What happens when you give 40,000 employees access to AI agents and let them build whatever they want? Prosus found out. The global tech investment company published the results in May 2026: 60,000 agents deployed, across dozens of portfolio companies, without any central mandate from headquarters.
The playbook they’ve written from that experience — distilled in a report called The Coming Age of AI Colleagues — is one of the most data-rich enterprise perspectives on agentic AI published so far this year. The headline finding is counterintuitive and important.
The Power Law of AI Agents
Prosus’s single most useful finding: approximately 2% of agents drive a disproportionate share of business impact.
The full distribution looks like this:
- 82% of agents save less than 20 hours per month
- 17% of agents save 20–173 hours per month
- <1% of agents deliver the equivalent of thousands of hours monthly — comparable to full-time roles
Financial value follows the same pattern. Most agents generate under $1M in annual value. Outliers reach into the tens of millions. One standout example: an agent handling communications and onboarding for a marketplace affiliate partner was projected to generate $83 million in new annual revenue.
The implication is clear. The naive approach to enterprise AI deployment — deploy as many agents as possible and see what sticks — will produce a long tail of marginal productivity gains. The strategic approach is to find the 2% early and go deep on them.
The 20 Use Cases That Converge Everywhere
Here’s what surprised Prosus the most: across different industries, geographies, languages, and without any central coordination, the portfolio companies independently converged on the same ~20 high-ROI use cases.
Euro Beinat, Global Head of AI at Prosus, described it this way: “our portfolio companies kept building for the same 20 ‘power law’ use cases… each delivering a strong, immediate ROI.”
The top categories, by share of usage:
- Data analytics & market intelligence (~18%) — agents that pull, process, and synthesize market data for decision-making
- Operations (~15%) — agents handling internal processes, workflow automation, and operational monitoring
- Personal AI assistants (~14%) — often built outside formal departments by individual employees finding their own leverage
These three categories alone account for nearly half of all agent usage. The consistency across companies is striking — it suggests these are natural fits for agentic systems, not just popular experiments.
Enterprise Lessons on Structure and Cost
Prosus observed that most agents mirror human seniority tiers in practice. “Junior” agents handle high daily volume — repetitive, structured tasks. “Senior” agents serve broader user bases and handle more complex judgment calls.
One practical insight on cost: many tasks work fine with smaller, cheaper models, but users stick with expensive frontier models out of habit or default settings. This creates a significant optimization opportunity. Systematically auditing which agents actually need the most capable model — and routing the rest to cheaper alternatives — can substantially reduce cost without affecting output quality.
The Toqan Platform
Prosus didn’t build 60,000 agents manually. They did it through Toqan, their internal AI platform that gives non-technical employees access to multiple AI models and lets them build agents without writing code.
The bottom-up, self-serve nature of Toqan is a large part of why the deployment scaled so quickly. Employees across the portfolio found their own use cases and built for them — the 20 convergent categories emerged organically from thousands of independent decisions, not top-down design.
At peak, agents active on Toqan were delivering the productivity equivalent of over 1,000 full-time employees in some measurement windows. That’s not a hypothetical projection — it’s observed output from the deployed systems.
What This Means for Your Organization
Prosus’s playbook is a useful calibration for any enterprise considering broad AI agent adoption:
Don’t optimize for agent count. Optimize for finding the 2% that drive real value, then build more like those.
Look for the 20 use cases. If Prosus’s cross-portfolio data is generalizable — and the consistency across industries suggests it is — the highest-ROI use cases for your organization probably look a lot like data analytics, operations automation, and personal productivity assistance.
Start simple, iterate quickly. Most of Prosus’s agents started as basic automations. The sophisticated ones emerged from iteration on simpler versions.
Audit model selection. The gap between “what model this task actually needs” and “what model users default to” is a cost lever that most enterprises haven’t touched yet.
The full report, The Coming Age of AI Colleagues, is available on the Prosus website.
Sources
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260517-0800
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