Enterprise software is a world of spreadsheets, APIs, and compliance rules, not unstructured chat. Standard large language models, built for text, consistently fail at the relational logic of finance and supply chains.
SAP CTO Philipp Herzig detailed the company's solution on No Priors: a specialized architecture called RPT1, or Relational Pre-trained Transformers. Designed to interpret tabular data directly, it aims for a 3% to 4% improvement in demand forecast accuracy. In global logistics, that margin translates to millions in saved costs, a tangible outcome generic models can't capture. The goal is to automate the feature engineering that still requires expensive data scientists.
"Democratization in the predictive space has lagged behind generative AI."
- Philipp Herzig, No Priors
This push for specialization reflects a broader industry split. On This Week in AI, Perplexity CEO Aravind Srinivas argued that coding, being open-ended and creative, prevents LLMs from becoming true commodities. Models are developing distinct personalities - Claude leads in front-end design, while Codeex is preferred for Swift and Rust. Simultaneously, companies like Apple are building a different moat: using its Silicon to run agent orchestration loops locally on devices, sidestepping cloud latency and privacy issues for sensitive data.
The real challenge isn't building the AI but deploying it at enterprise scale. Herzig highlighted the 'context bloat' problem. A retrieval-augmented generation (RAG) prototype works on ten documents but collapses when reconciling a thousand across 90 countries with different tax laws. SAP manages 20,000 APIs; an agent must disambiguate whether a user needs a 'sales order' or a 'maintenance order' based on departmental data.
"Startups frequently underestimate the complexity of enterprise scale. It breaks when it has to reconcile 1,000 documents across 90 different countries."
- Philipp Herzig, No Priors
This engineering bottleneck is the final barrier to autonomy. Until agents can be verified against a system of record with absolute certainty for global compliance, they'll remain in advisory roles with humans supervising. The race isn't for the smartest chat model; it's for the most reliable system of record.
