Cisco, PwC and Wyndham showed how they are actually retraining employees to work with AI

While part of the market discusses how many jobs AI will take away in the coming years, some large companies are betting on a different scenario: not to cut teams, but to quickly retrain them for new processes. At the Semafor World Economy forum, leaders from Cisco, PwC, and Automation Anywhere explained how they are trying to embed AI into work without destroying the workforce foundation.

Who Is Already Training

There is more discussion than systemic practice around corporate AI training. In the USA, support measures are only forming: in February 2026, a bipartisan AI Workforce Training Act was introduced with tax breaks for companies that train employees in prompt engineering, data literacy, machine learning, and AI ethics. But while regulators debate the rules, the real burden has fallen on business, and a Gallup poll named manager support as the main factor for successful AI implementation.

One telling example was provided by PwC's AI Director Dan Priest. The company helped Wyndham implement an agent system for processing customer requests, and this reduced call duration by at least 30%. The freed-up time did not go into layoffs: managers were able to retrain employees for tasks where human participation is more important, such as higher-quality guest communication, monitoring the agents themselves, and solving non-standard situations.

"The goal was not to replace these people,"

Priest described Wyndham's approach to automation. PwC used similar logic when working with Lucid Motors, where AI improved financial forecasting tools. The point was not to make fewer people needed, but to shift them to more valuable skills. Mikhir Shukla, head of Automation Anywhere, supported this same idea: effective implementation does not start with handing out trendy AI tools, but with rebuilding the work processes themselves and the role of humans within them.

How Training Is Structured

Cisco's approach is more rigid and formal. According to Liz Centroni, Director of Customer Experience, basic understanding of AI is now mandatory for everyone at the company. She claims that 98% of Cisco employees already use AI tools daily, and training is built as a practical program with an internal level system resembling belts in karate. This format, in the company's view, should make skills measurable and eliminate learning done for the sake of reporting.

• Basic AI literacy is mandatory for the entire company
• Courses differ by role and depth, not following one template for everyone
• Employees are trained on real scenarios and work processes with agents
• After automating routine tasks, people are shifted to higher-value tasks
• Progress is tracked through clear levels and completed modules

There is a separate emphasis on the fact that different employees perceive training differently. At PwC, they noticed that young specialists respond better to short video instructions for specific tasks, while more senior and experienced employees respond better to face-to-face discussions about what non-technical skills become more important when AI takes over some functions. A universal program for everyone here hinders more than it helps, because motivation and fears differ greatly between groups.

Shukla adds another principle: people should be trained not alongside an abstract AI course, but directly within real work. His logic is this: an employee should bring the model to the point where it starts making mistakes, and at that moment understand what their own unique value consists of. This approach ties upskilling not to a formal checkbox in an LMS, but to career growth, responsibility, and a new role for humans within an automated team.

Why Juniors Matter

Against the backdrop of discussions that agent systems make junior positions unnecessary, Priest takes the opposite position. In his opinion, companies still need to continue hiring entry-level employees, even if some starter tasks become automated. He describes the future structure not as a classic pyramid, but as an hourglass: many entry-level employees at the bottom, less middle management in the middle, and experts at the top who set the framework and control quality.

The logic here is pragmatic. Juniors are easier to develop for new processes, they are cheaper for the company, and they often more willingly master AI. But sharp cuts of people for the sake of quick efficiency can result in loss of institutional memory, breakdown of control, and expensive hiring a year or two later. Plus, responsibility for agent errors still rests not with the agent itself, but with the person who implemented and controls it, especially in regulated industries where the cost of failure is higher than usual.

What This Means

The clearest corporate strategies around AI right now look not like "we will replace people with bots," but like "we will remove routine work and quickly retrain our team for new work." This is an important signal for the market: the companies that will win are not those that talk loudest about automation, but those who can turn it into skill growth, not just a reason for layoffs.