AI's Business Value Depends on Data Fabric Context, Says SAP Executive
Irfan Khan, who serves as the president and chief product officer of SAP Data & Analytics, emphasizes that the primary challenge for successful enterprise AI lies in the quality and context of the foundational data rather than the performance of the models. According to a McKinsey survey, by 2025, half of all companies will implement AI in at least three areas of their operations. However, only 9% believe they are ready to merge AI with their data systems. Implementing a data fabric, which encompasses data federation, a semantic layer, and cross-fabric governance, facilitates AI's interaction with business insights. More than two-thirds of organizations utilizing data fabrics report enhanced data accessibility and control, promoting synchronized AI functions across different business areas.
Key facts
- Irfan Khan is president and chief product officer of SAP Data & Analytics.
- By end of 2025, half of companies used AI in at least three business functions per McKinsey survey.
- Only 9% of organizations feel fully prepared to integrate AI with data systems.
- One in five organizations consider their data approach highly mature.
- Data fabric is an abstraction layer spanning infrastructure and logical organization for AI.
- Data fabric requires federation, semantic layers with knowledge graphs, and cross-fabric governance.
- Over two-thirds of enterprises with data fabrics see improved data accessibility and control.
- AI without business context can produce technically correct but operationally flawed decisions.
Entities
Institutions
- SAP Data & Analytics
- McKinsey
- Capgemini