Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out.
Nearly two-thirds (64%) of enterprise decision-makers with responsibility for machine learning, application development, and decision management in their organizations are worried about job security, according to new research by business software company InRule.
There are many use cases for AI in the enterprise, from driving market and customer insights to testing new products, mitigating compliance, and addressing privacy risks, and many decision-makers report feeling overwhelmed by the options. At least one-third of decision-makers report too many use cases across business functions like sales, marketing, and customer experience. There were 53% of respondents in the survey who said customer experiences was the top business function for AI — and that they have too many AI use cases in that area.
The problem of having too many use cases will continue to increase as 67% of decision-makers said they expect their AI/ML usage to increase over the next year-and-a-half.
Challenges with collaboration impede AI success. More than half (51%) of decision-makers say their organization has too much data, and 42% struggle to identify and gain access to the right data. Organizational silos exacerbate the inaccessibility of data, hindering collaboration between experts and data scientists.
AI operations are critical to gaining essential insights about customers and markets, but there are myths and misconceptions that may stifle AI projects before they can get off the ground, InRule’s study found. One such misperception is that AI projects can’t be done without enough data scientists, when the reality is that there are many AI and ML tools available.
Another is that using AI can have unintended consequences that could harm the business. Sixty-four percent of decision-makers said it is “Important” or “Critical” for their organization to defend or prove the efficacy of its digital decisions. With the growing number of privacy regulations, enterprises have to be able to justify what they are doing with the data. Even so, 58% of decision-makers find defending or proving the efficacy of their digital decisions challenging. They are willing to share visual representations of their outcomes and inputs used, but less likely to show the code they used or the questions driving the decisions, the study found.
Part of that may be because many organizations don’t have the right tools, technology, process, and culture to identify the right questions for digital decisioning, InRule found. More than half (57%) of decision-makers report not having the tools and technology in place to identify the right questions for their digital decisions and 42% don’t have the right processes or a culture of collaboration, the study said.
The study, which consisted of three interviews and an online survey of 302 U.S.-based individuals, focused on decision-makers’ perceptions of AI. “AI is a critical source of industry competitiveness. The fastest path to AI solutions is to formulate and execute a strategy to scale AI use cases based on reality unencumbered by myths,” the report said.
Read the full report from InRule.
VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.
Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:
- up-to-date information on the subjects of interest to you
- our newsletters
- gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
- networking features, and more