Hire or retrain?
The best strategy combines both. AI will influence nearly every job, so organizations should invest in developing talent and skills to leverage AI in their everyday work.
AI adoption is more than just training; it’s a full mindset shift. Organizations are demanding “digital fluency” in their employees, which is seeing leading companies upskilling their entire workforce to drive competitive advantage.
That said, some skills are too specialized to develop in-house. AI architecture, model training, data science and feature engineering require years of training and experience.
People with these skills can demand higher salaries but the benefits of hiring quickly outweigh the short-term costs. The key goal for leaders should be to hire professionals who can demonstrably increase the AI maturity of the business, laying the foundation for long-term value creation.
Classroom or on-the-job training?
Both are useful in the right context. Engaging external partners for classroom sessions is the lowest-cost option but lectures are the least effective teaching method.2
Any training should have an on-the-job component. People should be able to immediately put their new skills to work and see the benefits.
“It’s communication, awareness and reskilling to drive adoption,” says Savi Thethi, EY Americas Technology Strategy and Transformation Leader. “More than just reskilling IT employees, it’s about creating digital fluency across the enterprise. Everyone will have access to the tools, understanding how to unlock their power is what will separate the leaders from laggards in the market.”
Who should be trained?
In a word, everyone. Organizations are going “all in on AI” and bringing the workforce along for the journey. AI skills will be core competency for many existing and emerging roles across industries, becoming a daily part of every employee’s workday.
Pathway to success
Finding the right collaborators across the business and selecting the right use cases is vital to achieving real outcomes and results.
“Putting AI to work requires confidence and fidelity in the data,” Thethi says. “Unfortunately, many organizations don’t have the data foundations in place to fully unlock the value of AI, falling short on expectations and ROI for AI investments.”
The good news is you don’t need to clean up your entire data landscape to get started.
Choose an area of the business where you’re confident in the data quality. Select an opportunity that can illustrate speed to value, quickly showing impact to growth, cost of goods sold (COGS) or selling, general and administrative (SG&A) expenses.
When should I start?
With just over 60% of IT decision-makers still early in their AI journeys (research or piloting), businesses should accelerate their plans or risk being left behind.3 That requires a coordinated effort that starts at the top.
The AI-ready workforce is more than just trained. It is empowered, agile and aligned to a common purpose. Organizations that invest in both the human and technological elements of becoming AI-driven will be the ones that redefine what’s possible.
The EY organization has undertaken a thorough analysis to identify four distinct scenarios for how AI could reshape business by 2030.
AI can reimagine business operations and customer experience if delivered in the right way. That means evolving a new kind of workforce that creates exponential value through human-AI collaboration.
