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The analytical capabilities of machine learning (ML) combined with the scalability of cloud computing provide a powerful duo to achieve truly transformational changes. Currently estimated to be between $2 and 5 billion, the cloud ML market has the potential to reach $13 billion by 2025.1 This projected exponential growth is a testament to the growing confidence among executives in cloud-based ML technology to further accelerate their AI programs.
Based on our analysis of Deloitte’s State of AI in the Enterprise, 3rd Edition survey, published July 2020, organizations that are already implementing a cloud ML approach are more likely to recognize the potential of AI technologies to transform their organization and industry. In our recent article, “Time, technology, talent: The three-pronged promise of cloud ML,” we uncovered three key findings:
These cloud ML adopters are defined as respondents who stated their organization is using cloud-enabled AI along with cloud-enabled ML platforms or tools. While they represent slightly more than 15% of the 2,737 respondents in the State of AI survey, cloud ML adopters are further along the maturity curve compared with nonadopters and are able to extract benefits from these technologies, with responses showing additional gains in decision-making, process efficiencies, and competitive advantage.
Characteristics of cloud ML users
Our analysis of the State of AI survey data has found that cloud ML users believe that AI technology is essential for business success. Cloud ML adopters are almost twice as likely, compared with the rest of the respondents (defined as “others”), to say that AI technologies have a critical strategic importance today, as well as two years from now. This translates to a much more effective and active AI program. There are certain characteristics that cloud ML users portray that help in delivering value to the organization through AI/ML; some of these include:
Faster ROI with cloud ML use
Cloud ML users are much more likely to have a larger volume of AI initiatives, can sustain and scale enterprise adoption, and can continue with bigger budgets. These practices in themselves have the potential to uplift any AI program and generate returns for the organization. However, based on our analysis, we find that respondents with a robust cloud ML strategy fare marginally better in terms of ROI.
Furthermore, cloud ML organizations are also able to manage and meet expectations around payback timelines, typically aggressively set by top executives. However, the same cannot be said for other organizations, where almost 40% take longer than expected to achieve intended returns (compared with only 25% among cloud ML adopters).
Cloud ML users are defined as respondents who stated their organization is using cloud-enabled AI approaches, which includes: ‘AI-as-a-service’ AND (‘Data science/ machine learning platforms or frameworks’ OR ‘Automated machine learning tools (AutoML)’); n=423; 15.5%
Others are the remaining 84.5% (n=2314) of the respondents
*Note: %age of respondents stating to Agree or Strongly agree to ‘My organization is slowing its adoption of AI technologies because of the emerging risks’
It is evident that, on top of enhanced strategic decision-making, competitive advantage, and improved process efficiencies and employee productivity, those with a cloud ML strategy see improved enterprise adoption, bigger budgets, and thus better return on investment. Organizations can respond in kind by thinking through their cloud ML strategy to advance enterprise AI programs. Whether that is with cloud AI platforms, cloud ML services, or AutoML (including low-code and no-code options), success will depend on the business cases, people, and technical requirements.
For more on this topic, check out our recent research, “Time, technology, talent: The three-pronged promise of cloud ML.”
 ReportLinker, “Global Data Lakes Industry,” April 30, 2020, https://www.globenewswire.com/news-release/2020/05/01/2025940/0/en/Global-Data-Lakes-Industry.html.
 Cloud ML users are defined as respondents who stated their organization is using cloud-enabled AI approaches, which includes “AI-as-a-service” and “Data science/machine learning platforms or frameworks” or “Automated machine learning tools (AutoML)”; n=423.
As the chief cloud strategy officer for Deloitte Consulting LLP, David is responsible for building innovative technologies that help clients operate more efficiently while delivering strategies that enable them to disrupt their markets. David is widely respected as a visionary in cloud computing—he was recently named the number one cloud influencer in a report by Apollo Research. For more than 20 years, he has inspired corporations and start-ups to innovate and use resources more productively. As the author of more than 13 books and 5,000 articles, David’s thought leadership has appeared in InfoWorld, Wall Street Journal, Forbes, NPR, Gigaom, and Lynda.com. Prior to joining Deloitte, David served as senior vice president at Cloud Technology Partners, where he grew the practice into a major force in the cloud computing market. Previously, he led Blue Mountain Labs, helping organizations find value in cloud and other emerging technologies. He is a graduate of George Mason University.