GoKawiilEnterprises told to unify agentic AI systems, not just scale pilots
An MIT Technology Review Insights webcast produced with NiCE examines how companies are moving AI agents from small pilots to enterprise-wide use. Chandra argues that agents need consistent access to data, context and back-end systems, and warns that scaling without integration can create new silos across teams.
GoKawiil's interpretation of the reporting above, not reported fact.
As AI agents take on more consequential business tasks, treating them like disconnected experiments rather than a unified workforce component risks governance, security and privacy failures. Framing agents as accountable to the same standards as human employees signals a shift toward more formal oversight structures as automation expands.
- AI agent effectiveness depends heavily on unified access to data and context, not isolated deployments.
- Scaling without coordination risks creating new organizational silos and governance gaps.
- Experts recommend focusing on high-value use cases and measurable outcomes rather than broad rollouts.
AI Engineering by Chip Huyen (O'Reilly) — If your team is moving AI from scattered pilots to production scale, this O'Reilly book walks through building applications on foundation models — from data and context pipelines to evaluation and deployment. It's a practical companion to the article's point that agent efficacy depends entirely on the data and context you can feed it.
See AI Engineering by Chip Huyen (O'Reilly) on Amazon → Affiliate link — we may earn a commission on purchases, at no extra cost to you. Product picked by AI based on this article; it is not a tested recommendation.Source: technologyreview.com — Mit Technology Review Insights, 2026-09-03
Published there as: “Scaling agentic AI pilots across the enterprise”
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