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Can You Scale AI Without Losing Control (or ROI)?

Drawing on survey insights from 100 AI leaders, The Expanding AI Enterprise 2026 explores the new scale paradox: AI portfolios are growing fast—fueled by GenAI, third-party services, and emerging agentic systems—while enterprise value struggles to keep pace. The report highlights the execution gaps that stall progress (cost, risk, and operational complexity), and offers a practical roadmap for profitable AI transformation through lifecycle oversight and embedded, scalable governance.

Key Findings at a Glance:

  • AI portfolios are getting bigger—fast. In 2025, most enterprises reported fewer than 100 AI/ML use cases. In 2026, the most common portfolio size has shifted to 101–250, with a growing share reporting 250+ in production.
  • Scale is still the bottleneck. Even as portfolios grow into the hundreds, many enterprises continue to operate fewer than 25 AI use cases at scale—a widening gap between activity and durable production value.
  • Speed is improving (especially with vendor services). Time-to-market is shortening as more teams rely on vendor-provided AI building blocks—moving faster from idea to pilot and initial production.
  • 60% of agentic use cases reach production within 3 months, while higher-risk agentic work typically reaches production in 6–12 months—accelerating the need for consistent oversight.
  • 49% of enterprises report using commercial AI lifecycle management and governance platforms in 2026—evidence that governance is becoming operational infrastructure, not a side project.
  • Fragmentation remains a stubborn blocker. Disconnected systems were a top barrier in 2025 (58%) and are still cited by more than one-third of organizations in 2026—reinforcing the need to standardize how AI is tracked, controlled, and scaled.

These findings point to a clear takeaway: AI is scaling faster than enterprise control, and the gap between “in flight” and “at scale” is where cost, risk, and ROI get decided.

Download the report to see where your organization stacks up—and what practical governance steps top performers are using to turn momentum into measurable value.

See what’s holding AI back—and how to fix it
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