How organizations are sharpening their skills to better understand and use AI

To successfully implement AI technologies, companies need to take a holistic approach toward retraining their workforces.

By Ben Lorica
August 26, 2019
How organizations are sharpening their skills to better understand and use AI

Continuous learning is critical to business success, but providing employees with an easily accessible, results-driven solution they can access from wherever they are, whenever they need it, is no easy feat. Additionally, delivering valuable content in a variety of formats—whether that is through books, videos, or live online training—is crucial to supporting employees to upskill and reskill on the job. These are some of the features O’Reilly online learning provides to its users to encourage personal and professional development, and there’s no better time to take advantage.

According to Deloitte, evolving work demands and skills requirements are one big reason why continuous learning is critical, and there is no sector experiencing this more abruptly than technology. Executives and employees alike are worried about how emerging tech, such as robotics and AI, are changing jobs and how people should prepare for them. In fact, a recent World Economic Forum report found that more than half (54%) of all employees will require significant reskilling and upskilling in just three years. So, what exactly are the skills data scientists and other tech titles are honing in response to this shift?

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I regularly track broad changes in consumption patterns and preferences on O’Reilly. For example, Figure 1 shows usage across a few select topics related to AI and Data. More precisely, it provides total usage across all content types in this subset of topics. We measure consumption with Units, a metric tuned specifically for the type of content (e.g., page views for books, minutes for videos):

AI and Data topics on
Content usage across a few select AI and data topics on Image by Ben Lorica.

Python is the largest topic on our platform, and it also happens to be a popular language among data scientists (the second largest topic is another programming language, Java). Overall content usage, across all topics combined, grew by 8% from 2018 to 2019 (January to July). Among the fastest-growing topics are those central to building AI applications: machine learning (up 58% from 2018), data science (up 53%), data engineering (up 58%), and AI itself (up 52%).

One of the main reasons Python has been ascendant as a programming language is because of its popularity among data scientists and machine learning researchers and practitioners. In fact, of the top 20 most-consumed Python titles on O’Reilly online learning in 2019, several were focused mainly on data science and machine learning applications, including:

In a survey we conducted earlier this year about AI adoption in the enterprise, respondents cited culture, organization, and lack of skilled people among the leading reasons holding back their adoption of AI technologies. As I noted in a recent article, adopting and sustaining AI and machine learning within a company will require retraining your entire organization. To succeed in implementing and incorporating AI and machine learning technologies, companies need to take a more holistic approach toward retraining their workforces. The rapid growth in consumption of content in training-relevant topics on the O’Reilly online learning platform (including machine learning, data engineering, data science, and AI) provide early signs that companies and individuals are taking training seriously.

Post topics: AI & ML
Post tags: Research

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