Large Language Models (LLMs) have captured enterprise attention. Natural language interfaces, copilots, summarisation, and automated content creation are now part of everyday discussions. 

Yet, many organisations find themselves asking a hard question soon after the first demos: 

“Why isn’t this scaling into real business value?” 

The answer is rarely the model itself. It is the operating model around the model. 

Enterprise AI success depends far more on governance, security, lifecycle management, and integration than on which LLM is selected. This is precisely where SAP AI Core and SAP AI Launchpad play a critical role. 

SAP AI Launchpad

Why LLM Adoption Breaks at Scale 

Most organisations begin their AI journey using one of the following approaches: 

1. Directly consuming LLM APIs from providers (OpenAI, Anthropic, Google, etc.) 
2. Using hyperscaler AI platforms 
3. Hosting open‑source LLMs themselves 

All three approaches work technically, but usually fail operationally. 

Common challenges customers experience include: 

1. Multiple API keys, vendors, contracts, and billing models 
2. No consistent governance across teams and applications 
3. Difficulty tracking usage, cost, and lifecycle ownership 
4. Rebuilding integrations every time a model changes 
5. High security and compliance risk due to uncontrolled access 

I will again speak in reference to the Gartner prediction. Gartner predicts that by 2026, 60% of AI projects will be abandoned because they are not supported by enterprise‑ready data and governance foundations. The issue is not AI ambition, it is enterprise readiness. 

Why “Just Using an LLM” Is a Risk 

AI without governance is not just inefficient, it is risky. Now let me talk about some real time instances of this risk.  

IBM Cost of a Data Breach Report highlights that: 

• The global average cost of a data breach reached USD 4.88 million 
• Over 70% of breached organisations experienced operational disruption 
• Organisations using AI and automation in security reduced breach costs by ~USD 2.2 million on average 

LLMs introduce new attack surfaces: 

• Prompts containing sensitive data – this seems to be one of the most pressing and non-compromising problems 
• Uncontrolled connectors 
• Hardcoded API keys 
• Shadow AI usage across teams 

Without a governed AI platform, these risks multiply quickly. 

What SAP AI Core and SAP AI Launchpad Actually Do?

Think of SAP AI Core and SAP AI Launchpad as two layers of the same enterprise AI foundation. 

1. SAP AI Core – The Runtime Engine 
2. SAP AI Core is the execution layer: 
   • It runs AI workloads
   • Hosts and serves models 
   • Exposes APIs to applications 
   • Scales securely using managed infrastructure 

It is where AI actually “runs”. 

SAP AI Launchpad – The Enterprise Control Plane 

SAP AI Launchpad is the governance and lifecycle cockpit: 

• AI scenario lifecycle management 
• Prompt lifecycle management (create, test, version, retire) 
• Consumption and usage analytics 
• Central administration and access control 
• A single UI across all AI runtimes (including AI Core) 

Together, they form a platform that allows enterprise‑grade AI, not just AI experiments.  

Multi‑Model Without Multi‑Integration Chaos 

Through SAP AI Core’s Generative AI Hub, customers can access multiple foundation models via a single, governed interface, including: 

• Azure OpenAI models 
• AWS Bedrock models (e.g. Anthropic) 
• Google Vertex AI models (e.g. Gemini) 
• Open‑source models hosted in SAP AI Core 
• Mistral AI models 
• IBM foundation models 
• SAP foundation models such as SAP RPT‑1 and SAP‑ABAP‑1 to name a few

Business value: 
Customers can choose the right model for the right use case and trust me that too without rewriting applications or breaking governance every time they switch. 

Why This Is Cheaper Over Time (Based on Illustrative Economics – my favorite) 

While individual LLM APIs may look cheaper initially, customers often underestimate hidden costs: 

• Duplicate integrations per model and per application 
• Separate governance implementations per team 
• Re‑certification and compliance reviews for every provider 
• Cost overruns due to lack of usage visibility 

With SAP AI Core + Launchpad: 

• One integration layer supports multiple models 
• One governance model scales across teams 
• Central usage statistics enable proactive cost control 
• Model switching becomes configuration, not redevelopment 

Illustrative outcome observed in practice (this are just illustrative numbers): 

• 25–40% reduction in duplicated integration effort 
• 20–30% reduction in governance and operational overhead 
• Faster time‑to‑production for new AI use cases 

Two Concrete Business Use Cases 

Use Case 1: HR – Workforce Attrition Intelligence and HR Copilot 

Problem: 
HR leaders struggle to identify attrition risk early and take consistent action. 

Solution pattern: 

• Structured prediction models identify attrition risk using tenure, compensation, overtime, engagement, and promotion history 
• An LLM‑powered HR copilot explains risk drivers and recommends retention actions 
• SAP AI Launchpad governs prompt versions, HR data access, and auditability 

Business value: 

• Earlier intervention 
• Reduced attrition costs 
• Consistent decision‑making across managers 
• Fully governed AI in a people‑sensitive domain 

Without AI Core + Launchpad, teams typically struggle to align security, explainability, and lifecycle ownership. 

Use Case 2: Finance – Payment Risk Prediction and AP Copilot 

Problem: 
Late payments and invoice exceptions negatively affect cash flow and working capital. 

Solution pattern: 

1. AI Core hosts prediction models to identify payment delay risk 
2. An LLM copilot summarises invoice issues, explains block reasons, and drafts supplier communications 
3. Launchpad manages lifecycle, roles, and consumption visibility 

Business value: 

• Improved cash‑flow predictability 
• Faster exception resolution 
• Lower operational effort in shared services 
• Governed AI embedded directly into finance processes 

How Long Does It Take to Get Started? 

One of the most overlooked advantages of SAP AI Core and AI Launchpad is how fast enterprise AI foundations can be enabled. 

Total setup time: ~15–20 minutes 

Step‑by‑Step: Turn On SAP AI Core 

Time: ~10 minutes 

• Go to SAP BTP Cockpit → Sub account → Entitlements 
• Edit entitlements and add SAP AI Core 
• Choose: 
  • Standard plan
  • Extended plan (required for LLMs) 
• Save 
• Go to Services → Instances and Subscriptions 
• Create a new SAP AI Core instance 
• Select Cloud Foundry space and create 

AI runtime is now active.

Step‑by‑Step: Turn On SAP AI Launchpad 

Time: ~5–10 minutes 

• In Entitlements, add SAP AI Launchpad (Standard) 
• Go to Instances and Subscriptions 
• Create a SAP AI Launchpad subscription 
• Assign role collections such as: 
  • genai_manager 
  • prompt_manager 
  • genai_experimenter 

Governance cockpit is now ready 

Validation Checklist

After setup, you should be able to: 

• Open SAP AI Launchpad 
• See connected AI Core runtimes 
• Access the Generative AI Hub 
• Explore available foundation models 
• Create and manage prompts 
• View usage statistics 

The Enterprise Bottom Line 

The biggest mistake organisations make is assuming that LLM adoption is a developer problem. 

It is not. 

It is a platform, governance, and operating model decision. 

LLMs generate text. Platforms generate value. 

SAP AI Core and SAP AI Launchpad provide customers with: 

• Freedom of model choice 
• Enterprise‑grade security 
• Lifecycle governance 
• Cost visibility 
• Faster path from experiment to production 

For organisations serious about SAP Business AI, this foundation is no longer optional, it is essential.