The tough economic climate, combined with the promise of AI delivering significant efficiencies, has pushed the role of technology firmly into the spotlight for research agencies.

New tools promise a lot. They can automate repetitive tasks like coding open-ends, accelerate insight generation, and enable more dynamic, interactive outputs—something increasingly expected in client briefs.

But in practice, adopting new technology is rarely straightforward.

As a relatively small team with a mix of technical skills, we’ve always considered ourselves fairly forward-thinking when it comes to tech. Not having legacy systems or multiple layers of complexity has its advantages. Over the past three years, we’ve tested a range of platforms—and adopted some of them too.

So, what have we learned?

1. No one tool does it all

Naively, that was our hope. But despite the sales pitches and promises of seamless integration, no single platform fully meets the needs of a research agency—especially when working across both qual and quant.

Our projects are simply too varied.

What we’ve found works better is building a stack of specialist tools, aligned to each stage of the research process:

• Ideation
• Scripting
• Fieldwork
• Analysis
• Reporting

Rather than searching for a one-size fits all option, we found it more effective to assemble partners who specialized in certain areas.

2. Free Trials are helpful—but can cost

Most tech providers offer free trials, which are essential for evaluation. But in reality, they come with a hidden cost: time.

Like Excel, where most users only scratch the surface of its functionality, you can’t properly evaluate a tool without really using it. And that takes effort.

We’ve found that to assess a platform properly, you need to:

• Run a live project through it, or
• Replicate a previous project in full

A quick “look around” simply isn’t enough.

For smaller teams especially, this creates a trade-off between investing time in testing versus delivering client work. It’s something to plan in advance.

3. Have a clear assessment framework

To bring more structure to our decision-making, we developed a framework for evaluating new tools.

This included criteria such as:

• User interface and usability
• Level of support and onboarding
• Strategic fit with our offer
• Pricing and scalability

We then weighted these factors based on what mattered most to us as a business.

This approach helped us:

• Compare tools consistently
• Benchmark against existing platforms
• Create a clear rationale for investment decisions

It also provided a useful audit trail for internal stakeholders when making financial decisions.

4. Get the balance between ‘human’ and ‘tech’ right

While many of the tools we tested were powerful, we found that outputs could sometimes lack context, nuance, or even accuracy.

It reinforced an important principle:
technology can accelerate thinking, but it can’t replace it.

As a team of experienced researchers who developed our skills using more traditional approaches, we’re acutely aware of the importance of critical thinking. And increasingly, that’s something that needs to be actively protected.

In response, we:

• Developed structured learning paths for junior team members
• Created more opportunities for hands-on, real-world experience
• Reintroduced more collaborative, face-to-face analysis sessions
• Redesigned workflows to ensure outputs are properly interrogated

The goal isn’t to resist technology—but to ensure it enhances, rather than dilutes, the quality of thinking.

5. Be specific about the problems you’re solving

It’s easy to get dazzled by the promises of new technology. But our experience has been that it’s best to start with the problem, not the tool.

For us, this meant focusing on two key challenges:

• Giving junior team members more exposure to insight generation and report writing, rather than purely data processing
• Improving how we visualise and maintain tracker data without constantly reworking reports

Being specific about these needs helped us identify tools that genuinely fit our workflow—even if they didn’t initially appear to be the most feature-rich.

Ultimately, the right technology isn’t the most powerful—it’s the one that solves real business problems, will fit into how your team works and frees up time for more thinking.

Finally…

For small research agencies, successful tech adoption isn’t about chasing innovation for its own sake. It’s about making deliberate, practical choices that improve how your team works day-to-day.

At Brand Ignite, this has been an ongoing process of testing, learning, and refining—and one which will be ongoing!