance of a unified data fabric and the deployment of advanced AI models and agentic technologies within the 麻豆原创 ecosystem.

In this new business environment, they noted that every enterprise is becoming a data company and every user experience鈥攆rom the front line to the boardroom鈥攊s becoming AI-driven.

鈥淎s developers, you don鈥檛 just code anymore,鈥� Alam said. 鈥淵ou also design intelligent workflows and supervise AI agents to shape real business outcomes.鈥� To help developers do this, 麻豆原创鈥檚 strategy centers on empowering them with applications, connecting them with data, and supercharging them with AI.

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麻豆原创 BTP is the foundation for AI agents

All this runs on 麻豆原创 Business Technology Platform (麻豆原创 BTP), which serves as the foundation for building and managing AI agents鈥攂oth 麻豆原创 built and custom developed. While 麻豆原创 BTP is the engine, the Business Transformation Management portfolio is the navigator, ensuring technology translates into real business impact. It aligns strategy, process, and people, turning AI-driven potential into sustainable transformation at scale.

Alam identified three broad themes during the keynote:

First, he said 麻豆原创 is continuing to make 麻豆原创 more open. As part of that, he announced 麻豆原创 Snowflake, which brings the full data and AI capabilities of Snowflake as a solution extension to 麻豆原创 Business Data Cloud (麻豆原创 BDC). This partnership is exactly what joint customers have been asking for, Alam shared.

Second, he said 麻豆原创 will provide developers with the most context-rich agentic platform, including the most deeply grounded set of ready-to-use agents that developers can customize to their needs. 鈥淭oday, you鈥檒l hear about creating custom Joule Agents using low-code and pro-code tools with agent builder in Joule Studio as part of 麻豆原创 Build鈥� Alam said.

鈥淲e鈥檙e also standardizing AI agent interoperability with the agent-to-agent protocol, allowing your agents to collaborate securely across ecosystems,鈥� he added.

Third, he announced 麻豆原创鈥檚 first foundation model built specifically for structured business data: 麻豆原创 RPT-1, pronounced 鈥溌槎乖� Rapid One.鈥� 鈥淥ur relational pretrained transformer delivers enterprise-grade accuracy and scale, outperforming both LLMs and AutoML for tabular AI, which is critical for building reliable high-value agents,鈥� Alam said.

鈥淚n short, we鈥檙e embracing an open ecosystem, supercharging agents with deep process and data context, and giving you the tools to amplify AI and agents,鈥� he said. As the keynote continued, Ameling and Herzig detailed these and other innovations, explaining how they will help developers work smarter and achieve more.

麻豆原创 BDC and Snowflake

Ameling expanded on the new partnership with Snowflake, which will bring Snowflake鈥檚 fully managed data and AI capabilities to 麻豆原创 customers. Together with the introduction of 麻豆原创 BDC Connect for Snowflake, he said this will result in cost savings and simplified data landscapes. 鈥淭his enables you to integrate 麻豆原创 and non-麻豆原创 data products seamlessly between 麻豆原创 BDC and Snowflake, so that you can deploy intelligent applications faster and share across your preferred data marketplace.鈥�

Ameling also positioned 麻豆原创 HANA Cloud as 鈥渢he database AI was looking for.鈥� With 麻豆原创 HANA Cloud and 麻豆原创 BDC, 麻豆原创 provides the best business data fabric to help address these challenges, he said. 鈥淲ithout 麻豆原创 HANA Cloud, you would have one database for each and every data representation, which leads to disaggregated data siloes that limit your AI potential. With 麻豆原创 HANA Cloud, we have all these powerful engines in one integrated, multi-model database.鈥�

Building on this, Herzig emphasized that every business requires a strong data foundation because 鈥淎I is nothing without well-organized data…On top of the data foundation sits our AI Foundation that allows you to not only use the latest frontier AI technologies out there in the market, but also to extend 麻豆原创鈥檚 out-of-the-box AI capabilities and build your own experiences deeply contextualized in your business processes and data.鈥�

麻豆原创 pioneers a tabular foundation model

Herzig then explained that 麻豆原创 RPT-1 was designed to address a crucial problem for developers and enable them to deliver much better predictive capabilities, enterprise-grade accuracy, and scale to business customers.

