Scaling AI with adaptive governance
File(s)
Author(s)
Lanzolla, Gianvito
Pagani, Margherita
Tucci, Christopher
Type
Journal Article
Abstract
Leaders with even a cursory understanding of AI know that while this powerful technology can help them to improve productivity and capture new opportunities, it can also expose their organizations to many risks. Those who know a bit more are aware that surfacing and mitigating those risks requires adopting responsible AI practices. And for those scaling AI implementation at their organization, it will become obvious that ad hoc attention to those practices is inadequate, and they will require a systematic capacity to govern AI at scale.
Yet building that capacity is proving far harder than most executives expect. They know what they need to accomplish: frameworks from governments and regulators1 define important guardrails and principles such as transparency, fairness, and accountability. But to implement controls and principles into day-to-day workflows and decision-making, organizations must
rethink AI governance. They must frame that task not as a compliance obligation, but as a strategic, adaptive capability that evolves as AI systems scale, use cases expand, and risks shift over time.
In this article, we show how leading organizations are doing exactly that. We introduce an approach to adaptive AI governance built on two principles: matching governance controls to the type of AI system and risk involved, and embedding those controls directly into
workflows, decision rights, and accountability structures.
Yet building that capacity is proving far harder than most executives expect. They know what they need to accomplish: frameworks from governments and regulators1 define important guardrails and principles such as transparency, fairness, and accountability. But to implement controls and principles into day-to-day workflows and decision-making, organizations must
rethink AI governance. They must frame that task not as a compliance obligation, but as a strategic, adaptive capability that evolves as AI systems scale, use cases expand, and risks shift over time.
In this article, we show how leading organizations are doing exactly that. We introduce an approach to adaptive AI governance built on two principles: matching governance controls to the type of AI system and risk involved, and embedding those controls directly into
workflows, decision rights, and accountability structures.
Date Acceptance
2026-03-15
Citation
MIT Sloan Management Review
ISSN
1532-9194
Publisher
Sloan Management Review Association
Journal / Book Title
MIT Sloan Management Review
Copyright Statement
Copyright © 2026 Copyright Owner. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Accepted