AI not only speeds up the research process but also leads to better, more informed decisions that product teams are already using to rapidly improve their product experiences. If you need to get your team on board, these eight stats will help make your case for testing out AI-powered research methods.
How AI-powered research methods speed your team up Let’s face it: speed-to-insight matters. Whether you’re trying to get a product out the door or just keep up with your competition, you can’t afford to be stuck in a slow research cycle. Modern companies are using AI-powered research methods to speed up their decision making process. Butternut Box is a good example of a company that's drastically shortened the time it takes their product teams to reach user insights that inform their product decisions. They were able to reduce their time-to-insight by a staggering 42 times after implementing Wondering into their research stack. With Wondering in their research tool stack, they can get insights that quickly, able to iterate faster, make better design decisions, and ultimately, create a product that better meets their users’ needs.
Butternut Box are not alone in caring about being able to speed up their research cycle and product launches. In fact, nearly half (45%) of all product launches are delayed by at least a month . A lot of that comes down to slow research and testing cycles.
How AI-powered research methods helps you scale your research As your organization grows, being able to scale your research practice often becomes a bottle neck. With more teams that need insights to inform their decisions, your previously well-staffed research team gradually starts to feel overworked and struggling to keep up with the demand for research projects to be completed on short timelines. Fortunately, large language models are today very adept at completing some specific tasks in your research workflow if implemented correctly. Let's look at Matsmart, an e-commerce company operating across Europe. They were able to boost the number of research studies they conducted by 2X after starting to use Wondering's AI-moderated research platform in their research. This helped run more frequent, targeted research studies to help them iterate faster and keep you in tune with what your users really want.
AI can also help you as a researcher uncover insights you might otherwise miss. When compared in our benchmarking study to a human expert-level user researcher, Wondering's AI-moderation and analysis was able to capture insights (29% of all insights) that the human researcher missed completely.
Scaling research with AI doesn’t just mean doing more research — it also allows you to do more without adding extra complexity. AI tools like Wondering make it simple to collect and analyze data from a wider range of sources with your current team. Whether you’re launching in new markets, expanding your product line, or just trying to understand a broader audience, AI gives you the flexibility to scale without the headaches.
How AI-powered user research will help you improve your product At the end of the day, all the research in the world doesn’t mean much if it doesn’t lead to better business and product outcomes. This is where examples are likely to help your stakeholders get a better picture of how AI-powered research can drive meaningful outcomes related to metrics they care about.
Take StreamElements as an example. They saw a 20% bump in their product conversion rates after they started using AI-powered insights from Wondering. That’s a big deal. Conversion rates are one of the most critical metrics for any software business, and a 20% increase translate into a significant boost in their revenue. This kind of result shows that AI research isn’t just about gathering data—it’s about making data-driven decisions that have a real impact on your bottom line.
The cost of bad UX Here’s a some stats that might make you wince:
35% of potential revenue is lost due to bad UX. That’s a lot of money left on the table simply because of poor design or functionality issues. AI-powered research tools help you catch these problems early, before they start eating into your revenue. By continuously improving your UX based on real user feedback, you can recover that lost revenue and turn frustrated users into loyal customers. The cost of bad UX decisions also permeate to the overhead costs for your product development teams too. The cost of fixing an error after development is 100x higher than if you fix it before development. AI-powered user research tools like Wondering aren’t just a nice-to-have—they’re a must-have for product and research organizations at companies that experience phases of rapid growth. The stats speak for themselves: faster insights, scalable research, and real business results. When you’re trying to get your stakeholders on board, use the stats in this article to help you build a compelling case, and don't hesitate to reach out to the Wondering team if you need support building your business case for AI-powered research at your organization!
