A version of this article appeared previously on the Sonar blog.
Artificial Intelligence is taking the world by storm. Every day, we use AI-powered tools whether we notice it or not: from predictive Google searches to virtual assistants we pester with questions during our shopping sprees, AI’s force-multiplier is too tempting to pass up, as it allows us to deliver better results faster, more easily and with fewer resources.
UX and qualitative research at large are no strangers to this technological revolution. In recent years, many of the most time-consuming steps once performed manually are being optimised by AI, saving UX’ers countless hours of repetitive – and quite frankly, boring – tasks.
However, critics are split on the current status and usability of AI. Some argue AI is not quite there yet, still burdened by limited functions and vocabulary. On the other hand, some argue AI is perhaps too intelligent, foreshadowing Artificial Intelligence is taking over the most analytical aspects of UX research from UX’ers any minute now.
So, what’s the truth? What are the actual benefits of AI in UX research? What can it do and not do at present? Let’s find out!
Better, Faster, Easier: 5 Benefits of AI for UX Researchers
1. Speed
Right off the bat: AI cannot – and in the future will not, replace UX designers and researchers in generating insights and doing the more analytical aspects of UX research. Rather, what it can do is to help get to insights faster, doing the heavy lifting on the most mechanical and repetitive aspects of UX research – quotes tagging and interview transcriptions above all, and leaving UX’ers more time to conduct deeper and bigger research.
That’s because AI is fast – much faster than humans can ever be at these tasks. Let’s compare manual and AI-powered interview transcriptions: where AI can provide instantaneous transcripts or transcribe at the same speed as the interview, manual transcriptions take on average 4 times the duration of the interview. Now consider that manual transcription can produce in parallel as many transcripts as the number of transcribers available, while the AI can work on 5, 100, 1000 transcriptions simultaneously…the time saved with AI is truly exponential, and that’s just for one task!
The sky is the limit when it comes to automation. On top of video and audio transcriptions, Sonar’s AI can speed up your research even more by automating quote generation, tagging, and data gathering from your surveys, giving you a massive head start on your UX studies.
2. Cost
So, AI helps you save a lot of time. What about the cost? Because of automation, running UX studies with AI-powered tools is also cheaper. Let’s go back to interview transcriptions. For 60$ on the low end, you can get your 1-hour audio or video interview manually transcribed. With AI, you can slash that price to as little as a couple of dollars.
But that’s not just the end of it. You can scale up cost savings even further by having a comprehensive AI-powered UX research platform with all your tools in one place. And with Sonar, you can design, collect, analyse and share your user research all in one platform!
3. Consistency
When people do research, they take very different approaches in the way they conduct and document it. On the other hand, AI is consistent, for good or bad. What it means is that, if not “trained” properly, Artificial Intelligence will make the same mistakes over and over again. Conversely, if fine-tuned with your study, you can get consistent – and instantaneous – results.
Using the same AI across multiple studies and projects means you can export consistent results and reliably mix or compare them with others from similar projects, exponentially expanding the study pool of your UX inquiries.
It’s a no-brainer that the dealbreaker between choosing AI or not lies in its quality – or rather, in how fine-tuned to your studies the machine learning algorithm is; this depends on how long it has been “trained” with similar interviews. Logically, the more AI becomes accustomed to specific vocabulary and is able to develop predictive patterns. And through hundreds of thousands of interviews, Sonar’s AI is one of the best in the market for UX Research and qualitative studies.
4. Ease of Use
If you’ve got this far, you may now ask “Is AI hard to use?”. While the short answer is “No!”, the long answer is “Is it comparatively harder than running qualitative research either manually or on multiple platforms?”
One of the key features of AI is that its algorithm learns constantly – not just from you, but also from other users on the platform where the algorithm operates. This empowers it to make recommendations based on other users’ best practices, creating a network that extends beyond your team. Thus, AI’s synergy allows you to learn from others and become an experienced user yourself.
Furthermore, machine learning algorithms usually presents themselves in intuitive and easy to master interfaces. Using our platform, with just a couple of clicks, you can browse through interview scripts, survey results, quotes and observations, meaning you have all the instruments necessary to generate insights at a glance and in one place.
5. More Quality and Quantity
To conclude, with an AI-powered platform that allows you to design studies and collect data faster, produces consistent results, and is easy to use, you can save time, resources, and produce UX studies that are larger in scale and deeper in scope. In other words, Artificial Intelligence forgoes the old quality vs quality tradeoff, allowing UX researchers to achieve more quality and quantity.
