# MIT Technology Review examines redesigning organizations for agentic AI [Canonical reading](https://escapemeridian.com/readings/mit-technology-review-examines-redesigning-organizations-for-c2f3d9d0) MIT Technology Review published this sponsored Insights article on May 26, 2026. It argues that organizations risk falling short of their ambitions for AI agents when they add them to human-designed operations instead of redesigning how work is organized. Prasun Shah of PwC UK Consulting compares this to “adding sticky tapes to parts of an operating model that is breaking.” Ema calls a broader redesign agentic business transformation (ABT): changing an organization’s technology stack, workforce, and measures of success. The article’s central idea is that AI agents may create value by coordinating work across applications, not merely by acting as another tool in existing processes. That would require reconsidering workflows and who makes decisions. In mixed human-and-agent teams, Shah says managers will face questions of trust, explainability, psychological safety, and status. The article also argues that measuring activity, such as interactions handled, can obscure whether customers benefit; it describes one Ema customer whose measured return on investment tripled after changing its metrics. It closes by urging leaders to begin internal discussion of systems-level change, while acknowledging that these questions are complex and still being worked through. How accountability should work when an AI agent makes a mistake, or when people and agents disagree, remains unresolved. Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source MIT Technology Review · Article Original source date: May 26, 2026 (2026-05-26T14:54:10Z). Date basis: Date from the publisher’s own records, through its API. Added to Escape Meridian: 2026-11-09T00:23:24.037Z. First observed: 2026-11-08T22:13:38.961Z. Retrieved: 2026-11-08T22:15:35.054Z. - [Rethinking organizational design in the age of agentic AI](https://www.technologyreview.com/2026/05/26/1137584/rethinking-organizational-design-in-the-age-of-agentic-ai/) ## Limits Text as retrieved on November 8, 2026; originally published May 26, 2026. This is sponsored content produced by Insights, MIT Technology Review’s custom content arm, and was not written by the publication’s editorial staff. The article attributes its estimates and predictions to named organizations or speakers; it does not establish that projected benefits or workforce changes have occurred. ## Open question Who should be accountable when an AI agent makes a mistake, or when people and agents disagree? ## Source-stated The article argues that layering AI agents onto operations designed for people may prevent organizations from realizing their full value. > They’re embedding AI employees into what is a human operating model,” layering on AI agents to existing workplace structures when “this is like adding sticky tapes to parts of an operating model that is breaking. - [Supporting source](https://www.technologyreview.com/2026/05/26/1137584/rethinking-organizational-design-in-the-age-of-agentic-ai/) ## Source-stated Ema describes agentic business transformation as integrating AI agents into the organization and changing its technology stack, workforce, and success measures. > According to Ema, ABT encompasses three core pillars: an organization’s technology stack, its workforce, and the metrics used for success. - [Supporting source](https://www.technologyreview.com/2026/05/26/1137584/rethinking-organizational-design-in-the-age-of-agentic-ai/) ## Source-stated The article says agents’ ability to coordinate across applications could make them more than another layer in an existing technology stack. > the value in AI agents isn’t as another layer in an existing technology stack but as a connective tissue - [Supporting source](https://www.technologyreview.com/2026/05/26/1137584/rethinking-organizational-design-in-the-age-of-agentic-ai/) ## Source-stated The article reports that one Ema customer’s measured return on investment tripled after it changed its metrics. > the measured ROI from agentic AI tripled within two quarters. - [Supporting source](https://www.technologyreview.com/2026/05/26/1137584/rethinking-organizational-design-in-the-age-of-agentic-ai/) ## Source-stated The article says leaders will need to consider accountability when AI agents make mistakes and when humans and agents disagree. > Who is accountable when an AI employee makes a mistake? What happens when AI and humans disagree? - [Supporting source](https://www.technologyreview.com/2026/05/26/1137584/rethinking-organizational-design-in-the-age-of-agentic-ai/) ## Source and contributor context MIT Technology Review · Publisher The publisher is identified here. No individual author has been inferred. ## Source media Media retrieval status: unchecked. