Explainer: A look at AI tools in the SAP Environment and How that Impacts You

This blog was written in collaboration with Aaron Lozano.

To say Gen AI is having a moment is an understatement. That attention can be both helpful and distracting, depending on where you are in your technology journey. On the one hand, we need to leverage all the tools available to us that will further our goals. On the other, we also need to make sure that we’ve accurately defined what tools we need and how they will benefit us. This blog will explore what AI means in the context of an SAP environment and how you can take advantage of it. Hint: You may already be using some of these tools if you’ve already implemented SAP S/4HANA.

Key terms you should know

SAP created the term Business AI to refer to many different aspects of its technology, whether it’s their Gen AI tool, Joule, or other embedded tools that have been a part of their system for a long time. To dig deeper, here a few definitions and (where applicable) examples of where you find that functionality in SAP S/4HANA:

• Generative AI (Gen AI): Creates new content or data based on existing patterns. In SAP S/4HANA, this can enhance functions like creating detailed reports or generating customer interaction summaries from raw data.
• Machine learning: A subset of AI that focuses on enabling computer systems to learn and improve from experience or data, incorporating elements from fields like computer science, statistics, and psychology.
• Deep learning: A branch of Machine Learning that uses multi-layered neural networks to analyze complex data patterns, and it may employ different learning methods like supervised, unsupervised, and reinforcement learning depending upon the task and available data.
• Predictive analytics: Statistical algorithms and machine learning techniques to identify the likelihood of future outcomes. SAP S/4HANA uses predictive analytics for sales forecasting, risk management, and maintenance scheduling.
• Computer vision: Allows computers s to interpret and make decisions based on visual data. In SAP S/4HANA, it can be applied in quality control for manufacturing by automatically identifying defects in products.
• Foundation models: AI systems that use self-supervised learning on vast data to perform a variety of tasks, such as writing documents or creating images. Their versatility and adaptability make them a significant advancement in AI, lowering the barriers to adoption.

You may have also heard about SAP’s AI copilot, Joule. Using natural language prompts, Joule provides seamless navigation to different applications to help you leverage the power of Gen AI. This allows users to ask questions and interact with Joule wherever they are. For example, a user can be in a customer report and have questions on product. Rather than leaving the customer report, they will be able to simply ask Joule the question. Whether they are in SAP S/4HANA, SAP SuccessFactors, SAP Start or any other SAP Cloud Solution Joule will help increase efficiency. This tool will be available in SAP S/4HANA Public Cloud Edition later this year, and Private Cloud Edition will soon follow. However, it will not be available for on-premise solutions.

You start by building a robust foundation in your ERP system

While we’re all excited about incorporating AI into our business processes, the most important part, especially with an ERP system, is having a foundation that your AI can build upon. For example, if there are problems in your master data, they will become more exaggerated with AI. Imagine if you have bad data, or outdated information in your system. Then imagine the impact of training your AI tools on that. To avoid these problems, it’s best to start with a roadmap to implement SAP S/4HANA that includes important data management tasks up front as well as implementing a few key tools specific to your business. Then, as you continue to enhance your SAP landscape you can incorporate more advanced functionality. In short, it’s a process that takes time and will be more successful if it’s planned and aligns with the rest of your business goals.

This can look different depending on your company and your industry. For example, before focusing on highly advanced AI tools and models, you might look at tools that include robotic process automation to automate administrative tasks like data entry and report generation, or use it for things like inventory management. Or you might consider something like SAP Analytics Cloud to better forecast cashflow or analyze multiple datasets from SAP and non-SAP sources. Then, later, you can look at things like a customer service ticket scenario where AI helps you craft an empathetic response to a customer that’s really struggling right now. It will depend on where your pain points are, what efficiencies are needed, and what you need to get out of your data.

You don’t have to work alone

To navigate any of these implementations, organizations often collaborate with specialized partners like NTT DATA Business Solutions. Our team provides both expertise in SAP and how to apply it to your industry, business needs and goals. This includes extensive experience with all aspects of SAP implementations from road mapping and consulting to helping you continue to optimize and maintain your system once it’s live.

Executing an AI integration is not a one-time project. Like many other elements of business, it’s a continual process of improvement. Even this technology is constantly evolving. We can help you make the technology work for you without overwhelming your people or resources.