AI’s Latest Twist: It’s Creating Jobs – and New Challenges at Work

A flurry of new data is complicating the narrative that AI will simply erase jobs. Contrary to widespread fears, the latest analysis by the World Economic Forum shows that AI has been a net job creator in recent years ([1]). LinkedIn’s global Economic Graph data indicates that, rather than just cutting positions, AI innovation has added about 1.3 million new roles, even as global hiring remains ~20% below pre-pandemic levels ([2]). Many of these new jobs – for example, AI engineers and data specialists – barely existed a decade ago.

At the same time, business leaders are eyeing workforce cuts in areas where AI can handle routine work. The World Economic Forum’s recent *Future of Jobs* survey of global employers found 41% plan to downsize due to AI over the next five years (rising to 48% of U.S. companies) ([3]). However, it’s not all doom and gloom: fully 77% of those organizations say they will retrain or upskill employees to take on new AI-driven tasks ([4]), and 47% intend to redeploy staff into newly created roles emerging from technology. In other words, leaders anticipate that while AI may automate certain jobs, it will also open doors to new opportunities within their companies.

Looking further ahead, forecasts remain cautiously optimistic – provided businesses manage the transition well. The World Economic Forum projects that by 2030, AI and automation could create 170 million new jobs globally while displacing 92 million existing ones ([5]). That’s a net gain of 78 million jobs worldwide, rather than a loss. But experts warn this upside will only materialize if organizations invest in human capital. Without concerted efforts in training and job redesign, AI’s benefits could be unevenly distributed, leaving some workers behind even as others leap ahead.

Forward-thinking organizations are proactively redesigning jobs and team structures to integrate AI – often in surprising ways. Consider the approach of global bank UBS: since 2024, an AI system now approves consumer loans without human intervention. But instead of cutting its loan officers, UBS shifted their roles; today these employees focus on setting the AI’s lending parameters, running scenario tests, and coaching the system ([1]). In essence, people are becoming AI trainers and quality controllers rather than being made redundant. This example underlines a broader point: companies that treat AI as a tool for *augmentation* – not pure automation – can retain valued employees by redesigning their work.

New roles are emerging across industries to support AI-enabled operations. A recent Gartner survey found 67% of leading organizations (those at the forefront of AI adoption) have created roles specifically for generative AI, and 87% have established dedicated AI teams ([2]). Companies are appointing specialists like model managers to oversee machine-learning models and AI ethicists to address unintended consequences of automated decisions ([3]). Many firms are even designating a Head of AI to drive strategy and ensure AI efforts align with business goals ([4]). By adding these roles and blending human expertise with intelligent systems, organizations can reorganize workflows so that employees and AI tools collaborate, each doing what they do best.

This human+AI teaming often requires rethinking processes from the ground up. Leading adopters – dubbed 'Reinventors' in an Accenture study – actively involve their workforce in co-creating AI-driven workflows, breaking down silos and building trust in new technologies ([5]). The difference between companies that successfully navigate this shift and those that stumble frequently comes down to culture and communication. People are far more likely to embrace AI at work when they understand its purpose, have a hand in shaping its use, and see leadership championing its responsible adoption.

As AI becomes part of everyday work, employees are experiencing a mix of excitement and trepidation. On the upside, many workers are already using generative AI tools and seeing concrete benefits. A new global survey from Boston Consulting Group found about half of employees who regularly use AI are saving at least five hours per week by automating tedious tasks ([1]). They report spending the time they save on new projects, creative work, and higher-value activities, which is exactly the kind of productivity boost leaders hope for.

At the same time, these AI power users are paradoxically more anxious about the future. The BCG study revealed that 49% of employees who use generative AI often believe their jobs may disappear in the next ten years – double the rate of those who aren’t using AI ([2]). This counterintuitive insight suggests that the more people understand and work alongside AI, the more they recognize its potential to change their roles. In workplaces where communication is lacking, employees may fill the void with fear, imagining worst-case scenarios about automation.

A key challenge for management is to address these mixed sentiments through transparency and support. Rapid change is already the norm. Nearly two-thirds of workers globally say the pace of change in their job has increased significantly over the past year ([3]), contributing to uncertainty and stress. Studies show that when AI systems aren’t transparent, employees often feel distrustful and view algorithmic decisions as unfair . To counteract this, leaders are learning they must engage employees early in any AI implementation, clarify how the technology will (and won’t) be used, and provide reassurance that people remain essential to the organization’s success.

