The AI Summit New York Identifies 10 Trends Shaping the Next Era of Enterprise AI

Industry leaders from Disney and Northwell Health examine the shift from AI experimentation to enterprise value and the priorities set to shape investment and adoption in 2027

NEW YORK, October 20, 2026--Artificial intelligence is entering a more consequential phase as businesses move beyond experimentation and confront new questions around value, autonomy, trust and infrastructure. Ahead of its return to the Javits Center December 9-10, The AI Summit New York has identified 10 emerging trends in its new report, AI in Action: From Experimentation to Enterprise Value, examining the forces expected to shape enterprise AI adoption and investment heading into 2027.

Where is AI delivering measurable returns? How much autonomy should AI systems have? Can organizations trust the systems making increasingly important decisions? And what happens as AI becomes embedded in the operating model itself? Five of the report's leading trends highlight questions confronting business and technology leaders as organizations look to turn growing AI investment into measurable results.

1. Agentic and Multi-Agent Systems: Agentic AI is moving beyond prompt-driven assistants toward systems that can plan, reason and take action across complex enterprise workflows. As organizations deploy teams of specialized AI agents, the opportunity for greater automation and productivity is also raising the stakes around governance, transparency and human oversight. This makes the ability to monitor and audit autonomous systems increasingly critical.

2. AI Governance, Safety and Compliance (TRiSM): As AI becomes more embedded in business operations, the focus is shifting from whether organizations can deploy AI to whether they can trust, govern and account for it. In 2026, enterprises are moving from responsible AI principles to operational controls, including risk classification, system documentation, continuous monitoring and human oversight, as evolving regulation and greater AI autonomy make trust, transparency and accountability business imperatives.

3. On-Device and Edge AI with Small Models: AI is moving closer to where data is created as more capable small language models and AI-enabled devices allow organizations to run everyday tasks locally rather than relying exclusively on large cloud models. This emerging hybrid approach of using on-device AI for frequent or sensitive tasks while reserving larger models for complex reasoning is giving enterprises new ways to improve speed and privacy while gaining greater control over the cost of scaling AI.