SAP digital transformations: The role of AI in business evolution

Insights

• Organizations are redefining SAP as a strategic digital core, not just a back-office system.
• Clean-core design and unified data are proving essential for AI success.
• Small language models are emerging as a trusted path to secure, scalable AI.
• Enterprises that align people, process, and technology are achieving measurable transformation.

Introduction

Many organizations are rethinking the role of SAP — not as a back-office system, but as the digital core of their business strategy. In the age of artificial intelligence (AI), success doesn’t depend on how much technology you deploy, but how well your systems are architected to learn, adapt, and scale. Clean core transformation, data harmonization, and AI integration aren’t just enablers. They’re imperatives. And the companies that get it right are pulling ahead.

What is SAP digital transformation?

SAP digital transformation refers to modernizing core enterprise systems by leveraging cloud-native technologies, intelligent workflows, and connected data. It goes beyond system upgrades to redefine how organizations operate, decide, and compete in an increasingly digital world. Once an enterprise resource planning (ERP) backbone, SAP is now evolving into a strategic operating system that enables enterprisewide agility and intelligence.

Why is SAP digitalization necessary for businesses?

In today’s volatile business environment, speed, transparency, and adaptability are essential. Yet legacy ERP systems often stand in the way. Fragmented and heavily customized SAP ERP Central Component (ECC) environments block visibility, delay decisions, and hinder innovation. With support for SAP ECC ending in 2027 and the global ERP software market projected to reach $110 billion by 2034, the urgency is real. SAP modernization is no longer an IT project, it is the foundation of enterprise intelligence.

The current state of SAP in business processes

AI, cloud-native platforms, and data-driven operating models are reshaping enterprise strategy. Yet many organizations remain constrained by outdated SAP ERP systems. Over time, customizations and technical debt have made these systems slow to adapt and costly to maintain.

Modern SAP digital transformation starts with the principle of the clean core — a simplified, standardized architecture free from legacy complexity. This structure supports frequent upgrades, real-time visibility, and trustworthy data, all prerequisites for AI integration.

SAP now promotes a three-layered architecture:

• Applications: Standardized processes in SAP S/4HANA.
• Data layer: Unified SAP and non-SAP data via SAP Business Data Cloud (BDC).
• AI layer: AI applied to harmonized data to enable automation, insights, and adaptive decisions.

This model creates a flywheel effect — applications generate operational data that feeds AI models, which in turn optimize the applications. With each cycle, the system becomes smarter, faster, and more autonomous.

Challenges in transforming SAP

Despite heavy investment, many digital transformation programs fail to scale. A common pitfall is approaching migration as a purely technical task without a clear operating model. Simply moving legacy processes into the cloud often locks in outdated logic, fragmented workflows, and poor-quality data. Instead of enabling transformation, it reinforces complexity.

In our Cloud Radar 2023 report, we found that successful cloud programs share certain traits: They keep business value at the center, apply disciplined cost management, and are built on strong governance and security. They also depend on technical depth and cross-functional teams that can align business and IT priorities. Without these elements, migration can quickly drain budgets. Costs continue to rise, and many companies struggle to predict spending or enforce controls.

SAP digitalization therefore requires starting from a solid foundation. Clean system design, consistent data, and smart workflows need to be built in from the start. Transformation succeeds when teams work together across business and IT, make decisions with the customer in mind, and execute with discipline.

The most common obstacles include:

• Weak operating models that undermine governance and delivery.
• Broken legacy processes migrated to the cloud without redesign.
• Poor data quality that limits AI effectiveness.
• AI initiatives cut off from core operations.
• Cloud cost overruns from poor visibility and forecasting.

For example, one manufacturer migrated a flawed invoice process without redesign, only to end up with faster dysfunction, unreliable automation, and unusable AI outputs.

In contrast, a global healthcare company specializing in women’s health deployed SAP S/4HANA across 76 countries and 15,000 users. Guided by a clean-core strategy and the RISE with SAP model, it established SAP as its digital backbone. In just three months, the company integrated acquisitions under $1 billion and cut onboarding costs. By eliminating shadow workflows, strengthening accountability, and creating the conditions for AI to thrive, it showed how transformation can succeed when guided by clear intent and a strong operating model.

From automation to agentic AI

Enterprise systems are evolving from automation to autonomy. The next step is agentic AI — a new generation of AI that doesn’t just assist with tasks but can plan, reason, and act independently, without waiting for human prompts.

Early AI tools supported developers and business users by suggesting code or automating simple actions. Today, agentic solutions can troubleshoot, iterate, and complete complex, multistep processes autonomously. This represents a foundational shift in enterprise productivity, with our research indicating potential gains of 60% to 90% across tasks like API generation, UI development, and database coding.

SAP is investing in the future with innovations like SAP Joule and SAP BDC. Joule is a generative AI copilot that delivers contextual insights and recommendations directly within business processes. BDC unifies SAP and non-SAP data, enabling real-time analytics and AI at scale.