All these transformations require new skills – and many companies are scrambling to catch up. There’s now a fierce competition for AI talent, and the supply isn’t keeping pace. In 2024, demand for AI experts surged: job postings seeking AI skills grew 3.5 times faster than overall job listings, and roles requiring AI knowledge command up to a 25% pay premium in some markets ([1]). Most large organizations are building in-house AI teams, yet there simply aren’t enough experienced practitioners to go around. One industry survey estimates that by 2026, 90% of companies worldwide will face a shortage of skilled tech workers to deploy AI solutions ([2]). This supply-demand gap is already putting upward pressure on wages and making it even more urgent for companies to train their existing employees.

In the past 48 hours, we’ve seen businesses doubling down on upskilling as a strategic response. For example, Amazon’s cloud division (AWS) just launched a new generative AI certification for developers ([3]), complete with free training resources, in hopes of accelerating the pipeline of AI-proficient talent. Amazon reports that professionals with its AI and machine learning certifications can command salaries up to 47% higher than their peers in IT roles ([4]). Similarly, IBM recently expanded its SkillsBuild program through partnerships with community colleges to train students and workers in AI, data analytics, and cybersecurity, aiming to reach millions of new learners by 2030 ([5]). These initiatives recognize that while AI can automate tasks, mass reskilling is needed to fill emerging roles and prevent a widening gulf between those who have AI skills and those who don’t.

Leaders are also realizing that investing in employees’ growth is directly linked to retention and morale. Today’s workforce is eager to develop AI expertise – but many feel their employers aren’t keeping up. Fewer than half of workers worldwide agree that their employer provides adequate opportunities to learn new skills ([6]). And in that same survey, a striking 67% of employees considering a job change said the lack of growth and learning opportunities would contribute to their decision to leave ([7]). The message for companies is clear: those that fail to support their people’s skill development risk losing talent. On the flip side, organizations that empower employees with new capabilities not only bridge the skills gap; they also foster a culture of innovation, resilience, and loyalty.

How leaders and policymakers respond to these trends is becoming a decisive factor in whether AI drives growth or disruption. The reality is that few companies have fully figured this out yet – in a recent McKinsey global study, only about 1% of organizations felt they had achieved AI maturity at scale ([1]). Surprisingly, the same research concluded that the biggest barrier to progress on AI isn’t employee pushback at all; it’s a lack of leadership urgency and clarity in defining how to adopt AI across the enterprise ([2]). In many firms, top executives have not yet articulated a clear vision for how AI should transform their business, leaving their workforce without direction and potentially hindering innovation.

Corporate governance is only starting to catch up. Only around 15% of S&P 500 companies currently disclose that their boards are providing oversight of AI in their organizations, and a mere 13% have even one board director with AI expertise ([3]). This gap at the very top means many companies lack guidance on aligning AI with strategy, managing risks, and reimagining work in an AI-driven world. On the other hand, a growing cohort of CEOs are treating AI as a company-wide priority – setting up cross-functional AI leadership committees and investing in their own learning to lead by example.

Pressure to get this right is also building from below. Regulators and labor unions are increasingly vocal about balancing innovation with protection of employees. In the EU, trade unions argue that the newly enacted AI Act – the world’s first comprehensive AI law – doesn’t go far enough to address everyday workplace issues, and they are urging an additional directive to set minimum standards for the use of algorithmic systems at work ([4]762323_EN.pdf#:~:text=Parliament%20www,algorithmic%20systems%20in%20the%20workplace)). In the UK, the Trades Union Congress (TUC) recently introduced an AI and Employment Rights Bill that would require employers to consult workers before implementing high-risk AI systems, ensure transparency, and provide explanations for AI-driven decisions ([5]). While that proposal did not advance, it signals a growing push for worker protections around AI. Even in the United States, unions in industries from film to shipping have put AI on the bargaining table, securing agreements that prohibit replacing jobs with AI without negotiation and guarantee human oversight of new technologies. For business leaders, the lesson is that adopting AI without investing in people and governance is increasingly a liability – and those who lead with a clear, ethical plan for human+AI integration will set their organizations apart.