The small team advantage: how customer science helps small teams do big things

Last month, Meta's Chief AI Officer Alexandr Wang sent a memo announcing 600 job cuts from the company's AI unit. His reasoning was interesting: "By reducing the size of our team, fewer conversations will be required to make a decision, and each person will be more load-bearing and have more scope and impact."

Wang isn't alone in this thinking. Across the tech sector, there's a growing conviction that smaller teams move faster, especially now that AI amplifies what individuals can get done. One engineer armed with AI can produce what previously took a small team. The same holds for a marketer who can research, write, and iterate at a pace that would have required several people only a few years ago.

But the efficiency argument misses something important: The power of small teams isn't just about doing more with less. It's about unlocking a power that large teams structurally cannot provide. And with customer science, small teams can now access that analytical depth, creative might, and production scale that used to exclusively belong to large teams with large budgets.

Customer science: A method for the madness

For years, B2B marketers built journeys based on how we imagined people made decisions. We created content for each stage of our theoretical funnel. We measured MQLs and click-through rates and told ourselves these metrics mattered. Meanwhile, actual buyers were doing something completely different. They were researching in bursts, circling back to earlier questions, involving new stakeholders at unexpected moments, and generally creating the kind of chaos that our linear journeys couldn't accommodate.

This is why customer science matters. Instead of building static campaigns around what we think buyers want, customer science uses data and AI to map what buyers actually do. It's a systematic approach to decoding real buyer signals, designing responsive programs around those insights, and scaling them into adaptive systems that prove their value in the market.

The method works through rapid cycles: Decoding what's happening, designing a response, scaling what works, then decoding again based on new signals. It's fundamentally experimental, inherently cross-functional, and thrives on speed of adaptation. Customer science allows small teams to retain and amplify their natural advantages of speed, candor, and coherence, while realizing the power of a large team.

The power dynamics of small teams

Small teams have always had inherent advantages. There are plenty of studies that show they move faster and produce more than large teams, but speed isn't really the edge. It's coherence.

When a group is small enough for everyone to see the whole system – the data, the signals, the impact – trust forms through shared visibility, not hierarchy. Large teams lose that feedback loop while small teams live inside it.

Small teams enjoy candor without politics. You can say "this campaign isn't working" or "that buyer signal contradicts our assumptions" without worrying about whose ego you're bruising or how it affects your next performance review. With customer science, where you're constantly testing and adapting, the ability to kill ideas quickly is as important as the ability to launch them. A trusted team can pivot in a Slack conversation. A large team needs a deck and a stakeholder alignment process.

You can admit what you don't know. When someone says, "I don't understand why these conversion rates dropped," or "I'm stuck on how to interpret this data," they're not confessing weakness—they're accelerating learning. Customer science requires moving fluidly between data analysis, creative development, and strategic thinking. In small, trusting teams, people can be forthright about their gaps and learn from each other quickly.

Decisions happen informally and fast. A quick chat replaces the meeting-with-a-deck ritual. When you're responding to actual buyer behavior, waiting for the formal approval process means the signal has already changed. Small customer science teams can see something in the data in the morning and have a new test running by the afternoon.

Roles become elastic. Customer science demands cross-functional fluidity because buyer signals don't respect organizational boundaries. Someone spots a pattern in the data, someone else has a creative hypothesis about how to respond, a third person knows how to test it quickly. This only works when people feel comfortable stepping outside their job descriptions (and letting others step into theirs). In small teams, "that's not my job" becomes "let me help figure this out."

Risk-taking is rewarded. Customer science is inherently experimental. You're testing hypotheses about buyer behavior, and many of them will be wrong. That's the point. But this only works if failure won't be weaponized. Small teams with trust can attempt ambitious, uncertain things because they know mistakes yield insights.

These five dynamics flow into a virtuous cycle. Candor enables the right to be wrong, which enables learning, which builds the trust that makes roles elastic, which powers fast decisions, which makes risk-taking possible, which requires more candor to work. It only exists in teams small enough that people actually know each other as people.

One of the biggest challenges in getting decisions brought to life internally is bigger decision making teams tend to water down the impact of a program as it tries to please too many stakeholders. Smaller teams that have representation from a broader pool of stakeholders with the authority to decide (and defend!) on their behalf leads to far better adoption and roll out.Jessie Tracy, Managing Partner, Pretzl

Don't fall for the load-bearing trap

Here's where Wang's memo reveals something troubling. That phrase "load-bearing" sounds empowering at first. You're critical. You matter. Your work has scope and impact. It's seductive language, especially at a time when tireless LLMs are constantly showing off all the parts of your job they can do.

But load-bearing is a term from structural engineering, and in structures, load-bearing elements have a specific characteristic: If you remove them, the building collapses. Think about what that means when applied to people. If you're load-bearing, you can't leave. The system has no redundancy, no resilience, no capacity to absorb shock.

And this is antithetical to customer science. Customer science requires continuous learning across the team, not single points of failure.

How to build resilient, small teams

So how do you get the power of small teams without the brittleness? How do you move fast without creating unsustainable pressure? The answer is that trust has to be paired with intentional resilience.

This starts with redundancy by design. Everyone on the team should know multiple domains, not as a backup but as standard operating procedure. In customer science terms, the person analyzing buyer data should understand enough about creativity to contribute ideas. The person designing programs should understand enough about measurement to interpret results. This isn't about everyone being interchangeable, but about everyone having enough context to step in when needed.

Every experiment, every insight, every pattern gets captured not because someone mandated it, but because the team values making knowledge portable, reusable, and valuable. AI can help here tremendously as systems for capturing and retrieving context.

This is when customer science itself becomes the operating system for your B2B marketing. When the methodology is shared, when the team has common language and common practices, you're not dependent on individuals. The approach is portable. New people can learn it. Knowledge compounds rather than concentrates.

The real competitive advantage

Buyers aren't waiting for you to catch up. Customer science with AI gives small teams the ability to decode patterns, design responses, and scale what works, capabilities that formerly required big teams, big budgets, and months of time. Now, with customer science as a guiding methodology, a small team can have both: the nimbleness and trust that comes with working closely together and the analytical horsepower that used to be reserved for enterprise marketing departments.

There's a heavy premium placed on smart people and strong tools in Silicon Valley. But the competitive moat is having a team structure that can actually leverage smart people and strong tools at speed and scale. And in B2B marketing, where understanding real buyer behavior is now the foundation of everything else, small teams doing customer science matters more than ever.