The analytics academy: Bridging the gap between human and artificial intelligence


Here is an excerpt from an article written by Solly Brown, Darshit Gandhi, Louise Herring, and Ankur Puri for the McKinsey Quarterly, published by McKinsey & Company. To read the complete article, check out others, learn more about the firm, and sign up for email alerts, please click here.

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As organizations rebuild their foundations to compete in the era of data and advanced analytics, in-house capability-building programs offer the best way to train workers up to the task.
The rise of artificial intelligence (AI) is one of the defining business opportunities for leaders today. Closely associated with it: the challenge of creating an organization that can rise to that opportunity and exploit the potential of AI at scale.Meeting this challenge requires organizations to prepare their leaders, business staff, analytics teams, and end users to work and think in new ways—not only by helping these cohorts understand how to tap into AI effectively, but also by teaching them to embrace data exploration, agile development, and interdisciplinary teamwork.Often, companies use an ad hoc approach to their talent-building efforts. They hire new workers equipped with these skills in spurts and rely on online-learning platforms, universities, and executive-level programs to train existing employees.
But these quick-fix tactics aren’t enough to transform an organization into one that’s fully AI-driven and capable of keeping up with the blazing pace of change in both technology and the nature of business competition that we’re experiencing today. While hiring new talent can address immediate resource needs, such as those required to rapidly build out an organization’s AI practice at the start, it sidesteps a critical need for most organizations: broad capability building across all levels. This is best accomplished by training current employees. Educational offerings from external parties have limitations, too: they aren’t designed to deliver the holistic, company-specific training or the cohesive, repeatable protocols essential for driving deep and lasting cultural changes, agile and cross-functional collaboration, and rapid scaling.

Quick-fix tactics aren’t enough to transform an organization into one that’s fully AI-driven and capable of keeping up with the blazing pace of change in both technology and the nature of business competition.

The answer to the talent challenge, in our experience, is creating an in-house analytics academy. These bespoke analytics-training centers are a relatively new development, and our experience to date suggests that they are poised to move from early adoption by select organizations to core elements of the AI transformations that lie ahead for most companies.

In this article, we explore what an analytics academy can do that other approaches largely can’t, as well as share best practices culled from companies that have launched academies.

It’s important to note that the current focus for analytics academies is to help their organizations successfully bring AI to scale. As a result, their first order of business is to reskill those who play an active role in this work—for example, helping business staff to acquire crucial analytics-translator skills. As more AI systems are deployed, a subsequent and equally important issue that all companies and society in general will need to answer is how to retrain workers when machines take on tasks humans once did. We believe academies hold the promise of playing a role in this retraining effort. But that is part of a larger conversation that is not the focus of our discussion here.

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Here is a direct link to the complete article.

Solly Brown is an associate partner in McKinsey’s Sydney office, Darshit Gandhi is an associate partner in the New York office, Louise Herring is a partner in the London office, and Ankur Puri is an associate partner in the Delhi office.

The authors wish to thank Ali Arat, Holger Hürtgen, Sebastian Kerkhoff, and Akanksha Midha for their contributions to this article.


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