McKinsey’s Superagency: What Workplace AI Demands of Agency Leaders

McKinsey’s superagency concept describes a future where AI doesn’t just assist workers but actively amplifies what they can do, shifting organisations from AI-augmented teams to AI-native ones. The implementation challenge isn’t technical. It’s structural, cultural, and commercial, and most agency leaders are not ready for it.

The McKinsey Global Institute’s research on workplace AI paints a picture of significant productivity gains, but those gains are not evenly distributed. They flow to organisations that have done the hard work of integrating AI into actual workflows, not just buying licences and calling it a transformation.

What Does McKinsey Actually Mean by Superagency?

The term superagency, as McKinsey frames it, refers to an elevated state of human capability made possible by AI agents that can plan, execute, and adapt across complex tasks. It’s not about replacing people. It’s about dramatically expanding what each person can accomplish. A strategist who once spent three days synthesising research can now do it in three hours and spend the remaining time on the thinking that actually moves clients forward.

This is a meaningful shift from the earlier wave of AI hype, which focused heavily on content generation and automation of low-skill tasks. The superagency model points toward AI that handles multi-step reasoning, integrates across systems, and operates with a degree of autonomy that makes it a genuine working partner rather than a sophisticated autocomplete.

For agency leaders, this matters because it changes the unit economics of service delivery. If your senior strategist can do the analytical work of three people, your margin structure changes. So does your talent model, your pricing, and your pitch.

If you’re thinking through how AI fits into a broader agency growth model, the resources at The Marketing Juice Agency Growth hub cover the commercial and operational dimensions in detail.

Why Most Agencies Are Getting AI Implementation Wrong

I’ve watched a pattern repeat itself across the agency world over the past two years. Leaders announce an AI strategy. They buy a stack of tools. They run a lunch-and-learn. Then six months later, three people are using those tools regularly, the rest have reverted to their old workflows, and the business has a line item on the P&L that isn’t producing a return.

This isn’t a technology failure. It’s an implementation failure, and it’s almost always rooted in the same set of mistakes.

The first mistake is treating AI adoption as a training problem. You can train someone to use a tool without changing the workflow the tool sits inside. If your briefing process, approval chain, and client delivery structure all remain unchanged, the tool becomes an add-on rather than an accelerant. People fit it around their existing work rather than redesigning their work around its capabilities.

The second mistake is starting with the wrong use cases. Agencies tend to pilot AI on content generation because it’s visible and fast. But content generation is also where AI is most likely to produce output that needs significant human editing, which can actually slow down experienced writers who are faster without the AI draft. The higher-leverage use cases are in research synthesis, campaign analysis, competitive intelligence, and brief development, areas where the time savings are substantial and the quality floor is easier to maintain.

The third mistake is underestimating the change management requirement. I spent several years turning around a loss-making agency and growing a team from around 20 people to close to 100. In that process, I learned that operational change lives or dies on the behaviour of middle management. If your heads of department aren’t modelling the new approach, the people below them won’t adopt it regardless of what the CEO announces. AI implementation is no different. You need visible champions at every level of the organisation, not just an enthusiastic executive team.

What a Real Superagency Implementation Looks Like

Genuine AI integration at the agency level is not a rollout. It’s a redesign. And it has to start with an honest audit of where time actually goes in your business.

Most agency leaders have a rough sense of this, but the detail is usually murkier than they think. When I’ve done time audits with agency teams, the results are consistently surprising. A significant portion of senior time goes into tasks that are high-effort and low-differentiation: writing first drafts of documents that then get reworked, pulling data from multiple sources and reformatting it, preparing internal status updates, and building presentations that repackage work that’s already been done.

These are exactly the tasks where AI produces the highest return. They’re time-intensive, they follow recognisable patterns, and they don’t require the kind of contextual judgement that genuinely needs a senior brain. Freeing your senior people from these tasks doesn’t just save time. It changes what they’re able to do with clients, which changes the quality of the work and the depth of the relationship.

The agencies that are implementing this well are doing a few things consistently. They’ve identified three to five specific workflow stages where AI is inserted as a standard step, not an optional one. They’ve built internal prompt libraries that encode institutional knowledge so that the AI output reflects their way of thinking, not a generic response. They’ve created feedback loops where the team is actively improving those prompts over time. And they’ve tied AI adoption to commercial outcomes, not just efficiency metrics.

Tools like AI-assisted pitch generation are one narrow example of where this can show up in the sales process. But the real gains are deeper in the delivery workflow, where the cumulative time savings compound across every engagement.

The Talent Question Nobody Wants to Answer

McKinsey’s superagency framing raises a question that most agency leaders are dancing around: if AI can do the work of multiple people, what happens to headcount?

The honest answer is that it depends on whether the agency is growing or static. In a growing agency, AI-driven efficiency means you can take on more work without proportional headcount increases. Your margin expands. You can price more competitively or protect margin while the market commoditises. In a static agency, the same efficiency gains create pressure on headcount that leadership has to address directly.

What I’d push back on is the framing that AI simply eliminates jobs. What it eliminates is the need for certain types of work at certain volumes. The more pressing talent question is whether your current team has the skills to work effectively alongside AI, and whether you’re hiring for those skills going forward.

