How companies are building sustainable AI and ML initiatives

A recent survey investigated how companies are approaching their AI and ML practices, and measured the sophistication of their efforts.

By Ben Lorica and Paco Nathan
January 29, 2019
Interfaces Interfaces (source: Pixabay)

In 2017, we published “How Companies Are Putting AI to Work Through Deep Learning,” a report based on a survey we ran aiming to help leaders better understand how organizations are applying AI through deep learning. We found companies were planning to use deep learning over the next 12-18 months. In 2018, we decided to run a follow-up survey to determine whether companies’ machine learning (ML) and AI initiatives are sustainable—the results of which are in our recently published report, “Evolving Data Infrastructure.”

The current generation of AI and ML methods and technologies rely on large amounts of data—specifically, labeled training data. In order to have a longstanding AI and ML practice, companies need to have data infrastructure in place to collect, transform, store, and manage data. On one hand, we wanted to see whether companies were building out key components. On the other hand, we wanted to measure the sophistication of their use of these components. In other words, could we see a roadmap for transitioning from legacy cases (perhaps some business intelligence) toward data science practices, and from there into the tooling required for more substantial AI adoption?

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Here are some notable findings from the survey:

  • Companies are serious about machine learning and AI. Fifty-eight percent of respondents indicated that they were either building or evaluating data science platform solutions. Data science (or machine learning) platforms are essential for companies that are keen on growing their data science teams and machine learning capabilities.
  • Companies are building or evaluating solutions in foundational technologies needed to sustain success in analytics and AI. These include data integration and extract, transform, and load (ETL) (60% of respondents indicated they were building or evaluating solutions), data preparation and cleaning (52%), data governance (31%), metadata analysis and management (28%), and data lineage management (21%).
  • Data scientists and data engineers are in demand. When asked which were the main skills related to data that their teams needed to strengthen, 44% chose data science and 41% chose data engineering.
  • Companies are building data infrastructure in the cloud. Eighty-five percent indicated that they had data infrastructure in at least one of the seven cloud providers we listed, with two-thirds (63%) using Amazon Web Services (AWS) for some portion of their data infrastructure. We found that users of AWS, Microsoft Azure, and Google Cloud Platform (GCP) tended to use multiple cloud providers.

Post topics: AI & ML, Data
Post tags: O'Reilly Radar Analysis, Research
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