Enterprises ramp up AI/ML spending despite deployment challenges

In response to the economic impact of COVID-19 companies are turning to their investments in AI to deliver both short-term cost-cutting and long-term technology innovation to drive revenue and efficiency.

A report from ML operations and management software specialist Algorithmia finds that 83 percent of organizations have increased their budgets for AI/ML and that the average number of data scientists employed has increased 76 percent year-on-year.

Organizations are also expanding into a wider range of AI/ML use cases. The survey finds that the percentage of organizations that have more than five use cases for AI/ML has increased 74 percent year-on-year. The top use cases that organizations are focusing on are related to customer experience and process automation.

This is not without challenges though. The top issue being AI/ML governance, with 56 percent of all organizations ranking governance, security and auditability issues as a concern -- and 67 percent reporting the need to comply with multiple regulations for their AI/ML.

In addition to governance challenges, organizations continue to struggle with basic deployment and organizational issues. 49 percent of organizations rank basic integration issues as a concern, and the survey finds that cross-functional alignment continues to be a major blocker to organizations achieving AI/ML maturity.

It's clear that businesses are taking the time to get things right though, the time required to deploy a trained model to production has increased year-on-year and 64 percent of all organizations take a month or longer to deploy a model. While 38 percent are spending more than 50 percent of their data scientists' time on model deployment.

Third-party solutions help, when compared to organizations that build and maintain their own systems from scratch, organizations that either integrate commercial solutions into their systems or use a third-party platform spend an average of 19-21 percent less on infrastructure costs. The average amount of their data scientists' time that's spent on model deployment is also 22 percent lower and the average amount of time they take to put a trained model into production is 31 percent lower for third-party solutions.

"COVID-19 has caused rapid change which has challenged our assumptions in many areas. In this rapidly changing environment, organizations are rethinking their investments and seeing the importance of AI/ML to drive revenue and efficiency during uncertain times," says Diego Oppenheimer, CEO of Algorithmia. "Before the pandemic, the top concern for organizations pursuing AI/ML initiatives was a lack of skilled in-house talent. Today, organizations are worrying more about how to get ML models into production faster and how to ensure their performance over time. While we don't want to marginalize these issues, I am encouraged by the fact that the type of challenges have more to do with how to maximize the value of AI/ML investments as opposed to whether or not a company can pursue them at all."

You can get the full report on the Algorithmia site.

Photo Credit: NicoElNino/Shutterstock