- AI strategy in SAP is a board-level decision, not an IT task. It reshapes operations, investment priorities, and talent models.
- Governance and data quality are leadership responsibilities. Without them, AI can’t scale safely or deliver trust.
- Partnerships matter. Working with experienced SAP partners accelerates maturity and minimizes risk.
The rapid evolution of Generative AI (GenAI) has made it a cornerstone of digital transformation, creating new opportunities for innovation, efficiency, and competitive advantage. For CTOs and CIOs, successful adoption in the SAP ecosystem requires a careful balance of technical precision, organizational alignment, and strategic foresight.
This article draws on industry insights, recent research, and Inclusion Cloud’s 20+ years as an official SAP partner to present an actionable framework for deploying GenAI at scale. It reviews the technological, strategic, and human factors essential for success, emphasizing the importance of aligning stakeholders and the board while ultimately maximizing the ROI of your SAP platform.
1. Foundational Success Factors for GenAI Adoption in SAP
– Aligning GenAI initiatives with business objectives
GenAI projects must originate from clearly defined business problems with measurable outcomes rather than technological novelty. Leading organizations anchor their efforts in use cases demonstrating measurable ROI, such as Intelligent Operations (increasing reliability while reducing cost), AI-based customer service workflows (up to 15% agent productivity gains and 60% faster resolution times in early adopters), or automation of routine tasks (up to 50% faster process execution and productivity) through AI-powered automation.
In the SAP ecosystem, AI projects must be grounded in concrete use cases where intelligence directly improves processes supported by core SAP solutions such as S/4HANA, SuccessFactors, Ariba, SAP BTP or Integrated Business Planning (IBP). For example, AMD built a GenAI Supply Chain Troubleshooter on SAP BTP to simplify complex supply chain interactions. This AI assistant reduced issue resolution time and costs by 90%, saving 3,120 hours of staff effort annually, and demonstrates how generative AI can dramatically streamline supply chain management
This alignment between process improvements and tangible business results ensures executive buy-in and prevents solution-first approaches that often lead to misallocated resources or frustration from the company board. While the field of GenAI implementation is broad and outcomes vary by task, our recommendation is designed to help you focus your efforts and create early success stories that build credibility and drive further investment.
In the video below, we share a real example of how AI is transforming critical operations. We used drones with AI-powered image recognition to inspect energy towers and detect signs of damage or wear. In the past, workers had to climb towers often as high as 200 to 300 feet, a process that was slow, risky, and heavily manual.
By connecting the drone data directly to SAP Plant Maintenance (SAP PM), inspection results were automatically converted into repair orders:
– Delineating the SAP data strategy
One critical step, often overlooked, is the upfront assessment and definition of your company’s data strategy to support BI and AI initiatives. Attempting to apply this strategy across all enterprise-wide processes can quickly become overwhelming. It is therefore essential to focus on specific business problems and initiatives that deliver tangible outcomes.
Data can come from many places: core applications like S/4HANA, SuccessFactors, Ariba, IBP, integration services on SAP BTP, or even external sources outside SAP. The real differentiator is the ability to consolidate this information within Datasphere and the Business Data Fabric, creating a single semantic layer where enterprise data is transformed into AI-ready knowledge.
While Datasphere and the Business Data Fabric prepare enterprise data for AI, SAP is extending this vision with the Business Data Cloud. This layer allows organizations to share and collaborate on data across their ecosystem, unlocking new GenAI scenarios in different business units, while maintaining governance and security.
Another strategy widely utilized, that help reduce cost and gain flexibility, is the creation of these data pipelines using different ETLs and data management tools like Databricks on top fo Azure, or Microsoft Fabric, to mention a few. This requires a deliberate strategy to extract SAP data and move into the cloud of choice for data pipeline creation. This also reduces SAP vendor lock-in.
Your data strategy should include the following core components:
- Data Assets: Identify the precise data assets required. Define granularity, refresh cycles, and sensitivity classifications. For example, sales data at the store/product level, refreshed weekly, may be classified as restricted to the Sales organization and executive leadership.
- Data KPIs: Establish the KPIs that support AI capabilities, ensuring they reflect a single source of truth across SAP and non-SAP applications.
- Data Governance and Security: Apply SAP’s robust role-based authorization model to safeguard sensitive assets while extending governance to external data.
- Data Quality: Define thresholds and monitoring mechanisms, using Datasphere to enforce quality and consistency across diverse sources.
While many other elements contribute to a comprehensive data strategy, these represent the minimum viable foundation.
– Building cross-functional AI competency in SAP
SAP-native AI requires collaboration between IT architects, integration experts (PI/PO, Integration Suite, API Management), ethical AI specialists, data scientists experienced with AI Core and AI Launchpad, and business experts who understand processes inside out.
Leading enterprises are adopting Center of Excellence (CoE) models to centralize GenAI expertise while fostering close collaboration with business units. This structure promotes cross-departmental knowledge sharing and preserves agility in project delivery.
For organizations just beginning their AI journey, it is critical to partner with experienced SAP partners who prioritize measurable business outcomes over technology deployment alone. It’s equally important to assess organizational readiness and training needs upfront.
