As companies look to expand their use of artificial intelligence (AI) and machine learning (ML) to keep up with the demands of their customers, they are facing hurdles getting these projects to production and ultimately deliver the desired results to their bottom line. In fact, 88% of AI/ML decision-makers expect the use cases that require these technologies to increase in the next one to two years, according to a commissioned study conducted by Forrester Consulting on behalf of Redis Labs. The research looked at the challenges keeping decision-makers from their desired transformation when deploying ML to create AI applications.
The study revealed that companies are developing increasingly more models based on real-time data. Still, more than 40% of respondents believe their current data architectures won’t meet their future model inferencing requirements. Most decision-makers (64%) say their firms are developing between 20% to 39% of their models on real-time data from data streams and connected devices. As teams develop more models on real-time data, the need for accuracy and scalability is becoming increasingly critical. Significantly, thirty-eight percent of leaders are developing roughly a third of models on the real-time spectrum.
