The rapid integration of artificial intelligence (AI) into business operations has prompted enterprises to deploy AI agents as co-pilots, assistants, and autonomous task executors. According to McKinsey’s annual AI report, nearly two-thirds of companies were experimenting with AI agents by late last year, with a significant percentage utilizing AI across various business functions. However, despite the initial success of pilot projects, only a small fraction of companies have effectively scaled their AI agents. A primary obstacle hindering this expansion is the inadequacy of the data infrastructure that underpins these AI systems.
Experts assert that the delays in AI implementation are not primarily due to the limitations of the AI models themselves but rather stem from the absence of a robust data architecture capable of providing essential business context. Irfan Khan, President and Chief Product Officer of SAP Data & Analytics, emphasizes that companies must prioritize establishing a well-defined data architecture in the coming months, as the evolving AI landscape is unpredictable. To achieve immediate wins, organizations need to adopt an AI-centric mindset and ensure their AI models are supported by reliable data. The effectiveness of AI agents is increasingly dependent on the integrity of an enterprise’s data infrastructure and governance rather than solely on the advancement of AI models.
As businesses aim to scale AI technologies, it becomes crucial to implement a modern data framework that supplies relevant context alongside the data itself. Contrary to traditional views that categorize structured data as high-value and unstructured data as less significant, the advent of AI complicates this distinction. High-value data for AI agents is determined more by its business context than by its format. Critical business functions, such as supply chain management and financial planning, rely heavily on context-dependent data. Khan points out that the real challenge lies not in the lack of data but in the absence of business grounding, which is essential for realizing the true potential of AI. To foster trust in data, organizations must establish shared definitions, semantic consistency, and reliable operational contexts that align data with its intended business meaning, overcoming the ‘trust debt’ that has hindered many companies from achieving AI readiness.
Source: Building a strong data infrastructure for AI agent success via MIT Technology Review