As these tools evolve, so does the role of developers. Their focus is shifting from writing code to overseeing intelligent agents. To fully capitalize, enterprises must align team structures, upskill talent, and design systems with AI-readiness in mind. Our Enterprise AI Readiness Radar research confirms this: the organizations creating the most value are those that embed AI across every part of their business.

Trust, transparency, and training

AI readiness depends not just on technology, but on trust, particularly around how enterprise data is handled. Many large organizations remain reluctant to share sensitive information with external AI platforms, citing concerns about privacy, control, and competitive exposure.

As a result, more enterprises are adopting small language models (SLMs). These compact, domain-specific models can be trained and run entirely within secure environments, enabling faster deployment, lower risk, and full data sovereignty. The significance of SLMs is underscored in Infosys’ Top 10 Imperatives for 2025 report, which identifies them as a key enterprise priority.

Yet even with secure architecture and protected data, enterprise trust in AI decision-making remains limited. Most organizations are comfortable using AI for analysis and recommendations, but hesitant to let it act autonomously, especially in core systems like SAP. A common solution is a human-in-the-loop approach: AI generates a decision, but a human must approve it before execution. This approach helps to safeguard accountability.

Building AI-ready organizations requires more than systems. It requires people who know how to work with intelligent tools. This starts with hands-on access to AI, not just policy discussions. Ethical and legal concerns must be addressed, but they shouldn’t paralyze progress.

With the right governance, companies can safely empower employees to explore, understand, and trust AI. Responsible Enterprise AI in the Agentic Era research finds that while almost all executives have faced damaging AI incidents, those with stronger responsible AI practices build the trust, clarity, and confidence needed to scale. The result is turning fear into fluency, and compliance into capability.

The essentials for successful SAP digitalization

The most effective business transformations follow a disciplined, repeatable approach. Hitachi Energy is unifying 32 ERP systems and 45,000 employees under one SAP S/4HANA platform. Standardized processes and clean data are enabling intelligent workflows that respond autonomously to changing business needs, from supply chain disruptions to shifting customer demand, showing that smart design and planning lead to smart execution.

Based on our research and client experience, we recommend five priorities:

1. Start with a strategic clean core
   Reduce complexity by eliminating embedded customizations and enabling modular innovation. A fit-to-standard analysis compares existing processes with SAP’s standard functionality to determine what truly requires customization, what can shift to the cloud, and what legacy code can be retired. One life sciences company applied this approach with SAP, Infosys, and Amazon Web Services (AWS) to build a clean, cloud-first foundation that improved supply-chain agility and inventory accuracy.
2. Connect enterprise data
   Unify SAP and non-SAP data to provide the context AI needs to deliver value. Begin by cleaning, harmonizing, and standardizing data, then leverage SAP BDC for advanced use such as real-time analytics, predictive modeling, and cross-enterprise insights. A North American heating, ventilation, and air conditioning (HVAC) manufacturer did this by integrating SAP with Databricks, harmonizing internal and external data, and deploying 25 AI use cases that unlocked significant operational and commercial impact.
3. Lead with change
   Leading companies prepare their people to work confidently with intelligent tools. This goes beyond training: It means redefining roles, clarifying accountability, and creating an environment where employees understand how AI works and why they can trust it. Infosys’ AI Business Value Radar shows that companies investing in trust, transparency, and workforce readiness consistently outperform those that don’t.
4. Execute in fast cycles
   Speed and continuous learning are critical. Companies should combine agile sprints with disciplined program management to accelerate both the blueprinting and build phases. A global pharmaceutical company applied this approach to launch more than 20 AI applications, including demand forecasting, clinical trial data analysis, automated quality checks, and personalized patient engagement. Using 90-day sprints through an innovation pod, the company scaled innovation quickly without sacrificing governance.
5. Architect AI-native workflows
   AI should be built into workflows, not added later. Embedding AI ensures processes are more adaptive, automated, and insight-driven. For instance, a global beverage company embedded AI into planning workflows to align supply with real-time demand, improving forecast accuracy and responsiveness.

Following these recommendations will help companies ensure their transformation becomes measurable, resilient, and AI-ready.

The path forward

SAP S/4HANA is no longer just a system upgrade. It is the engine of business reinvention. But the real differentiator isn’t the technology. It is how deliberately an enterprise is designed to harness it. The organizations pulling ahead are not those with the biggest cloud budgets or the most AI pilots. They are the ones bold enough to simplify, disciplined enough to align data and workflows, and wise enough to empower people with systems that learn, adapt, and act.

Transformation is not about chasing innovation for its own sake. It is about building a core designed to continuously evolve and respond to what comes next.

In the age of intelligent transformation, the future doesn’t reward caution. It rewards architecture built for change and leadership prepared to unlock it.