#ai-adoption

#ai-adoption

The UK is entering a pivotal phase in the evolution of its digital economy as artificial intelligence (AI) shifts from experimental innovation to mainstream dependency. Platforms such as ChatGPT now attract hundreds of millions of weekly active users worldwide, while Microsoft 365 Copilot has been rapidly adopted across the enterprise landscape, with nearly 70% of Fortune 500 companies integrating it into daily workflows.

Focus the conversation on tasks, not titles-what parts of the job may become easier and what parts will require more human judgment, context, and problem-solving. Be explicit about where AI fits and where it doesn't. Often, the aim of introducing AI is to strip out busywork so people can spend more time on problem-solving and generating ideas that move the work forward.

Salesforce says it's at the vanguard of the AI revolution and has even toyed with renaming itself Agentforce in honor of its bet on AI agents. The company is rapidly adopting AI internally as well, and a survey obtained by Business Insider reveals how that's actually playing out behind the scenes. The results - which were broadly positive - show that most employees feel AI is increasing their productivity, although fewer say it's lightening their workloads.

A common perception is that AI adoption requires specialized data scientists or massive upfront infrastructure investments. In reality, the true value of modern AI lies in its ability to solve common, costly operational challenges that affect nearly every organization. AI can analyze vast amounts of data far faster than human teams, delivering predictive insights that optimize supply chains, streamline administrative work and improve decision-making across departments.

A musician may begin learning a new piece and find themselves lost in the weeds, fumbling while thinking about fingering options, phrasing decisions, and micro-adjustments to dynamics. A golfer may end up actually lost in the weeds after needlessly obsessing over specialized techniques, swing plane, and ball flights. And the manager rolling out a new AI workflow? Their simple automation idea can devolve into scattershot attempts at broad goals, governance concerns, and vague existential questions about productivity.

Indeed survey data paints a stark picture of uneven AI engagement across advanced economies. The chart below shows a clear frontrunner: Ireland stands out, with roughly seven in ten workers using AI at least monthly for work, followed by Australia, Germany, and North America. At the other end sits Japan, where fewer than one in five workers report professional AI use - less than half the level seen in the US or UK.

Companies in most industries are investing heavily in artificial intelligence: 88% of companies reporting regular AI use. Yet many leaders report familiar frustrations. AI adoption stalls. Performance gains plateau. Employees experiment with new tools but don't integrate them deeply into how work actually gets done, leaving executives increasingly concerned about ROI. Erin Eatough is a co-founder and chief science officer at Fractional Insights and professor of organizational psychology at Michigan State University.

PRAGUE-(BUSINESS WIRE)- monday.com, the AI work platform that turns strategy into execution at scale, today announced key expansions to its partner program at its sixth annual Partner Summit. The updates are designed to unlock the AI opportunity across the partner ecosystem and enable joint customers to achieve unprecedented efficiency, impact, and growth. The multi-day event brings together over 500 professionals from over 40 countries to drive scalable growth across the monday.com ecosystem and build momentum through 2026.

I'm a director on the global strategy team at Freshworks, where I drive high-priority strategic initiatives that shape the company's growth, investment decisions, and execution, including on AI adoption. Previously, I spent nearly a decade at PwC advising Fortune 500 companies across healthcare, financial services, and technology on growth strategy and digital transformation. As part of my role, I led the upskilling of over 50,000 employees on automation tools.

Advancements in AI technology make our personal and work lives easier and more efficient. Or so they say. From search engines to customer service chatbots to athletic facilities, AI platforms are being introduced at a rapid pace. However, does AI make everything better or more efficient within your company? It depends on who you ask. As a competitive adult tennis player, I enjoy following professional tennis, especially the top men and women players.

Mark Cuban expects legions of workers will be needed to implement AI at companies, creating a huge opportunity for tech-savvy young people. The tech billionaire and former "Shark Tank" investor made the prediction during an August interview with TBPN, a tech talk show and podcast. AI guru Rohan Paul shared a clip of Cuban's comments over the weekend, which was widely reposted; Cuban himself shared three responses from other AI gurus on his X feed.

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.

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.