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?
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.
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