How to Choose an SAP Integration Pattern: A Decision Framework
Knowing what the available SAP integration patterns are is one thing. Knowing which one is right for a specific scenario is another.
The Autonomous Enterprise: Better Decisions in Motion
Business leaders are being asked to make faster, better decisions in an environment that is becoming harder to predict.
Demand shifts quickly, supply networks are more exposed to disruption, cost and margin pressure remain constant, and the decisions that determine whether a company can respond with confidence rarely sit inside one function.
The enterprise is left with a critical question: How do you move fast enough to capture opportunity without putting fulfillment, margin, or customer trust at risk?
Many of the world’s largest organizations navigate this challenge on a regular basis. It is exactly the kind of moment that exposes the limits of how enterprises currently operate. Connecting the dots across functions, systems, and decisions still takes too much time, too much manual effort, and too much stitching across fragmented landscapes. By the time teams have gathered the data, aligned the functions, modeled the trade-offs, and agreed on a response, the environment has already shifted.
This is why we introduced the Autonomous Enterprise at SAP Sapphire. The goal is to sense change earlier, understand its impact across the enterprise, coordinate the right response, and keep people in control of important decisions. This is a fundamental shift in how businesses can operate: intelligence that is continuous, decisions grounded in real-time context, and an enterprise that moves as a connected system rather than a collection of disconnected parts.
Autonomy at scale
An Autonomous Enterprise is an organization that can continuously sense what is happening across its operations, reason over those signals using business context and established rules, and act across end-to-end processes without depending on manual coordination at every step. AI assistants and agents advance work across the enterprise in alignment with the goals, policies, and constraints defined by humans.
Every AI-driven action is auditable and traceable. Human judgment is deliberately embedded in decisions that require accountability and exceptions that fall outside defined parameters.
Three principles underscore the Autonomous Enterprise:
- Process knowledge: Deep, industry-specific understanding of how a business truly runs
- Business data: Enriched, connected, contextual data that gives AI something real to work with
- Governance: The backbone that keeps everything upright, traceable, and within policy
Beneath it all is the SAP platform, ensuring every layer works in concert, every agent operates within guardrails, and every outcome can be traced back to a decision made by a human.
Intelligence that works across the business
The average business landscape probably doesn’t look like one system, one vendor, or one clean stack. Your processes still have to run end to end across all of it: record to report, plan to make, source to pay, hire to retire, order to cash. If AI is going to work in the enterprise, it has to work across this landscape, not inside one application or vendor boundary.
IDC shows that more than 50% of business decisions still take between one and seven days. That is the gap we are closing—from days to moments.*
At the core of the Autonomous Enterprise is the SAP Autonomous Suite. Joule becomes the way you interact, as a single entry point into your business. In the middle, the SAP Autonomous Suite connects your core domains: finance, supply chain, spend, HCM, and customer experience. And underneath, everything is grounded in your business context, your data, your processes, your rules, your governance.
With SAP’s unified foundation of applications, data, and business context, AI is embedded directly into how work gets done, enabling autonomous, end-to-end execution rather than isolated use cases.
The operating model behind this is built on a clear division of responsibility: people set priorities, policies, and guardrails. Assistants understand role and process context and coordinate activity across domains. Agents carry out the defined work, detecting signals, triggering actions, and resolving routine tasks continuously in the background.
And while automation is a part of this, the bigger shift is intelligence and optimization. The system is no longer following predefined workflows. It is using business context to understand what is happening, and what should happen next. This is the shift from systems of record to systems that help run the business.
Autonomous Finance shows what changes
Finance offers a clear example of how this model changes the work itself. Many finance organizations still contend with manual steps, fragmented data, and slow cycles. In a volatile environment, that lag translates directly into slower responses to risk, missed opportunities, and diminished confidence in the decisions that shape performance.
With Autonomous Finance, more of that work can be handled by the system, allowing finance teams to spend less time chasing numbers and more time shaping decisions. The function begins to move from reconciling the past to shaping the future.
Autonomous Finance is not one capability, one agent, or one use case. It is built across the entire finance process, from planning to revenue management, treasury, closing, compliance, and tax. Within each area, assistants are supported by specialized agents working continuously in the background. Some focus on forecasting, some on billing, some on cash, and some on closing. The important point is that these capabilities are connected, so decisions in one area can flow into the others. Connected assistants, specialized agents, continuous optimization. That is the model.
