Ben Rand · May 27, 2025
Harvard Business School Working Knowledge: AI suggestions helped service agents
Markdown · JSON · Original source
Ben Rand’s Working Knowledge article describes a study by Shunyuan Zhang and Das Narayandas of AI suggestions shown to customer-service agents during online chats with customers of an unnamed meal delivery company. The researchers report that agents using suggestions responded faster and received higher customer-sentiment scores than agents without them; the reported improvements were larger for less-experienced agents. The article argues that AI can support human service rather than simply replace it, especially by helping newer agents handle conversations. It also describes limits: results varied with customer intent, improvements were less substantial for repeat complaints, and customers sometimes seemed confused when transferred from a chatbot to an agent who responded very quickly. The researchers suggest that managers consider who benefits from assistance, including newer service employees and companies seeking to retain customers who ask to cancel subscriptions. These findings come from one company, and the article does not establish that the measured sentiment changes led to higher revenue, better employee retention, or results in other settings. How would the reported effects change across other companies, customer intents, and ways of transferring conversations from chatbots to human agents?
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
Harvard Business School · Article
Source details
Original source date: May 27, 2025 (2025-05-27T08:00:00-05:00).
Date basis: Date stated by the publisher.
Added to Escape Meridian: 2026-11-05T20:54:15.869Z. First observed: 2026-11-05T15:42:07.080Z. Retrieved: 2026-11-05T20:53:16.563Z.
Referenced by
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Limits
The study concerns one unnamed meal delivery company; the article does not establish whether the findings generalize to other companies or service settings. Customer sentiment was evaluated by the AI tool based on emotional tone at the beginning and end of the interaction; the article does not establish that sentiment changes produced higher revenue or profitability. The article presents employee retention and freemium customer conversion as possible benefits, not outcomes demonstrated by the reported study. The source does not establish effects on employee retention or later subscription conversion.
Open question
How would the reported effects change across other companies, customer intents, and ways of transferring conversations from chatbots to human agents?
Source-stated
The study analyzed online chats at an unnamed meal delivery company, comparing agents who received AI response suggestions with agents who did not.
Some agents received real-time response suggestions from the AI tool, while others did not.
Source-stated
The article reports faster responses and higher customer-sentiment scores for agents using AI suggestions, with larger reported improvements for less-experienced agents.
AI helped human agents respond to chats some 20 percent faster—improving performance even more for less experienced agents.
Source-stated
The article says results varied by customer intent; improvements were less substantial for repeat complaints, while cancellation inquiries showed improved sentiment.
The study found that AI improved sentiment when customers asked to cancel their subscriptions. The researchers think this could be because AI recommended alternative subscription options.
Source-stated
The authors say very rapid transfers from chatbots to human agents sometimes left customers confused about whether they were still interacting with a chatbot.
the transfer happened so quickly that customers thought they were still communicating with a chatbot.
Source-stated
The researchers suggest AI assistance may help newer service employees and may be useful to firms seeking to retain customers who ask to cancel subscriptions.
The research suggests that AI is effective for: Training new employees in service roles
Source and contributor context
Ben Rand · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
Media declared by the original publisher; availability checked separately from the editorial reading.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Harvard Business School Working Knowledge
Harvard Business School Working Knowledge publishes articles explaining business research on management, technology and workplace behavior.
Editorial provenance
Version 1. Publication ID: 7151804c-aab6-415a-82dd-fce49bea11d7.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-05T20:54:15.869Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: useful_perspective.
Observation: 827bab4bbd10a5af19ff72d149d0c6d8a7060ad50df5133ec87eea26dfe932e0. Source text hash: 901b8aee25c81ccdeceec4da045ce0caf6d09fa230ec376c14c697348ea16a1e.
Revision and source-check history
Version 1 · 2026-11-05T20:54:15.869Z
Harvard Business School Working Knowledge: AI suggestions helped service agents
Ben Rand’s Working Knowledge article describes a study by Shunyuan Zhang and Das Narayandas of AI suggestions shown to customer-service agents during online chats with customers of an unnamed meal delivery company. The researchers report that agents using suggestions responded faster and received higher customer-sentiment scores than agents without them; the reported improvements were larger for less-experienced agents. The article argues that AI can support human service rather than simply replace it, especially by helping newer agents handle conversations. It also describes limits: results varied with customer intent, improvements were less substantial for repeat complaints, and customers sometimes seemed confused when transferred from a chatbot to an agent who responded very quickly. The researchers suggest that managers consider who benefits from assistance, including newer service employees and companies seeking to retain customers who ask to cancel subscriptions. These findings come from one company, and the article does not establish that the measured sentiment changes led to higher revenue, better employee retention, or results in other settings. How would the reported effects change across other companies, customer intents, and ways of transferring conversations from chatbots to human agents?
