AI at Work: Unexpected Winners, Unintended Consequences

This week dealt a sobering reminder that even the biggest tech players aren't immune to misreading the immediate returns on AI. IBM's stock plummeted over 25% in a single day (the company's worst one-day drop since 1987) after an AI-driven sales disappointment in its latest earnings ([1]). CEO Arvind Krishna revealed that many clients held off on buying the company's advanced AI-infused mainframe and software offerings, choosing instead to pour budgets into hardware like high-priced memory chips ([2]). In other words, customers are spending on the picks and shovels of the AI gold rush, such as computing power, but not necessarily seeing reason to invest as heavily in IBM's software right now.

JPMorgan's finance chief offered similar words of caution, not about revenue but about costs. On an earnings call, CFO Jeremy Barnum advised employees to be prudent in deploying AI, quipping that "you really don't need the latest cutting-edge, incredibly expensive model to summarize an analyst report" ([3]). The message: use the right tool for the job and make sure it delivers value. This pragmatic tone comes as companies across industries begin to grapple with the hidden costs of AI at scale – for example, Uber's CTO recently admitted the firm blew through its entire 2026 budget for AI coding tools in just four months due to overuse ([4]). Such anecdotes suggest that the rush to embrace AI can carry unanticipated expenses, underscoring the need for oversight and a clear ROI game plan.

Even global financial stewards are cautioning against over-exuberance. The Bank for International Settlements (BIS) warned that today's frenzied AI investment could turn into a 'debt-fueled boom to bust' scenario if businesses over-borrow for AI bets that don't pay off. For CEOs and boards, the takeaway is to maintain a balanced approach: aggressively pursue innovation, but calibrate investments with realistic timelines for returns. The early AI leaders may reap outsized rewards, but as IBM's stumble shows, those betting big on AI without solid short-term results to show for it can face harsh market backlash.

In contrast to the turbulence facing some tech firms, other industries are finding ways to turn the AI wave into immediate gains. This week, two of Wall Street's largest banks stunned observers with AI-powered windfalls. Goldman Sachs and JPMorgan Chase each reported record quarterly revenues for Q2, driven by a massive surge in stock trades, IPOs and deal-making linked to the AI boom ([1]). Goldman's equity trading division saw revenues rocket 72%, while JPMorgan's rose 27%, helping to deliver what CEO Jamie Dimon called "one of our best quarters ever" ([2]). Executives noted that an AI-fueled capex super cycle (companies worldwide racing to invest in everything from data centers to specialized chips) has boosted demand for financing and deals across global markets ([3]).

It's a counterintuitive twist: the biggest short-term winners of the AI revolution may not be the obvious tech innovators, but the businesses savvy enough to align with AI-driven trends in their own sectors. In banking's case, that meant positioning themselves to fund and facilitate the flood of AI investments across the economy. More broadly, new research indicates that nearly three-quarters of all economic value created by AI so far is being captured by just one-fifth of firms ([4]). These AI leaders differentiate themselves not by dabbling in one-off pilots, but by using AI as a catalyst to reinvent products, services and processes. They are twice as likely to redesign workflows for AI integration rather than simply layering AI on top of old ways of working ([5]), and they focus heavily on data readiness, governance and trust to drive outcomes.

By contrast, the majority of companies remain stuck in experimentation mode, struggling to scale beyond proofs of concept. A BCG survey found that while more than 75% of managers use generative AI tools at least weekly, only about 51% of frontline employees do the same ([6]). This stark adoption gap – essentially a human bottleneck – shows that technology alone can't transform an organization. Without redesigning jobs and workflows to integrate AI from the ground up, and bringing all employees along, even heavy investments may yield only limited gains.

Rapid moves to automate work with AI are also exposing new people risks. One of this week's most unsettling stories came from Meta, where a lawsuit alleges the social media giant's 'constellation' of internal AI systems selected employees for layoffs without accounting for those on protected leave ([1]). Twenty-six former staff claim the algorithm disproportionately flagged people on medical or family leave for termination, a practice they argue is discriminatory. Just weeks prior, a federal judge ruled that Workday must face a similar lawsuit alleging its AI-powered hiring tools unfairly screened out candidates with disabilities, violating anti-bias laws ([2]). These cases highlight the legal and ethical pitfalls of rushing into AI-driven workforce decisions, and reveal how high the stakes can be if companies get it wrong.

Even when AI isn't directly eliminating jobs, it's changing how employees feel about their futures. Fear of replacement by intelligent systems (often shortened to FOBO – "fear of becoming obsolete") has grown markedly. In Mercer's latest Global Talent Trends survey, the share of workers who believe AI will eliminate more jobs than it creates climbed from 28% to 40% over the past two years ([3]). Such anxiety, if left unaddressed, can undermine morale and productivity. Indeed, 62% of employees think leaders are underestimating the emotional and mental toll of rapid AI-driven change on the workforce ([4]).

Leaders championing AI must therefore also lead with empathy and transparency. Clear communication about how AI will – and will not – be used is essential to maintaining trust. Some organizations are proactively involving employees in their AI transformations, inviting staff participation in developing AI use cases and establishing clear ethical guidelines. By treating employees as partners in the change rather than imposing AI from above, companies can foster a more inclusive culture and mitigate fear of the unknown.

The most urgent and promising work ahead lies in equipping the workforce with the skills and roles needed for an AI-powered future. Experts maintain that with proper investment in talent, AI will ultimately create more jobs than it destroys by 2030 (around 170 million new roles globally versus 92 million displaced) ([1]). But these new roles won't automatically go to the same people who lose jobs; they often demand different skill sets and adaptability. The World Economic Forum estimates 59% of workers worldwide will need significant retraining by 2030 to meet the changing demands of jobs transformed by AI ([2]). Organizations that delay reskilling risk facing major talent shortages even as they adopt advanced technologies.

Many leading companies are not waiting. In logistics, FedEx launched a sweeping AI literacy program late last year, partnering with Accenture to train its 500,000 employees in using AI tools effectively and creatively on the job ([3]). In banking, the UK's Lloyds Banking Group recently opened a company-wide AI Academy to upskill all 60,000 staff in practical AI skills. Such initiatives not only build critical capabilities, they also send a powerful message that employees will be supported to grow alongside AI – a key factor in easing FOBO anxieties and gaining buy-in for change.

Investors and boards are taking note of these human-centric strategies. According to Mercer's research, 77% of investors are more likely to back companies committed to robust AI training and upskilling for their people ([4]). That external pressure aligns with internal imperatives: successful AI adoption requires rethinking organizational structures, creating new hybrid roles, and fostering continuous learning. In the end, the organizations that thrive in an AI-driven world will be those that blend innovation with empathy – investing in technology and talent hand-in-hand to build a future-ready workforce.