Liz Herbert, Vice President & Principal Analyst

Show Notes:

For many companies, the ERP system is the backbone of the business. And as generative AI emerges as a productivity booster across the enterprise, one growing question among tech leaders is: How will genAI enhance ERP systems? In this episode, Vice President and Principal Analyst Liz Herbert reviews the evolution of AI in ERP, shares use cases for genAI in ERP, and highlights the biggest challenges to adoption.

The conversation begins with a discussion about the history of AI use in ERP systems. ERP vendors like Oracle and SAP have been incorporating machine learning algorithms and adaptive intelligent apps into their systems in various ways for nearly a decade. The adoption of AI in ERP has been steady but slow, with advancements in natural language processing and other AI capabilities gradually being integrated into these behemoth systems.

Throughout the episode Herbert touches on various use cases for genAI in ERP. She says one of the early use cases is leveraging genAI to automate the time-consuming process of writing collections emails to customers. Instead of having accounting staff members look up how much each customer owes and drafting each individual email, genAI could automate the entire process and dramatically reduce time to get those reminders out.

More complex use cases could involve analyzing and modelling changes a specific business function is considering making. How would a specific price change impact revenues? What if a certain supplier was switched to reduce shipping times? GenAI’s ability to analyze data and provide options could be very valuable in these areas, says Herbert.

The depth and breadth of genAI’s use in ERP may vary based on business function. For example, while functions such as finance and accounting may be better suited to leveraging genAI because their data is more consistent, they also tend to be more conservative in use of new tech due to the high stakes involved in accurate reporting. Additionally, many organizations still need to modernize their ERP systems and overcome integration challenges with legacy systems before they can focus on integrating genAI.

The discussion also touches on the role of specialized large language models that cater to specific industry needs and individual business requirements. While ERP vendors will provide their own genAI capabilities within their offerings, there will also be a role for specialized models and add-ons from third-party vendors or services firms. While long-established ERP vendors like SAP and Oracle maintain deep domain knowledge and extensive customer bases, genAI combined with the emergence of cloud platforms does provide startups more opportunity to disrupt the ERP space in certain areas.

Pricing models for AI capabilities within ERP are still evolving, Herbert says: “AI will be coming from lots of sources and part of the secret sauce of doing it well is going to be to figure out that right mix of sources for you.”

The episode closes with Herbert previewing some of the sessions and tracks at the upcoming Technology & Innovation Summit North America September 9-12 in Austin. Herbert will present a session entitled “The Future Of Enterprise Software” and the event has dedicated tracks for AI and Business Applications. View the agenda here.