AI’s Role in SAP Automation, Now and In The Future

AI is set to become the decisive factor in whether SAP automation programs deliver incremental efficiency or truly transform how work gets done. SAPinsider’s “AI Adoption and Maturity in the SAP Ecosystem” research shows that most organizations are still early in their AI journey, but the patterns emerging today point clearly to how AI will reshape SAP automation in the coming years.

Automation Demand is the Top AI Driver

For SAPinsiders, demand for automation and cost reduction is the single most common driver of AI strategy, cited by more than a third of respondents. This puts AI and automation on the same critical path: organizations are not adopting AI as a science experiment, but as a way to make processes faster, cheaper, and less manual.

Other leading drivers, such as enhancing the customer or employee experience and SAP S/4HANA or cloud migration initiatives, reinforce AI’s role within business processes rather than as a standalone analytics layer. When organizations modernize core SAP platforms or look to differentiate experiences, AI-driven automation becomes a natural extension of those efforts.

Broad AI Adoption, Shallow Integration Into Workflows

On the surface, AI use appears widespread: the vast majority of respondents report using AI in their processes. But when asked how AI actually shows up in workflows, the picture is much more cautious.

Nearly half of organizations are still in an experimental phase, piloting AI in select, often non-critical areas. Another sizable group uses AI to support specific tasks with partial automation, where humans remain firmly in control. Only a relatively small minority has embedded AI into core workflows across functions with integrated automation or autonomous and adaptive capabilities.

This means AI’s role in SAP automation today is often assistive rather than orchestrating end-to-end processes. Many organizations are still using AI as a smart “plug-in” to existing workflows rather than rethinking those workflows with AI at the center.

Leaders Automate More, and Better

To understand how AI changes automation outcomes, the research segments organizations into AI Beginners, Adopters, and Leaders based on a maturity score. Most respondents fall into the middle, with a smaller portion at the extremes. The jump from Adopter to Leader is where automation really starts to look different.

Leaders report far broader AI use cases, higher end-user adoption of AI tools, and much stronger AI-related KPI improvements. Faster decision-making is the single most common outcome, and Leaders are significantly more likely than other groups to report that AI has helped them make decisions more quickly. They are also much more likely to report greater automation and efficiency, as well as an enhanced customer or employee experience.

These patterns signal a clear trend: as organizations move from ad hoc AI pilots to integrated, governed AI programs, the role of AI in automation shifts from marginal gains to step-change improvements.

AI-driven Automation is Already Changing KPIs

The KPI data shows that AI is not just improving “AI metrics” but core operational performance when tightly coupled with automation. AI Leaders report strong year-over-year improvements across cost savings from AI-driven automation, reductions in manual intervention and process errors, and the volume of AI-driven decisions or transactions. They also see improvements in time-to-value for AI use cases and in revenue attributed to AI-enhanced offerings.

By contrast, Beginners and Adopters generally see modest, often single-digit improvements across these same KPIs. The implication for SAP automation is straightforward: simply adding AI features to a few processes will not deliver the kind of efficiency, quality, and speed gains that Leaders report. The organizations seeing outsized benefits are those using AI to reshape how decisions are made and where humans versus machines participate in SAP workflows.

Embedded AI and Copilots as the On Ramp

One of the clearest stories in the data is how organizations are actually accessing AI today. Embedded AI capabilities within enterprise applications are the most widely used or previously used AI technology. Generative AI services and natural language interfaces follow closely, reflecting the rapid rise of chatbots, virtual assistants, and copilots.

For SAP customers, that translates into heavy use of embedded AI in SAP applications and analytics, particularly among more mature organizations. Copilots and AI agents emerge as accessible entry points into AI at earlier stages of maturity.

This pattern suggests that AI’s first major role in SAP automation will be as an accelerator and advisor within existing processes—surfacing recommendations, summarizing content, generating code or configurations, and handling routine interactions—before moving into deeper, autonomous execution.

The Future of SAP Automation

The data paints a clear arc for how AI will shape SAP automation. In the near term, AI primarily augments existing SAP processes through embedded features, copilots, and targeted use cases such as content generation, forecasting, and virtual assistants. Automation gains are real but localized.

As we move farther out, organizations expand their AI toolsets, formalize governance, and deepen platform usage, AI begins to orchestrate decisions inside automated workflows—driving routing, exception handling, and optimization across multiple SAP functions. In the long term, AI and automation become inseparable from the operating model. AI agents, embedded deeply in SAP and surrounding systems, carry out significant portions of end-to-end processes, with humans supervising, handling edge cases, and focusing on higher-value work.

As AI adoption climbs and more organizations move from experimentation to scaled use, the question will not be whether AI is part of SAP automation but how central its role will be. The research makes it clear: the organizations that use AI to reimagine, not just accelerate, their SAP processes are already pulling ahead.

What This Means for SAPinsiders

For SAPinsiders planning the next phase of their automation roadmaps, the research suggests three priorities.

• Treat AI and automation as a single transformation agenda. AI initiatives that sit apart from automation programs tend to stall at the pilot stage. Align AI investments with your most important automation and cost reduction goals—particularly in finance, supply chain, and customer-facing processes—so that AI is evaluated on its contribution to concrete outcomes like faster decision making, reduced manual effort, and higher process accuracy.
• Move from “copilot only” to governed AI platforms. Copilots and embedded AI are an excellent on-ramp, but they are not enough on their own to support end-to-end intelligent automation. Organizations that want AI to drive significant automation gains need to invest in platforms such as SAP BTP and related AI services, along with scalable development, deployment, and monitoring capabilities that bring models into production and keep them there with appropriate controls.
• Build ownership and end-user adoption into your automation plans. In many organizations early in their AI journeys, there is no clear owner of the AI strategy, making it difficult to coordinate technology choices or measure impact. Leaders replace “no clear owner” with shared ownership models spanning IT and the business, and they pair that with serious change management efforts so that AI-powered tools are actually used by end users. For SAP automation programs, this means designing experiences and training that help employees trust and rely on AI, rather than treating it as a bolt-on feature.To learn more about the latest AI trends for SAPinsiders, read the “AI Adoption and Maturity in the SAP Ecosystem” benchmark report.