At Rise of AI 2023 in Berlin, Feiyu Xu, Senior VP and Global Head of AI at SAP, presents SAP's view of generative AI for enterprise and industry. The talk opens with remarks from Thomas, an SAP board member, who says SAP identified more than 80 generative AI use cases within a few weeks, warns that almost three quarters of AI models come from the US and 15 percent from China, and argues Europe must not miss this shift, pointing to SAP's role in the LEAM initiative for large European AI models. Xu then explains SAP's two-pillar strategy of embedding AI into business applications and enabling low-touch, no-touch innovation across four core business processes, describes advantages of generative over narrow AI such as reduced annotation needs, and outlines a three-step adoption path for companies: use existing models as-is, fine-tune on proprietary data, or invest in further research for data types like tabular, time-series, chemical and biological data. She closes on SAP's AI ethics governance, including a monthly cross-functional steering committee.
Key takeaways
- SAP identified more than 80 generative AI use cases within a few weeks of the technology's emergence, according to board member Thomas.
- Almost three quarters of AI models come from the US and 15 percent from China, which Thomas says makes Europe's LEAM initiative urgent.
- Xu organizes SAP's AI strategy around four business processes: lead-to-cash, design-to-operate, recruit-to-retire, and source-to-pay.
- Xu argues self-supervised learning's reduced annotation burden is what makes low-touch, no-touch enterprise AI applications realistic.
- Xu proposes a three-step adoption path for companies: use models as-is, fine-tune on proprietary data, or invest in further research for underserved data types.
Detailed notes
SAP's early generative AI momentum and Europe's position
Thomas, an SAP board member responsible for product success, opens by describing rapid movement inside SAP: within a few weeks the company identified more than 80 generative AI use cases, including creating test scripts and code documentation, supporting employee HR queries, defining business process models in natural language, and customizing product information for customers. He warns that almost three quarters of AI models come from the US and 15 percent from China, and that Europe must not miss this paradigm shift, citing analyst estimates that the global AI software market grew 22 percent from 2021 to 2022, before accounting for ChatGPT's impact. He points to SAP's participation in LEAM, the large European AI models initiative, and notes SAP set AI ethics guidelines as early as 2018 and introduced a global AI ethics policy in 2022. As an example of AI's impact, he cites the Hamburg Port Authority, which handles millions of cargo containers a year with no room to expand and uses AI to run its infrastructure more efficiently.
SAP's strategy: build AI in, make innovation adaptable
Xu describes SAP's AI approach around two pillars. The first is building AI directly into business applications so customers' own processes become more efficient, since technology alone has no value without application. The second is adaptable innovation: because SAP serves more than 400,000 enterprises across more than 20 to 25 industry verticals, each with different workflows, applications must be flexible, low-touch and no-touch, which she argues was nearly impossible with narrow AI but becomes achievable with foundational, generative models. She organizes SAP's business processes into four end-to-end flows: lead-to-cash (from first customer contact through contract and payment), design-to-operate (product design, customer feedback, supply chain resilience), recruit-to-retire (writing job descriptions, finding candidates, supporting employee development), and source-to-pay (finding suppliers, managing procurement risk and payments), with AI needed throughout all four.
Why generative AI matters for enterprise
Xu frames generative AI's advantages against narrow AI: where narrow AI mainly classifies, for example recognizing a face to unlock a phone, generative AI can create content, raising new authentication and provenance questions about where content originates. Self-supervised learning sharply reduces the annotation effort needed, which she says is central to making low-touch, no-touch applications possible. She also highlights emerging capabilities: a single AI system that can perform multiple tasks, unlike earlier narrow models built for one task each, potentially replacing many micro-services with one industry foundational model. She cites ChatGPT reaching 100 million active users within two months as evidence of how fast adoption can move, expressing hope generative AI will similarly raise adoption of SAP's own AI applications. She expects customer interaction to shift toward chat-based natural language, for example requesting vacation in a sentence instead of filling in start and end dates, and predicts developers will increasingly become debuggers and evaluators of AI-generated code rather than writers of it, illustrated by using GitHub Copilot to build a search engine in a couple of days.
A three-step path for companies
Xu lays out three adoption stages for companies working with foundational models. The first, and lowest-hanging fruit, is using models as they are, which an audience member describes using for coding, including debugging assistance and building a web scraper in about an hour with GPT-4. The second is fine-tuning models on a company's own data, such as SAP's proprietary programming language, which requires working through legal conditions with vendors. The third is continued research into data types large language models still do not handle well, including business process, tabular and time-series data, and chemical and biological data, which she argues requires sustained investment rather than pausing AI development. She illustrates simple applications already possible: retrieval-augmented generation combining embeddings and a large language model to answer business process questions, and a document-processing pipeline, demonstrated by an audience member named Wolf and his team, that uses OCR to convert an invoice image into structured data and then extracts key business information with a language model.
AI ethics governance at SAP
Xu closes by describing SAP's approach to AI ethics in practice, not only on paper. SAP set its AI guiding principles in 2018 as an early European mover and holds a monthly steering committee with legal, human resources, engineering and marketing experts who review use cases and identify data-related challenges, inviting external AI ethics experts when needed. A dedicated trustworthy AI workstream accompanies use cases from ideation through development, deployment and continuous improvement.
Q&A highlights
Asked about the biggest barrier to adoption, Xu says identifying the truth and originality of AI-generated content is a central challenge, less concerning for text but more so for video, making content authentication important for building trust. Asked whether enterprises mostly buy "wrapped" AI products rather than building their own large language model capability, as Deloitte AI reports suggest, Xu agrees this reflects SAP's own approach: SAP mostly sells business AI applications built on top of large language models rather than general-purpose models themselves, and customers typically ask either how SAP can use large language models to improve their business processes, or how to apply AI successfully in their own specialized domains, such as the chemical industry.
Quotes
The question whether AI will revolutionize the way we live and work is no longer relevant. The only relevant question now is how we will shape this revolution together.
Thomas (SAP board member)
Europe must not miss this paradigm shift.
Thomas (SAP board member)
In the future we will not have programmers, we mainly have debuggers.
Feiyu Xu
Questions answered in this talk
What generative AI use cases has SAP identified?
According to Thomas, an SAP board member, at Rise of AI 2023, SAP identified more than 80 generative AI use cases within a few weeks, including test script and code documentation generation, HR query support, and customer support automation.
What is SAP's AI ethics governance process?
According to Feiyu Xu, SAP's Global Head of AI, at Rise of AI 2023, SAP set AI guiding principles in 2018 and a global AI ethics policy in 2022, with a monthly steering committee of legal, HR, engineering and marketing experts, plus external AI ethics advisors, reviewing use cases from ideation through deployment.
How will generative AI change how people interact with enterprise software?
According to Xu, customers will increasingly interact with business software through natural language chat instead of clicking through forms, for example requesting vacation in a single sentence rather than manually filling in start and end dates.