Superagency: What It Actually Means When AI Empowers Individuals, Not Just Companies

McKinsey published something interesting in early 2025 — a concept they’re calling “superagency.” The idea: AI doesn’t just automate tasks, it gives individuals capabilities that were previously only accessible to well-resourced institutions.

A solo researcher can now run literature reviews that used to require a team. A one-person startup can produce design, copy, and code that used to require three different specialists. A mid-level manager can synthesize organizational data that used to require a data science function.

That’s the promise. But there’s a gap between the concept and what most organizations are actually building — and it’s worth being specific about what’s missing.

What ‘Superagency’ Actually Means

The word is doing some heavy lifting in McKinsey’s framing, so let’s unpack it.

Traditional agency: you have the authority to act within a defined scope. A manager has agency over their team’s decisions. A specialist has agency over their domain.

Superagency: AI extends your effective scope of action far beyond what your individual skills or resources would normally allow. You can act as if you have capabilities you don’t actually have.

A solo founder with Claude and a few specialized workflows can now:
– Run competitive intelligence that used to need a research firm
– Generate first-draft legal language (with review, not instead of it)
– Build functional prototypes without a developer
– Synthesize customer feedback across thousands of data points

This isn’t AI replacing these functions. It’s AI extending the individual’s reach into domains they couldn’t previously access at reasonable cost or speed.

The distinction matters. “AI doing things for you” and “AI expanding what you can do” lead to very different design decisions.

Where Most Organizations Get It Wrong

Most enterprise AI deployments are improving for the wrong level.

They’re building AI tools for the organization — workflow automation, process efficiency, cost reduction. All legitimate goals. But they’re largely ignoring the individual empowerment angle.

My observation from talking to teams across different industries: the employees who are getting the most from AI right now are the ones who figured out superagency on their own, often without organizational support. They’re using personal AI subscriptions, building their own prompt libraries, running their own workflows.

Meanwhile, the organization is rolling out an enterprise AI tool with guardrails designed for compliance, not empowerment. The individuals who most need expanded capability — the individual contributors, the mid-level people without budget for specialized help — are often the last ones considered in the deployment.

That’s a significant miss.

The Asymmetry Problem Nobody Talks About

Here’s something that deserves more attention: superagency is not evenly distributed, and the distribution follows existing power dynamics in uncomfortable ways.

People who already have high baseline skills get disproportionate benefit from AI. A strong writer with AI becomes a much stronger writer. A mediocre writer with AI becomes… a slightly more productive mediocre writer who now produces mediocre content faster.

This creates an asymmetry problem inside organizations. The people who were already high-performing get a larger capability expansion from AI than the people who needed the most support.

If you’re designing AI deployment for your team, this matters. The goal of superagency — genuinely expanding individual capability — requires different design decisions than just “give everyone access to the same AI tool.” It may require targeted workflows for specific roles, more training for people with lower baseline skill in a domain, or explicit coaching on how to use AI to close skill gaps rather than amplify existing strengths.

Superagency Requires Metacognitive Skills That Most Orgs Don’t Develop

There’s a prerequisite for superagency that rarely gets acknowledged: you need to know what you don’t know.

To get use from AI across domains you’re not expert in, you need to be able to:
1. Recognize when an AI output is plausible but wrong (domain judgment)
2. Know what questions to ask to get useful outputs (prompt engineering)
3. Know when AI is the right tool and when it isn’t (tool selection)
4. Evaluate quality in domains outside your expertise (cross-domain assessment)

These are metacognitive skills. They’re about thinking about thinking — knowing the limits of your own knowledge and the limits of the model’s.

Most AI training programs skip this entirely. They teach the mechanics of using tools. They don’t teach the judgment layer that determines whether the tool outputs are actually useful.

Without that judgment layer, superagency becomes super-confidence — acting with the reach of someone with expanded capabilities but without the discernment to know when those capabilities are actually working.

What Genuine Individual Empowerment Looks Like

I’ve seen superagency work well in a handful of contexts. The patterns:

Domain extension with appropriate verification. A marketer using AI to understand technical product specs, then verifying the key claims with an engineer before writing. The AI expanded their domain reach; the verification kept the quality.

Speed-to-competence in new areas. A first-time manager using AI to understand employment law basics well enough to have an informed conversation with HR — not to replace HR, but to arrive at the conversation with a starting framework.

use on high-skill tasks. A researcher using AI to handle the systematic parts of literature review (categorization, pattern-finding in abstracts) while reserving their own time for the interpretive work that actually requires expertise.

What these have in common: the individual retained judgment about where AI outputs needed verification, extension, or outright rejection. Superagency worked because the human stayed in a supervisory relationship with the model, not a dependent one.

The Organizational Implication

If superagency is real — and I think it is — then the organizational design question becomes: how do you systematically enable it, rather than leaving it to individuals who figure it out on their own?

This means:
– Providing AI access at the individual level, not just the team or department level
– Training for metacognitive skills alongside tool training
– Designing workflows that extend individual reach into adjacent domains with appropriate verification steps built in
– Measuring outcomes that reflect genuine capability expansion, not just efficiency

The organizations that crack this will have an advantage that’s genuinely hard to replicate. Not because they have better AI tools — those are increasingly commoditized. Because they’ve built a workforce where individuals are systematically more capable than their job descriptions would suggest.

That’s the superagency opportunity. Most organizations are leaving it on the table.

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