My take: Understanding business context is key to generative AI success

SAP Chief Marketing Officer Eric van Rossum shares three guiding principles that can help enterprises get greater value out of their generative AI implementations

By Eric van Rossum, chief marketing officer for cloud ERP and industries, SAP

In the past year, conversation engines based on large language models have revolutionized how we search for information and generate new content—literally overnight. Traditional approaches to ideating and implementing creative ideas, developing code, and making sense of large amounts of information have been disrupted. A new way of working has been established. Tasks that traditionally took days or hours can be shortened to a few minutes with impressive results.

This will fundamentally change how users work with software. More and more, search requests and business processes will be executed using natural language and voice input. Imagine a world where artificial intelligence takes the lead in executing operational tasks under a business user’s supervision, freeing up the user for more strategic, value-adding tasks.

In my view, it’s critical that we stop thinking only about how companies will use the technology and instead ask ourselves how the supply chain, finance, or any other business functions will look and how they will define their roles in the future.

That said, AI is not yet mainstream. We know from the innovation adoption curve that usually only a few companies step ahead as innovators and early adopters. Those companies taking the first steps now, starting with exploring and testing AI features, are building up their competitive advantage.

We at SAP believe that three guiding principles will enable enterprises to get the most out of their generative AI implementations, both in the core and across business applications. First, AI must be relevant to businesses, so that it can drive immediate impact. Second, it must be reliable, so organizations can make confident AI-based decisions, grounded in business data. Third, AI must be responsible, based on the highest ethical, security, and privacy standards so that users can trust its output.

With those guiding principles in mind, companies should develop a clear strategy anchored in business goals. AI shouldn’t be considered a stand-alone functionality, but rather an integral, embedded capability in all business processes to support a company’s digital transformation.

A good place to start is by asking these two questions: Where do we need additional decision-making support in our core business processes? And how can we use AI to make our employees more productive (do more with less), improve the quality of their work (do what they’re doing even better), and even drive top-line growth?

Customers should prioritize their AI investments according to business value, which is why all of our use cases at SAP are reviewed and evaluated based on business value during the development process.

Organizations will measure the success of their AI investments in different ways, but a good place to start is, again, to see if AI is making your employees more productive, improving the quality of their work, or driving greater top-line growth.

AI, and particularly generative AI, can increase employee productivity and efficiency. Especially in times of increasing pressure, a productivity tool like this can enable companies to stay competitive.

What will likely distinguish success among those who adopt AI is not just how many AI features they start using but also the extent to which their AI, and specifically their digital assistant, understands their full business context. For instance, it’s one thing to have an AI assistant tell you which location has the highest emissions. It’s another for the tool to explain the factors that are contributing to those emissions—and this is what will give a business a leading edge over competitors. The second agent understands that emissions are driven by supplier networks, transportation routes, transportation methods, vehicles, types of energy in your factories, materials procured, and much more.

And by the way, much of that data sits in your enterprise resource planning system, which is why it’s essential to have AI at the core of your business acting as a copilot that understands the full context of your business. That’s what will differentiate companies from their competition.

Acknowledgments

Editorial consultant: Ed Burns

Design consultant: Heidi Morrow

Cover image by: Meena Sonar