Use Case

AI Governance and Adoption

A robust framework for supporting AI governance and tracking AI initiatives

As AI proliferates across your organization and is incorporated into more and more applications and business processes, robust AI governance and adoption practices should become an essential part of enterprise architecture management. This requires full visibility into the IT landscape so you can chart the potential risks, benefits, and use cases for a myriad of AI technologies.

The latest extension to the SAP LeanIX meta model addresses AI technologies, enabling you to gain the visibility and control you need for efficient AI governance and adoption.

Read on to learn how this out-of-the-box extension helps you maximize the value and minimize the risk of your AI initiatives.

80 %

of companies are leveraging generative AI

90 %

of IT experts say they need a clear view of AI use in their organizations

14 %

say they actually have the overview of AI that they need

Report

SAP LeanIX AI Survey Results 2024

SAP LeanIX surveyed 226 IT professionals working for organizations across the world to find out how they're dealing with AI adoption and governance.

Access the report

How it works

Comprehensively evaluate AI initiatives

Capture AI-related information on business capabilities and applications including AI usage, AI risk, and AI potential, giving you a complete understanding of where the technology is implemented across your IT landscape
Categorize where AI has the potential to enhance productivity, user interactions, or transform existing processes
Identify which applications benefit the most from introducing AI technologies and prioritize your initiatives accordingly

Track AI-associated risks

Gain full visibility of your potential AI risk exposure for all applications
Cross-reference AI risk with AI usage and potential AI benefits
Support AI risk management and mitigation efforts by providing key information to stakeholders
Identify the applications scored with the highest AI risk to prioritize further investigation and mitigation actions

Chart AI adoption and standardization

Add AI technologies, such as LLMs, to your lifecycle visualizations, capturing the maturity and adoption of different AI technologies in your organization
Leverage the maturity and level of adoption assessment to identify AI technologies that are candidates for standardization
Track emerging LLMs and other AI technologies that are at the initial stages of adoption and evaluation in your organization
Guide AI governance policy on AI adoption and standardization through insights captured in the technology lifecycle report

Envision and plan your AI-related transformations

Plan your AI initiatives and document key information such as timeline, resources, and associated applications or IT components
Create detailed roadmaps for transforming business capabilities and applications with AI and ensure alignment with all relevant stakeholders
Maintain an overview of your AI projects and track progress
Keep EA documentation up-to-date and automatically execute impacts after the completion of a project