#ai-adoption

#ai-adoption

Across large enterprises, AI is moving quickly from experimentation into daily work. That shift is forcing leaders to confront issues they can't delegate to technology: how performance is measured, how people are supported through change, and how values show up when machines start doing more of the work. Not every company is approaching those questions in the same way. Some organizations are responding by racing for efficiency.

A year or so ago, most legal departments were still testing. AI pilots. Workflow trials. Small process experiments. Everyone was learning cautiously. The stakes were relatively low, and the work was labeled "innovation," which made imperfection forgivable. Then something shifted. Those same pilots became part of day-to-day delivery, and the business started relying on them. Sometimes intentionally, because early results looked good.

On a personal basis, that means people using AI services want to be able to veto big decisions such as making payments, accessing or using contact details, changing account details, placing orders, or even just seeking clarity during a decision-making process. Extend this way of thinking to the working environment and the resistance is likely to be equally strong in professional settings.

If you look at the dot com bubble, that's the best example that we have of what happened in the past. A lot of companies went bankrupt, but there were some companies that actually did really well, like Amazon, for example. So I think [that's] what will happen with AI. There are use cases that are tangible and real, and there's a lot of hype... post-bubble, that hype is not going to last, but tangible results that actually solve problems and then lead to results that team will remain and will thrive afterwards.

Throughout her life, Accenture CEO Julie Sweet hasn't been afraid to throw out the playbook, and, in the age of AI, both she and her Fortune 500 clients are in the middle of another reinvention. Going into her freshman year at Claremont McKenna College, Sweet, who grew up in a middle class Tustin, Calif. family, decided to study international relations and learn Chinese.

Image Credit: AI Generated Image I admire artists and industrial designers who challenge assumptions. Ross Lovegrove is one of them. If you've never heard of him, he is one of the most visionary creators in the world, and designs all sorts of devices, including door handles, computers, fragrance bottles, and concept cars. In an article in the popular design magazine Wallpaper, he claims that the potential of working with AI is utopian. That says a lot coming from someone considered by many as a futurist.

AI is no longer optional at banks. The road map, and showing how it pays off, is the hard part. Alexandra Mousavizadeh, the cofounder and co-CEO of Evident, which tracks AI use in the financial industry, said some AI capabilities are "table stakes" for banks at this point - think back-office functions like reviewing legal documents and routine onboarding tasks. Beyond that, though, Mousavizadeh banks need to double down on their "competitive edge."

"We really pride ourselves on using AI to the maximum possible amount," said Yanisse. "We gave every employee a monthly stipend to try AI tools, and we did AI days and demos. After one year, 95% of the employees use prompting daily."

But if you dig deeper into how businesses in this industry are actually approaching AI deployments - if you ask questions like how they are governing their data, how they are ensuring data quality, and how easily are they connecting AI tools directly to data platforms - you'll soon realize that claims about AI adoption in financial services don't always align with reality.

AI has changed the speed of business. The tools are smarter. The workflows are tighter. Automation now powers everything from emails to reports to meeting recaps. Execution has never been easier. However, as the systems move faster, something else is slowing down. Leadership. It does not always happen in obvious ways. The systems are running. Deadlines are met. Messages are delivered. Teams are producing.

Let a thousand flowers bloom.

For all the talk of AI-driven productivity, many organisations still haven't seen wage costs fall or even clearly stabilise. In a recent PwC survey of more than 4,400 chief executives, only 12% said their AI investments had delivered both higher revenue and lower costs, while more than half reported no meaningful business impact at all. That gap between expectation and reality is becoming harder to ignore.

We are entering a new phase that is much more employer-centric in terms of who holds the keys,

"It's not that Gen Z has confidence necessarily in the market, but they do have a confidence in their ability to adapt," Kyle M.K., Indeed's senior strategy advisor, tells Axios. "This is a group that - for a majority of their lives - they've seen a lot of disruption." "They just have a lot of confidence in themselves to plan accordingly," he adds, "especially as we go through some of this transformative change that we're seeing with AI and the economy."

The gap between what large corporations can do with technology and what small businesses can afford has been shrinking for years. But in 2026, that gap has nearly closed entirely when it comes to artificial intelligence. Tools that would have cost six figures to develop five years ago are now available for less than £50 per month. The question for UK SMEs is no longer whether they can afford to use AI, but whether they can afford not to.

Speaking with Fortune at Davos, PwC Global Chairman Mohamed Kande argued that the shortfall is not about AI's capability, but execution, noting that many companies "forgot the basics," including clean data, disciplined processes, and governance. The takeaway many leaders are drawing is that AI is failing to live up to expectations. That conclusion is wrong. The problem is not the technology. It is how leaders are framing the opportunity and how they are measuring success.

One explanation that came up again and again was incomplete adoption among employees. Many professionals are understandably worried about what these tools will mean for their jobs, or at least skeptical of their usefulness as AI slop abounds. To bulldoze through this hesitation, bosses have stepped up the pressure, making AI use mandatory and incorporating it into performance reviews. But a number of executives at the roundtable advised against strong-arming.

Some 12% of employed adults say they use AI daily in their job, according to a Gallup Workforce survey conducted this fall of more than 22,000 U.S. workers. The survey found roughly one-quarter say they use AI at least frequently, which is defined as at least a few times a week, and nearly half say they use it at least a few times a year.

Shares of Amazon ( NASDAQ:AMZN) are up 0.5% this week, but retail investor sentiment tells a darker story. The company's social sentiment score dropped to negative 0.15 on Reddit and X over the past week, a sharp reversal from its neutral-bullish 0.12 average over the prior quarter. Among select tech peers, Amazon stands alone in sustained bearish sentiment while companies like NVIDIA ( NASDAQ:NVDA), Alphabet ( NASDAQ:GOOGL), Meta Platforms ( NASDAQ:META), and Apple ( NASDAQ:AAPL) enjoy bullish or neutral enthusiasm from the retail crowd.

Results of the survey, conducted in April, have been compiled into GitLab's 2024 Global DevSecOps Report, which was announced June 25. Among the findings, 78% of respondents said they are currently using AI in software development or plan to in the next two years, an increase from 64% of respondents who said they were using or planning to use AI in development last year.

Big companies are busy hiring chief AI officers and setting up tiger teams to pilot agentic products. However, LinkedIn cofounder Reid Hoffman says this overlooks where automation actually pays off - in the "unglamorous layer" of day-to-day work. Speaking with AI engineer Parth Patil on his "Possible" podcast, Hoffman said a company's AI transformation involves employees "being able to talk to each other about it and do collective learning."