Until now, Herzig said, 鈥淲e still had to go back to good old machine learning 鈥o train what we call 鈥榥arrow鈥� AI models that are specifically made for each [business] task. Therefore, you really had to train a hell of a lot of models.鈥� For example, to solve 10 predictive tasks across 10 different entities like company codes or plants would require training 100 different models.

鈥淲hat we really want to do is get rid of all these models and just introduce one giant model that only requires a small amount of data to learn from,鈥� he said. That鈥檚 what 麻豆原创 RPT-1 is. 鈥淲e believe 麻豆原创 RPT-1 is the most capable predictive foundation model that鈥檚 out there today,鈥� he said, delivering much higher prediction quality while being very fast and super-efficient in terms of resource requirements.

Joule, Joule Agents, and AI assistants

The 麻豆原创 CTO also emphasized that the company is committed to providing developers with the most context-rich agentic platform. He noted that 麻豆原创 has already shipped 20 Joule Agents across lines of business and will have approximately 40 by the end of the year, and that these agents can leverage more than 2,100 pre-delivered Joule skills. In addition, more than 300 embedded AI scenarios across product lines are available for customers to date, including Joule Agents, growing to 400 use cases in total by the end of the year.

Ameling added: “Our promise is simple: build with intent. You describe the outcome and 麻豆原创 Build uses AI agents to generate code, logic, and UIs for you, all with seamless access to your applications and data while you stay in the flow… [麻豆原创] Joule for Developers enables vibe coding experiences to make intent-based development simple and intuitive.”

麻豆原创 is taking it one step further by providing extensions to work with VS code, Windsurf, Cursor, OpenAI Codex, Claude Code, Cline, and more directly in 麻豆原创 Build. “You choose a tool, and we meet you where you are,” Ameling said. He also announced that fine-tuned ABAP LLMs with ABAP 1 on AI Foundation will be published in Q4 this year.

麻豆原创 is redefining how developers interact with AI through innovations in Joule and agentic AI, but also in terms of physical AI. Herzig welcomed Torsten G. Mueller, Group CIO and COO BPS at Sartorius, to discuss how the partnership between Sartorius, NEURA Robotics, and 麻豆原创 is bringing to life robots that understand the what, when, and how based on live business context. 鈥淭his is how we鈥檙e really imagining the future, right? Humans and robots working in harmony through Joule, through AI,鈥� Herzig said.

Quantum computing

Looking to the future, Herzig ended the keynote by talking about another 鈥渃ompute paradigm that is still hard to seize鈥�: quantum computing. While he made it clear that 麻豆原创 is not building a quantum computer, 麻豆原创 is teaming up with quantum hardware leaders, like IBM鈥攚hose Director of Research and IBM Fellow Jay Gambetta joined via video鈥攖o help evaluate quantum computing for business processes and applications.

鈥淲e believe quantum will join classical and AI compute in your stack, and we鈥檙e embedding it into the processes and apps you鈥檙e already using, so it just shows up in the workflows of your enterprise,鈥� Herzig said. 鈥淎nd of course, with the cloud, we scale it all. Now, 麻豆原创 has got you covered, and you鈥檝e got your business covered.鈥�

At 麻豆原创, we believe in creating an ecosystem that simplifies your data landscape and preserves the mission-critical business context of all of your data. This is made possible with a business data fabric.

This architectural approach marks the culmination of decades of progress 鈥� from cubes to warehouses to lakehouses 鈥� now converging toward a business data fabric architecture that brings the true meaning of data together for AI projects to succeed at scale.

Simplify your data landscape with a business data fabric

Earlier this year, we announced 麻豆原创 Business Data Cloud (麻豆原创 BDC), making end-to-end business data fabric capabilities available for everyone that relies on mission-critical 麻豆原创 data, including fully managed 麻豆原创 Databricks.

This week at 麻豆原创 TechEd Berlin, we announced the 麻豆原创 Snowflake solution extension for 麻豆原创 BDC that brings Snowflake鈥檚 data and AI capabilities directly to 麻豆原创 customers.

Together with 麻豆原创 BDC, this provides organizations with the flexibility to choose the right compute and storage for every data and AI workload 鈥� extending their business data fabric while maintaining governance, interoperability, and semantics. As a solution extension, 麻豆原创 Snowflake will be available directly through 麻豆原创, providing a simplified operational experience for customers.