The impact across these areas compounds. Finance teams reclaim meaningful capacity as manual reporting, reconciliation, and transaction processing give way to continuous intelligence. Cash cycles compress. Close timelines shorten. Forecasting becomes more accurate and more responsive to changing conditions.
Because these capabilities are connected, improvements in one area reinforce the others: faster billing flows into better cash visibility, which flows into stronger planning confidence, which flows into more decisive action at the executive level. Compliance strengthens as well, not through added controls, but through better intelligence embedded in the process itself, supporting requirements across ISO, SOC, and SOX with greater accuracy and less manual effort.
The result is not incremental improvement in isolated tasks. It is a fundamentally different operating posture for the finance function, one where the system handles orchestration and people direct outcomes.
Industry AI adds depth
Autonomous domains give breadth across business functions, while Industry AI provides the depth of knowledge. The same supply chain problem looks very different in life sciences, in industrial manufacturing, in agribusiness, in retail, or in energy. The rules, regulations, data models, and value chains are different.
SAP is not starting from generic AI and trying to teach it how an enterprise works. We start with decades of industry and process knowledge, already embedded in the systems that run the world’s most complex businesses. Our AI is grounded in sector-specific processes, end-to-end value chains, operational realities, and compliance requirements. And our ecosystem extends this with specialized expertise, so organizations can adapt the intelligence to their markets and their industries.
This is not AI for the sake of AI. This is AI applied to the real operating model of each industry.
The path forward
That is the real shift: not AI operating in isolated tasks, but AI helping the enterprise continuously sense, reason, act, and learn. People remain in control throughout, while the system handles the orchestration required to bring together the right data, context, and decision at the right moment.
The Autonomous Enterprise marks a shift from managing processes to directing outcomes. It moves organizations from reacting to events to anticipating them, and from stitching together decisions after the fact toward helping the business move as one connected system.
This does not require waiting for a perfect, fully transformed landscape. Organizations can begin by applying AI on top of existing landscapes and evolving their business as they go. That work is already underway with many of our customers. What they have in common is that they are starting now, moving faster, making better decisions, and building the foundation for a more autonomous enterprise, step by step.
This is a journey. And it begins with the recognition that the enterprise of the future will not be defined by how efficiently it executes predefined processes, but by how intelligently it can sense change, weigh trade-offs, and move with confidence when it matters most.
For more on SAP’s broader Autonomous Enterprise announcement, read The Future of the Enterprise Is Autonomous. For more details on 2026 SAP Sapphire announcements, see the SAP Sapphire Innoation News Guide.
Manoj Swaminathan is general manager and chief product officer of SAP Autonomous Suite, Finance & Spend, and member of the Extended Board of SAP SE.
Eric van Rossum is chief marketing officer of SAP Global Product Marketing and chief product officer of SAP Industries and Globalization.
*IDC Resource Map for SAP, SAP Custom Survey 2026: Enterprise Process Automation Survey– April 2026, sponsored by SAP, doc #US54531626 _RMD , May 2026
AI Value at Scale with ZF Group | SAP Sapphire Madrid 2026
See how ZF Group is using SAP RISE, SAP Business Data Cloud, and AI to modernize a complex global manufacturing landscape.
At SAP Sapphire Madrid 2026, Thomas Buck, Chief Information Officer at ZF Group, shares how ZF Group is preparing for AI at scale while transforming a highly customized SAP ECC environment shaped by years of growth and acquisitions. With more than 150,000 employees across 29 countries and 160+ plants, ZF is using RISE with SAP and SAP Business Data Cloud to move toward clean core, make data AI-ready, and build a stronger foundation for innovation.
The conversation explores how ZF is applying AI to an industry-specific quality management challenge: the 8D problem-solving process. Today, teams manually review historical reports, search for similar cases, identify root causes, and assess whether issues could recur across other products. Working with SAP through a forward-deployed engineering approach, ZF built a custom AI framework with four agents to help analyze historical data, compare current cases, support root cause analysis, and propose solutions based on past cases.
ZF also shares the expected business value, including faster processes, improved quality, and potential savings in the mid double-digit millions of euros. The discussion closes with how WalkMe helps users adopt new AI-enabled ways of working and how SAP Max Success Plan supports ZF’s broader transformation journey.
