Algorithmia, a leader in ML operations and management software, has published its 2021 Enterprise Trends in Machine Learning report, outlining the priorities and challenges of enterprise IT departments pursuing AI/ML initiatives. A key takeaway from the blind study, which included 403 business leaders involved in machine learning initiatives at companies with $100M or more in revenue, is that enterprise IT departments are increasing machine learning budgets and headcount despite the fact that many haven’t learned how to translate increasing investments into efficiency and scale.
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The AI/ML landscape has changed significantly in the past year due to the economic impacts of COVID-19. Companies are turning to their investments in AI to deliver both short-term cost-cutting and long-term technology innovation to drive revenue and efficiency in these uncertain times. This has led to a doubling-down of AI/ML efforts, with enterprises increasing the size of both their budgets and their teams for 2021.
Algorithmia’s report uncovered 10 key trends for enterprises to focus on as they head into 2021. Here’s a look at some of the top themes in its findings:
Key Finding #1: Organizations Are Increasing AI/ML Budgets, Staff and Use Cases
Organizations were increasing their investments in AI/ML before the pandemic, according to Algorithmia’s 2020 report, and the economic uncertainty of COVID-19 has added to the urgency. The 2021 survey revealed that 83% of organizations have increased their budgets for AI/ML and that the average number of data scientists employed has increased 76% year-on-year.
In addition, organizations are expanding into a wider range of AI/ML use cases; the survey found that the percentage of organizations that have more than five use cases for AI/ML has increased 74% year-on-year. Notably, the top use cases that organizations are focusing on are related to customer experience and process automation—areas that can offer top- and bottom-line benefits during times of economic uncertainty.
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Key Finding #2: Challenges Span the ML Lifecycle, Especially with Governance
Organizations are experiencing challenges across the ML lifecycle, with the top challenge by far being AI/ML governance. 56% of all organizations rank governance, security and auditability issues as a concern—and 67% of all organizations report needing to comply with multiple regulations for their AI/ML.
In addition to governance challenges, organizations continue to struggle with basic deployment and organizational challenges. 49% of organizations ranked basic integration issues as a concern, and the survey found that cross-functional alignment continues to be a major blocker to organizations achieving AI/ML maturity.
Key Finding #3: Despite Increased Budgets and Hiring, Organizations Are Spending More Time and Resources—Not Less—on Model Deployment
Despite the increase in budgets and headcount, organizations are now spending more time and resources on model deployment than they did before. Algorithmia found that the time required to deploy a trained model to production increased year-on-year, and that 64% of all organizations take a month or longer to deploy a model. 38% of all organizations are spending more than 50% of their data scientists’ time on model deployment—and organizations with more models spend more of their data scientists’ time on deployment, not less.
The bottom line is, organizations have increased their AI/ML resources without solving underlying challenges with operational efficiency. This has exacerbated the problem and led to organizations spending more time and resources on model deployment.
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