For many customers, integrating data across multi-cloud and hybrid environments adds complexity, especially when bringing transactional and analytical workloads together. Too often, that process comes with a hidden data tax: it strips away the business context and semantics that give data its meaning.

We recently announced 麻豆原创 BDC Connect, a capability that gives customers bi-directional, zero copy data and metadata sharing in 麻豆原创 Business Data Cloud with their existing Databricks and Google Cloud environments. Today, we are excited to announce 麻豆原创 BDC Connect for Snowflake, making it dramatically easier for customers to harmonize and govern mission-critical data wherever it resides.

Connect all your data

As organizations simplify their data landscapes, building a trusted data foundation rooted in business context has become a top priority. A found that nearly half are heavily investing more in harmonizing business data, underscoring the growing importance of using data built on industry standards.

To help accelerate this progress, 麻豆原创 delivers fully managed data products as a core component of 麻豆原创 Business Data Cloud, now with new innovations that expand coverage and connectivity:

• Data product studio: A new capability in 麻豆原创 BDC allows users to create, model, and manage reusable data products using both visual tools and SQL-based transformations from a single workspace. Users can define schema, lineage, and logic to blend 麻豆原创 and non-麻豆原创 data into governed assets with full version control and lifecycle management. It is a simpler way to cross-pollinate data products across lines of business and ensure a consistent definition of data across your business data fabric.
• Additional 麻豆原创 data products: are now available through 麻豆原创 BDC, spanning 麻豆原创 Cloud ERP, 麻豆原创 SuccessFactors, sustainability, and customer experience solutions and more 鈥� bringing even broader coverage across business domains.
• Bi-directional data sharing: We announced data sharing between 麻豆原创 BDC and 麻豆原创 HANA Cloud, unlocking the value of data across both transactional and analytical workloads. Customers can reuse existing objects, such as 麻豆原创 HANA calculation views, directly in 麻豆原创 BDC 鈥� preserving business logic, KPIs, and governance as they extend models across their business data fabric.

Amplify your agents and applications with an AI database

We鈥檙e expanding 麻豆原创 HANA Cloud with new capabilities that make it the AI database for building agents and intelligent applications, helping developers connect and understand all types of data: structured, spatial, graph, vector embeddings, and more, all within a single in-memory engine.

Today, we鈥檙e expanding these multi-model capabilities with three major innovations that make it easier to build agentic AI experiences.

• Expanded knowledge graph capabilities to enable accurate agents: 麻豆原创 HANA Cloud knowledge graph engine will now enable customers to automatically generate knowledge graphs from 麻豆原创 HANA Cloud metadata. The automated graph will include tables and columns and can demonstrate data relationships. These knowledge graphs are customizable and composable, so developers can review data mapping, modify the graph structure, execute a semantic search, and use it to ground agents with the business context it needs to reason accurately.
• Model Context Protocol (MCP) support for 麻豆原创 HANA Cloud: We鈥檙e expanding 麻豆原创 HANA Cloud with Model Context Protocol (MCP) support, giving Joule Agents access to rich multi-model engines in 麻豆原创 HANA Cloud. While SQL remains the default database standard, MCP allows Joule Agents to go beyond rows and columns to interface with unstructured data 鈥� understanding relationships, locations, and meaning across all data types. In practice, that means Joule Agents can navigate relationships between customers and suppliers, analyze geographic dependencies through spatial data, and perform semantic searches through vector embeddings.
• Tabular AI capabilities: This integration with 麻豆原创 AI Core allows users to run AI workloads such as forecasting, anomaly detection, and predictive modeling directly on structured business data from 麻豆原创 HANA Cloud. Along with ready-to-use tabular AI models, customers also gain access to the 麻豆原创-RPT-1 AI model, a new transformer-based foundation model. Embedded natively in 麻豆原创 HANA Cloud, this delivers predictions without task-specific pre-training, enabling developers to use simple SQL procedures to bring AI closer to their 麻豆原创 data and generate semantically rich outputs.