Chapters:
00:00 – Welcome and ZF’s AI journey
00:28 – ZF’s global manufacturing scale
01:13 – Moving from complex ECC landscapes to clean core
01:56 – RISE with SAP and SAP Business Data Cloud
03:08 – Industry AI and the 8D quality process
04:24 – Building a four-agent AI framework
05:07 – Forward-deployed engineering with SAP
06:05 – AI value and expected savings
07:14 – Driving adoption with WalkMe
08:31 – What’s next for ZF and SAP
Watch the full Customer Success Keynote. 👉https://sap.to/6050B8GXnq
Watch all SAP Sapphire replays on demand. 👉https://sap.to/6052B8GXns
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About SAP:
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Joule Studio: Build AI Agents and Apps in Minutes | Demo
See how Joule Studio turns a single business prompt into an enterprise-ready AI solution – from agent to workflow to deployment.
In this demo, Joule Studio shows what AI-first development can look like for the enterprise. Starting with one business prompt, Joule Studio helps create a complete solution that includes an AI agent, coordination workflows, and a real-time dashboard – then tests and deploys it to production in minutes.
Joule Studio is SAP’s development solution for building AI agents, apps, and workflows on an SAP-managed runtime. It is designed to help teams move from business intent to working solutions faster, using natural language to shape requirements, specifications, and code-first development outputs.
The demo also highlights how Joule Studio supports enterprise needs beyond speed. Built-in SAP business context helps ground generated solutions in how real business processes run, while governance, lifecycle management, agent observability, and inherited controls help teams build and scale with confidence.
For developers and business builders, Joule Studio brings agents, apps, and workflows into a single AI-first experience – so custom enterprise solutions can move from idea to execution without adding unnecessary complexity. It is part of SAP’s broader vision for the X, where people set direction and AI helps execute trusted work across the business.
00:00 – Start with a business prompt
00:15 – Generate the solution plan
00:35 – Build the AI agent
00:55 – Create coordination workflows
01:15 – Add a real-time dashboard
01:35 – Test the solution
01:55 – Deploy to production
Try Joule Studio: https://sap.to/6052BBlr0q
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About SAP:
As a global leader in enterprise applications and business AI, SAP stands at the nexus of business and technology. For over 50 years, organizations have trusted SAP to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit: https://sap.to/6057BBlr0v
#JouleStudio #SAPBusinessAI #AIAgents
SAP and AWS Enable Next-Generation AI with Bi-Directional Zero-Copy Data Sharing with SAP Business Data Cloud
Today at SAP Sapphire in Orlando, SAP and Amazon Web Services (AWS) announced plans to build SAP Business Data Cloud (SAP BDC) Connect for Amazon Athena, a new offering that will provide bi-directional zero-copy integration between Amazon Athena and SAP Business Data Cloud.
Building on their long-standing partnership, AWS and SAP are making it easier for customers to access mission-critical SAP data products across AWS services, including Amazon Bedrock, Amazon Quick, and Amazon SageMaker. This will allow teams to deliver self-service analytics and build AI agents across all lines of business—without waiting for IT teams to replicate, prepare, and provision SAP data.
“The next era of business will be defined by how well organizations turn intelligence into action at scale,” said Muhammad Alam, member of the Executive Board of SAP SE, SAP Product & Engineering. “By bringing together SAP Business Data Cloud and widely adopted AWS AI and analytics capabilities, customers can unlock the true potential of data and AI.”
Enabled by SAP BDC Connect, the integration delivers near real-time, zero-copy access to semantically rich SAP data products directly through Amazon Athena, keeping data in place, preserving its original business context, and eliminating replication delays. Customers can query and analyze this data immediately, or choose to store and transform it for use across the broader AWS environment. This gives teams a governed, secure environment within AWS to easily build reports, dashboards, and AI agents.
“SAP and AWS share a commitment to helping customers put their most valuable data to work,” said Ruba Borno, vice president of Global Specialists and Partners at AWS. “By combining SAP Business Data Cloud with the AWSsecure, global infrastructure and advanced AI services, organizations can unlock mission-critical SAP data and act on it at the speed and scale their business demands.”
