AI readiness in SAP companies – How to make it happen

SAP companies face a twofold challenge: On the one hand, the use of artificial intelligence (AI) accelerates the automation of business processes; on the other hand, many organizations lack the structural prerequisites to actually deploy AI productively. In practice, there is often (still) a gap between the availability of SAP Business AI, SAP Joule, and SAP Business AI Platform on the one hand, and measurable business benefits on the other.

This gap has a name: AI readiness. The term describes a company’s ability not only to technically deploy AI applications and AI agents but also to seamlessly embed them into existing processes, data structures, and governance frameworks. This article systematically categorizes the relevant areas of action and outlines the specific steps SAP companies should take on their path to AI readiness.

Key takeaways:

• AI readiness encompasses four dimensions: strategy, data, governance, and employees. These dimensions must be addressed simultaneously.

• High data quality and consistent master data management form the foundation for any productive AI deployment in an SAP environment.

• Robust AI governance aligns regulatory requirements – such as the EU AI Act – with internal corporate guidelines.

• Change management and targeted skill development among employees determine whether AI projects scale beyond the pilot phase.

• IBsolution supports SAP companies in systematically establishing the technical and organizational prerequisites for AI deployment.

Productive AI requires the right foundation

What does AI readiness mean in the SAP context?

AI readiness refers to a company’s willingness and ability to systematically integrate artificial intelligence into its value-creation processes. In the SAP context, this is not merely about activating SAP Business AI or SAP Joule. What is crucial is that the underlying system landscape, the data foundation, and the organizational structures can withstand the demands of productive AI deployment.

Specifically, AI readiness encompasses four interrelated areas of action: strategic alignment, the quality and availability of corporate data, the establishment of governance structures, and employee empowerment. If any one of these elements is missing, AI initiatives are usually limited to isolated pilot projects.

One aspect that is often underestimated concerns the architecture of the SAP landscape itself. Systems that have evolved over time – with distributed identities, heterogeneous data platforms, and inconsistent authorization models – significantly complicate the deployment of AI. Against this backdrop, AI readiness is not a one-time project but an ongoing development process.

Why SAP companies should act now

Several developments are increasing the pressure on companies to take action. The EU AI Act, the European regulation on artificial intelligence, has been in effect since August 2024. It requires companies that use or provide AI systems to conduct a risk-based assessment and documentation of their applications. High-risk systems are subject to stricter requirements regarding transparency, data quality, and human oversight.

At the same time, SAP is driving the integration of artificial intelligence across its entire product portfolio. The vision of the Autonomous Enterprise requires that AI agents are able to access business data in a context-aware and secure manner. Companies that fail to prepare their IAM structures and data infrastructure in a timely manner risk falling behind this rapidly evolving technological development.

There is also an economic factor at play. According to a recent Bitkom survey from September 2026, only 11% of companies in Germany are using agent-based AI. The most common reasons for not adopting artificial intelligence are a lack of expertise (85%), uncertainties regarding legal requirements (66%), and a lack of data (or poor data quality) (53%). These barriers can be systematically overcome through structured AI preparation.

The four pillars of AI readiness

AI readiness is not a monolithic endeavor. It can be broken down into four pillars that build on one another and should be addressed in parallel: strategy, data, governance, and people. Each pillar addresses specific issues, which are discussed in detail below.

Pillar 1: Developing an AI strategy based on business objectives

The first step toward AI readiness is to define artificial intelligence not as a technology project, but as a strategic initiative. The starting point is the specific business objectives: Which processes should be improved through AI? What measurable results are being targeted? And what resources are available for implementation?

In SAP-based companies, various application areas present opportunities. Predictive maintenance in manufacturing, automated invoice verification in finance, intelligent demand forecasting in the supply chain, or context-based decision support in human resources are typical scenarios. Prioritization is key: Companies should start with use cases that promise high business value with comparatively low complexity.

A proven approach is to structure the AI strategy across three levels. At the strategic level, overarching corporate goals are aligned with AI potential. The tactical level defines the target architecture and the necessary technological building blocks. At the operational level, specific use cases are identified, prioritized, and transferred to an implementation backlog.

What matters in strategy development

A robust AI strategy is characterized by commitment and measurability. Clear key performance indicators (KPIs) should be defined for each prioritized use case: reduction in turnaround times, improvement in forecast accuracy, reduction in error rates, etc. These metrics serve as the benchmark for evaluating the progress of AI initiatives.

Furthermore, the strategy must address the organizational framework. Who is responsible for the AI initiatives? What budgets are allocated? How will the results be integrated into existing corporate management systems? Without answers to these questions, the AI strategy remains a paper tiger.

Pillar 2: Building the data foundation for AI deployment

Without high-quality data, any AI initiative will be ineffective. This is particularly true for SAP companies, as the complexity of their data landscape is often above average. ERP systems, CRM solutions, HR applications, production systems, and analytics platforms generate data in various formats, structures, and levels of quality.

