Upskilling Your Workforce for the AI Era
Advice for companies and employees for incorporating AI into existing work, and seizing opportunities to elevate skills.
In the age of artificial intelligence (AI), businesses have a pivotal responsibility to help employees learn new skills. And it’s not just for the employees’ sake. With AI (including generative AI, or GenAI) transforming how people work, the ability of businesses to remain competitive and survive in the long term hinges on an upskilled workforce.
Consumer-grade AI tools like ChatGPT—which can answer questions about complex topics and perform tasks ranging from reviewing contracts to planning family trips—have been a wake-up call for employers and their workers. These tools show that there may be a range of work tasks the technology can perform successfully. It is a prospect that raises opportunities to make productive use of these capabilities along with uncertainties about people’s jobs.
Chip Kleinheksel, principal in Deloitte’s Enterprise Performance offering portfolio, points out that various AI technologies – such as robotic process automation, machine learning, document recognition, and optical recognition capabilities – have been in use for years. But the rising awareness of GenAI’s potential applications has resulted in a greater desire to understand and use AI.
AI has far-reaching implications in virtually every area of any organization. For instance:
- In sales, AI models can help predict future sales trends, impacting product development and inventory management.
- In HR, AI can assist in screening resumes and candidates, matching them to suitable roles.
- In finance, AI can help predict future financial conditions and company performance to aid in budgeting and planning.
- In operations, AI can identify when machines or equipment are likely to fail, enabling predictive maintenance and minimizing downtime.
Related Viewpoints on SAP Insights
- 8 Examples of Artificial Intelligence in Action – Enterprise use of AI is growing fast with practical business applications.
- Advanced Manufacturing Skills Workers Will Need – AI-driven factories require problem-solving, design thinking, coding, and creativity.
- How to Upskill: From Strategy to Practical Steps – Tips for leveraging learning management systems effectively.
Categories of Upskilling
The need for upskilling falls into two categories: technical and analytical.
- Technical Skills: Architects and data scientists require expertise in hybrid architecture and cloud platforms. They should be able to work with foundation AI models and integrate AI with enterprise ERP systems. The focus is on connecting systems and applications effectively.
- Analytical Skills: Business users must develop decision-making capabilities. As AI becomes integrated, their role shifts from task execution to validating automated processes and making data-driven decisions.
Challenges in AI Upskilling
The challenge for many organizations is that AI technology is new and evolving rapidly, leading to a shortage of required skills and competencies. This creates a familiar challenge: learning how to integrate new technology into daily work and using it effectively.
Companies play a crucial role in enabling this transition. However, only a few organizations have successfully implemented reskilling programs, and even those have seen limited impact.
Moving Forward
Organizations must focus on building effective reskilling programs with strong leadership involvement, clear strategy, and practical execution. This includes addressing challenges, aligning business goals, and taking actionable steps to ensure workforce readiness for the AI-driven future.
Source : SAP