With SAP BDC Connect for Amazon Athena customers can:
- Create a single, trusted foundation for all their business data, SAP and beyond
- Get AI up and running faster with business data that is ready to use
- Build intelligent applications and agents powered by the business data that matters most
- Fast-track insights and innovation with self-service analytics that reduce time-to-insight from weeks to hours
General availability
SAP Business Data Cloud is already available on AWS in the U.S. East (N. Virginia), Europe (Frankfurt), Asia Pacific (Tokyo), Canada (Central), Asia Pacific (Sydney), South America (São Paulo), Asia Pacific (Seoul), Asia Pacific (Singapore) Regions, and the AWS European Sovereign Cloud.
SAP to Acquire Prior Labs to Establish a Globally Leading Frontier AI Lab in Europe
Acquisition doubles down on SAP’s early mover advantage in tabular foundation models
WALLDORF and FREIBURG — SAP SE (NYSE: SAP) and Prior Labs, the pioneer of Tabular Foundation Models (TFMs), announced that they have entered into a definitive agreement for SAP to purchase Prior Labs, accelerating SAP’s success in TFMs that started with SAP-RPT-1, and bringing one of the world’s leading TFM research teams into the SAP family.
Prior Labs will continue to operate as an independent entity, with SAP committing to invest more than €1 billion over the next four years to scale it into a globally leading frontier AI lab for the structured data that runs the world’s businesses. Terms of the deal were not disclosed. The transaction is still pending regulatory approval.
Large language models (LLMs) struggle to make accurate predictions on structured business data because they have only a rudimentary understanding of tables, numbers and statistics. Unlike LLMs, TFMs are purpose-built for this type of data and can accurately predict business outcomes based on tabular data such as payment delays, supplier risks, upsell opportunities, customer churn risk and more.
“Early on, SAP recognized that the greatest untapped opportunity in enterprise AI wasn’t large language models; it was AI built for the structured data that runs the world’s businesses,” SAP CTO Philipp Herzig said. “We built SAP-RPT-1 to prove that conviction for enterprise data. Prior Labs has built a leading TFM on public benchmarks and built one of the leading research teams in this category. Combining their frontier model work with enterprise data and customer reach is how we intend to lead this category globally.”
“Over the last 18 months, Prior Labs has built an incredible team, increasing the velocity in tabular foundation models,” Prior Labs CEO Frank Hutter said. “Joining the SAP family gives us the resources, data environment and customer reach to take this category to its full potential.”
Once the transaction is closed, with Prior Labs, SAP will have the special opportunity to establish an industry-leading AI research lab and shape a new category in TFMs. The lab will operate as an independent unit to ensure research velocity, while SAP provides long-term investment and a direct path to productization across the SAP portfolio with SAP AI Core and SAP Business Data Cloud as well as the agentic layer with Joule.
With over 3 million downloads, Prior Labs’ TabPFN is a widely adopted open-source tool for tabular AI, supporting a dynamic developer ecosystem. SAP is fully committed to further support this open-source strategy. The Prior Labs cofounders Frank Hutter, Noah Hollmann and Sauraj Gambhir lead a team of world-class AI researchers and practitioners. The company works with leading scientists in the field, including Yann LeCun, ACM A.M. Turing Award winner and executive chairman at Advanced Machine Intelligence, and Bernhard Schoelkopf, director of Max Planck Institute for Intelligent Systems and ELLIS president, both of whom will serve on Prior Labs’ scientific advisory board as it scales to a globally leading frontier AI lab.
Accelerating Innovation
Prior Labs’ TabPFN-2.6 is the top-performing model on TabArena, the top benchmark for TFMs. TabPFN-2.6 matches the accuracy of a four-hour automated machine learning pipeline — instantly, in a single model, at a fraction of the complexity.
With a conversational interface layered on top, business users can ask questions in natural language, generate or select datasets and run “what-if” scenarios without needing to be data science and machine learning experts. With Prior Labs’ models, SAP will provide in-context learning, allowing users to provide data records to receive instant, reliable predictions without any model training. A single TFM can adapt to any business use case on the fly, resulting in faster time to value with GDPR compliance.
With Prior Labs, SAP will deliver TFMs with superior predictive capability that understand tables natively, learning statistical reasoning directly from data and will power agentic AI systems capable of understanding high-level goals, combining tables, language and images to reason, integrate domain knowledge, infer causality and adapt dynamically.
