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Upskilling is necessary, but it’s not nearly enough to match the demands of an AI-centred workplace. Net job growth is predicted to be a long-term impact of AI, but these jobs will be different from the ones that have existed in the past. Business leaders need to reevaluate exactly what they’ll need from the workforce of the future.
Many new jobs will affect your tech teams, and team members will need to adapt by learning new ways of working and thinking. AI model development is very different from software development. Software is usually rules-based and typically follows unchanging rules to turn data (such as invoices) into output (payments). An AI model, on the other hand, is constantly changing and works with probabilities, not certainties. It might look at both data and output to continuously adapt to new vendors and new invoice formats, and then adjust its own rules to predict the probable size of future invoices.
Ever-changing, continuously learning AI means that agile software development, with its linear, iterative approach and rigid handoffs, won’t work. Instead, AI teams have to be constantly testing, experimenting and learning—like scientists. With time, this approach will have to guide not just your AI and technology teams, but your entire workforce. Your company can get there, but it has to act now.
How to build an AI-ready workforce
- Hire for the hottest jobs of the year.
If your company is building its own AI, it needs machine learning and model ops engineers with skills that cross software engineering and data science. ML engineers help integrate, scale and deploy models. Model ops engineers monitor and improve post-deployment model performance and stability.
Democratize with care.
You should certainly democratize AI so all levels of employees can reduce tedious work and increase innovation with plug-and-play AI tools. But to make AI democratization work, you’ll need to offer the right training and governance. You’ll also want to limit democratization for more sophisticated and risky models and use cases to data scientists and data engineers.
- Build a culture that cultivates AI.
Your people will need to work more with data and adopt an experimental mindset, questioning and continuously seeking to improve data and models. Teams will also need to accept continuous independent evaluation of their AI models—a big cultural shift for many.
Learn about the five predictions and what your company can do to make the most of AI
About the survey
PwC’s annual AI Predictions survey, now in its fourth edition, explores the activities and attitudes of US business and technology executives who are involved in their organization’s AI strategies. Among this year’s 1,032 survey respondents, 71% hold C-suite titles and 25% were from companies with revenues of $5 billion and up. They are from the following industries: industrial products (20%), consumer markets (20%), financial services (18%), tech, media and telecommunications (17%), health industries (17%), and energy, utilities and mining (8%). The survey was conducted by PwC Research, PwC’s global Center of Excellence for market research and insight, in October 2020.
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