5 Important AI Trends for SAPinsiders in 2026

Artificial intelligence is no longer a side project in the SAP ecosystem as it is becoming a core part of how organizations plan to run and transform their businesses in 2026. AI adoption is broad, but most organizations are still early to mid-maturity, so the next phase is about turning experimentation into outcomes that actually move KPIs.

AI Maturity: Stuck in the Middle

The data in SAPinsider’s AI Adoption and Maturity research report shows that organizations have started their AI journeys, but many remain stuck in the middle. A large share describes their AI strategy and capabilities as either ad hoc or foundational, with limited deployment beyond pilots and targeted use cases. At the process level, nearly half are experimenting with AI in non-critical areas, and only a minority have AI embedded into core workflows with integrated automation or adaptive capabilities.

This gap between experimentation and integration matters because the biggest benefits such as better forecasting, more automation, improved customer and employee experience, and increased revenue show up most clearly once AI is part of end-to-end processes. The organizations that qualify as AI Leaders in our maturity analysis are more likely to use multiple AI platforms, scale beyond chatbots and simple forecasting, and report stronger year-over-year gains in AI-related KPIs.

Governance as a Differentiator

The bright spot in the research is that SAPinsider organizations understand the importance of governance, even if their AI deployments are still limited. Many respondents have foundational AI governance in place, including data privacy controls and model documentation, while others are in exploratory stages discussing responsible AI. Only a relatively small segment, however, says governance is fully operationalized or embedded and automated across the AI lifecycle.

AI Leaders separate themselves by treating governance as an enabler rather than a roadblock. Responsible AI governance is one of the top actions they are taking to support adoption, and they are more likely to have clear ownership models that are often shared across senior stakeholders, with CTO leadership common, that make it easier to align AI efforts with broader transformation agendas.

Data, Platforms, and the SAP Backbone

AI success in the SAP ecosystem is tightly linked to data quality and the platforms used to build and deploy models. Executive leadership support and access to high-quality, trusted data stand out as top requirements for successfully adopting and scaling AI, with integration into SAP and other enterprise systems close behind. AI must be connected to a strong, unified data foundation rather than isolated data sources to drive impact across processes.

Platform choices reinforce this story. SAP Business Technology Platform (BTP) emerges as a central piece of the AI backbone, especially for more mature organizations. AI Leaders often rely on SAP BTP alongside platforms such as Snowflake, Microsoft Azure Machine Learning, and Databricks, and they use multiple frameworks and services to build, train, and deploy AI models at scale. Treating data and platforms as shared, strategic assets rather than project-by-project decisions is a key step for SAPinsiders looking to scale AI.

Embedded and Generative AI as Everyday Tools

One of the most important trends for SAP customers is how quickly embedded AI and generative AI have become everyday tools. Embedded AI capabilities within enterprise applications are among the most widely used AI technologies in the research, highlighting that even those we categorize as AI Beginners can tap into AI functionality in SAP and other cloud applications without standing up custom models.

Generative AI services for text, code, and image generation, along with natural language and search interfaces, are close behind as organizations roll out copilots, chatbots, and AI agents. Within the SAP stack, adoption of SAP AI Launchpad, SAP AI Core or AI Foundation, SAP BTP AI services, SAP Business AI, and AI-powered SAP Analytics Cloud features all rise with maturity, while Joule Copilot and Joule AI Agents often serve as accessible entry points at earlier stages.

AI Outcomes, Leadership, and the Business Case

The research draws a clear line between AI maturity and business outcomes. Faster decision-making emerges as the most common AI-related outcome across maturity levels, but AI Leaders are far more likely than others to report gains in areas such as automation and efficiency, customer and employee experience, forecasting accuracy, risk and compliance management, and revenue or profitability.

At the same time, a significant share of organizations at the Beginner level say they have not yet seen meaningful outcomes from AI, underscoring the risk of remaining in pilot mode. Ownership and investment patterns help explain the difference: less mature organizations often lack a clear owner for AI strategy, while Leaders favor shared ownership and CTO-led models and are much more likely to have increased AI investment, including significant year-over-year spending.

What This Means for SAPinsiders

There is no single path to AI maturity in the SAP ecosystem, but the research offers a useful blueprint for where to focus next.

• Treat AI as part of your core SAP transformation, not a side project. Demand for automation and cost reduction, better customer and employee experiences, and SAP S/4HANA or cloud migration initiatives are among the top drivers for AI adoption.AI roadmaps should be tightly aligned to these drivers with clear business cases and ROI frameworks so that AI investments translate into measurable performance improvements.
• Build a governed AI backbone on SAP BTP and connected data. Use SAP BTP as a foundation for AI, combining SAP’s embedded and generative AI capabilities with a unified data strategy that brings SAP and non-SAP data together.Establish reusable assets—models, prompts, agents, and pipelines—and connect them to SAP S/4HANA, SAP SuccessFactors, and SAP Analytics Cloud so AI moves from pilot to production with monitoring, explainability, and lifecycle management built in.
• Formalize ownership and invest in adoption, not just tools. Replace “no clear owner” scenarios with shared ownership across IT and business leaders, including roles such as CIO, CTO, and CDO, and consider an AI center of excellence to coordinate strategy and governance.Focus as much on change management and user experience as on technology deployment so that AI-powered tools, including SAP Joule and other copilots, achieve high end-user adoption and deliver the intended business impact.

To explore the full data behind these findings, compare your organization with AI Beginners, Adopters, and Leaders, and access detailed charts and recommendations, download the SAPinsider benchmark report “AI Adoption and Maturity in the SAP Ecosystem.’