A Maturity Model for the Research Team of the Future

In my previous post, I argued that AI is absorbing the manual work of research, and that the teams who thrive will be the ones who adapt to what research needs to become, not defend what it used to be.

In this post, I’ll share a practical guide for building a research team fit for the future. At Qualtrics, we've built a maturity model that helps research teams see where they stand and what the next step looks like. It draws on the same conversations with research leaders and industry experts that shaped the first post.

Three Pillars, One Motion

The model rests on three pillars that make up how a research team gets work done and how impactful that work is to the business.

• Technology: the platforms that let a research practice execute, measure impact, and scale.
• Competency: the skills and actions required to design, manage, and execute research.
• Culture: the mindsets and beliefs that encourage research-centric behavior, both inside the team and across the business.

Research teams that change one pillar significantly without the others will find the benefits unlikely to stick. New tools with old habits give you a faster version of the old team and new skills with lagging tools never shift how the business sees you –they simply frustrate everyone on the team.

The pillars also build on each other in a particular order. Technology enables competency, because you can’t practice skills you have no tools for. Competency earns the culture, because the business won’t give influence to a team that can’t yet deliver.

To make these real, imagine each pillar has a starting point and a destination. In technology, research teams must move from disconnected, project-based tools to an integrated ecosystem that synthesizes data sources and delivers speed and rigor, not one or the other.

In competency, teams move up the maturity model by going from being great at manual execution and getting the method perfect to making the hard calls, owning the result and handling the politics.

In culture, a research team matures by going from being the protective gatekeeper of “truth” to an integrated strategic partner across the business.

All three pillars point to the same change: the team is judged less by the research it delivers, and more by what the business does with it.

We've defined three levels of maturity, and outlined how the differences in each level show up in all three pillars. The progression is one of accountability. At level 1, you are accountable for the output: did the study go out, and what came back? At level 2, you are accountable for the system: are the tools producing good work? At level 3, you are accountable for the outcome: did the business make the right call, and was research a key reason?

Level 1: Great Work, Most Exposed to AI Disruption

Most teams will recognize themselves here. When it comes to technology, the research team at this level runs like a complex filing cabinet: every project lives in its own tool, reports are delivered and then filed, and when people leave, the knowledge goes with them.

In competency, a level 1 research team operates like an execution factory, valued for throughput: survey out on time, data clean, deck delivered.

In culture, a level 1 research team is like a ticket queue, where a brief comes in, a report goes out, and the work often confirms a decision that has already been made. It is also the first budget line cut in a downturn.

None of this is bad work. Much of it is excellent. But it is also the work AI now does, which makes this team the most exposed.

Level 2: Faster, But Still a Service

Level 2 is where the efficiency gains are. The technology underpinning a level 2 research team operates like an augmented workbench: AI handles open-ends and summaries, the business self-serves within guardrails, and validated experience simulation lets people test ideas early. Validated is the important word: a platform that publishes how its simulated responses compare with real ones, not a chatbot with a persona prompt. Without validation, it’s just hype.

The core competencies of a level 2 team looks like a control tower, with researchers steering instead of performing every step. They know when a decision needs real people and when the quick option is good enough, and they start to own the story of the research and what it means for the business.

The level 2 culture is an enablement engine, built on the understanding that for the small stuff, 80% right in an hour beats 100% right in a month. The business self-serves within standards the research team sets, and it expects the team to bring a point of view, not just a summary.

Once all three pillars reach level 2, it’s tempting to stop. Resist that, because at level 2 you are still a service, just a faster one. A service waits for the brief, gets measured on turnaround and cost, and competes with every self-serve tool and vendor that can offer a quick answer. Its value depends on how efficiently it fulfills requests, not on whether the business decides better because of it.

Level 3: From Findings to Decisions

At Level 3, the team is measured on what happens after the research, not on what it produces.

The technology becomes an intelligence system. Data sources are connected by design, so nobody stitches spreadsheets together for each new question, and the system flags signals and anomalies without being asked. Synthetic data and experience simulation are used throughout the research cycle, alongside real research, not instead of it. The researcher's job shifts from finding to judging.

That shift makes the researcher a decision architect. Researchers track decision outcomes and feed what they learn into future work. They keep command of what the organization already knows, and of how that knowledge has been used to drive decisions and deliver results, so new work builds on old instead of starting from zero. And they tailor the same finding for different audiences to drive action: risk for the CFO, growth for the CMO, operations for the COO. The deck isn’t the finish line.

Culturally, the team has earned a place in the inner circle. The business sees research as the function responsible for the probability of success of its major bets, so leaving research out of a major decision would be flagged as a business risk. The function is measured on decision outcomes, not research outputs, and researchers are in the room as decisions are made, not just presenting in advance or analyzing afterwards. If your team were left out of a big decision next month, would anyone notice?

Start Where You Are

You don't have to transform everything  at once. Start by finding your weakest pillar: that’s your bottleneck, and bringing it up to the level of the other two should be your first step.

Remember that the three pillars reinforce each other, so a step forward in one makes the next step easier in the others. A better-connected system gives researchers time to build judgment, and a team that exercises judgment gives the business a reason to bring research into the room. The goal isn't to reach Level 3 for its own sake. It's to make sure that when the business makes its next big decision, research is part of how it gets made.