Introducing SAP Business AI for Professional Services

Artificial intelligence (AI), and above all, generative AI, marks a paradigm shift on how computer systems learn from data, solve complex problems, and even provide creativity when it’s needed. SAP believes that this paradigm shift will fundamentally change work, business, and society. SAP is committed to helping organizations through this transformative moment by creating business outcomes that were unimaginable before through SAP Business AI.

SAP Business AI is deeply embedded in the SAP business processes and analytics, helping change how people interact with business software and making work more efficient and more delightful than ever before.

Enterprise AI needs three things to deliver real value. SAP has all three. 

• Deep process and industry  knowledge

Fifty years of business process expertise encoded in our applications, our knowledge graph, and our purpose-built models.

It knows how finance connects to procurement and what compliance rules govern a transaction.

That knowledge is industry specific. Over 120 mission-critical processes across 25+ industries. Not learned from public data. Earned.

• Semantically rich business data.

Our data carries business meaning. A customer is linked to a contract. A supplier is scored for risk.

AI reasons over real business context across SAP and non-SAP sources. The full picture, not fragments of it.

• Enterprise-grade governance.

Governance is in the architecture, not an afterthought on top. ISO 42001 for AI management. ISO 27001 for security. ISO 9001 for quality.

SAP has been in governance, risk, and compliance longer than most AI companies have existed. This isn't a feature we added. It's the operating system we've always run.

The foundation that SAP provides makes something possible that wasn’t possible before. Not a better version of the old model. A different model entirely.

This is not a business run by AI. It's a business where people finally get to lead at the level they were hired for.

The decisions that require judgment, creativity, accountability. That's where customer employees spend their time.

AI handles the coordination and execution across business operations. End to end.

That’s the shift.

One of the key elements of the Autonomous Enterprise is SAP Industry AI.

The Industry AI portfolio offers agentic use cases bundled into function-specific domains, addressing industry-specific value chains. Use cases are defined and built along the business process and are going across lines of businesses.  

Example: With our Industry AI domain "Autonomous Asset Management", we are building towards a closed-loop agentic AI solution orchestrating the full asset mgmt. lifecycle through multiple assistants and agents, built along the industry-specific value chain. The function "Asset Management" is being addressed for multiple industries, starting with Oil, Gas and Energy. 

The Industry AI offering is SAP's strategic path to transforming enterprise software from systems of record into systems of autonomous execution, purpose-built for the realities of specific industries. Industry AI targets end-to-end processes that define how value is created, regulated, and delivered across industries. These processes are deeply contextual, tightly integrated, and often subject to the highest levels of regulatory and operational complexity. 

Industry AI translates sector-specific process logic, data models, and regulatory requirements directly into agentic solutions. Rather than bolting intelligence onto existing applications, Industry AI brings autonomous decision-making and execution into the core of industry value chains — accelerating value realization while providing a disciplined path from co-innovation to scalable, productized solutions.

In 2026 SAP has defined seven autonomous industry domains, unlocking value across all the entire value chain, but the model is built to scale. Scaling happens in three ways: 

1. by adding new domains,
2. by expanding each domain to cover more industries,
3. by growing the number of use cases for every domain-and-industry combination.

This creates a flexible Industry AI portfolio that can expand across the value chain while staying grounded in the specific processes, pain points, and business outcomes of each industry. 

Project delivery is a persistent operational concern wherever complex, multi-party engagements define how revenue is earned and value is delivered. Four structural pressures define the operating environment: 

• Manual data entry consumes delivery capacity — repetitive input across project setup, time tracking, and billing takes time away from higher-value work and increases the risk of errors. 
•  Resource matching is high-touch and slow — staffing decisions require manual comparison of skills, availability, cost, and project needs, which can lead to mismatches, roll-offs, and delivery disruption.
•  Forecasting relies on stale, limited data — spreadsheet-based analysis often uses static project data, so decisions are based on information that may already be outdated.
• Billing processes are complex and error-prone — manual, multi-step billing workflows increase invoice errors, delay cash collection, and create client disputes. The result: instead of solving these challenges, people are overburdened with manual processes, fragmented systems, and constant firefighting — where risk, cost, and performance balance are the key drivers for business continuity. 