Building an AI-ready data foundation involves several areas of focus. First, companies must consolidate and harmonize their master data. Duplicates in business partner data, incomplete material master records, or inconsistent plant assignments distort any automated analysis. Various established tools, such as SAP Master Data Governance (MDG), are available for centralized maintenance and quality assurance of master data.

In addition, establishing a unified data platform is becoming increasingly important. SAP Business Data Cloud (BDC) consolidates SAP and non-SAP data on a central platform while preserving the business context. In the future, SAP Knowledge Graph will describe the meaning, relationships, and context of business data, thereby providing AI agents with a reliable foundation for making informed decisions.

How to systematically evaluate data quality

A structured approach allows you to reliably check and evaluate data quality. In practice, a three-step process has proven effective:

• Create a data inventory
  Systematically record all relevant data sources, data owners, and quality metrics.

• Resolve quality issues
  Remove duplicates, fill in missing required fields, and resolve inconsistencies between systems.

• Set up continuous monitoring
  Define metrics, establish thresholds, and automatically trigger correction processes in the event of deviations.

As a component of SAP MDG, SAP Data Quality Management (DQM) supports this process with rule-based validation mechanisms and automated quality monitoring. Using the rules mining function, data quality rules can even be derived semi-automatically from the existing data set.

Pillar 3: Establish AI governance and meet regulatory requirements

Governance is the framework that holds strategy, data, and technology together. Robust AI governance defines responsibilities, quality standards, and compliance requirements for the entire lifecycle of an AI application. It ensures that AI systems remain transparent, traceable, and controllable.

In the regulatory landscape, the EU AI Act serves as the central framework. The regulation distinguishes between prohibited practices, high-risk systems, and low-risk AI applications. Companies that operate AI systems must, among other things, ensure that the data used meets quality requirements, that human oversight is guaranteed, and that the systems are adequately documented.

Implementing AI governance in the SAP ecosystem

In the SAP environment, governance takes on an additional technical dimension. The question of what permissions AI agents should have to access business data is of central importance. Without robust identity & access management (IAM), the use of AI becomes a security risk. As part of SAP Cloud Identity Services, the SAP Authorization Management Service (AMS) provides a technical foundation for managing authorizations for AI agents in a consistent and context-sensitive manner.

A practical governance framework for AI in the SAP environment should include the following elements:

• Risk classification
  Each AI use case is assessed based on its risk level and documented accordingly.

• Roles and responsibilities
  Responsibilities for the development, operation, and monitoring of AI applications are defined.

• Data policies
  Binding standards for data quality, data access, and data protection are established.

• Monitoring and auditing
  AI systems are continuously monitored, and regular audits ensure compliance with the defined standards.

Pillar 4: Empowering employees and shaping cultural change

Technology and governance alone are not enough to achieve AI readiness. The success of AI initiatives depends largely on whether employees support and actively shape the change. AI readiness is therefore also a matter of corporate culture and change management.

The EU AI Act emphasizes this aspect in Article 4 on AI competence. According to this provision, providers and operators of AI systems must ensure that their staff possess a sufficient level of AI competence. This includes technical fundamentals as well as an understanding of ethical and regulatory frameworks.

In practice, a lack of knowledge has proven to be the main barrier to AI adoption. Companies should therefore invest early on in training programs tailored to their respective target groups. For IT managers, technical training on SAP Business AI and SAP Business AI Platform is a priority. Business users need hands-on workshops that demonstrate the benefits of AI through concrete scenarios from their day-to-day work. Executives, in turn, need an understanding of the strategic implications and governance requirements of AI adoption.

How an effective AI skills program is structured

A suitable AI skills program addresses three target groups with different focuses:

• IT and technology
  Technical training on SAP AI tools, data architecture, authorization models, and SAP Business AI Platform.

• Business departments
  Workshops featuring specific use cases from the respective work environment, teaching AI fundamentals and mindsets for collaborating with AI systems.

• Management
  Strategic context, governance requirements, decision-making frameworks for AI investments, and risk management.

It is important that training initiatives are not one-time events. AI technologies are constantly evolving. An ongoing learning program that integrates new features, regulatory changes, and practical experience ensures sustainable AI competence within the company.

Step by step toward AI readiness: a structured approach

The implementation of the four pillars of AI readiness can be mapped out in a structured process model comprising five phases that build upon one another.

Phase 1: Assessment and status quo analysis

The first step is to determine the company’s current level of maturity. This involves systematically assessing the existing system landscape, data quality, governance structures, and employees’ AI competencies. The result is a nuanced picture of the strengths and areas requiring action across all four pillars. Based on this, priorities can be established and the next steps can be planned.

