Artificial intelligence (AI) might dominate business headlines, but genuine intelligence begins with data. No algorithm can deliver reliable insights if it’s learning from inconsistent, incomplete, or inaccurate information.
Automation provides the missing link. It transforms raw enterprise data into trusted, actionable intelligence by capturing, validating, and connecting it across systems. In large, complex data environments such as SAP, automation ensures that information flows accurately and efficiently—forming the groundwork for meaningful AI outcomes.
Automation and Data Quality: The Foundation of AI Readiness
Automation isn’t just about speed, it’s about consistency, control, and governance. By eliminating manual errors, standardizing workflows, and enforcing rules, automation creates the structure that analytics and AI systems require.
According to the 2025 Precisely–ASUG Research Report, nearly 60% of SAP organizations are live or in the process of migrating to S/4HANA, yet automation adoption remains flat at 57%, showing no improvements from last year. This gap reveals a critical disconnect. While companies are moving forward with modernization, many haven’t fully embedded the automation practices needed to make those projects scalable and sustainable.
The biggest barrier? Complexity. Sixty-two percent of survey respondents cited process complexity as their top challenge—surpassing integration and process definition issues. SAP’s intricate web of business rules and compliance checkpoints makes accuracy at scale difficult to achieve.
Automation and data quality go hand in hand here. The same research found that poor data quality was a leading barrier for nearly a third of organizations during migration, while complex data transformation was close behind. Embedding automated validation, cleansing, and transformation directly into process design reduces risk and improves every downstream decision. When automation enforces quality at the source, AI receives a steady supply of structured, trustworthy data to learn from.
Creating Trustworthy, Connected Data
Accurate data is only part of the equation. AI also depends on data that’s complete, contextual, and connected across systems. Automated data-quality processes make that possible by applying consistent business logic, reconciling inconsistencies, and ensuring datasets align across SAP and non-SAP environments.
According to the Precisely–ASUG findings, organizations that built data-quality automation into their modernization programs accelerated timelines and reduced rework compared to those relying on manual correction..
Clean, well-governed data doesn’t just improve AI—it enhances agility across the enterprise.
Beyond process automation, data enrichment deepens the reliability of insights. Location Intelligence and Data APIs help organizations validate addresses, confirm locations, and add real-world context to enterprise records. This enrichment ensures analytics and AI models interpret data accurately—whether evaluating supply-chain routes, customer regions, or compliance risks. When data is both verified and contextualized, organizations can make confident, data-driven decisions at scale.
ReportTransforming SAP® Processes Through Automation – 2026 Trends & Challenges
Whether you’re deep into your migration, refining your automation strategy, or just beginning to explore how to modernize your SAP® landscape, this report delivers the insights you need to move forward with confidence.
Keeping Workflows Familiar—and Future-Ready
Modernization should enhance productivity, not disrupt it. As SAP environments evolve, many organizations must manage multiple user interfaces—SAP GUI, GUI for HTML, and Fiori. The Precisely–ASUG research shows that 54% of companies still operate in these mixed UI environments, even as full Fiori adoption has doubled to 18% in the past year.
Cross-interface workflow automation minimizes this complexity, allowing teams to work within familiar screens while maintaining consistency across systems. By automating the underlying logic that connects data and processes, organizations can move toward S/4HANA and next-generation interfaces without interrupting day-to-day operations. This unified experience supports both short-term productivity and long-term readiness for AI-driven innovation.
Scaling Automation for Intelligent Operations
When automation is built into the fabric of business processes, data quality improves continuously—and that’s when AI can truly deliver measurable value. Each automated transaction or validation produces a reliable signal that enhances forecasting, anomaly detection, and other advanced analytics.
The Precisely–ASUG report reveals a clear shift in how organizations scale, with 75% now viewing no-code and low-code capabilities as essential to their automation strategies. This approach empowers business users to address routine automation opportunities while IT focuses on more complex, data-intensive projects. The key to success lies in balancing empowerment with governance so that every automation contributes to cleaner, more consistent data.
Supporting research from MIT Technology Review Insights, in partnership with Snowflake, found that 78% of organizations say poor data foundations hold them back from capitalizing on AI. By embedding automation directly into business processes, companies can strengthen those foundations—connecting workflows, enforcing standards, and transforming fragmented operations into coordinated, intelligent systems that enable AI to deliver measurable value.
The Path Forward: Building AI on a Trusted Foundation
Across the SAP ecosystem, one truth stands out: modernization and intelligence succeed only when the data foundation is sound. Automation, data quality, and transformation are not separate stages—they reinforce one another to deliver reliable insights and resilient operations.
The 2025 Precisely–ASUG study shows momentum in this direction. While overall adoption levels may have steadied, automation maturity is rising, and organizations are shifting focus from where to start to how to scale. Those that embed automation and data governance into their S/4HANA strategies will be best positioned to leverage AI responsibly and effectively.
Automation doesn’t just optimize workflows— it creates the connected, governed data ecosystem on which intelligent systems depend. As enterprises accelerate digital transformation, assessing automation maturity will increasingly define true AI readiness.
Because intelligent insight doesn’t begin with AI. It begins with automation of your data and processes.
Partner with Precisely
Precisely helps organizations turn automation into a strategic advantage. From workflow orchestration in SAP, Snowflake and Salesforce, to data quality, validation, and enrichment through our Location and Data APIs, Precisely empowers enterprises to build trusted data foundations that fuel AI success.
If your organization is modernizing on S/4HANA or exploring ways to strengthen AI readiness, connect with our experts to assess your automation maturity and identify where to begin.
Let’s unlock the full potential of your data—together.
FAQ:
How does automation support S/4HANA modernization and clean-core strategies?
Automation standardizes and accelerates data preparation, validation, and transformation during migration. By shifting manual logic and custom code into governed workflows, it supports SAP’s clean-core strategy—simplifying upgrades, maintaining compliance, and improving long-term maintainability.
How does automation improve data quality inside SAP?
Automation embeds validation rules and business logic directly into workflows, ensuring data is accurate, complete, and standardized as it’s created. This eliminates downstream rework and delivers consistent, trusted data for analytics and AI models.
Why is automation so important for AI success?
AI is only as strong as the data it learns from. Automation enforces governance and consistency at scale, creating structured, reliable data pipelines that allow AI systems to deliver meaningful, trustworthy insights.
What role does data enrichment play in AI readiness?
Data enrichment adds verified external context—such as geocodes, addresses, or industry classifications—that improves accuracy and reliability. Precisely’s Location Intelligence and Data APIs provide this validation and context, enhancing analytics and AI-driven decision-making.
How can automation and AI work together in SAP environments?
Automation provides the structure and governance that AI depends on, while AI enhances automation through predictive and adaptive capabilities. Together, they form a continuous improvement loop that drives efficiency, insight, and resilience across SAP operations.
