The Small Team Advantage Isn't About Size
Sparq's CEO, Ingrid Curtis, breaks down why AI rewards small teams over big ones. The advantage comes from decision-making authority sitting close to the work, not headcount. Coordination, not skill, slows large teams down. The fix: decide who owns each call and place that authority where the work happens.
If your organization is still staffing projects the way it did years ago, you’re not alone. For years, big teams were the norm. The more complex the project, the larger the team needed to handle it. Bigger team, more talent meant better results.
But that isn’t the case today. AI makes every team member more productive. Projects that used to take quarters can now take just weeks.
In the AI era, big teams may be more of a liability than a benefit. Small teams can move faster on complex work because their decisions don't have to travel far. When something breaks, the person who understands it is already there to fix it. But when something breaks in an organization with large teams, the request gets routed, sits in a queue, and then eventually gets addressed.
No one designed big teams to be slow. The reality is that they usually just are, especially these days.
The instinct is always to hire
When your organization needs more capacity, what do you do? Hire more people, of course. And while it makes sense on paper, there’s a difference between coverage and capacity that most org charts don't reflect. Coverage measures how many people share responsibility for a problem; capacity measures how quickly that problem gets resolved.
- Coverage means responsibility is distributed.
- Capacity means things get resolved.
Add enough people to shared-ownership of a problem and you get the first without the second. You get more distribution, less resolution, and an unwelcome surprise: more coordination. That means more meetings where half the attendees are there to stay informed. And more time spent getting people up to speed on decisions that should have already been made.
The teams that feel slow usually don't have a skill problem. They have a problem with who's authorized to do what or how to action the direction.
Finance teams aren't waiting for end-of-year reviews to ask these questions. Neither are the senior leaders signing off on headcount. They want to know what changed, what it cost, and what moved. If you think headcount justifies itself, try explaining it to finance six months in.
The structural question
It isn’t really about team size. It's about who is allowed to make the call.
Some things worth actually looking at:
- When something breaks, who owns the fix? Can they make the call without asking someone above them?
- How many people does a decision pass through before anyone acts?
- When a senior leader gets pulled in, should it have gotten to them at all?
- Are the people doing the work allowed to make judgment calls, or are they waiting?
Most leaders already know the answers. They know exactly where things slow down. The problem is that you put those structures (e.g., oversight, risk management, accountability) in place for good reasons. Taking them apart feels like introducing the exact problems you designed them to prevent.
But slow approval chains and clear authority aren't the same thing. One bogs the work down. The other just means the person closest to a problem can actually do something about it.
The companies that stay fast as they grow tend to be the ones that have figured this out. Not because they run lean, but because authority travels with the work instead of sitting two levels above it. The ones that didn't figure it out keep hiring and keep wondering why nothing speeds up.
The boring solution
The organizations that successfully integrate AI into their workflows and teams aren’t doing anything special. They’ve just decided who makes the decisions, ensured they’re close enough to the work to make good ones, and stopped sending sign-off requests to people whose job is to approve rather than to know.
That's it. It doesn't require a restructure. It requires honesty about where things get stuck and a willingness to change who's authorized to unstick them.
Most organizations already know what that looks like. The harder question is whether they're willing to act on it before the cost of not doing so becomes impossible to ignore.
Read the original article in HR Technology Insights here: In 2026, Large Teams Will Be a Millstone Around Your Neck
Ingrid Curtis is CEO of Sparq, an AI-accelerated product engineering and data consultancy. She stepped into the role in January 2026 after seven years as President and six as COO, following an earlier run as VP of Client Services; 17 years at Sparq in total. Ingrid has led the company's growth from a single-market engineering firm into an AI-first partner for enterprise and mid-market organizations, with a focus on outcomes over headcount and talent as the firm's core asset. She holds a degree in Management Information Systems from Babson College and serves on the board of the Technology Association of Georgia.
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