From feature overload to functional intelligence
As enterprise AI becomes more embedded into the fabric of everyday tools, the biggest challenge facing organizations isn’t AI adoption; it’s AI management. Gone are the days when AI features like meeting transcriptions or document summarization stood out as cutting-edge.
Today, they are expected. According to McKinsey’s 2024 State of AI report, 72% of organizations have adopted at least one form of generative AI, and over half report using it in more than one business function. But this surge in adoption has led to a new operational crisis: AI sprawl.What Is AI Sprawl and Why Does It Matter Now?
AI sprawl is the unchecked proliferation of AI tools and systems across departments, applications, and infrastructure without a unified strategy. The result? A chaotic digital ecosystem where:
Redundancy is rampant (e.g., multiple summarization tools embedded in different apps)
User experiences are inconsistent
Data governance becomes unmanageable
Security vulnerabilities go undetected
For example, companies eager to integrate AI across their tech stacks often deploy similar capabilities in silos – an AI assistant in a messaging platform, a different one in email, another in help desk software – without a shared interface or policy layer. This fragmented approach increases operational costs, confuses users, and makes compliance audits a nightmare.The Rise – and Limits – of Vertical AI
Most enterprise AI today is what we call « vertical AI »: narrow capabilities embedded directly into a specific tool, often by that tool’s own vendor. These AI features are excellent at solving bounded problems but struggle at scaling across workflows or departments.
IDC research notes that organizations are spending up to 30% more per seat due to overlapping AI functionality across their application ecosystems (IDC).
