News Analysis
Despite billions in AI investments, big tech is experiencing a reality check: generative artificial intelligence is not yet delivering the sweeping economic transformation many expected (or hoped for). Even Microsoft CEO Satya Nadella urged caution, warning against chasing artificial general intelligence (AGI) milestones without real-world impact. Instead, he argues, AI’s true potential should be measured by tangible productivity gains — like the kind seen during the Industrial Revolution. So far, those gains have yet to materialize.
Salesforce’s recent earnings report underscores the struggle. The company’s AI-powered Agentforce platform has seen slower-than-expected adoption, contributing to a disappointing revenue forecast and a 5% stock dip. Meanwhile, OpenAI’s most advanced AI models, touted as game-changers, still require heavy supervision and lack the speed needed for real-world disruption.
As tech giants continue to bet big on AI, the growing disconnect raises a crucial question: is AI truly revolutionizing productivity, or are we still waiting for its promised breakthrough?
Skills Gaps and Complicated AI Stacks Hamper GenAI Adoption
On one level at least, while generative AI continues to reshape industries, a new challenge is emerging: developers are struggling to keep up. A January survey carried out by IBM, indicated that skills gaps, overly complicated AI stacks and other issues are hampering technical employees from adopting AI.
According to the author, Ritika Gunnar, general manager for data and AI at IBM, the survey found AI specialists and data scientists felt confident in their abilities, yet only 24% of application developers consider themselves experts in the field.
The knowledge gap is compounded by a lack of clear frameworks and best practices for AI development, noted Gunnar, with 33% of developers citing the absence of a standardized AI development process as a major challenge.
At the same time, developers are overwhelmed by the sheer number of tools required to build AI applications. Almost three quarters (72%) of respondents use between five and 15 different tools, with 13% relying on 15 or more. Despite the need for performance, flexibility, ease of use and integration, many of these qualities remain rare in existing tools — further complicating the AI development process.
IBM advocates for a simplified AI development stack, including open-source frameworks and streamlined tools, to address these challenges. Worth noting that IBM does indeed provide such solutions, including its watsonx.ai platform, watsonx Code Assistant and IBM’s Application Integration solutions, but it’s unclear how effective this approach will be.
What It Will Take for Worker Adoption to Take Off
The picture is even less optimistic for workers. The enthusiasm shown by big tech in their GenAI investments isn't matched among workers, Cheney Hamilton CEO and research analyst at Bloor Research told Reworked.
The problem isn’t in the AI itself, she said, it’s the fact that businesses are trying to fit AI into outdated job models, without understanding how people actually work. “We’ve seen this firsthand, with 90% of employees saying their job description doesn’t match what they actually do day-to-day. If businesses don’t know what work really looks like, how can they expect AI to improve it?” she asked.
She added that while companies assume AI is intuitive, there is still a long way to go to reach the kind of sophistication that delivers big productivity gains. Employees need real training in how AI works, but also how it makes their job easier and what they can use it for. Without this training, AI acceptance will stall, Hamilton said.
“Businesses need to stop guessing what AI should automate and start looking at the current real human workflows,” she said. “AI should enhance what people already do, not just replace tasks business leaders and managers assume are redundant.”
Behind the GenAI Spend vs. Adoption Disconnect
NTT DATA's Marisa Zdroik attributes the disconnect between big tech's AI investments and worker adoption rates to several factors:1. Rapid Technological Advancements
Big tech companies are investing heavily in AI infrastructure and development, with spending expected to exceed a quarter trillion dollars next year. The problem, said Zdroik, is that the pace of technological evolution can be overwhelming for workers, which makes it difficult to keep up and fully integrate AI into their workflows.
2. Lack of AI Maturity
A small percentage of companies consider themselves mature in AI deployment, Zdroik continued. This means that while AI tools are available, they are not yet fully integrated into business processes.
3. Leadership and Organizational Readiness
Successful AI adoption requires strong leadership and organizational readiness. The reality is companies are still figuring out how to best integrate AI into their operations without robust best practices and lessons learned to guide them.
"Addressing these challenges involves investing in employee training, fostering a culture of innovation and ensuring that AI tools are user-friendly and accessible,” Zdroik told Reworked.
What's Your GenAI Adoption Strategy?
The worker adoption lag boils down to a gap in digital readiness and enablement, WalkMe CIO Uzi Dvir told Reworked. AI adoption doesn’t just happen by default, he continued.
By not prioritizing a digital adoption strategy with the right technology and change management approach, employees may find it challenging to adapt AI into their workflows, or even fail to understand the need to try.
“This ultimately leaves AI tools to sit unused or underutilized. Successful AI integration requires bridging the gap between executive vision and employee readiness through comprehensive digital adoption and practical implementation support,” he said. Enterprises that follow this will be positioned to realize the transformative potential of AI.
AI offers huge opportunities, he added, but complexity is slowing adoption. Employees lose as much as 36 working days per year due to a combination of software inefficiencies and outdated technologies highlighting the need for user-friendly AI integration.
“To close the AI skills gap, businesses must focus on how employees use digital tools, identify friction points, and provide targeted upskilling,” he said. “AI should enhance workflows, not complicate them.”
Effective change management is crucial for successful AI adoption, as it's more about guiding employees than the technology itself, he said. “Organizations that succeed prioritize a people-first approach, offering real-time guidance within employees' existing workflows. AI tools only deliver value when employees are properly trained and equipped to use them.”
AI challenge
The massive capital expenditures on AI by tech giants like Microsoft, Google, Amazon and Meta — projected to exceed $320 billion in 2025 — stand in stark contrast to the tepid adoption rates in workplaces across America, Serena Huang, F100 AI advisor and keynote speaker, told Reworked.
This disconnect represents one of the most significant challenges in the current phase of AI deployment. She said that while C-suite leaders may underestimate actual usage, even the most optimistic assessments reveal adoption is far below what would justify such extraordinary investments.
Huang argues the disconnect stems from several interrelated factors:
First is the fundamental misalignment between how AI tools are developed and how work actually happens in employees’ day-to-day, she said. “The result is powerful but poorly integrated tools that create friction rather than reducing it. The 'why' behind the employer pushing these AI tools doesn’t always resonate with employees as a result.”
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The AI hype cycle has led to corporate AI initiatives that were driven by FOMO [Fear of Missing Out] rather than clearly defined use cases connected to broader business strategy, she added. When leadership lack a strategic vision for how AI transforms their business model in the longer-term, implementations become more of a technological solution rather than the true transformation it can be.
Most critically, however, is the profound employee distrust related to AI's purpose and impact. Huang noted that many workers fear AI implementations are primarily to cut costs through job elimination or to monitor their performance and productivity, rather than to augment capabilities.
“Without addressing this trust through transparent communication about AI's intended benefits and limitations and plans to upskill/reskill employees, adoption will continue to lag, regardless of the amount of AI training and communication the company has done,” she said.