True upskilling for artificial intelligence is about much more than training and requires a citizen-led approach. This is one of five PwC priorities for making the most of AI in the coming year.
Upskilling is the new corporate mantra, and unless you’re running an AI-only startup (and maybe even then), your workforce needs it. But the old kind of upskilling — offering learning opportunities focused on a siloed technology — is not enough to get your employees or your company ready for AI at scale.
True upskilling requires more than offering training courses. As executives in our survey recognized (50%), you also need to give immediate opportunities and incentives for people to apply what they’ve learned, so that knowledge turns into real-world skills that improve performance. Such a citizen-led approach is not only the most effective way to teach tech chops like creating data sets, building a machine learning model, or using Python or R notebooks. It also helps create a digital, AI-ready mindset that focuses on lifelong learning and cross-functional ways of working and problem-solving.
Companies also need cross-skilling: giving specialists in one area (such as data science) enough basic skills in another (such as the business) so they can speak each other’s language. Such cross-skilling is critical not just for collaborating on AI-related challenges, but also for deciding which problems AI can solve. Your teams should be “multilingual,” integrating multiple tech and non-tech skills. That helps non-tech employees come up with tech solutions and tech employees come up with business solutions, while also helping them learn the basics of each other’s skill sets.
While this trend of democratizing AI — making it accessible to your entire workforce — is a positive one, data scientists and AI specialists will still need to keep a close eye on AI model development and training, data and model governance, and how IP rights and openly sourced software and datasets are handled. Also look at how to cultivate more homegrown data scientists and AI operations and infrastructure folks to fill crucial roles. Thirty-eight percent of survey respondents said they are implementing credentialing programs for data scientists and more advanced AI skills.
38% of survey respondents said they are implementing credentialing programs for data scientists and more advanced AI skills.
Plan to offer opportunities. Most of what companies call “upskilling” is really just “up-knowledging.” Turning knowledge into performance that benefits the business requires first identifying what skills are needed, then giving employees opportunities (including a digital platform) to apply, perfect, and share what they’ve learned.
Create a citizen-led culture. The best workforce programs create a new culture. That comes from business leaders setting direction and goals, then standing back: giving employees the tools, platform and incentives (through both compensation and recognition) to learn skills then use them in new ways to perform their work.
Set a multilingual target. Make it a priority to give different specialists the ability to speak the language of other specialities. For faster cross-skilling and cross-functional collaboration, create “multilingual” teams, with data engineers, data ethicists, data scientists, and MLOps engineers part of application development and business teams.
Data and Analytics Leader, China and Hong Kong, PwC US
US Technology and Process Leader, PwC US
