SAP Is Always Evolving—Companies Must Take the Initiative!
SAP is accelerating the pace of change: artificial intelligence, portfolio shifts, cloud migration, new support models, data platforms, and usage-based licensing models are gradually transforming the operational, contractual, and cost models of many customers.
Strategic Realignment: AI, Cloud, and Customer Value
One example is the reorganization of SAP’s Executive Board: Christian Klein has long made AI a top priority. At the same time, a new Executive Board division—the “Customer Value Group”—is being created to more closely bundle customer-related activities and drive the migration to the cloud. Strategically, this makes sense: SAP wants to more closely manage the journey from the first customer interaction to value creation. For customers, this also means that SAP is likely to align its value propositions, sales strategies, and service offerings even more closely with its own transformation agenda.
New Cost Model: From User Licenses to Usage-Based Models
At the same time, SAP is revising its pricing model for the AI era. If, in the future, the scope of AI usage becomes more relevant than the number of users, the predictability of costs will change significantly. Traditional user- and subscription-based models are increasingly being supplemented by usage-, request-, or action-based models and could lose importance in the long term. This aligns with the logic of autonomous processes but increases the demands on monitoring, budgeting, and internal governance.
SAP Business AI: More Features, More Need for Control
This trend is particularly evident in the area of SAP Business AI. SAP is increasingly moving away from individual AI functions toward an agent-based ecosystem in which assistants serve as the central user interface and, in the background, orchestrate specialized agents for specific domains such as finance, procurement, supply chain, HR, or customer experience. In the future, users will no longer select individual AI functions but will instead initiate complex business processes that are executed autonomously by multiple agents. SAP already has 400 of these agents in its portfolio. By the end of the year, that number is expected to reach 700.
At the same time, the complexity of the commercial model is increasing. Terms such as “Freemium AI,” “Embedded AI,” and “Customized AI”; AI offerings like “Base AI,” “Premium AI,” and “Premium BTP”; solutions such as “Joule 4 Developers” and “Joule Studio” (V2 was recently announced); and the multitude of new assistants and agents must be understood in conjunction with the underlying licensing metrics, “AI Units” and “BTP Capacity Units.” The key factor is no longer just the consumption of a single AI function, but rather the question of what costs arise when multiple specialized agents interact autonomously with one another within a process chain. For example, if a single agent action consumes 0.002 AI Units, what AI Unit costs does a fully automated end-to-end process incur?
From the customer’s perspective, this is precisely where the critical issue lies: Technical usage is set to become simpler, while commercial and governance-related management will become more complex. When an assistant orchestrates multiple agents in the background, companies must be able to track what usage is triggered, what costs are incurred, what data is processed, and how this usage can be allocated internally.
In addition, 320 AI capabilities are being moved from the paid Premium AI model to the Base AI model and will no longer consume AI units. The strategy is clear: The AI capabilities that will then be free are intended to make it easier for customers to get started with the autonomous enterprise, and the resources freed up as a result should be shifted to the paid model as much as possible.
SAP for Me: Measurement tools are only being developed in parallel
SAP for Me will therefore continue to be expanded. New metering, budgeting, and reporting functions are intended to provide transparency regarding AI unit consumption, feature usage, and user-specific analytics. This is fundamentally positive. At the same time, however, it also shows that the necessary control mechanisms are, in some cases, only being developed in parallel with the market launch of new AI offerings. Customers must therefore adapt to transitional phases in which usage, costs, and responsibilities are not yet fully transparent.
Success Plans: Support Becomes Part of the Transformation
Another key component is the Success Plan model. SAP is supplementing its existing support and service offerings with three tiers: Foundational, Advanced, and Max. For cloud customers, the model is designed to be simpler and clearer. The Foundational level is included in the cloud subscription, while Advanced and Max offer additional services such as proactive risk detection, AI-powered guidance, the Success Plan Advisor, dedicated managers, or customer-specific AI prototypes.
From SAP’s perspective, this is a more structured model. From the customer’s perspective, however, it creates another layer of costs and management overhead. Support is no longer just technical assurance but is becoming more integral to the ongoing transformation. Companies must assess which services they actually need, which can already be handled internally, and whether additional Success Plans are economically justified.
Business Data Cloud and API Policy: Data, Integration, and Dependencies
Added to this is the strategic role of the SAP Business Data Cloud. Through acquisitions and partnerships in the data sector, SAP aims to more closely integrate and harmonize external and internal corporate data and make it usable for AI. Technically, this can bring significant advantages. At the same time, dependencies, integration efforts, and requirements for data quality, architecture, and governance are increasing. The more AI applications rely on harmonized enterprise data, the more important it becomes to determine where data is stored, how it is processed, and which platform will serve as the central control point in the future. Furthermore, users face the challenge of understanding the underlying licensing model of SAP Business Data Cloud Capacity Units, ensuring cost transparency, and applying it to upcoming use cases.
The SAP API Policy also shows that customers must remain vigilant. When SAP more strictly defines which interfaces are officially supported and how API usage is permitted in cloud and AI scenarios, this affects more than just technical architecture decisions. It also impacts existing integrations, partner solutions, innovation projects, and long-term planning certainty. Companies with established SAP landscapes and non-SAP integrations, in particular, should verify whether existing scenarios are securely supported for the long term.
On-Premise Customers: Innovation Versus Cloud Commitment
The situation remains challenging for on-premise customers. While SAP is making select AI features available for certain on-premise or hybrid scenarios, it ties this to clear investments in cloud ERP. Thus, the strategic direction remains clear: Access to innovation is increasingly linked to a commitment to the cloud. Customers are offered transition options, but there is no fundamental departure from the “cloud-first” strategy.
What Companies Need to Clarify Now
The big picture is clear: SAP is investing heavily in AI, data platforms, autonomous processes, and cloud-based business models. This presents both opportunities and risks for customers: automation, better process support, new assistants, faster migrations, and smarter analytics. At the same time, complexity is increasing across nearly all areas of management.
In the future, companies will need to do more than just know which SAP products they use. They must understand:
- Which AI features are included in the cloud subscription?
- Which solutions in Premium AI and Premium BTP consume how many AI Units or BTP Credits?
- Which assistants and agents are in productive use, and what are the associated costs?
- What data is used for AI scenarios, and how is it processed and stored?
- Which APIs are still officially supported?
- Which Success Plan services are required?
- What costs arise from usage, overage, or new service packages?
- What internal governance processes are necessary to properly manage and control usage and ensure compliance?
As a result, SAP application management is increasingly becoming an ongoing task. License management, architecture, procurement, business units, IT, data protection, and C-level executives must collaborate more closely.
Conclusion: Actively Manage SAP Dynamics
The most important recommendation for customers is therefore: Don’t wait until the next contract renewal or audit is due. Companies should continuously review their SAP roadmap, cloud strategy, AI usage, and license management. This includes clear responsibilities, regular usage monitoring, internal rules for AI and agent usage, transparent cost center logic, and an assessment of the contractual implications of new SAP policies.
We’d be happy to help you review your SAP roadmap, licensing strategy, and AI governance early on and transparently identify risks and opportunities for optimization. We also report regularly on current developments at SAP in the Vendor Observer Competence Center. You can find more information on this here.
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