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Tuesday, 7 December, 2021

Enterprise trends in machine learning report analysis

Each year Algorithmia produce an Enterprise trends report about AI and Machine Learning. The comprehensive report is based on 400 company leaders with revenue over $100m or more.

Key findings #1
Organisations are increasing AI/ML budgets, staff and cases. The pandemic seems to have fuelled more urgency with 83% of org increasing their budgets for AI/ML. The average amount of data scientist have increased to 76%.

Key findings #2
56% of organisations rank governance, security and auditability issues as a concern. 67% of orgs report needing to comply with multiple regulations when using AI/ML.

Key findings #3
Despite an increase investment and staff, organisations are spending more time and resources on model deployment than before. Most projects took a month longer than thought. Most orgs are spending more money but not improving efficiency.

Key findings #4
Algorithmia has also learnt that most orgs see improvement with 3rd party solutions to mange their AI/ML operations (MLOps) which allows them to spend upto 21% less on infrastructure.

Read the 2021 report here

In summary
It is encouraging to see that most of the business leaders were more concerned about improving the their AI/ML rather than doing it at all. There is no question that the pandemic has had a huge affect on every industry. It looks like it has fuelled further investment and further interest in AI/ML.

Also – Top 10 trends include

  1. Priority and budgets for AI/ML are increasing significantly year-on-year.
  2. Organizations are expanding into a wider range of AI/ML use cases, with particular focus on process automation and customer experience.
  3. Most organizations have more than 25 models in production, but there’s a gap between AI/ML “haves” and “have-nots”.
  4. Governance is by far the top challenge for AI/ML deployment, with more than half of all organizations ranking it as a concern.
  5. The second greatest AI/ML challenge is technology integration and compatibility, with 49% of organizations ranking it as a concern.
  6. Successful AI/ML initiatives require organizational alignment across multiple decision-makers and business functions.
  7. Organizational alignment is the biggest gap in achieving AI/ML maturity.
  8. The time required to deploy a model is increasing, with 64% of all organizations taking a month or longer.
  9. 38% of organizations spend more than 50% of their data scientists’ time on deployment—and that only gets worse with scale.
  10. Organizations that use a third-party machine learning operations solution save money and spend less time on model deployment than those that build their own solution.

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