Original source
Harvard Business School · Article
Source details
Original source date: May 27, 2025 (2025-05-27T08:00:00-05:00).
Date basis: Date stated by the publisher.
Added to Escape Meridian: 2026-11-05T20:54:15.869Z. First observed: 2026-11-05T15:42:07.080Z. Retrieved: 2026-11-05T20:53:16.563Z.
Limits
The study concerns one unnamed meal delivery company; the article does not establish whether the findings generalize to other companies or service settings. Customer sentiment was evaluated by the AI tool based on emotional tone at the beginning and end of the interaction; the article does not establish that sentiment changes produced higher revenue or profitability. The article presents employee retention and freemium customer conversion as possible benefits, not outcomes demonstrated by the reported study. The source does not establish effects on employee retention or later subscription conversion.
Open question
How would the reported effects change across other companies, customer intents, and ways of transferring conversations from chatbots to human agents?
Source-stated
The study analyzed online chats at an unnamed meal delivery company, comparing agents who received AI response suggestions with agents who did not.
Some agents received real-time response suggestions from the AI tool, while others did not.
Source-stated
The article reports faster responses and higher customer-sentiment scores for agents using AI suggestions, with larger reported improvements for less-experienced agents.
AI helped human agents respond to chats some 20 percent faster—improving performance even more for less experienced agents.
Source-stated
The article says results varied by customer intent; improvements were less substantial for repeat complaints, while cancellation inquiries showed improved sentiment.
The study found that AI improved sentiment when customers asked to cancel their subscriptions. The researchers think this could be because AI recommended alternative subscription options.
Source-stated
The authors say very rapid transfers from chatbots to human agents sometimes left customers confused about whether they were still interacting with a chatbot.
the transfer happened so quickly that customers thought they were still communicating with a chatbot.
Source-stated
The researchers suggest AI assistance may help newer service employees and may be useful to firms seeking to retain customers who ask to cancel subscriptions.
The research suggests that AI is effective for: Training new employees in service roles
Source and contributor context
Ben Rand · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
Media declared by the original publisher; availability checked separately from the editorial reading.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Harvard Business School Working Knowledge
Harvard Business School Working Knowledge publishes articles explaining business research on management, technology and workplace behavior.
Editorial provenance
Version 1. Publication ID: 7151804c-aab6-415a-82dd-fce49bea11d7.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-05T20:54:15.869Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: useful_perspective.
Observation: 827bab4bbd10a5af19ff72d149d0c6d8a7060ad50df5133ec87eea26dfe932e0. Source text hash: 901b8aee25c81ccdeceec4da045ce0caf6d09fa230ec376c14c697348ea16a1e.
passed · 2026-11-05T20:54:15.869Z · anthropic/claude-opus-5.5
Publication: 7151804c-aab6-415a-82dd-fce49bea11d7
attribution — pass: The reading correctly attributes the article to Ben Rand at HBS Working Knowledge and the study to Shunyuan Zhang and Das Narayandas. It keeps findings framed as the researchers' reports and the article's claims rather than established fact.
Featuring Shunyuan Zhang and Das Narayandas. By Ben Rand on May 27, 2025
summary — pass: The summary is understandable to a non-specialist. It explains the setup (agents with and without AI response suggestions), the reported gains, the larger effects for newer agents, the limits by customer intent, and the transfer-confusion finding. The complement-not-replacement framing matches the article. The question follows from the stated boundaries.
AI currently works best as a complement to human intelligence, rather than a replacement
date — pass: The originalPublished value of 2025-05-27 matches the article byline date. The forthcoming paper's earlier submission is not presented as this source's date.
By Ben Rand on May 27, 2025
support — pass: Each claim is grounded in the article. The limitations accurately note the single company, the AI-scored sentiment, and that the revenue, retention and freemium-conversion benefits are presented as potential rather than demonstrated. No unsupported benefits or costs are added.
uniquely human service strengths with the potential to positively impact revenue and profitability by making for happier customers
media — pass: The selected media is the verified direct link to the original Working Knowledge article. It is the most relevant choice, ahead of the author portrait.
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