Three years after ChatGPT’s launch, business leaders are still waiting for the promised AI revolution to materialize in their workplaces. At the World Economic Forum in Davos, executives gathered to discuss why AI adoption has been slower than expected and what needs to change to unlock its transformative potential.
The core problem isn’t the technology—it’s implementation. Executives identified three major barriers preventing AI from delivering on its promise. First, incomplete employee adoption remains a significant hurdle. Many workers are skeptical about AI’s usefulness or worried about job security, leading to resistance. Companies initially responded with mandates and performance review requirements, but this approach backfired. Francine Katsoudas, Cisco’s chief people, policy, and purpose officer, revealed that mandatory AI training “actually had a bit of a negative impact.” Instead, giving employees choice—like providing engineers access to multiple AI tools—proved far more effective.
Second, there’s a critical skills gap. Even willing employees often lack the expertise to maximize AI’s potential. Kyle Lutnick, executive vice chairman of Cantor Fitzgerald, said his company plans to hire more recent college graduates specifically because of their AI fluency. However, Elizabeth Faber, Deloitte’s global chief people and purpose officer, emphasized that “investment has been primarily on the technology and not so much on the people,” calling for a fundamental shift in training priorities.
The third and perhaps most challenging barrier is organizational inertia. Faber noted that “84% of work processes have been left in their legacy state when adopting AI and have not been redesigned.” True AI transformation requires companies to fundamentally rethink team structures, workflows, and job descriptions—something most established businesses haven’t done. The AI revolution is most visible in early-stage startups building from scratch in the post-ChatGPT era.
The timeline for meaningful change has extended significantly. When asked how many expected workforce reductions in 3-5 years, only two of 15 executives raised their hands. Gina Vargiu-Breuer, SAP’s chief people officer, explained that while SAP maintains flat headcount due to growth, “if productivity goes up and growth is slowing down, then I think we have to look at that with different eyes.” The consensus: workplace transformation will take years, not months, as companies complete the painstaking work of skills mapping, process redesign, and workforce training.
Key Quotes
When we asked our employees to take mandatory training for AI, not only did it not drive sustainable usage, it actually had a bit of a negative impact.
Francine Katsoudas, Cisco’s chief people, policy, and purpose officer, explained how forcing AI adoption backfired. This insight challenges the common executive impulse to mandate new technology use and highlights why employee buy-in matters more than compliance.
Investment has been primarily on the technology and not so much on the people. That needs to shift.
Elizabeth Faber, Deloitte’s global chief people and purpose officer, identified a fundamental misallocation of resources. Companies have spent heavily on AI tools but neglected the training and organizational changes needed to actually use them effectively.
84% of work processes have been left in their legacy state when adopting AI and have not been redesigned.
Faber’s statistic reveals why AI hasn’t delivered promised productivity gains. Most organizations are simply adding AI to existing workflows rather than reimagining how work should be done, limiting the technology’s transformative potential.
If productivity goes up and growth is slowing down, then I think we have to look at that with different eyes.
Gina Vargiu-Breuer, SAP’s chief people officer, acknowledged that workforce reductions are inevitable once AI productivity gains materialize and growth slows. Her candor reflects the reality many executives privately acknowledge but rarely state publicly.
Our Take
The Davos consensus reveals that we’re in the “trough of disillusionment” phase of AI adoption—past the initial hype but before real transformation. What’s striking is how executives are rediscovering lessons from previous technology revolutions: you can’t just bolt new tools onto old processes and expect magic.
The most telling insight is that AI-native startups are already seeing the benefits that established companies aren’t. This suggests the problem isn’t AI’s capability but organizational rigidity. Companies built around AI from day one don’t have legacy processes to unwind or resistant workforces to convince.
The extended timeline is actually more disruptive than a quick transition would be. It creates years of uncertainty for workers while giving companies time to methodically redesign roles and reduce headcount strategically rather than through mass layoffs. The executives’ reluctance to publicly acknowledge coming workforce reductions—only two raised hands—suggests the human impact will be significant enough that leaders are uncomfortable discussing it openly.
Why This Matters
This article reveals a critical disconnect between AI hype and workplace reality that has major implications for businesses, workers, and investors. Despite billions in AI investments, most companies haven’t achieved meaningful productivity gains because they’re trying to layer new technology onto old processes—a historically common mistake with transformative technologies.
The slower-than-expected adoption timeline provides both opportunities and warnings. For workers, it offers breathing room to develop AI skills and adapt to changing job requirements. For policymakers, it creates space to develop appropriate regulations and safety nets. However, executives’ acknowledgment that transformation is coming—just more slowly—suggests the disruption will be more profound when it finally arrives.
The emphasis on organizational redesign over simple tool deployment signals that companies treating AI as just another software upgrade will fall behind competitors who fundamentally reimagine their operations. The fact that only 16% of organizations are developing AI-native work processes suggests massive competitive advantages await early movers. This also explains why AI-native startups may have structural advantages over established enterprises, potentially reshaping competitive dynamics across industries.
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Source: https://www.businessinsider.com/ai-hype-hasnt-changed-work-yet-bosses-employees-2026-1