Phase 2: AI strategy and use case prioritization

Building on the current state assessment, an AI strategy is developed based on the company’s business objectives. Potential use cases are identified, evaluated in terms of their business value and technical feasibility, and prioritized.

For each prioritized use case, a business case is created that quantifies the expected improvements, the required investments, and the relevant risks. This results in a robust portfolio of AI initiatives that forms the foundation for operational implementation.

Phase 3: Building the data foundation and ensuring quality

In this phase, the identified data quality issues are systematically resolved. Master data is consolidated, data silos are eliminated, and a unified data platform is established. SAP Business Data Cloud, SAP Master Data Governance, and Reltio serve as the technological cornerstones here.

At the same time, a permanent data quality management system is established that defines key metrics, sets thresholds, and automatically triggers corrective actions in the event of deviations. This step is crucial because the quality of AI applications declines over time if the underlying data is not continuously maintained.

Phase 4: Implement the governance framework

The fourth phase focuses on establishing the governance framework. This includes setting up an AI steering committee, defining roles and responsibilities, creating guidelines for the responsible use of AI, and integrating the framework into existing compliance structures.

In the SAP environment, companies should also examine their IAM architecture for AI deployment during this phase. Authorizations for AI agents must be defined, identities harmonized across systems, and access control mechanisms implemented.

Phase 5: Piloting, scaling, and further development

In the fifth phase, the prioritized use cases are implemented in pilot projects. Following successful validation, the solutions are rolled out into production and scaled gradually. Ongoing monitoring ensures that the defined success criteria are met.

Lessons learned from the pilot projects are incorporated back into the AI strategy and governance framework. This creates a continuous improvement process that continually optimizes the company’s AI readiness.

Common pitfalls in AI preparation in SAP companies

Experience shows that certain mistakes occur frequently in AI preparation projects. Being aware of these pitfalls helps to avoid them proactively.

Prioritizing technology over strategy

The most common mistake is to begin by selecting technology before business objectives and use cases have been defined. AI tools and AI platforms are means to an end. Without a clear understanding of which problems need to be solved, a premature focus on technology leads to misguided investments and frustration within business units.

Underestimating data quality

Many AI projects fail not because of algorithms, but due to poor data quality. A company may have brought its architecture up to “clean core” standards and yet still be dealing with duplicates in business partner data, incomplete material master records, or inconsistencies between plants. These shortcomings skew any automated analysis and diminish the value of AI applications.

Failing to involve employees

Artificial intelligence is transforming workflows and decision-making processes. Without involving affected employees early on, resistance will arise that jeopardizes the success of AI initiatives. Open communication about goals, impacts, and opportunities for participation is essential. Companies that actively involve their workforce in the transformation process achieve more sustainable results.

Treating governance as an afterthought

Establishing the necessary governance structures afterwards is time-consuming and prone to errors. Companies should define clear guidelines for AI deployment from the very beginning. This applies to technical authorizations as well as organizational responsibilities and regulatory compliance.

Conclusion: How SAP companies can systematically build their AI readiness

AI readiness is not a state that is achieved once and then remains static. It is an ongoing development process that requires strategic foresight, operational consistency, and a willingness to adapt continuously. SAP companies that invest in their AI readiness now are laying the groundwork to use artificial intelligence as a true lever for value creation.

The four pillars of AI readiness provide a clear framework for guidance: Strategy sets the direction, data provides the foundation, governance creates the framework, and empowered employees bring AI initiatives to fruition. Those who systematically address these areas will make the transition to AI-driven value creation significantly faster and more securely.

FAQs about AI readiness in SAP companies

What is AI readiness in the SAP environment?

AI readiness describes an SAP company’s ability to productively integrate artificial intelligence into its business processes. It encompasses strategic alignment, data quality, governance structures, and employee competencies.

Why is data quality so important for AI deployment?

AI models make decisions based on the data provided. Erroneous, incomplete, or scattered data leads to distorted results. IBsolution supports SAP companies in consolidating their master data, establishing a unified data platform, and ensuring high data quality on an ongoing basis.

What regulatory requirements must companies comply with?

The EU AI Act requires companies that use AI systems to conduct risk-based assessments and maintain documentation. High-risk systems are subject to requirements regarding transparency, data quality, and human oversight.

How long does it take to achieve AI readiness?

The time required depends on the company’s level of maturity. For initial pilot projects, companies should expect a timeframe of six to twelve months. A structured phased approach that combines assessment, strategy development, and implementation accelerates the process. Together with IBsolution, companies build the foundation they need to deploy AI productively and purposefully.

What role does change management play in AI implementation?

Change management determines whether AI projects scale beyond the pilot phase. Without the active involvement of employees, resistance arises that jeopardizes success. People enablement should therefore be planned from the outset as an integral part of AI readiness.