Get started today

A business data fabric provides a deeply integrated ecosystem for all your data. And with 麻豆原创 Business Data Cloud and 麻豆原创 HANA Cloud, you have a fully managed solution for every data and AI workload that ensures business context remains intact across your data landscape.

Irfan Khan is president and chief product officer of 麻豆原创 Data and Analytics.

BERLIN 鈥� At 麻豆原创 TechEd in 2025, (NYSE: 麻豆原创) brings AI deep into the development process to level up how developers build.

New AI-driven capabilities in the 麻豆原创 Build solution, an expanding data ecosystem and powerful Joule Agents empower developers to move from idea to impact with unprecedented speed and confidence. As AI transforms the nature of professional work, 麻豆原创 also pledges to equip 12 million people worldwide with AI-ready skills by 2030.

鈥溌槎乖粹�檚 announcements today give developers the tools they need to deliver at the speed of AI,鈥� said Muhammad Alam, member of the Executive Board of 麻豆原创 SE. 鈥淚nnovations across 麻豆原创鈥檚 unique flywheel of applications, data and AI put developers in the driver’s seat — where they belong.鈥�

Opening the Developer Ecosystem

麻豆原创 Build, the company鈥檚 flagship solution for enterprise application development and automation, now gives developers more freedom to build, extend and automate using the tools they love most.

For instance, developers who prefer agentic development solutions like Cursor, Claude Code, Cline and Windsurf can now use 麻豆原创 development frameworks with new 麻豆原创 Build local Model Context Protocol Servers. Visual Studio Code users will be able to access 麻豆原创 Build capabilities directly in their development environment with a new 麻豆原创 Build extension. This extension will also be made available later on Open VSX Registry for other development environments. 麻豆原创 and n8n also announced plans for an integration so Joule Studio agents and n8n agents can work together.

And with new agent building capabilities in Joule Studio, developers have the tools they need to extend 麻豆原创鈥檚 ready-to-use agents and build new agents grounded in 麻豆原创 business data and context that can act autonomously based on changing business conditions.

Putting Data to Work

Every intelligent application starts with trusted data. 麻豆原创 is giving developers more ways to put that data to work through 麻豆原创 Business Data Cloud.

The solution now connects with more of the data and AI platforms developers use every day. A new 麻豆原创 Snowflake solution extension for 麻豆原创 Business Data Cloud brings Snowflake鈥檚 fully managed data and AI capabilities directly to 麻豆原创 customers, giving them the flexibility to choose the right compute and storage for each data and AI workload, while maintaining governance, interoperability and business context. 麻豆原创 also announced a new 麻豆原创 Business Data Cloud Connect partnership with Snowflake. This complements existing integrations with Databricks and Google Cloud, giving developers more freedom to choose how they work with 麻豆原创 data.

With a new data product studio capability in 麻豆原创 Business Data Cloud, developers can turn raw data into ready-to-use assets known as data products that support analytics, AI and application development.

An expanded capability in the 麻豆原创 HANA Cloud knowledge graph engine can automatically generate knowledge graphs. This capability maps relationships across 麻豆原创 database tables, columns and data models, revealing how data fits together and why it matters. Developers will be able to see how their data connects across systems and uncover underlying business insights.

Bringing AI Autonomy to Life

麻豆原创 is evolving its AI portfolio to give developers the intelligence and orchestration power they need to take AI from insight to action.

麻豆原创 introduced its first enterprise relational foundation model, a new class of AI that predicts business outcomes rather than the next word in a sentence. 麻豆原创-RPT-1, or the first-generation Relational Pre-trained Transformer, can make fast and accurate predictions for common business scenarios like delivery delays, payment risk or sales order completion. 麻豆原创 launched a free playground environment for developers today.

New AI assistants in Joule coordinate multiple agents across workflows, departments and applications, bringing automation and autonomy to life. These assistants plan, initiate and complete complex tasks spanning finance, supply chain, HR and beyond. Today, 麻豆原创 introduces new agents built for technical users. For example, an agent for business process analysis will help teams understand how processes run, identify inefficiencies and uncover opportunities to optimize workflows and drive measurable improvements.

Lastly, as AI changes the nature of work for everyone, 麻豆原创 is pledging to equip 12 million people worldwide with AI-ready skills by 2030. 麻豆原创 will expand hands-on training and certification programs that integrate practical AI-ready tools, including through its partnership with online learning platform Coursera.