The people who thrive in a superagency environment are not necessarily the most technically skilled. They’re the ones who can prompt well, evaluate output critically, and know when the AI is confidently wrong. That last skill is underrated. AI systems produce plausible-sounding output even when it’s inaccurate, and the people who can catch that are worth a significant premium.

I judged the Effie Awards for a period, which gave me a close look at what separates genuinely effective marketing from work that just looks impressive in a case study. The pattern was consistent: the best work came from teams with strong critical faculties, people who questioned assumptions rather than executing briefs at face value. That same faculty is exactly what you need to work with AI well. The tool is only as good as the thinking that surrounds it.

AI and the Pricing Problem for Agencies

There’s a commercial tension at the heart of agency AI adoption that doesn’t get discussed enough. If AI makes your team significantly more efficient, your time-based billing model starts to work against you. You’re delivering the same output in half the time, which means you’re either leaving money on the table or your clients are expecting you to pass the savings on to them.

This is a genuine structural problem, and it’s pushing agencies toward output-based and value-based pricing models faster than anything else has. If you’re billing for a campaign strategy document, the price should reflect the value of the strategy, not the hours it took to produce it. AI makes that argument easier to have with clients, because the time compression is visible enough that the old billing model becomes harder to defend.

For agencies that haven’t started thinking about this, the shift in agency pricing models is worth examining carefully. The direction of travel is clear, and AI is accelerating it.

The agencies that will handle this well are the ones that reframe AI efficiency as a quality argument rather than a cost argument. We’re faster not because we’ve cut corners, but because we’ve removed the low-value work from the process. The thinking is better, not just quicker. That’s a harder sell than “we’re cheaper now,” but it’s a more defensible position and a more profitable one.

Building AI Into the Agency Without Breaking What Works

One of the more underappreciated risks of aggressive AI adoption is that it can erode the institutional knowledge and craft that makes an agency distinctive. If everyone is generating first drafts from the same AI models with similar prompts, the outputs start to converge. The work loses the idiosyncratic quality that comes from a specific team with a specific point of view.

This is not a reason to avoid AI. It’s a reason to be deliberate about where human craft is non-negotiable and where AI can carry the load without cost to quality.

Early in my career, I found myself holding the whiteboard pen in a Guinness brainstorm when the founder of the agency had to leave for a client meeting. He handed it over without ceremony and walked out. The internal reaction was somewhere between panic and determination. What that moment taught me was that there’s no substitute for the thinking that happens when experienced people are genuinely engaged with a problem. AI can accelerate the research, frame the brief, and synthesise the competitive landscape. It cannot replicate what happens in that room when the right people are fully present and working the problem.

The agencies that get this right will use AI to clear the path for that kind of thinking, not to replace it. The ones that get it wrong will use AI to produce more output faster without improving the quality of the thinking behind it. The market will sort those two groups out fairly quickly.

For agencies using AI to improve content production specifically, Buffer’s analysis of AI tools for content marketing agencies is a useful ground-level look at what’s working in practice. The tools themselves are less important than the workflow design around them.

What Agency Leaders Should Actually Do This Quarter

The McKinsey superagency framework is useful as a directional concept, but it can also become a reason to wait for more clarity before acting. That’s the wrong response. The agencies building AI capability now will have a structural advantage in 18 months that will be very difficult for laggards to close.

consider this a credible implementation plan looks like at the agency level, without the theatre.

Start with a workflow audit, not a tool selection. Map three to five core processes in your agency: how a brief gets written, how a strategy gets developed, how reporting gets produced, how new business pitches get built. Identify the specific stages where time is being spent on tasks that AI could handle. That’s your implementation roadmap.

Then run a focused pilot on one process, not a broad rollout across everything. Pick the process with the highest time cost and the most standardised output. Build a prompt library for it. Measure the time savings. Document what the AI gets wrong and build checks for those failure modes. Only then expand to the next process.

Build internal governance from day one. Decide what AI-generated output requires human review before it goes to a client. Decide what data can be input into external AI tools and what can’t. These decisions are easier to make before an incident than after one. I’ve seen agencies lose client trust over data handling issues that could have been prevented with a simple policy document and thirty minutes of team training.

Finally, tie AI adoption to a commercial outcome, not a capability metric. “Our team is now using AI tools” is not a business result. “We’ve reduced the time to first strategy draft by 40% and reinvested that time in client-facing work” is a business result. The former is theatre. The latter is the point.

For a broader view of what’s driving agency growth and where operational priorities are shifting, the Agency Growth section of The Marketing Juice covers the commercial and strategic dimensions that sit alongside AI implementation.

The superagency isn’t a future state you arrive at. It’s a direction you commit to and build toward, one workflow at a time. The agencies that treat it as a destination will keep waiting for the right moment to start. The ones that treat it as a practice will be significantly harder to compete against by the time the rest of the market catches up.

About the Author

Keith Lacy is a marketing strategist and former agency CEO with 20+ years of experience across agency leadership, performance marketing, and commercial strategy. He writes The Marketing Juice to cut through the noise and share what works.

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