In today’s AI rush, businesses are seeking artificial intelligence solutions for two reasons: to solve known business problems and to “stay ahead of the curve.” While the latter may provide a boon for AI startups in the short term, generative AI platform Native AI says this has already started to lead to long-term problems for the industry.
“Hype can only take this industry so far,” says Native AI CEO Frank Pica. “Soon, providers of AI technology and applications will need to prove value to partners in order to thrive, or they will not survive once the hype wears off.” Pica says that many genAI startups focus exclusively on upstream value because it’s difficult to measure, so they can get away with phantom solutions for longer. But Native AI has found a competitive advantage in pursuing multiple use cases that produce measurable impact and ROI for businesses.
Native AI aggregates consumer feedback from third party retailer websites and ingests first party data as well, such as customer lists and market research study participants. Using Natural Language Processing, the dashboard surfaces common themes and trends. Their flagship product, Digital Twins, enables insights professionals and researchers to chat with digital representations of consumers from almost any data source.
While there are many ways to apply insights, Native AI is seeing the most long-term traction from brands and market research firms that have identified immediate and practical applications for the insights. According to Native AI, there are several use cases that have emerged over the past couple of years, including product review tracking, statistical analysis, and market research study qualification.
Product review tracking is the most basic and obvious use case. Brands often find it challenging to keep up with new feedback across sales channels, so they use Native AI to view, categorize, and tag reviews all in one place. Because this task can be a huge drain on brand resources, it’s easy to measure time saved by using a platform that does the legwork of aggregation and organization. These are some of Native AI’s early customers, as this functionality has been available long before Digital Twins.
The next two use cases are more novel. For years, researchers have understood the importance of qualitative feedback but have struggled to demonstrate as much impact as quantitative metrics. Now with Natural Language Processing, it no longer requires manual labor to group feedback by them and generate high-impact visualizations like charts and graphs. Beyond simply time saved, market research firms, in particular, are finding upsell value in offering such detailed analysis to their prospects.
“Native AI has structured a number of partnership agreements that enable research firms to sell Native AI’s services as a no-risk add-on,” says Native AI’s SVP Revenue Branden Smythe. “It’s a win-win because our solution is often complementary, creating a 1+1= 3 scenario.” Smythe says that Native AI frequently partners with other market research solution providers to service major clients.
The third use case drills down to the individual Digital Twin response level. Increasingly, market research firms are using Digital Twins to predict responses to new questions from past market research study participants. Some firms use this as a replacement for recontact studies, and others use these predictive responses to qualify past study participants for future studies. This can have a measurable improvement on recruitment metrics, which reduces costs and improves participant retention.
Native AI’s GenAI uses RAG (Retrieval-Augmented Generation), which applies context from the source data to the chat model, allowing for improved prediction accuracy over former data science methods. Data sets don’t usually contain verbatim responses that can be easily searched. For example, if a consumer has previously mentioned eating at fast food restaurants, a RAG model can draw some logical inferences about other types of food preferences, even down to individual ingredients. While these types of predictions will never be 100% accurate, the added signal can make a huge difference at scale.
Native AI believes that once the hype period of genAI is over, businesses will settle on AI applications that drive measurable impact. Sometimes it will be obvious, like replacing an existing manual function with an AI automation; sometimes it requires a little more creativity, like rethinking how a process can be improved with predictions at scale. But based on the number of new use cases that have emerged over the past year, Native AI is confident that the true value of genAI is yet to be fully realized.
