Algorithmia Blog - Deploying AI at scale

Executive summary: the 2020 state of enterprise machine learning report

Read it 12 December >

Reflects data only from survey Group B. Respondents were allowed to choose more than one answer.

In the last 12 months, there have been numerous developments in machine learning (ML) tools, applications, and hardware. Google’s TPUs are in their third generation, the AWS Inferentia chip is a year old, Intel’s Nervana Neural Network Processors are designed for deep learning, and Microsoft is reportedly developing its own custom AI hardware.

This year, Algorithmia has had conversations with thousands of companies in various stages of machine learning maturity. From them we developed hypotheses about the state of machine learning in the enterprise, and in October, we decided to test those hypotheses.

Following the State of Enterprise Machine Learning 2018 report, we conducted a new two-prong survey this year, polling nearly 750 business decision makers across all industries at companies that are actively developing machine learning lifecycles, just beginning their machine learning journeys, or somewhere in between. Sign up to receive the full 2020 report on 12 December 2019 when it publishes.

2020 key findings and report format

The forthcoming 2020 report focuses on seven key findings from the survey. In brief, they are:

  1. The rise of the data science arsenal for machine learning: most all companies are building data science teams to develop ML use cases. There are discrepancies in team size and agility, however, that will affect how quickly and efficiently ML is applied to business problems. 
  2. Cutting costs takes center stage as companies grow in size: the primary business use cases center on customer service and internal cost reduction. Company size is the differentiator. 
  3. Overcrowding at early maturity levels and AI for AI’s sake: the pool of companies entering the ML arena is growing exponentially but that could bring about an increase in “snake-oil AI” solutions. 
  4. An unreasonably long road to deployment: despite the rapid development in use cases, growth in AI/ML budgets, and data science job openings, there is still a long road to model deployment. We offer several hypotheses why. 
  5. Innovation hubs and the trouble with scale: we anticipate the proliferation of internal AI centers (innovation hubs) within companies designed to quickly develop ML capabilities so the organization can stay current with its competition. Machine learning challenges still exist, however, stymying the last-mile to sophisticated levels of ML maturity. 
  6. Budget and ML maturity, an emerging disparity: AI/ML budgets are growing across all company sizes and industries, but several industries are investing more heavily. 
  7. Determining machine learning success across the org chart: hierarchical levels within companies are determining ML success by two different metrics. The director level will likely play a large role in the future of ML adoption.

The report concludes with a section on the future of machine learning and what we expect in the short-term. 

What to expect in the 2020 report

Our findings are presented with our original hypotheses, as well as our analysis of the results. Where possible, we have provided a year-on-year comparison with data from 2018 and included predictions about what is likely to manifest in the ML space in the near term.

We have included graphics throughout to bring the data to life (the banner graphic of this post is a bubble chart depicting the use cases of machine learning and their frequency in the enterprise).

We will continue to conduct this annual survey to increase the breadth of our understanding of machine learning technology in the enterprise and share with the broader industry how ML is evolving. In doing so, we can track trends in ML development across industries over time, ideally making more informed predictions with higher degrees of confidence.

Following the report and future-proofing for machine learning

We will soon make our survey data available on an interactive webpage to foster transparency and a greater understanding of the ML landscape. We are committed to being good stewards of ML technology.

This year’s survey report should confirm for readers that machine learning in the enterprise is progressing at a lightning pace. Though the majority of companies are still in the early stages of ML maturity, it is incorrect to think there is time to delay ML efforts at your company. 

If your organization is not currently ML–oriented, know that your competitors are. Now is the time to future-proof your organization with AI/ML.

Sign up to receive the full 2020 State of Enterprise Machine Learning report when it publishes on 12 December.