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