After a troublesome adolescence during which artificial intelligence (AI) mostly appeared in movie plots and presentations delivered by so-called “futurists,” AI has finally come of age. It’s now starting to change the way both consumer- and enterprise-focused products perform.

Beyond the Robots

The real (non-Hollywood) AI has emerged because of faster processing speeds, improved (and cheaper) storage, and our current approach to managing Big Data. The sum of these forces has allowed us to start applying AI to help all software systems drive the decision-making process. The hope is that we’ll build up to a new level of context-aware use cases.

The emergence of real-world AI comes from the combination of all these factors, plus a new approach to microprocessor chipset engineering where a degree of AI is hard-etched onto the circuit boards powering our machines. You can see why AI has become core to how we’re now able to make digital transformation happen.

ERP AI Introduces Decision Support

AI is changing all technology, and enterprise resource planning (ERP) software is no exception. ERP with AI (or ERP AI, pronounced like Shar-Pei) is primed to deliver decision support. The machine brain can help back up, ratify, correlate, and in some cases, completely automate and replace decisions that normally, a human being would need to make.

ERP AI actions could include, for example, answering typical business questions for sales, procurement, finance, production, supply chain, and project cost estimates.


To answer this need, SAP has engineered AI within the SAP S/4HANA 1805 release. Said to be “the most AI-driven HANA yet,” this new version is integrated with SAP CoPilot digital assistant technology to add a conversational user interface. The release ships with three new SAP CoPilot scenarios, as well as nine new instances of machine-learning-driven decision-support scenarios.

This brings a conversational user experience to ERP, which is designed to significantly reduce the number of clicks and typed commands users need to perform to negotiate their way through multiple screens to get to what they need.

Taking the Human out of Manual Tasks

When SAP talks about these nine new machine learning scenarios in the context of ERP, this is intelligence that is applied to specific business operations such as sales, finance, procurement, production, and the supply chain.

As Sven Denecken, SVP, Head of Product Management, Co-Innovation SAP S/4HANA at SAP SE explains here, “When it comes to paying for your procured goods and services, machine learning can dramatically reduce the number of manual tasks that need to be carried out, particularly in cases where the goods and invoices receipts do not match. This exception handling process used to involve several mundane routine manual steps.”

What’s Noteworthy in the SAP S/4HANA 1805 Release?

Here are some of the release highlights in SAP S/4HANA 1805, each aimed at a specific business function:

  • Finance: Account reconciliation powered by machine learning leads to intelligent recommendations instead of manual resolutions. This will speed up the exception management process and help improve accuracy.
  • Sourcing and Procurement: Smart buying via a conversational user experience (natural language interaction) eases and accelerates requisitioning, which helps increase productivity.
  • Sales: Sales quotes can quickly become sales orders through a conversational user experience (natural language, voice, or text), giving salespeople more time to focus on what they do best.
  • Enterprise Portfolio and Project Management: AI-powered project cost forecasting helps reduce budget overruns and drives better project investment decisions. The conversational user experience powered by SAP CoPilot gives project managers a hands-free, interactive experience that allows them to stay on top of their game by sharing insights on the status of their projects anytime, anywhere.

The Bottom Line on AI in SAP ERP

Has SAP put fully proficient AI and machine learning intelligence into ERP? The tech analyst and press community might argue that this may be a significant advance, but natural language interfaces have been criticized for being narrow in scope.

The bottom line is, users will only move to chatbots, AI, and machine-learning powered intelligent software agents—or any system interactions that involve speech recognition—if they are easier, faster, more efficient, more intuitive, and ultimately more productive than the way we have done things in the past.

Learn how to tap into the latest innovations in artificial intelligence. Register for our webcast, Industrial AI for Intelligent Enterprise to hear insights from Jerry Chen, Business Development in Machine Learning and Data Science at NVIDIA.