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**ZDNET’s key takeaways**
Organizations are recognizing that their employees must undergo carefully designed, structured education programs in order to successfully prepare for the growing presence of artificial intelligence in their daily work. Instead of resorting to reductions in staff, forward-thinking companies are being encouraged to place stronger emphasis on upskilling—equipping their teams with new, AI-relevant capabilities—to maintain long-term productivity and morale. Additionally, entry-level employees continue to represent an essential component of a healthy organizational ecosystem, despite misconceptions that emerging technologies make such roles obsolete.
At this week’s Semafor World Economy summit, I observed a recurring theme during multiple panel sessions: moderators persistently asked chief executive officers whether they anticipate AI will eradicate traditional jobs. Although many CEOs consistently gave variations of what is commonly called the *augmentation argument*—the belief that AI functions as a tool that enhances human capability rather than supplanting it—there remains an undeniable trend showing that new opportunities, particularly at the entry level, are becoming scarce. Analysts already forecast that by 2029, artificial intelligence systems could rival human performance across a substantial portion of professional tasks.
Beyond the debates around government regulation and compliance, a central strategy to mitigate AI’s disruptive economic consequences lies in ensuring workers are equipped with relevant AI knowledge and applied skills. However, the pressing question remains: where are the discussions around upskilling translating into meaningful, measurable progress, and where are they still only serving as aspirational talking points?
During the summit, I engaged with numerous industry leaders to explore exactly how they are redesigning employee training programs to prepare their workforces for this rapidly evolving near future—and to uncover which methods have proven most effective so far.
**Where upskilling initiatives succeed**
When organizations fail to implement structured programs, employees are often left to figure out how to acquire new technological skills entirely on their own. On the public policy front, encouraging signs have appeared. The bipartisan *AI Workforce Training Act*, introduced earlier this year, proposes tax incentives for corporations that commit to training their employees in areas such as prompt engineering, data literacy, applied machine learning, algorithmic ethics, and other AI-relevant disciplines. Meanwhile, the Trump administration’s updated AI regulatory framework echoes similar priorities by emphasizing educational apprenticeships and training pipelines in AI.
Still, policy often moves slowly, and until such frameworks become law, corporations remain the most immediate agents of change. According to a recent Gallup poll, employees are far more likely to adopt AI effectively when they receive explicit support from their managers—highlighting leadership’s pivotal role in fostering technological adaptation and reducing fear of automation.
Dan Priest, the chief AI officer at PwC, routinely collaborates with clients across sectors to formulate AI implementation strategies. In his extensive experience, he has observed diverse approaches—ranging from formal corporate academies to peer-led learning clusters—but concludes that the most successful leaders regard upskilling as an inherent aspect of strategy itself, not as an optional add-on. As a concrete example, PwC helped Wyndham Hotels integrate an AI-driven agentic system capable of managing customer requests, leading to significant improvements, including a 30% reduction in call handling time. Crucially, employees were trained to supervise and refine these AI agents, which allowed managers to redirect their time toward teaching new capabilities such as delivering enhanced, personalized guest experiences. The clear intent, Priest underscored, was never to replace human workers but to empower them, and Wyndham’s reinvestment in its staff ultimately determined the program’s success.
Priest recounted a similar engagement with Lucid Motors, where AI was integrated into financial forecasting. Rather than leading to headcount reductions, the collaboration stimulated employees’ skill growth and expanded their functional expertise. In contrast to these flexible client-driven approaches, technology giant Cisco has enforced company-wide AI education requirements. Executive Vice President Liz Centoni explained that every employee must attain at least a foundational comprehension of artificial intelligence principles. Currently, roughly 98% of Cisco’s workforce uses AI tools daily. Its long-standing tradition of technical learning for both employees and customers has evolved into structured, hands-on training programs, complete with a belt-ranking system—modeled after martial arts levels—to signify mastery: white, blue, and green belts for progressively advanced modules.
Centoni elaborated that true AI readiness is not merely a matter of adding tools onto existing workflows. Instead, Cisco’s training encourages employees to question how roles themselves must transform to fully leverage AI. This concept aligns closely with Automation Anywhere CEO Mihir Shukla’s philosophy, which frames successful upskilling as a strategic redesign of how work is performed altogether. He emphasizes the importance of building “autonomous” functions—whether in IT, logistics, finance, or healthcare—at a systemic level, well beyond simply introducing new digital instruments. Employees learn by participating directly in AI-integrated workflows, bridging theory and practice. Shukla shared that his engineering teams were recently challenged to develop end-to-end autonomous software requiring absolutely no human coding, a project that simultaneously refined their technical judgment and understanding of automation limits.