To support this, we use a maturity index that helps assess your level of engagement with the SAP ecosystem and identify the next steps toward greater automation and cost savings.
2. Technical Architecture Considerations for SAP
A. Composable GenAI on SAP BTP
Enterprise GenAI systems demand infrastructure capable of handling the computational intensity of LLMs and the unpredictability of generative outputs. In SAP, this backbone is delivered through SAP Business Technology Platform (BTP), which combines hyperscaler capacity with secure, enterprise-grade AI services.
A composable SAP GenAI stack can be seen in layers:
- Datasphere and Data Intelligence unify data, or equivalent data lake and data governance (i.e. Azure + Databricks).
- Integration Suite and API Management connect processes, or equivalent integration capabilities (i.e. Mulesoft, Kong, Boomi and many more).
- AI Core, AI Launchpad, and AI Business Services manage models and pre-built AI.
- Analytics Cloud and Extension Suite bring insights and custom apps to end users.
- Industry Cloud solutions further embed AI in sector-specific processes.
For companies already running S/4HANA and BTP, these layers enable scalable, enterprise-wide GenAI. Those still on legacy systems (ECC, PI/PO, etc.) must first establish a migration path so data can flow into modern platforms like Datasphere and Integration Suite.
We explored this in more detail here: Why It’s Time to Plan Your PI/PO Migration to SAP Integration Suite.
B. SaaS GenAI activations with Joule
Not every enterprise needs to design a full composable stack to start capturing value. SAP also offers embedded GenAI capabilities that reduce complexity and accelerate adoption. The most visible example is Joule, the AI copilot integrated into many SAP applications.
These embedded solutions handle much of the infrastructure automatically, from compute to monitoring, and offer ready-to-use AI capabilities aligned to core business processes.
However, success still depends on data readiness and governance.
3. Data Governance and Quality Assurance in SAP
The adage “garbage in, garbage out” takes heightened significance with generative systems. Enterprises must implement robust data pipelines featuring:
- Security and governance framework: It is important to apply all your data governance and security rules within your data platform and GenAI implementations to ensure proper use of all data assets. For example, failing to define appropriate access controls for employee compensation or salary data may result in inadvertent exposure through a prompt in your GenAI solutions.
- Define data quality rules: This refers to all detailed validations you must perform on data at the source and during refinement to guarantee that its quality is fit for use. You should define data quality rules in the context of your activation needs, so you do not overinvest in this area.
- Automated validation checks for data assets and training datasets: Once rules are defined, you will need to guarantee the consistent application of them across all your data assets.
- Data Anomaly detection: For more sophisticated data sets, like in data streaming, it may be required to apply anomaly detection techniques. These techniques go beyond traditional rule based data quality checks, but explore and resolve data anomalies to increase overall quality.
As you transition from traditional BI implementations, often reliant on extensive data manipulation to support multiple dashboards, it’s important to understand that GenAI operates at a fundamentally deeper level of granularity, automation, and speed. Data imperfections that may go unnoticed in BI environments can become critical issues in GenAI use cases, potentially hindering performance and reliability.
Conclusion
As we’ve seen, GenAI implementation is far more than an IT project. Focusing only on the technology risks overlooking what truly matters: the business value. GenAI enables organizations to automate persistent challenges, freeing teams from time-consuming tasks and creating space for innovation.
To make these first steps easier, we created a YouTube series for midsize companies. Each short, practical “pill” walks business leaders through essential actions like assessing data readiness, identifying the right use cases, and securing executive alignment.
Watch the first episode here:
And we remain at your disposal to explore next steps tailored to your specific business needs. Contact us!
Q&A: AI in the SAP Ecosystem
Q: How can AI create real business value in SAP beyond automation?
AI transforms SAP from a process execution platform into a decision-making engine. It allows you to predict demand, optimize workflows, and generate insights in real time—turning data into proactive business actions.
Q: What’s the biggest challenge for leaders adopting AI in SAP?
The main challenge is balancing innovation with governance. Many companies rush to experiment without securing their data architecture or defining ownership. A structured roadmap avoids these pitfalls and ensures measurable results.
Q: How do we know if our SAP landscape is ready for GenAI?
Readiness depends on your data quality, integration maturity, and system version. Companies running S/4HANA and BTP are best positioned, but even those on ECC can start by improving data pipelines and integrating with Integration Suite.
Q: Is GenAI adoption expensive or resource-heavy?
It depends on your approach. Embedded AI (like Joule) delivers quick wins with lower investment, while composable AI on BTP offers long-term scalability and customization. Starting small with measurable pilots helps manage costs and risk.
Q: How should leaders organize teams for AI in SAP?
The most effective setups combine IT architects, data scientists, and business domain experts in a Center of Excellence (CoE). This ensures consistency, knowledge sharing, and alignment between technology and strategy.
Q: How do we ensure data privacy and compliance when using GenAI?
SAP’s built-in role-based access and governance tools help, but leaders must also define internal policies—especially for sensitive HR, finance, or supplier data. Embedding these controls directly in data pipelines prevents unwanted exposure.
