If there’s one big trend we can all agree on in 2023, it’s this: AI is all the buzz.
At InterQ, we’re especially taking note of this, because we’ve worked with numerous companies in the AI space to deliver insights, and it’s worth spending some time discussing how vital qualitative research is for successful AI-product rollouts, as well as for ongoing AI improvements.
Qualitative research (which includes UX – but the case studies below detail other methods that are helpful in the product launch and refinement phase) may be easy to overlook when companies are developing AI products, since AI lives prominently in the quantitative space of data, but from our experience, qualitative research is essential when developing, implementing, and improving AI-based products.
To illustrate this, we’ll outline a few case studies, where we’ve used qualitative research to help AI companies be more successful.
Case study: How qualitative research helped a large healthcare company push through a new AI product
A large healthcare company hired InterQ to talk to HR leaders about a new AI product they were developing. A concern was that HR and their employees may push back against the product – seeing it as too “big brother.”
Using the methodology of focus groups, our moderator presented the concept to HR leaders in focus groups held in major U.S. cities. We led discussions about the products’ benefits, use-cases, and how it could impact their healthcare costs, long-term. From these focus groups, themes emerged around the product’s AI concerns, and the healthcare company was able to understand how to build a better AI product and message it so that the concerns would be allayed.
Ultimately, the product ended up being a cornerstone piece of their technology, but had they tried to sell and market it without the knowledge that the focus groups illuminated, it may never have gone to market.
Case study: How qualitative research helped a rising AI sales software company develop and market a new product
Here’s another example illustrating how crucial qualitative research is in the product-release process. The client was a rising star in the AI sales software category, and they wanted to roll out a new product that would live on top of its core AI product. First, however, they needed to understand whether there was a market need – and they wanted to uncover how this need was being met and managed by sales teams.
InterQ interviewed around 40 sales leaders, in an in-depth interview format (both current and non-customers). We asked about their current processes and had detailed conversations about the current tools and cost/benefits that teams were seeing (and missing). We then presented an outline of the new AI product to test messaging and get concept feedback.
The results from the research helped the market and product teams understand how to position this new product, and, most importantly, which types of sales orgs would most benefit from a tool like this. This allowed the sales software team to be more efficient and hyper-focused in their rollout.
Case study: How qualitative research helped an AI-based financial lending product break through to those who needed short-term loans
The final case study we’ll discuss today was with a client that had developed an AI tool that helped those with poor credit scores get short-term loans. This is an underserved market that is often preyed on, and the high interest rates that people with poor credit are offered only exacerbate financial difficulties. The new AI lending tool was able to take into account various other factors to lend people short-term loans with lower interest rates, but the company needed to be able to position the product without coming off as too “techy” or alienating its core audience.
InterQ conducted in-depth interviews with various persona groups that the company’s products were targeting. In these discussions, we were able to listen to people’s stories about how predatory the marketplace is, and we learned how individuals piece together various high-interest loans to cope – but often are unable to get out of the debt cycle as a result.
In the second half of the interview, we took participants through the company’s website and messaging. This helped us understand how people reacted to and perceived the messaging – and what questions they had. The findings were extremely helpful in understanding how to position the product, how to discuss AI, and how to reconfigure the product messaging so that it could be tailored to meet the needs of the customers that the lending company was trying to assist. As a result of the qualitative research, the company continues to grow steadily, and they’ve branched into other loan products.
Qualitative research in AI isn’t just about UX research
We hope these case studies illustrate how impactful – and essential – qualitative research is when developing and positioning AI products. Doing the research upfront on the product features, messaging, and sales-targets is essential for positioning AI products for success, and this becomes even more important as the AI field becomes more crowded and the technology gets lost in a sea of sameness and “AI buzz.”
This is not to discount the importance of also conducting UX research with AI products, as usability testing is extremely important too; just don’t neglect the upfront qualitative research that is so essential for understanding market fit, branding, and sales.