After the close, SAP and Prior Labs plan to turn top AI research into enterprise-ready innovation, allowing customers to get even more value out of their tabular business data. True intelligence requires moving beyond correlation to understand causation. Answering “What will happen?” is useful, but answering why it will happen is transformative.
The transaction is expected to close in Q2 or Q3 of 2026, subject to customary closing conditions, including regulatory approvals.
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About Prior Labs
Prior Labs is the pioneer of Tabular Foundation Models, a new category of AI purpose-built for structured data. Founded by Frank Hutter, Noah Hollmann & Sauraj Gambhir, Prior Labs’ TabPFN model series, published in Nature, set the state-of-the-art on tabular benchmarks across hundreds of independent academic studies. Prior Labs is scaling tabular foundation models to handle millions of rows, real-time inference, and entirely new data modalities, while building the infrastructure to deploy them in production across some of the most demanding industries on earth.
Headquartered in Freiburg, Germany, and offices in Berlin and New York City, Prior Labs has built one of the leading AI research teams globally, with researchers recruited from Google, Apple, Amazon, Microsoft, G-Research, Jane Street, Goldman Sachs, and CERN. www.priorlabs.ai
About SAP
As a global leader in enterprise applications and business AI, SAP (NYSE: SAP) stands at the nexus of business and technology. For over 50 years, organizations have trusted SAP to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit www.sap.com.
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How SAP S/4HANA Has Improved Financial Close
This post covers trends in financial close, including hard close, soft close, continuous accounting, fast close, and predictive close.
How Deloitte Automates Accounting Advisor with SAP Business AI | SAP Partner
Teams make better, faster accounting decisions when guidance is easy to find and approvals are streamlined. In this short partner story, Deloitte shares its Automated Accounting Advisor: a Hack2Build-winning solution built on SAP Business Technology Platform (SAP BTP). The app uses the Generative AI Hub, the SAP HANA Cloud vector engine, and GPT-4.0 to rapidly create proposals tailored to customer-specific accounting guidelines.
Here’s the core idea: instead of starting from scratch, accountants can leverage generative AI that’s grounded in the organization’s own accounting standards, producing fast, relevant proposals that support day-to-day work. Deloitte also emphasizes keeping the human in the loop, so professionals review and validate outputs while still gaining speed and productivity.
For finance and accounting teams looking to modernize advisory workflows, without compromising governance, this is a quick look at how SAP platform services and partner innovation can deliver customer value fast.
Explore SAP Business AI: https://www.sap.com/products/artificial-intelligence.html
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About SAP:
As a global leader in enterprise applications and business AI, SAP stands at the nexus of business and technology. For over 50 years, organizations have trusted SAP to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit: https://www.sap.com/index.html
#SAPBusinessAI #Finance #SAPPartner
The Future of Finance with SAP Cloud ERP Private | Efficient & Agile
Move from ECC to SAP Cloud ERP Private for agility, compliance, and growth.
Finance teams face rising costs, constant change, and increasing compliance demands. Legacy ERP systems weren’t built for today’s reality: manual processes, siloed data, and limited analytics slow decision-making and growth.
In this video, learn how CFOs and finance leaders can transform operations with SAP Cloud ERP Private. Gain real-time insights, predictive planning, and intelligent automation to accelerate cash flow, streamline financial close, and reduce manual workload by up to 70%.
SAP Cloud ERP Private embeds IFRS, GAAP, and ESG standards for compliance and sustainability, while enabling agility through integrated planning and universal journal capabilities. Turn finance from a back-office function into a growth engine with SAP Cloud ERP Private.
Chapters:
00:00 – Why Finance Needs Agility
00:28 – Challenges with Legacy ERP
00:32 – Introducing SAP Cloud ERP Private
01:08 – Real-Time Insights & Predictive Planning
01:36 – Intelligent Automation & Compliance
02:00 – Finance as a Growth Engine
Learn more about SAP Cloud ERP Private: https://www.sap.com/products/erp/s4hana-private-edition.html
#SAP #CloudERP #FinanceTransformation
Why Reusability Matters in SAP Integration Suite
Integration is no longer just a technical necessity—it’s a core business capability.
