People & Organization on YUNO AI Studio

Buying Outcomes, Not Just Labor

Thu, 05 Nov 2026 00:00:00 +0000

HR and procurement no longer buy only labor. They buy an outcome that might come from a combination of people and AI.¹ Some talent firms now design models that include both. One is rPotential, a spin-off of the staffing group Adecco.

In our reading, buying outcomes changes where the playbook starts, and the seven actions follow from it.

1. Map tasks and outcomes

Start each sourcing discussion by breaking a role or project into its tasks and the outcomes they produce. High-volume data validation or repetitive call-center functions might go to AI agents. Tasks that need complex judgment, persuasion, or deep domain expertise may stay with people or use a hybrid approach.

You Already Know How to Buy AI Agents

Thu, 05 Nov 2026 00:00:00 +0000

When a company needs more hands, it can hire, bring in a temp, or outsource the whole function. AI agents arrive through the same three doors:¹

Client-owned digital labor.
You license or build the agent and run it in-house.
Leased digital labor.
You rent agents from a third party, “in a manner akin to traditional temp staffing.”
Fully outsourced AI subdepartments.
A vendor runs an entire process, such as order fulfillment or a call center, with AI and a small team of human experts.

At least one staffing group is reorganizing around the idea. In 2025 the Adecco Group launched r.Potential with Salesforce to help companies “manage human and digital labour at scale.”² By August 2026, Adecco said half the revenue in its Adecco unit was already “agent-enabled,” in the company’s term, and it raised the goal to 70 percent by year-end.³

Workslop Is a Tax Organizations Levy on Themselves

Fri, 12 Jun 2026 00:00:00 +0000

AI-generated junk work is now common enough to have a name. When an employee pastes unverified AI output into a deliverable and sends it on, the recipient inherits the job of figuring out what is wrong with it. Researchers at Stanford’s Social Media Lab and BetterUp Labs call this workslop, and their survey of 1,150 U.S. employees found that 41 percent had received some in the past month¹.

The instinct in most executive teams is to read this as a discipline problem: people being careless with a new tool. The survey data points somewhere less comfortable. More than half of respondents admitted to sending work they knew was subpar, and the reason they gave was workload pressure combined with vague instructions to use AI more. They were complying with an incentive someone above them created.

What Gets Documented, Gets Rewarded

Sun, 17 May 2026 00:00:00 +0000

Gen AI in performance reviews is being sold as the cure for manager fiction—surface the artifacts, replace storytelling with retrieval, anchor the review in what got written down. Boston Consulting Group reports its internal tool cuts review-writing time by 40 percent. Citi and JPMorgan have shipped their own. The pitch is that managers will stop telling stories and start reading evidence.

It is the wrong fix, framed as the right one.

Don't Put AI on the Org Chart

Sat, 09 May 2026 00:00:00 +0000

The CEOs announcing “AI employees” on their org charts believe they’re sending a signal. Modernity. Scale. Seriousness about the technology. New research from Boston Consulting Group and Boston University, published this month, suggests the signal is real—but pointed in the wrong direction.

The team ran a randomized experiment with 1,261 managers in HR and finance from the U.S., Canada, and the European Union¹. Each manager reviewed workplace documents seeded with errors. The only variable changed across the three conditions was the byline: the document came from an AI tool, from a human teammate named “Alex,” or from an AI employee named “ALEX-3.”

Why AI Adoption Stalls Even When the Tools Work

Sat, 09 May 2026 00:00:00 +0000

The CEO has signed the enterprise license. Training is rolling out. Dashboards say usage is climbing. And yet the productivity story the board was promised is not landing. New research published this month by Guy Champniss¹ suggests the issue may not be the tool, the rollout, or the training. It may be a category of cost most adoption strategies do not measure.

Champniss surveyed more than 1,200 full-time employees across 10 sectors in the U.S. and UK. He measured how often people use AI, how complex the tasks are, and how often they avoid the tool on tasks where they know it would help. He then measured what he calls psychological debt—the accumulated motivational cost of using AI inside a workflow that was not designed with the human side in mind. The results are uncomfortable for any leader treating AI rollout as a logistics problem.

In the AI Era, the Most Important Capability Isn't Technical

Fri, 08 May 2026 00:00:00 +0000

Hugging Face has 250 employees.

It hosts more than 3 million AI models, datasets, and applications. A new model is added every 10 seconds. It serves more than 5 million daily users. In August 2024, with 220 employees on staff, the company became profitable while keeping most of its platform free.

For context: that head count is smaller than the regional bank branch network of any mid-sized city. Smaller than the engineering team at most Series B SaaS companies. Smaller than the AI division of a single Fortune 500 company.

No One in the C-Suite Wants to Admit They Don't Understand AI

Fri, 08 May 2026 00:00:00 +0000

In 2024, Clément Delangue, CEO of Hugging Face, sat for a long interview about the future of AI. He said this:

“No one knows a profitable, sustainable business model for AI.”

A few minutes earlier in the same conversation, asked about a controversial model that had been hosted on his platform, he said: “We are still just scratching the surface when it comes to ethics reviews.” Most of ML research, he added, is in the same place.