Over the last decade, machine learning has rapidly infiltrated the enterprise world, riding on tidal wave of expectations and optimism about how machine learning and data science can reshape big business.
Recently, Algorithmia published a white paper based on a survey of organizations applying machine learning, with some interesting insights into the state of enterprise machine learning in 2020.
Every organization is trying to build a data science team, but experts are hard to find.
Organizations are ramping up their hiring efforts to build larger data science teams. In 2018, 18 percent of companies employed 11 or more data scientists. In 2019, however, that number climbed to 39 percent. As we can attest to, the problem most organizations face is finding skilled data scientists with industry expertise who can actually make an impact. As Deloitte
predicted back in 2016, there is a serious shortage of data scientists to fill these roles.
Bigger organizations are investing most heavily in machine learning to reduce costs.
A larger organization's first priority with machine learning is often to reduce costs. The white paper states, "The survey data showed that large companies are using ML primarily for internal applications (reducing company spend and automating internal processes), while smaller companies are primarily focused on customer-centric functions (increasing customer satisfaction, improving customer experience, and gathering insights). This suggests that as companies grow, they prioritize customer service less than cost-saving measures and applications that improve their product lines."
Predictably, “industries with customer-facing products or services (retail, manufacturing, healthcare, etc.) prioritize ML applications that improve customer service, and industries involved with security, compliance laws, and proprietary data (financial institutions, government agencies, insurers, etc.) focus more so on ML use cases that help solve those challenges.”
Organizations still underestimate the infrastructure investment required to deploy models.
At Strong, our focus is on applying machine learning in the real world. We know it's not easy, and the organizations surveyed in this paper can corroborate: When they looked at the actual time spent deploying models across companies of all sizes, at least 25 percent of data scientist time was spent on deployment efforts. "Put simply, a quarter of data science capability is lost to infrastructure tasks." Because of that, the time it takes to deploy a model is somewhere between 31 and 90 days for most companies. Eighteen percent of companies had deployment timelines longer than 90 days, and a smaller set still with timelines over a year!
At the end of the day, this has serious consequences in terms of the impact of this ML work: A shocking fifty-five percent of companies surveyed have not yet deployed a machine learning model.
Challenges aside, organizations are investing more in machine learning than ever before.
Forty-three percent of companies have increased their AI/ML budgets between 1 and 25 percent in the last year, with the banking, manufacturing, and IT industries having seen the largest budget growth. In general, companies are doubling down on their tech investment efforts. This means that companies in very early deployment stages (just starting to develop ML models) will have to triple their efforts to stay competitive in their industry.
If your organization is considering building new strategies around machine learning, get in touch with our team of machine learning consultants to maximize the impact of your investment.