Outsourced recognition

Outsourced recognition is what happens when the expression of noticing another person is delegated to a machine, so the words arrive without the seeing that used to produce them. The thank-you still gets sent. The apology is better phrased than you would have managed. The feedback is warmer, more specific and more generous than the version you would have written at half past six on a Thursday. And the thing those messages existed to carry, evidence that a particular person chose to attend to you, is no longer in them. The term is mine, and the distinction it protects is not sentimental: recognition is the mechanism by which people know they are not interchangeable, and the cheapest thing in any organisation to automate and the most expensive to lose.

Why the distinction matters#

Because the surface and the substance have come apart, and only the surface is visible. Kindness is a trained capacity to recognise another person when recognising them is inconvenient, and the friction of composing the message was where the noticing actually happened. Sitting down to write what someone did, and why it mattered, is the act of attending to them; the paragraph is only the receipt. Remove the friction and you keep the receipt and lose the transaction.

The failure mode is erosion rather than cruelty, and it runs in a direction nobody intends: you can now express more care than at any point in your life while seeing fewer people than ever. There is an inversion in it that I find hard to look away from. We are polite to machines that cannot receive it, and efficient with the humans who can.

Two studies on what recognition costs#

Two studies make the point better than argument does. Yin, Jia and Wakslak, writing in PNAS in 2024, found that AI-generated replies made recipients feel more heard than replies written by untrained humans, and that labelling the reply as coming from AI removed that advantage. Same words, same quality, different sense of being received. The value was never in the phrasing. It was in the belief that a person chose to write it.

And Ayers and colleagues, in JAMA Internal Medicine in 2023, had licensed professionals blind-rate chatbot and physician answers to 195 real patient questions: the chatbot's responses were rated empathetic or very empathetic 45.1 percent of the time against 4.6 percent for the doctors. So the machine is not merely adequate at the performance of care. On the observable surface, it is already better. So the surface is the wrong place to look.

What it looks like#

A manager generates the note for someone's ten years of service. It is a better note than they would have written. Nobody notices anything. The employee reads a paragraph produced by a system that has never met them, and the ritual survives while the thing the ritual existed to do has stopped happening.

A leader runs difficult feedback through a model to soften it. The delivery improves and the thinking that difficulty was forcing, about what the person actually needs to hear and why it is hard to say, never takes place. A condolence message is drafted in four seconds by something that does not know the person died.

Where to draw the line#

Draw a line around the small acts and keep them manual. Thank-yous, apologies, condolences and feedback on someone's work are cheap to automate and they are the only artefacts in an organisation that carry the message that a specific person was seen by another specific person. A badly written note that you wrote does the job. A beautiful one you did not write does not, and increasingly people can tell.

If you use AI at all here, use it after the noticing rather than instead of it: write what you actually observed in your own words first, then let the model tidy the prose. That ordering, human at the start, preserves the part that matters and improves only the part that does not.

The same two words, sold as a service#

A reader who types this phrase into a search box is more likely to arrive at a procurement question than at this argument, and the honest thing is to say so on the page rather than to let the definition carry it alone. In human resources, outsourced recognition means handing an employee recognition programme to a supplier: the points, the badges, the long-service awards, the catalogue the points are spent in. It is a settled market with a category of its own. The SHRM Human Resource Vendor Directory listed 301 vendors under Employee Recognition Programs when it was checked on 14 September 2026.

That older sense is about who administers a scheme. This one is about who does the noticing. A company can run its recognition programme entirely in house and still have every word inside it written by a model, and a company can buy the whole apparatus from a supplier while the manager using it has genuinely watched somebody all quarter. The two questions are independent, and only the second one decides whether the message carries any evidence of attention. Naming the older sense here is a precaution rather than a complaint: a term with an incumbent meaning in an adjacent profession gets read as that meaning by default, and the distinction this page turns on survives only if it is stated.

Development of the idea#

I set the argument out in the Box of Amazing essay The Thing That Proves You're Human (25 January 2026), which opens with Primo Levi and the schoolteacher who simply talked to him, and argues that kindness is trained attention rather than warmth. The wider treatment of what survives automation is in what stays human, and the framework is developed in SuperSkills (Kogan Page, 2026).

Key research and primary sources

• Yin, Y., Jia, N. and Wakslak, C. J. (2024). AI can help people feel heard, but an AI label diminishes this impact. PNAS, 121(14), e2319112121.
• Ayers, J. W. et al. (2023). Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions. JAMA Internal Medicine, 183(6), 589-596.

Related SuperSkills research#

See what stays human, empathy, Human at the Start and using AI without dependency.

About this research#

Rahim Hirji is the author of SuperSkills (Kogan Page, 2026), keynote speaker on AI and human capability, and founder of The SuperSkills Intelligence Company. Outsourced recognition is his term, introduced in January 2026. The studies cited are attributed to the researchers who produced them and are separate from the interpretation, which is his. This is a living reference, reviewed and updated as significant new evidence appears.

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