From Embedded AI to Development Platforms, these are the Most Common AI Vendors and Technologies for SAPinsiders

Artificial intelligence has quickly become a core focus for SAP customers, and the SAPinsider research report “AI Adoption and Maturity in the SAP Ecosystem” shows that organizations are standardizing around a mix of SAP-native tools, cloud provider platforms, and specialist providers to build out their AI stacks. For 2026, the question is less about whether SAPinsiders will use AI and more about which vendors and technologies they will lean on to move from pilots to production.

AI maturity still skews early to mid-range for most organizations. Many are experimenting with copilots and embedded features, while a smaller group of AI Leaders is using a broader set of tools and platforms to operationalize AI at scale. That maturity progression is closely tied to the partner ecosystem companies build and the technologies they choose.

SAP AI: Embedded Capabilities and Joule as the Entry Point

On the SAP side, AI adoption often starts with what is already available in core applications. Embedded AI in SAP S/4HANA and SAP SuccessFactors, as well as AI-powered features in SAP Analytics Cloud, are among the most widely used capabilities. These features become more prevalent as organizations mature and look to automate forecasting, recommendations, and process steps inside familiar workflows.

As teams move beyond basic experimentation, they increasingly turn to SAP Business Technology Platform (BTP), AI services, SAP Business AI, and SAP Conversational AI for more focused use cases such as document processing, conversational interfaces, and intelligent automation. These services help bridge the gap between “out-of-the-box” AI and more tailored solutions that reflect specific business needs.

Joule Copilot and Joule AI Agents play a distinct role as an accessible entry point for many SAPinsiders. Copilots are typically used for content generation, code assistance, and virtual assistant scenarios, allowing organizations to expose AI to end users quickly without launching full custom model projects. For many, Joule acts as the first real touchpoint for AI in the SAP environment before they adopt more advanced tools like SAP AI Core and SAP AI Launchpad.

Microsoft and Cloud Providers as the Non-SAP Backbone

Outside of SAP’s own offerings, Microsoft stands out as the primary AI partner for a large share of respondents. Azure AI services and various Copilot offerings are central to many organizations’ AI strategies, thanks in part to their deep integration with productivity suites and existing cloud platforms.

As organizations mature into AI Leaders, they broaden their vendor mix. Cloud providers such as Amazon Web Services and Google Cloud become more common, especially where organizations are running large-scale data workloads or using specialized AI services. IBM, NVIDIA, and other providers also enter the picture for more advanced or industry-specific capabilities.

OpenAI-based tools are particularly popular among organizations at early and mid levels of maturity, often accessed through APIs or embedded in copilots. As organizations progress, they rely somewhat less on direct OpenAI integration and more on enterprise-grade offerings built on top of cloud provider and SAP platforms.

Platforms for Building and Deploying Models

Beyond embedded features and APIs, the platforms used to build, train, and deploy AI models are one of the clearest markers of AI maturity. Many AI Beginners are still not using dedicated AI platforms at all, and when they do, SAP BTP is typically the first stop, because it sits close to their SAP data and processes.

AI Adopters, the mid-level of AI maturity, are more likely to bring in Microsoft Azure Machine Learning and Databricks alongside SAP BTP. This combination allows them to standardize model development and deployment, work with larger data sets, and support more advanced data science workflows.

AI Leaders take a multi-platform approach. They commonly use SAP BTP together with Snowflake, Azure Machine Learning, Databricks, TensorFlow, PyTorch, and enterprise AI environments such as IBM Watsonx. Rather than betting on a single platform, they choose the right environment for each scenario—SAP-centric use cases on BTP, large-scale experimentation and training on data platforms, and specialized models on targeted AI services.

AI Technologies in Use

When respondents are asked about AI technologies and development platforms, embedded AI within enterprise applications comes out on top. This is not surprising given that even organizations with limited AI experience can tap into features that are already built into SAP and other enterprise solutions.

Generative AI services for text, code, and images, as well as natural language and search-based interfaces, are next in line. These capabilities underpin chatbots, virtual assistants, and copilots deployed across both SAP and non-SAP environments. For many organizations, this is where employees feel AI most directly in their day-to-day work.

Other categories, such as AutoML and low-code or no-code AI tools, traditional machine learning environments, cloud-based AI/ML platforms, AI-enabled business process automation, and AI agents, show strong current use or near-term plans. Edge AI and federated learning are less prevalent today but are on a growing number of roadmaps, especially where real-time or privacy-sensitive scenarios are in play.

A well-chosen mix of vendors and technologies will not guarantee AI success on its own, but the research shows that organizations that build a thoughtful ecosystem and evolve it as they mature are better positioned to turn AI from a set of tools into a driver of real business outcomes.

What This Means for SAPinsiders

For SAPinsiders planning AI investments in 2026, the vendor and technology choices uncovered in the research point to three practical priorities.

Start with embedded and copilot-style AI, then build out platforms. Use embedded AI features in SAP S/4HANA, SAP SuccessFactors, and SAP Analytics Cloud, along with SAP Joule and Microsoft Copilot, to deliver quick wins. As adoption grows, invest in platforms like SAP BTP, Azure Machine Learning, and Databricks to standardize how you build, train, and deploy models.

Think in ecosystems, not single vendors. Rely on SAP for SAP-centric AI while using Microsoft and other cloud providers as strategic partners for broader AI capabilities. As your program matures, consider where specialized tools such as Snowflake, IBM Watson, NVIDIA AI, or Hugging Face can add differentiated value in specific scenarios.

Align technology choices with your maturity level and governance model. Beginners should focus on tools that reduce complexity and risk, emphasizing embedded AI, copilots, and strong foundational governance. As you move into Adopter and Leader territory, expand into multi-platform environments, advanced AI services, and agents, supported by clear ownership, robust governance, and tight integration into SAP and non-SAP processes.

To dive deeper into the data behind these findings, benchmark your organization against AI Beginners, Adopters, and Leaders, and see detailed breakdowns of the top vendors and technologies in use, download the SAPinsider benchmark report “AI Adoption and Maturity in the SAP Ecosystem.”