[0928] Bridge the gap between human and AI
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[0928] Bridge the gap between human and AI

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在这个以数据和前沿分析为核心的时代,组织如何保持足够的竞争力?如何增强组织在数据,分析和人工智能技术方面的能力以应对激烈的竞争?或许在公司内部成立学院,培养和提升相关能力比只是依赖于引入外部人才更有效。
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.

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.

The rise of the analytics academy
Our experience suggests that analytics academies can be an extremely effective avenue for developing an AI-educated workforce in a concerted manner, providing a mechanism to deliver three critical building blocks needed for successful AI efforts:

A common vision, language, and protocol across training interventions ensures that all stakeholders (executives, business teams, analytics teams, and frontline staff) align around the core elements necessary to embed AI into their business successfully, to apply the same methodologies when identifying and developing solutions, and to understand one another’s roles and responsibilities. Doing so institutionalizes knowledge and learnings from previous AI use cases, ensures sponsorship from leaders, and fosters community building so that teams run like well-oiled machines capable of building a minimum viable product much more quickly. It also enables organizations to deploy talent where it’s needed, which not only maximizes expertise across the business but also boosts retention of highly sought-after experts, such as data scientists, whose job satisfaction is often closely intertwined with opportunities to learn and grow by working on a variety of different business problems.

Customized content linked to a company’s goals, starting point, and industry context ensures that training translates into business value. To this end, academies design learning programs that consider their company’s transformation road map as a whole, as well as its unique cultural barriers and skills gaps that could derail progress. They tailor learning journeys to their business and worker needs, articulating how skills will enable the desired outcomes. For example, academies ensure leaders are well versed in AI so they can develop and execute a strategy that pushes them ahead of the competition. They offer business staff the technical knowledge to translate business needs into AI solutions. They enable data scientists, data engineers, and other technical experts not only to stay on top of rapid innovations in their field but also to learn how to collaborate with their business colleagues effectively so that they focus on the business problems that will drive the greatest value. And they consider how to shift workers who will be using AI tools from their tried and tested ways and assumptions to relying more on new AI-driven insights.

Active apprenticeships help bring classroom theory to life, enable participants to learn by doing, and facilitate employee growth from a “learner” who has a classroom understanding of a topic to a “practitioner” who is skilled at capability delivery and, ultimately, to an “expert” who can lead in their function. In some cases, such as building translator expertise, this fieldwork is especially critical. Just like US medical school graduates need residency training to build their diagnostic chops, translators benefit from similar guidance as they learn.
There is no one-size-fits-all approach for delivering these components. Companies often structure their academies a bit differently based on where they are in their AI journey and where they need to go. Often, however, it is effective to start with executives and leadership in order to create alignment, establish a common aspiration and understanding, and enable role modeling for the broader transformation.

One global metals producer focused first on developing its leadership training program so it could effectively guide the company’s AI transformation—which would ultimately reduce operational costs across more than 15 manufacturing sites. Senior business and operational executives who previously didn’t back AI initiatives, because they were either doubtful of the technology or simply out of their comfort zones, became strong supporters of their company’s transformation.


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