Earlier this year we set out to understand how organizations are reacting and adapting to machine learning, its rate of adoption in the marketplace, and how the industry is evolving. We wanted to understand what our customers’ challenges are as Algorithmia plans to develop products, services, and content to help move the industry forward. We heard from over 500 decision makers at companies representing various sizes and industries. We want to share their knowledge with the industry at large.
What we found was a mix of expected and astonishing. First, as we expected, organizations that make a concerted effort to focus on machine learning and artificial intelligence across their customer lifecycle are more successful. These organizations have higher rates of brand loyalty, lower costs of operations, and many other benefits which we will discuss later.
Second, data scientists and machine learning engineers at companies of all sizes find that their number one challenge is deploying models across their infrastructure. This seems at odds with the first finding, considering companies must be able to deploy models in order to reap the rewards. The main problem is that not all enterprises are experts at deploying models, nor do these organizations make a concerted effort to focus on machine learning.
We found that in machine learning and artificial intelligence organizations:
- Data scientists are facing many roadblocks such as deployment, model control, etc.
- Companies are increasing their investment in machine learning on average by 25 percent
- Large enterprises are taking the lead in this initiative
- Machine learning leadership has no central location within an organization to date; they tend to be spread across the organization
- There are a broad number of use cases and applied applications for machine learning to date
Data scientists are facing many roadblocks
Most data science and machine learning teams are not able to focus on adding value. Rather, they spend the majority of their time on infrastructure, deployment, and data engineering. This leaves less than 25 percent of their time for training and iterating models, which is their primary job function. Across all organizations we surveyed, only 8 percent of respondents consider their organization “sophisticated” in their machine learning programs. The remainder considered themselves early adopters.
If data scientists cannot focus their time on advancing these systems to become sophisticated, organizations risk being stuck in mediocrity. Budgets are also growing faster for organizations that consider themselves “sophisticated.” 51 percent of these companies have increased their machine learning budgets by at least 25 percent this year.
Companies are quickly increasing their investment in machine learning
Overall, 80 percent of respondents say their organization’s investment in machine learning has grown by at least 25 percent in the past 12 months. What is most interesting is that this number climbs to 92 percent in organizations with greater than 10,000 employees. It is safe to say that organizations of all sizes are accelerating their investment. However, large enterprises seem to be willing to invest more.
Big companies are taking the lead
Employees within larger organizations feel significantly more satisfied with their progress than smaller organizations. The employees in this larger sector are roughly 300 percent more likely to consider their model deployment “sophisticated.” The market is moving quickly to develop tools that will help smaller organizations catch up, but this gap remains for the foreseeable future.
Companies have not decided where machine learning leadership should come from
Overall, 37 percent of respondents say their machine learning efforts are being directed primarily by management, while 55 percent say their efforts are emerging from engineers or other technical teams.
Qualitatively, many data scientists are fighting existing systems and processes without clear understanding from management. Without guidance and goals, this leads to confusion and a lack of organizational management to help companies move beyond these challenges.
One hypothesis for this is that as companies get larger, management begins to set the priorities more. We noted that 33 percent of companies with more than 10,000 employees say management sets priorities, remove roadblocks, and ensure data scientists are free to do their jobs.
Another interesting note is that business roles (management, product management) set priorities more often than technical roles (DevOps, ML engineer, R&D). Data scientists are in the middle.
Companies are trying a wide variety of use cases
Among enterprises of 10,000 employees or more, the most significant use case is increasing customer loyalty (59 percent), followed by increasing customer satisfaction (51 percent), and interacting with customers (48 percent).
In general, larger and more sophisticated companies noted more use cases overall than smaller and less mature companies. Our finding is as companies get better at machine learning, they get smarter about where to focus their efforts, and gain clarity around the results.
For larger organizations, cost savings are increasingly significant: 43 percent of companies between 1,001 and 2,500 employees, 41 percent of companies between 2,501 and 10,000 employees, and 48 percent of companies with more than 10,000 employees put cost savings as a use case.
The goal of this research and blog post is to give people in the industry a baseline understanding of the current maturity of the competitive landscape. The data from our survey shows that companies are rapidly maturing and running into common challenges. We hope this helps you navigate our quickly evolving field.
Would you like to read the report and make your conclusions? Get the Full Report “The State of Enterprise Machine Learning”[Download]