Visit the . Get 麻豆原创 news via  and .

About 麻豆原创

As a global leader in enterprise applications and business AI, 麻豆原创 (NYSE:麻豆原创) stands at the nexus of business and technology. For over 50 years, organizations have trusted 麻豆原创 to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit鈥�.

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Global Customer Center: +49 180 534-34-24
United States Only: 1 (800) 872-1麻豆原创 (1-800-872-1727)

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Marcus Winkler, +49 6227 7-67497, marcus.winkler@sap.com, CET
麻豆原创 麻豆原创 Room; press@sap.com

This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2024 Annual Report on Form 20-F.
漏 2025 麻豆原创 SE. All rights reserved.
麻豆原创 and other 麻豆原创 products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of 麻豆原创 SE in Germany and other countries. Please see  for additional trademark information and notices.
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BOZEMAN, Mont. and WALLDORF, Germany 鈥�&苍产蝉辫; (NYSE: SNOW), the AI Data Cloud company, and (NYSE: 麻豆原创), a global leader in enterprise applications and business AI, today announced a new collaboration to enable organizations to leverage Snowflake鈥檚 AI Data Cloud and 麻豆原创 Business Data Cloud (麻豆原创 BDC) together with semantically rich data.

The joint effort will make Snowflake鈥檚 data and AI platform available as an 麻豆原创 solution extension for customers of the 麻豆原创 BDC solution. The new offering, 麻豆原创 Snowflake solution extension for 麻豆原创 Business Data Cloud, unites 麻豆原创鈥檚 deep expertise in mission-critical business processes and semantically rich data with Snowflake鈥檚 unified platform capabilities for building AI and machine learning solutions. 麻豆原创 and Snowflake are also enabling zero-copy sharing between 麻豆原创 BDC and Snowflake to help customers get richer insights, build enterprise-grade intelligent applications, and unlock AI-enabled innovation that fuels business transformation.

鈥淏y tightly integrating 麻豆原创 and Snowflake, we鈥檙e making it simple for enterprises to connect their critical business data with its rich context in 麻豆原创 with the power of seamless AI app and data agent development at scale in Snowflake,” added Christian Kleinerman, EVP of Product, Snowflake. 鈥淓nterprises can now innovate faster with Snowflake and 麻豆原创 BDC and seamlessly share data between the platforms鈥攝ero-copy and fully governed.鈥�

麻豆原创 Snowflake brings Snowflake into the open data ecosystem of 麻豆原创 BDC and the business data fabric鈥攅mpowering customers with greater openness and choice while extending 麻豆原创 BDC with Snowflake鈥檚 AI, analytics, data engineering, Marketplace, and collaboration capabilities. Customers can use 麻豆原创 BDC with 麻豆原创 Snowflake as a cloud-scale compute and storage option to extend the value of their data. Leveraging bidirectional, zero-copy data access data and AI teams can work with semantically rich 麻豆原创 data products in real time, within a unified governance framework. As a result, customers can harmonize 麻豆原创 and non-麻豆原创 data while optimizing total cost of ownership across workloads and build agents and AI applications in 麻豆原创 Snowflake fueled by trusted 麻豆原创 data products.

鈥淏ringing Snowflake to 麻豆原创 Business Data Cloud empowers our customers with openness and choice,鈥� said Irfan Khan, President and Chief Product Officer for 麻豆原创 Data and Analytics, 麻豆原创 SE. 鈥淭ogether, we combine 麻豆原创鈥檚 decades of leadership in mission-critical business applications with Snowflake鈥檚 modern data platform to deliver a unified, enterprise-ready, and 麻豆原创-supported experience that extends the value of business data across the entire ecosystem.鈥�

With 麻豆原创 Snowflake, customers can:

• Build a trusted, AI-ready data foundation to harmonize 麻豆原创 and non-麻豆原创 data: Unify their data landscape with an integrated business data fabric鈥攅nabling more seamless zero-copy sharing, enriched modeling, and a complete, business-ready view of their data in real time for all data engineering, analytics, and AI and machine learning workflows across the enterprise.
• Accelerate AI business value with semantically rich data: Simplify AI governance, ground AI in organizational knowledge, and build tailored agents鈥攈elping to ensure more secure, context-rich, and intelligent applications across the enterprise.
• Develop intelligent applications grounded in mission-critical business data: Build, deploy, and continuously optimize intelligent applications faster with a harmonized and democratized data foundation powered by semantically rich, trusted data products that accelerate the pace of innovation and production.

