When we think of artificial intelligence (AI) and machine learning (ML), we tend to think of a technology that is new and at the cutting edge.
In reality, AI and ML have been around since the 1950s and 1960s.
The concept of the technology hasn’t changed; what’s evolved is the technology that makes AI and ML easier to use and applicable to more industries.
The companies that are further along in their innovation journeys, those identified as digerati and digital experimenters, have already mastered the foundational technologies.
AI and ML are becoming a tool that smart companies are using to innovate on the foundation they have already put in place.
This adoption of advanced solutions enables digital transformation leaders to create a wide variety of use cases.
Digital beginners struggle to leverage this advanced technology, creating a competitive gap.
AI and ML have expanded the use cases companies can utilize.
Examples of these use cases include creating real-time, next best offers for customers similar to how Netflix chooses new content to show to viewers, and the ability to approve a mortgage in seconds as opposed to days or weeks.
Additionally, companies are able to apply technology to massive amounts of data that wasn’t otherwise scalable.
Industry leader, TIBCO released insights from a survey “CXO Innovation Survey” that polled 600+ c-suite executives about the key AI/ML use cases that are being used in businesses today.
According to the survey, data security is considered the top AI/ML use case among c-suite executives.
Here are the top rated AI and ML use cases: ● Data security (27.
98%): use cases around data security include risk identification, early detection, operational improvement, and corrective action ● Real-time analytics (24.
4%): AI and ML can be used to implement real-time analytics to identify fraudulent transactions, dynamic pricing, product offers, and more ● Personalized data visualizations and dashboards (24.
4%): used to identify anomalies in the data, support predictive analytics, and suggest performance improvements ● Data integration, preparation, and management (23.
21%): AI and ML is crucial to data and having a strong understanding of your data: who, what, when, where, and why ● Sales/revenue forecasting (22.
62%): used for accurate sales forecasting, contributes to year-over-year growth, and enhanced business control ● Personal security (19.
64%): use cases include home surveillance, access control for events, and military defense As AI and ML grow it’s up to smart, innovative companies to determine how they are best going to utilize it in a wider number of use cases.
The implementation of these use cases is going to differentiate digital transformation leaders and laggards, ultimately is driving disruption.
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