AI has become everyone’s favorite buzzword. Yet behind the hype, most enterprises are still asking a simple question: waiting for IT’s AI masterplan is not an option – how do we make this work with what we have and start seeing real value right now?

For SAP customers, that question is amplified by years of investment in structured, governed systems that already hold the lifeblood of their business. SAP data represents clarity and trust which is exactly what AI needs yet often struggles to understand.

I see this every week: business leaders want results now, not in two or three years when their transformation roadmap is complete. The good news is that you don’t need to start over. You just need to start smart.

 

Why Most AI Initiatives Stall and What to Do Instead

In Gregor’s post “What 100 Executives Taught Me About AI”, he shared that executives aren’t short on ambition: they’re short on clarity. And MIT’s State of AI in Business 2025 report agrees: 95% of AI projects fail to reach production because organizations don’t know where to begin.

The problem isn’t just technical; it’s cultural. Too often, AI is treated as an IT rollout rather than an organizational redesign. It’s not a department; it’s a mindset shift. AI is reshaping how decisions are made, how teams collaborate, and how knowledge flows across systems and domains. The companies that succeed treat AI as both a technology and a new form of shared understanding.

That’s why the growing industry focus on semantic interoperability matters so much. Industry leaders are realizing that inconsistent data definitions and misaligned semantics aren’t just technical headaches but also cultural ones. When teams define “customer” or “revenue” differently, their systems learn differently too. Fixing that gap isn’t just data modeling; it’s also business alignment.

This is where semantic modeling connects the dots between technology and culture. It gives AI systems context while giving organizations a common language. It’s the foundation that lets business logic, governance, and collaboration finally speak to each other and that’s what makes AI truly work and where the real magic happens.

 

SAP Data: The Perfect Place to Start

SAP data already tells a story, but one that is not easily understood: it’s organized by business objects, tied to governance, and traceable to real-world business processes. It’s also not messy consumer data; it’s mostly structured knowledge that is unfortunately, essentially redacted by cryptic field and table names and many other complexities.

The challenge is that in SAP’s semantics, those relationships between customers, orders, materials, and cost centers are often buried inside tables, configurations, and custom logic. AI can’t see or interpret them unless we expose them intelligently.

That’s where semantic modeling comes in.

At Cirql One, we’re building an AI-ready semantic layer that sits between enterprise systems and AI solutions. Think of it as a translator: it helps AI understand the meaning behind your SAP data while maintaining the security models and compliance standards already built into your SAP environment.

 

What We Do Differently

Most data platforms focus on moving data. Ours focuses on understanding it and doing the hard work so our customers don’t have to.

The Cirql One platform automatically interprets SAP data structures and translates them into real business concepts – the kind people actually work with every day, like Customers, Orders, or Invoices.

It supports:

• Modelling business meaning, not just data structures, connecting SAP tables (like KNA1, MARA, VBAK, VBRK) to the real-world objects they represent and make that meaning usable for AI and analytics out of the box.
• Keeping governance intact, meaning domains, authorizations, lineage, and auditability stay under enterprise control while the semantics become shareable.
• Integrating seamlessly with SAP BTP, Datasphere, BDC, Snowflake or any other cloud partner helping to enable the gradual adoption of your enterprise modernization strategy.
• Enabling AI discovery safely by facilitating experimentation without losing compliance or control.

Our architecture follows a clean, modular principle: semantics first, AI second. This means the system can interpret relationships before attempting automation, which dramatically improves trust and explainability.

 

Making Progress Without Waiting

SAP’s Clean Core and Business Data Cloud are perhaps the right long-term direction but for many enterprises they’re a blueprint that stands a few years out. In this climate, enterprises just can’t afford to wait until the end of a multi‑year transformation – they need to start seeing value from AI now or risk falling behind.

Cirql One helps build that bridge. Our approach complements SAP’s and our customers’ modernization strategy by making AI discovery and data understanding possible today. It helps organizations take the next step with confidence to:

• Explore AI opportunities and test ideas within their current SAP environment.
• Identify and validate areas where AI can create measurable improvements.
• Build a foundation of semantic understanding that will support future modernization efforts, whether through BDC, S/4HANA, or the next big thing.

It’s about helping enterprises realize the benefits of AI responsibly and effectively, starting from where they already are.

 

Start Smart, Stay Ready

SAP has spent decades perfecting systems of record. AI is now building systems of reasoning, and the real opportunity lies in connecting the two.