In addition to 麻豆原创 Snowflake, the partnership also includes 麻豆原创 Business Data Cloud Connect for Snowflake, a capability enabling bidirectional, zero copy data sharing with Snowflake. Enterprises already using Snowflake can leverage 麻豆原创 BDC Connect to integrate their existing instances of Snowflake with 麻豆原创 Business Data Cloud for more-seamless, zero鈥慶opy access, providing Snowflake users with real-time access to semantically rich 麻豆原创 data products鈥攚ithout duplication.

麻豆原创 and Snowflake are supporting thousands of customers, including industry leaders like AstraZeneca, as they transform their industries with this partnership.

“AstraZeneca is constantly pushing the boundaries of science and is pioneering in life-changing medicines,鈥� said Russell Smith, Vice President of ERP Transformation Technology, AstraZeneca. 鈥淒ata and AI are central to achieving this aim, and our close collaboration with 麻豆原创 and Snowflake compliments our ability to access, process and analyze real-time data. This announcement will accelerate our mission and recognizes that every minute matters to make breakthroughs for patients.”

麻豆原创 Snowflake is planned to be generally available in Q1 2026. 麻豆原创 BDC Connect for Snowflake is planned to be generally available in H1 2026.

Learn more

• Learn more about the power of 麻豆原创 and Snowflake together .
• Discover what makes the 麻豆原创 Business Data Cloud unique .
• For more information on 麻豆原创 Snowflake, visit the and read this .
• Stay on top of the latest news and announcements from Snowflake on and .

Visit the . Get 麻豆原创 news via  and .

About Snowflake

Snowflake is the platform for the AI era, making it easy for enterprises to innovate faster and get more value from data. More than 12,000 customers around the globe, including hundreds of the world鈥檚 largest companies, use Snowflake鈥檚 AI Data Cloud to build, use and share data, applications and AI. With Snowflake, data and AI are transformative for everyone. Learn more at (NYSE: SNOW).

About 麻豆原创

As a global leader in enterprise applications and business AI, 麻豆原创 (NYSE:麻豆原创) stands at the nexus of business and technology. For over 50 years, organizations have trusted 麻豆原创 to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit鈥�.

Note to editors:
To preview and download broadcast-standard stock footage and press photos digitally, please visit . On this platform, you can find high resolution material for your media channels.

For customers interested in learning more about 麻豆原创 products:
Global Customer Center: +49 180 534-34-24
United States Only: 1 (800) 872-1麻豆原创 (1-800-872-1727)

For more information, press only:
麻豆原创: Scott Malinowski, scott.malinowski@sap.com
麻豆原创 麻豆原创 Room; press@sap.com
Snowflake: Tom Hannigan, press@snowflake.com

This press release contains express and implied forward-looking statements, including statements regarding (i) Snowflake鈥檚 business strategy, (ii) Snowflake鈥檚 products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, and (iv) the integration, interoperability, and availability of Snowflake鈥檚 products with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under the heading 鈥淩isk Factors鈥� and elsewhere in the Quarterly Reports on Form 10-Q and the Annual Reports on Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future events.
漏 2025 Snowflake Inc. All rights reserved. Snowflake, the Snowflake logo, and all other Snowflake product, feature and service names mentioned herein are registered trademarks or trademarks of Snowflake Inc. in the United States and other countries. All other brand names or logos mentioned or used herein are for identification purposes only and may be the trademarks of their respective holder(s). Snowflake may not be associated with, or be sponsored or endorsed by, any such holder(s).

This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2024 Annual Report on Form 20-F.
漏 2025 麻豆原创 SE. All rights reserved.
麻豆原创 and other 麻豆原创 products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of 麻豆原创 SE in Germany and other countries. Please see  for additional trademark information and notices.
Please consider our . If you received this press release in your e-mail and you wish to unsubscribe to our mailing list please contact press@sap.com and write Unsubscribe in the subject line.鈥