The result: teams spend too much time managing operational friction instead of delivering value to clients. 

What Is Autonomous Project Delivery? 

Autonomous Project Delivery is one of SAP’s seven Industry AI domains. It extends the SAP Autonomous Suite with services-specific process knowledge, data models, and workflow logic — covering the full Sell-to-Manage project lifecycle across five operational phases.

Autonomous Project Delivery creates value through eight connected areas across the project lifecycle. The process starts with opportunity management and customer agreements, then moves into project setup, staffing, time and expense, billing, financial planning, and receivables. Together, these areas help services organizations move from manual, siloed project delivery to autonomous project delivery. 

• Project Opportunity  This area helps account owners manage the pipeline and identify profitable business opportunities. It supports better decisions on which opportunities to pursue and how to move them forward. 
• Customer Agreement  This area helps translate agreement terms into project requirements. It reduces risk, rework, and manual interpretation before the project begins. 
• Project Management  This area supports project setup, planning, and delivery alignment. It helps teams create accurate project plans and keep execution connected to scope, staffing, and financial expectations. 
• Project Staffing  This area helps resource managers fill open demand and match the right people to the right projects. It supports better utilization, fewer staffing gaps, and smoother delivery. 
• Time and Expense  This area helps consultants submit time and expenses accurately and on time. It reduces manual effort, improves compliance, and prevents downstream billing issues.
• Project Billing  This area supports billing execution and invoice preparation. It helps reduce work in progress, lower billing delays, and improve client satisfaction. 
• Financial Planning and Analysis  This area helps practice leaders improve forecasting, staff utilization, and financial planning. It supports better visibility into project performance and business growth. 
• Receivables  This area focuses on accelerating cash collection and reducing DSO. It helps minimize revenue risk and improve financial outcomes. 

Beyond industry domains, AI is already a central, integrated capability of the SAP enterprise applications and business technology. AI-powered capabilities based on deep learning, machine learning, and generative AI are embedded in multiple SAP applications and the catalog of features is steadily growing.

These features include document processing, personalized recommendations, automations, natural language processing, forecasting and predictions, and others infused in:

• Cloud ERP applications – SAP S/4HANA

• Human Capital Management - SAP SuccessFactors

• Spend Management and business network – SAP Concur and Ariba

• Customer Relationship Management – SAP Sales Cloud

• SAP Business Technology Platform

• SAP Signavio

Existing and upcoming AI scenarios span across both SAP S/4HANA Public and Private editions.

The pre-built SAP Business AI commercial model is structured across two layers: Base AI and Premium AI each aligned to how value is delivered.

Base AI represents the foundation. It provides AI capabilities included in the standard software designed for broad adoption, with minimal friction. This includes AI embedded directly into SAP applications and Joule Base interactions. These capabilities are available as part of cloud subscriptions and do not consume AI Units.

Premium AI introduces value-scaling commercialization. This layer covers execution-driven AI capabilities where value increases with usage. It includes SAP-built agents and selected high-value AI features, such as Document AI and advanced GenAI services. These capabilities are commercialized through AI Units and measured based on consumption, such as actions executed or requests processed. There are limited user-based exceptions in this layer, specifically Joule for Consultants, where value is directly tied to individual productivity.

You can explore the available pre-built features by visiting the SAP Business AI feature catalogcatalog and AI Estimator :

SAP Business AI runs on one foundation: the SAP Business AI Platform.

SAP Business AI today have three separated elements (BTP, BDC ,AI foundation plus the business transformation management), these will be unified under one SAP Business AI Platform.

SAP Business AI Platform is where agents get built, where they get context, and where they are governed.

• Already over three hundred agents have been built by partners in Joule Studio, leveraging SAP fifty years of encoded process expertise.
• 7.3 million data fields in the Knowledge Graph. When agents query your data, they get it right with ninety-eight percent SQL reasoning accuracy and governing at enterprise scale.

Over fifty-five thousand live agents managed through SAP AI Agent Hub across customers today.

Additional Resources