AI efficiency in market research: What teams need to know now

AI has become impossible to ignore in market research. That part is obvious. What’s less obvious is what AI efficiency actually means in practice.

This isn’t just a story about faster survey programming, quicker summaries, or dashboards that build themselves. That’s only part of the picture. The bigger pressure now is that market research teams are being asked to move faster and prove more value at the same time.

That’s why the AI conversation is maturing. The question is no longer ‘Can AI automate this task?’ It’s ‘How does AI help research teams get from insight to action more effectively?’

The pressure isn’t new, but the environment is

One of the strongest themes from Forsta’s recent AI Efficiency webinar was that the core pressure on insights teams hasn’t really changed. Researchers have been dealing with shrinking resources and rising expectations for years. In that sense, AI hasn’t created the problem; it’s simply arrived in the middle of it.

Our Chief Customer Experience, Research Officer Luke Williams described this well: Teams on both the MR and CX sides are being asked to do more with less, while also facing growing scrutiny around ROI. Executives know there is more data available than ever before. They also know AI is changing what seems possible. The result is an expectation that insights teams should be able to produce more value, more quickly, and with fewer bottlenecks.

That expectation lands heavily on market research teams. Agencies feel it from clients who want faster turnaround and clearer commercial relevance, while in-house researchers feel it from stakeholders who are under pressure to move quickly and make better decisions.

So while AI can automate parts of the workflow, the real issue isn’t just efficiency for efficiency’s sake. It’s what that AI efficiency creates room for.

Speed only matters if it leads somewhere

This is where market research can borrow something useful from CX.

Customer experience teams have long been pushed to think not only about measurement, but about what happens next. Data alone is not enough. The challenge has always been moving from signals to decisions, and from decisions to change. That same lesson applies to MR right now.

Forsta’s General Manager of Market Research Tobi Andersson made the point that the familiar big buckets of the research process still remain: Design the questionnaire, collect the data, clean and prepare it, build the outputs, then discuss what it all means with the buyer. Those stages are not disappearing. What is changing is how teams move through them.

Instead of manually scripting surveys, cleaning data, and repeating setup work for each project, AI is starting to take on more of that foundational workload. As Tobi described the perfect future, using AI, a questionnaire written in Word could form the basis of a survey, with routing and structure generated automatically, and flow all the way through to accessibility recommendations for data visualization.

That doesn’t remove the need for researchers. What it changes is where their time goes. And that matters, because the most valuable part of research was never the repetitive setup. It was the interpretation, the nuance, the skill to spot what matters, and the quality of the conversation that follows.

What AI is already good at

For market research teams, AI is already proving useful. A consistent theme throughout the webinar was the welcome reduction in manual drudgery. Work that once took a lot of copying, pasting, routing, cleaning, and formatting can now be streamlined significantly. That alone is meaningful.

AI also helps with summarization and pattern detection. Luke’s framing here was useful: AI works well as a thought partner, not a thought replacer. It can surface patterns, highlight anomalies, summarize large amounts of material, and get researchers from a standing start to a more informed first draft.

What AI still does not replace

This is the part worth holding onto.

There is a lot of noise in the market about AI replacing human expertise. That fear is understandable, but it tends to flatten the reality. As Tobi pointed out, the market research industry has gone through several waves like this before. Postal surveys were supposed to disappear. Then CATI. Then traditional approaches were going to be overtaken by online panels, or passive data, or scraped data. Each shift changed the mix – but none erased the need for researchers.

This moment is no different.

AI may be able to help script, clean, and summarize, but it can’t replace what Tobi described as ‘that fingertip feel’ – the subtle judgment that comes from experience, context, and knowing what a piece of data actually means for a client or stakeholder.

Luke made a similar point. AI does not replace context, critical thinking, or strategic interpretation. It can identify patterns and summarize information, but it doesn’t have any meaningful grasp on the competitive realities that shape business decisions.

AI efficiency is not the same thing as value

There is a temptation, when AI is discussed, to focus entirely on time savings. Faster setup. Faster analysis. Faster reporting. Those gains are real, and they matter, but the more interesting question is what teams do with the time they get back.

Luke put it bluntly: If AI reduces a hundred steps in your day, what are you going to do with the time that creates? More work? More creativity? More strategic thinking? More time spent helping stakeholders understand what matters? Take a break?

That is where market research teams can turn AI efficiency into differentiation.

If the time saved simply disappears into more output, AI becomes a throughput story. If that time is reinvested into better stakeholder conversations, stronger interpretation, sharper storytelling, and more commercially useful recommendations, it becomes a value story. And that’s a much more powerful place to be.

What should market researchers do now?

1. Start using these tools. Luke was especially direct on this point: Mastery of AI is a differentiator right now, but it won’t remain one for long. The sooner teams understand what these systems can and can’t do, the better positioned they’ll be to apply them effectively.
2. Keep humans in the loop. Not as a defensive slogan, but as a practical necessity. Governance, validation, and quality control might not be glamorous, but they are what keep AI from becoming a trust problem.
3. Treat AI as infrastructure for better research. AI is a supportive tool – not a substitute for research thinking.The strongest use cases are the ones that remove friction from the workflow so researchers can spend more time on insight quality, communication, and action.

From AI efficiency to impact

For all the focus on automation, Tobi’s perspective is a useful anchor.

The structure of market research isn’t going anywhere. The same core stages still exist, and the same need for judgment and interpretation remains. What’s changing is how quickly teams can move through those stages – and where they spend their time.

This is where research-specific AI capabilities begin to hold real value.

Tools like Forsta’s Research Agent are useful – not because they replace researchers, but because they reduce some of the drag that slows good teams down. They help market researchers get to stronger outputs faster, freeing them up to spend more time refining what matters.

The future of AI efficiency in market research isn’t about cutting humans out of the process. It’s about making more room for the parts of research that humans are best at: Judgment, storytelling, challenge, interpretation, and helping stakeholders make better decisions.

That is the AI efficiency that matters.