**Tailoring training to varied experience levels**
Within PwC itself, Priest discovered that a uniform approach to upskilling is ineffective across generational lines. For younger recruits, short, targeted video demonstrations illustrating specific AI applications resonate strongly, offering quick and flexible learning. Senior professionals, however, often benefit more from immersive discussions that help them reinterpret their leadership and soft skills in light of AI-driven transitions. Priest has observed a pronounced enthusiasm among younger employees eager to master AI, though he cautions that levels of openness often correlate with career tenure. For example, asking a specialist with two decades of experience to reinvent their professional identity can be daunting. Effective leadership, therefore, must communicate clearly which aspects of their work are evolving while also reaffirming what remains constant.
Priest advises organizations to focus their initial upskilling energy where it is most strategically necessary, identifying specific employee groups that will benefit first based on corporate objectives. He does not view selective implementation as neglect, emphasizing that any temporary gaps in coverage are typically short-lived. Centoni noted that Cisco applies a similarly tailored model in which courses vary depending on job type and required technical depth, reinforcing the concept that personalization ensures relevance and engagement.
**The priority of talent development**
Echoing many arguments about AI’s positive potential for work, Centoni remarked that the process of upskilling at Cisco has unearthed previously hidden expertise among long-time employees. Specialists whose institutional knowledge had been buried beneath repetitive tasks are now able to focus on higher-level innovation once automation handles their routine obligations. This transformation encourages management to rethink their criteria for future recruitment, shifting attention from fixed technical skills to candidates’ adaptability and capacity to thrive alongside intelligent systems.
Although Centoni declined to confirm whether these programs have directly prevented layoffs, her comments strongly suggested that thoughtful upskilling naturally aligns with workforce stability. Shukla shared the same conviction, describing AI proficiency as integral to career growth. He encourages employees to explore the boundaries of their models—discovering where automated systems fail—to better understand their human creative advantage. Both he and Priest reject the notion that rapid staff reductions are an effective optimization strategy; companies seeing genuine results tend to prioritize human capital investment first. Priest aptly summarized, “It all centers around talent,” pointing to the ongoing competition for skilled tech professionals in Silicon Valley. Likewise, Shukla warned that poorly planned downsizing often destroys invaluable organizational knowledge, forcing companies to rebuild essential roles later at greater expense.
Given recent high-profile layoffs by major technology firms such as Snap, Meta, Oracle, and Block—many of which have cited AI efficiencies or restructuring motives—this perspective is particularly pertinent. Even though the causal link between AI and these decisions remains ambiguous, the narrative can influence less tech-savvy industries to adopt similar measures prematurely. Priest further cautioned that maintaining a human workforce is not only strategically wise but legally critical. In heavily regulated sectors, accountability ultimately lies with people, not algorithms; when an AI system errs, responsibility cannot be deferred to the software itself.
**The enduring importance of entry-level roles**
Interestingly, several Semafor attendees quietly expressed that AI tools appear to make recent graduates or junior employees redundant. Priest disputed this perception, insisting that entry-level recruitment is vital to maintaining a resilient organizational structure. He described the traditional workforce as a pyramid—broad at the base with numerous newcomers and narrowing upward through leadership ranks—but envisioned the future organization as more of an hourglass, where continual investment in capable entry-level hires becomes as important as cultivating top-tier expertise. Junior employees, he argued, are often more agile, ambitious, and cost-effective to train than middle managers, who may struggle to adapt quickly. However, Priest cautioned that balance is key: while senior staff may hesitate to adopt new AI tools, younger employees occasionally lean too heavily on automation, outsourcing excessive portions of their responsibilities to algorithms. Managing this equilibrium ensures that both experience and innovation are preserved within a company’s evolving culture.
Through these multifaceted examples, one message emerges unequivocally: genuine corporate progress in the AI era depends not on replacing people with machines but on empowering them to become technologically fluent collaborators. Those organizations that approach upskilling as an investment in long-term human potential, rather than a short-term cost-saving measure, will ultimately lead the transformation of work in the age of artificial intelligence.
Sourse: https://www.zdnet.com/article/how-companies-are-upskilling-workers-for-ai/
