It’s hard to find a corner of working life that hasn’t been touched by artificial intelligence – and in particular, by generative AI (Gen AI). In design, marketing, operations, and now research and analytics, AI is automating what once required hours of human effort to improve customer experience. Understandably, that has raised concerns, notably around accuracy and job security.
Research teams are under pressure. Many are stretched thin, trying to meet growing stakeholder demands with limited time, and an already shrinking workforce as people move into other roles, such as more general marketing positions. And, while sentiment towards AI is generally positive, 47% are concerned about job loss as a result according to MRII.
But while some roles may change, the core value of research – human understanding – is perennial. AI isn’t the end of research. It is the inflection point as the industry shifts to a smarter, more efficient, and more strategic way to work that should lead to better results. And, given Gartner research found 38% of Gen AI initiatives are focused on customer experience and retention, it’s vital that research teams become fluent. And quickly.
The end of the status quo?
Research and analytics teams have historically been tasked with a broad, often overwhelming scope of work – stakeholder interviews, usability testing, survey design, data synthesis, reporting – all while being expected to deliver faster results more frequently. These increasing demands, with limited resources, often lead to trade-offs between speed, depth, scale and quality.
However, the existing process has not kept pace with how modern organisations operate. Researchers often still rely on manual methods to analyse interviews, summarise feedback, and synthesise findings. These steps are time-consuming and repetitive, but also essential.
This makes research ripe for disruption. Not because it is broken – but because AI offers a way to take the industry into its next phase. One that’s faster, more efficient, and more impactful.
The technology offers something these teams desperately need: time. With the ability to quickly process large volumes of data, generate first-draft summaries, or map out early trends, Gen AI can relieve teams of the repetitive and easily automated tasks that take up disproportionate amounts of time. This creates space to do the work that really matters – interpreting findings, making decisions, and advocating for the user. Research by MIT Sloan, Microsoft Research, and GitHub found that Gen AI coding tools could reduce programming time by over half, for example. In such a time-intensive industry as research and analytics, saving anything close to that many hours could be revolutionary.
Disrupting research, at last
Gen AI models are already delivering benefits in research. From analysing and summarising responses to identify themes, through to creating a list of the key points that should be included in a report, it is a huge time saver. In some cases, researchers can even use it to help them iterate the next phase of discovery by helping them to create new questions based on pre-existing data.
Outside of research, we’re already seeing this impact across marketing, content, and design. Copywriters use AI to suggest headlines, designers automate component creation, and product teams generate user stories from feedback. Research is next.
Start and end with the human
It is tempting to see AI as a replacement for human insight. But it’s not. It is a tool – and like any tool, it has limitations.
ChatGPT for example is a powerful system, but its training data is always out of date. I even asked it to check. GPT-4-turbo, which is the latest model at time of writing, is trained on data up to December 2023.
In contrast, consumer expectations can shift overnight. For research to be relevant, it needs to reflect what’s happening now – not what happened last quarter.
That is why researchers remain essential. Even as automation accelerates, people need to stay involved throughout the process. AI can flag trends, but it takes a human to evaluate what’s relevant and what is noise. AI can summarise a transcript, but only a person can judge whether that summary captures the nuance of a user’s frustration, confusion or delight.
The end of the process is especially critical. Final insights need to be validated, contextualised, and translated into action – and that only happens with human judgment. Without humans in the loop, there’s a real risk of misinterpreting the data or drawing the wrong conclusions.
Take sentiment analysis for example. AI can label phrases as positive or negative. But only a human can understand when sarcasm, cultural nuance, or intent defy those labels. Likewise, personalised recommendations based on user data require a human touch to remain ethical, inclusive, and effective.
AI will change research, but it won’t replace the need for people. Instead, it elevates the role of the researcher from processor to interpreter, from executor to strategist.
From collectors to curators
AI is already ushering in a deluge of new experiences, and it’s an exciting opportunity to redefine research’s role to move humanity forward. Rather than serving as collectors of data, we can become curators of insight – strategically selecting what matters and framing it for action that delivers a tangible result.
Tasks like transcription, clustering, and first-pass summaries are already being streamlined. But this creates space for researchers to focus on strategy, insight curation, and cross-functional collaboration.
To succeed does require researchers to supercharge themselves though. Fluency with AI tools, comfort with automation, and confidence in synthesis and storytelling will help to deliver positive results. With less time spent compiling and more time interpreting, researchers can influence decision-making earlier and more effectively to drive the business’ growth at speed.
In the AI-augmented research landscape, human skills like empathy, interpretation, and strategic thinking become even more valuable. Because in the end, AI might help you hear from more people – but it still takes a human to truly listen and plan for the future.