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Agentic AI AI systems that plan and carry out a series of steps toward a goal, often by using other software, rather than giving one answer. - [Definition reference](https://developers.google.com/machine-learning/glossary#agentic) ## AI agent An AI system that chooses and carries out actions toward a goal, often using software tools. - [Definition reference](https://www.ibm.com/think/topics/ai-agents) ## Interpretability Research into how a model reaches its outputs, often by examining its internal workings. - [Definition reference](https://developers.google.com/machine-learning/glossary#interpretability) ## Editorial provenance Version 1. Publication ID: 4d39e9b6-8956-2283-70b2-8f07a7b04f72. Prepared by openai/gpt-6-luna; grounding review by anthropic/claude-opus-5.5. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-09T00:21:14.326Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: bridge\_admitted\_by\_model. Observation: a95cffe7c430c1318187406a4e3fc8c1dd4c1ffb12b58e04d2c51412853bad51. Source text hash: a7e4a89a9d1ea2a7bcbf7f89e3e5d5c748d016e0a1b14d2b8316af192faeb95f. ## Revision and source-check history ### Version 1 — 2026-11-09T00:23:24.037Z MIT Technology Review published this sponsored Insights article on May 26, 2026. It argues that organizations risk falling short of their ambitions for AI agents when they add them to human-designed operations instead of redesigning how work is organized. Prasun Shah of PwC UK Consulting compares this to “adding sticky tapes to parts of an operating model that is breaking.” Ema calls a broader redesign agentic business transformation (ABT): changing an organization’s technology stack, workforce, and measures of success. The article’s central idea is that AI agents may create value by coordinating work across applications, not merely by acting as another tool in existing processes. That would require reconsidering workflows and who makes decisions. In mixed human-and-agent teams, Shah says managers will face questions of trust, explainability, psychological safety, and status. The article also argues that measuring activity, such as interactions handled, can obscure whether customers benefit; it describes one Ema customer whose measured return on investment tripled after changing its metrics. It closes by urging leaders to begin internal discussion of systems-level change, while acknowledging that these questions are complex and still being worked through. How accountability should work when an AI agent makes a mistake, or when people and agents disagree, remains unresolved. ## Original source MIT Technology Review · Article Original source date: May 26, 2026 (2026-05-26T14:54:10Z). Date basis: Date from the publisher’s own records, through its API. Added to Escape Meridian: 2026-11-09T00:23:24.037Z. First observed: 2026-11-08T22:13:38.961Z. Retrieved: 2026-11-08T22:15:35.054Z. - [Rethinking organizational design in the age of agentic AI](https://www.technologyreview.com/2026/05/26/1137584/rethinking-organizational-design-in-the-age-of-agentic-ai/) ## Limits Text as retrieved on November 8, 2026; originally published May 26, 2026. This is sponsored content produced by Insights, MIT Technology Review’s custom content arm, and was not written by the publication’s editorial staff. The article attributes its estimates and predictions to named organizations or speakers; it does not establish that projected benefits or workforce changes have occurred. ## Open question Who should be accountable when an AI agent makes a mistake, or when people and agents disagree? ## Source-stated The article argues that layering AI agents onto operations designed for people may prevent organizations from realizing their full value. > They’re embedding AI employees into what is a human operating model,” layering on AI agents to existing workplace structures when “this is like adding sticky tapes to parts of an operating model that is breaking. - [Supporting source](https://www.technologyreview.com/2026/05/26/1137584/rethinking-organizational-design-in-the-age-of-agentic-ai/) ## Source-stated Ema describes agentic business transformation as integrating AI agents into the organization and changing its technology stack, workforce, and success measures. > According to Ema, ABT encompasses three core pillars: an organization’s technology stack, its workforce, and the metrics used for success. - [Supporting source](https://www.technologyreview.com/2026/05/26/1137584/rethinking-organizational-design-in-the-age-of-agentic-ai/) ## Source-stated The article says agents’ ability to coordinate across applications could make them more than another layer