Businesses are at a crossroads with the growing impact of machine learning and artificial intelligence. Companies need to retrain their workforce to stay productive and competitive.
Human–AI collaboration is improving customer experiences and creating new job roles. AI upskilling can help build a stronger business culture, as Google’s long-running investment in ML capability shows.
AI Disruption to the Way We Work
AI is changing how work gets done, and it is widening the skills gap in AI, ML, and data roles. The World Economic Forum estimates job disruption of about 22% by 2030, with around 170 million new roles created and 92 million displaced globally.
Source: World Economic Forum (Future of Jobs Report 2025 press release)
Automation may reduce some tasks and job categories, but it also creates demand for new skills and new work. For many organizations, the practical challenge is speed: roles evolve faster than traditional hiring and training pipelines can keep up.
That is why business managers often implement an AI upskilling program to shift capability internally instead of relying only on external recruitment.
Benefits of AI Upskilling for Businesses
AI upskilling helps build future-ready teams. It is a deliberate investment in knowledge and capability that helps organizations adopt AI tools safely, interpret results correctly, and identify high-value use cases.
Over time, most businesses will become “AI-enabled” in some way, even if they do not build models themselves. Targeted upskilling can build AI literacy, improve implementation outcomes, and support a stronger return on investment.
Organizations that offer reskilling and upskilling programs can also improve retention and internal mobility. For example, some training industry summaries report a strong association between learning programs and employee retention outcomes.
Additionally, Forrester Research has described Google Cloud as a leader in unified data and AI infrastructure and architecture. The Q4 2021 Forrester Wave™: AI Infrastructure report outlines the evaluation approach and criteria used in its assessment.
Google AI Upskilling Success Story
Google made major bets on AI over many years, embedding ML capabilities across teams and products. A key part of that success has been building internal ML capability, not only hiring specialists.
In its formative years, Google could hire many top AI graduates. As the company scaled, it became harder to fill roles quickly enough with external hiring alone.
Google responded by positioning itself as a machine learning–first organization. Rather than relying only on recruiting, leadership encouraged broad ML education across engineering.
As part of that push, it promoted internal training initiatives such as the Machine Learning Ninja Program, which aimed to lift practical ML knowledge among software engineers through structured learning and mentorship.
These programs helped expand the pool of engineers able to understand ML concepts, collaborate effectively with specialists, and apply ML techniques appropriately in product and infrastructure work.
How to Create an Effective AI Training Program
1. Align your AI strategy with your organization’s goals
To improve the odds of success, managers often connect AI training to business outcomes and measurable capability needs. For example, when AT&T found that many roles would require stronger STEM-aligned skills in future, it launched a large-scale reskilling initiative.
Many organizations support this work with a career portal or learning platform that helps employees identify skill gaps and plan development pathways.
2. Identify skill gaps using data
Use workforce and performance data to locate skills gaps, then prioritize roles where reskilling has the highest operational impact. Some organizations have used app-based learning and structured internal learning pathways as part of their approach.
For example, reports on large-scale corporate learning initiatives include summaries such as the Accenture strategy and its Connected Learning Platform model.
3. Tweak your training over time
Track outcomes and refine the program. Use completion data, internal mobility, project performance, and manager feedback to improve content and sequencing. Compare different training options, such as these AI courses online, and keep iterating as tools and job needs change.
Pivot to Stay Ahead of the AI Upskilling Curve
Many leaders recognize that automation and AI are changing skills requirements, but fewer organizations execute sustained upskilling at the pace required.
Managers can reduce risk by starting with clear role priorities, practical training paths, and ongoing measurement. This preserves capability internally, reduces hiring pressure, and supports retention as jobs evolve.