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Incorporating AI and machine learning technologies into everyday workflows isn’t as easy as the testimonials would have you believe. That’s the top-level finding from a survey of 750 business decision makers conducted by Algorithmia, which found that while machine learning maturity in the enterprise is generally increasing, the majority of companies (50%) spend between 8 and 90 days deploying a single machine learning model (with 18% taking longer than 90 days). Most peg the blame on failure to scale (33%), followed by model reproducibility challenges (32%) and lack of executive buy-in (26%).

“The findings of our 2020 [State of Enterprise Machine Learning] study are consistent with what we’re hearing from customers,” said Algorithmia CEO Diego Oppenheimer. “Companies are growing their investments in machine learning, and machine learning operationalization is maturing across all industries, but significant room for growth and improvement remains. The model deployment lifecycle needs to continue to be more efficient and seamless for ML teams. Nevertheless, companies with established ML deployment lifecycles are benefiting from measurable results, including cost reductions, fraud detection, and customer satisfaction. We expect these trends to continue as ML technologies and processes arrive to market and are adopted.”

Growth in hiring

Perhaps unsurprisingly given the barriers to adoption, machine learning expertise remains in high demand. Just over half of people polled by Algorithmia say that their companies employ between 1 and 10 data scientists, and 5% say they employ more than 1,000; 39% say they have 11 or more. That latter figure is an uptick from 18% in 2018, when the last State of the Enterprise Machine Learning survey was published.

Predictions of an industry-wide data scientist shortage seem prescient, given this context. In 2016, Deloitte anticipated a gap of 180,000 workers by 2018, and the number of data scientist job listings on LinkedIn increased more than 650% from 2012 to 2017.

Algorithmia anticipates that as the demand for data scientists grows, junior-level hires might be given less opportunity to structure AI efforts within their teams, as much of the program scoping is likely to have been done by predecessors. However, it could also mean that leadership alignment is likely to be granted, and that AI teams will have more ownership of and leeway in project execution.

Adoption and implementation challenges

Despite the fierce search for data science talent in the enterprise, nearly 55% of companies represented in the report say they haven’t yet deployed a machine learning model (up from 51% of companies last year). A full one-fifth are still evaluating use cases or plan to move models into production within the year, and just over 22% have had models in production for two years or fewer.

That jibes with a recent study conducted by analysts at International Data Corporation (IDC), which found that of the organizations already using AI, only 25% have developed an “enterprise-wide” AI strategy. Firms responding to that survey blamed the cost of AI solutions and a lack of qualified workers, as well as biased data and unrealistic expectations.

As alluded to earlier, moving models into production remains a challenge for most organizations, according to Algorithmia. At least 20% of companies of all sizes say their data scientists spend a quarter of their time deploying models, owing to pervasive scaling blockers like sourcing the hardware, data, and tools and performing the necessary optimizations. Versioning and reproducibility of models — which affect key processes like pipelining, model retraining, and evaluation — is yet another tall order for many.

Whatever the factors or combination of factors, budgets aren’t likely to blame. About 43% of respondents say their AI and machine learning spending grew between 1% and 25% from 2018 to 2019, while 21% say budgets for programs grew an average of 26% to 50%. In fact, only 27% of those surveyed noted that their spending hadn’t changed, which Algorithmia attributes to companies with AI maturity — i.e., those with deployed models at least two years old — doubling down on their efforts.

AI use cases

It’s not all doom and gloom.

Gartner reported in January that AI implementation grew a whopping 270% in the past four years and 37% in the past year alone. And according to the McKinsey Global Institute, the subsequent labor market shifts will result in a 1.2% increase in gross domestic product growth (GDP) for the next 10 years and help capture an additional 20% to 25% in net economic benefits — $13 trillion globally — in the next 12 years.

Algorithmia reports that among organizations that have deployed AI successfully, reducing company costs was among the most popular use cases, followed by generating customer insights and intelligence and improving customer experience. Of course, applications varied depending on the segment. For instance, banks and financial services firms are mostly focused on retaining customers and detecting fraud, while the energy sector — including utility companies — are laser focused on forecasting demand fluctuations. Respondents in consulting and professional services industries say that reducing customer churn was their top priority, while the education market’s top use case was interacting with customers.

It shouldn’t come as a shocker, then, that 9 in 10 of C Suite survey respondents characterized AI as the “next technological revolution” in an Edelman survey. Around 94% of tech executives responding to that study said AI will create innovative “smart” homes, while over 74% collectively said it would be “instrumental” in ramping up the development of autonomous cars, such as those from Alphabet subsidiary Waymo, Uber, GM’s Cruise, and others.

“This year’s survey should confirm … that machine learning in the enterprise is progressing in haste,” wrote the authors of the Algorithmia report. “Though the majority of companies are still in the early stages of [machine learning] maturity, it is incorrect to think there is time to delay your [machine learning] efforts. If your company is not currently [machine leraning]–minded, rest assured your competitors are, and the rate of AI’s development is bound to increase exponentially.”


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