in an existing technology stack. > the value in AI agents isn’t as another layer in an existing technology stack but as a connective tissue - [Supporting source](https://www.technologyreview.com/2026/05/26/1137584/rethinking-organizational-design-in-the-age-of-agentic-ai/) ## Source-stated The article reports that one Ema customer’s measured return on investment tripled after it changed its metrics. > the measured ROI from agentic AI tripled within two quarters. - [Supporting source](https://www.technologyreview.com/2026/05/26/1137584/rethinking-organizational-design-in-the-age-of-agentic-ai/) ## Source-stated The article says leaders will need to consider accountability when AI agents make mistakes and when humans and agents disagree. > Who is accountable when an AI employee makes a mistake? What happens when AI and humans disagree? - [Supporting source](https://www.technologyreview.com/2026/05/26/1137584/rethinking-organizational-design-in-the-age-of-agentic-ai/) ## Source and contributor context MIT Technology Review · Publisher The publisher is identified here. No individual author has been inferred. ## Source media Media retrieval status: unchecked. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Agentic AI AI systems that plan and carry out a series of steps toward a goal, often by using other software, rather than giving one answer. - [Definition reference](https://developers.google.com/machine-learning/glossary#agentic) ## AI agent An AI system that chooses and carries out actions toward a goal, often using software tools. - [Definition reference](https://www.ibm.com/think/topics/ai-agents) ## Interpretability Research into how a model reaches its outputs, often by examining its internal workings. - [Definition reference](https://developers.google.com/machine-learning/glossary#interpretability) ## Editorial provenance Version 1. Publication ID: 4d39e9b6-8956-2283-70b2-8f07a7b04f72. Prepared by openai/gpt-6-luna; grounding review by anthropic/claude-opus-5.5. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-09T00:21:14.326Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: bridge\_admitted\_by\_model. Observation: a95cffe7c430c1318187406a4e3fc8c1dd4c1ffb12b58e04d2c51412853bad51. Source text hash: a7e4a89a9d1ea2a7bcbf7f89e3e5d5c748d016e0a1b14d2b8316af192faeb95f. ### uncertain — 2026-11-09T00:21:14.326Z — anthropic/claude-opus-5.5 Publication: 4d39e9b6-8956-2283-70b2-8f07a7b04f72 attribution — uncertain: Shah and Chatterjee are quoted accurately, and the reading notes this is sponsored Insights content. It never says the sponsor is Ema, yet Ema is the source of the ABT framework and the ROI anecdote. The title also credits MIT Technology Review as the examiner, which can read as editorial work. Smallest repair: state that the piece was produced in partnership with Ema, the enterprise agentic AI platform quoted in it, and attribute the title to the sponsored article. > In partnership withEma summary — uncertain: The argument, the three ABT pillars, the shift from output to outcome metrics, the manager concerns and the closing call are faithful. However, Ema appears without identification. A general reader cannot tell that it is the vendor that coined ABT and sponsors the piece, and that context matters for weighing its framework and customer anecdote. Smallest repair: describe Ema as the enterprise agentic AI platform that coined the term with HFS Research and sponsored the article. The question follows from Shah's own list. > Enterprise agentic AI platform Ema describes this change as agentic business transformation (ABT), a term it coined last year in partnership with HFS Research date — pass: May 26, 2026 matches the UTC calendar day of originalPublished (2026-05-26T14:54:10Z). The modification about 23 minutes later is not flagged as a substantive edit, and the claims rely on the main article body. The later Deep Dive sidebar items are not used. McKinsey's 2030 projection is not presented as an outcome. This is a publisher page, so no archive disclosure is needed. support — pass: Each claim matches its excerpt. The ROI tripling is reported as one Ema customer's measured result, not as a general effect. The accountability question and the acknowledgement of unresolved complexity are source-stated. The limitations correctly say that projected benefits are not established. > These are complex lines of inquiry that experts continue to grapple with. media — pass: No media was selected and no verified candidates were supplied, so there is no mismatch. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/c2f3d9d019600bf9a946604ae6860e4c20a1f5c1e5c269a25722d3e0161efb85/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/mit-technology-review-examines-redesigning-organizations-for-c2f3d9d0.json)