The use of AI in any organization starts with having their data ready for that journey. “In order to actually reap the benefits of AI in terms of giving predictive or descriptive types of capabilities, it has to be fed clear, contextualized and relevant data,” says Astrix Principal Software & Systems Architect Dave Dorsett in a new AIScoop video interview.

However, this can prove challenging for many science- and research-based organizations to tackle on their own. Dorsett argues that collaborative efforts between subject matter experts and technical leads lead to more effective outcomes.

“There’s a bit of work and experience that are required in the selection of the right problems, in order to actually get back any kind of return on a technical investment,” says Dorsett. “The process of producing the data or using the data or building the models requires balanced guidance that is informed by both the technology side and the science side of things.”

According to Dorsett, taking a collaborative approach to AI and machine learning applications will ultimately allow science-based organizations to achieve advancements more rapidly and better leverage the systems they already have in place.

“We have, as an industry, a tremendous amount of research and development data locked away in our systems – and one of the challenges is findability and usability of that information that we already have,” he says. “AI has the ability to help scientists find what they need when they actually need it.”