Artificial intelligence has moved beyond experimentation. Across industries, it is no longer a side conversation owned by innovation teams or data specialists; it is becoming part of how work is designed, how decisions are made, and how value is created. That was the clear message from three roundtable discussions with 27 business leaders at Staffing Industry Analysts’ Contingent Workforce Strategies Summit in London.

The roundtable, facilitated by Jamie Eaton at Magnit Global, focused each participant on sharing practical applications of AI and how it’s impacting roles, skills and talent strategy – not the theoretical. In summary, it’s clear AI is already changing organizational approaches, reducing time spent on low-value tasks, and forcing leaders to rethink what they hire for, what they train for, and what they define as workforce capability.

The wider research supports what these leaders described. The World Economic Forum reports that employers expect 39% of workers’ core skills to change by 2030, with AI and big data, analytical thinking, resilience, flexibility, and lifelong learning all rising in importance. According to the WEF, 77% of businesses have already prioritized reskilling and upskilling their workforce to work alongside AI — the most common talent strategy globally. LinkedIn’s 2025 Workplace Learning Report similarly points to a growing skills crisis, with many executives worried that their organizations do not yet have the capabilities needed to execute strategy in an AI-shaped environment. In other words, the challenge is no longer whether AI will affect work. It is whether leaders can redesign work fast enough to capture the upside without hollowing out future capability.

1. Work Is Changing First at the Task Level

The most striking theme from the roundtables was that AI is not simply replacing whole jobs; it is decomposing them. Leaders repeatedly described a world in which administrative, repetitive, and low-complexity work is being absorbed by AI, while human effort is shifting upward toward judgment, exception handling, client interaction, and process redesign. That helps explain why many participants spoke about a new workforce “pyramid” — with fewer junior roles devoted to routine support work and more emphasis on experienced talent that can supervise, interpret, and improve AI-enabled outputs.

Examples from the discussions showed how broad this shift already is. In HR and recruitment, AI is being used for résumé analysis, competency mapping, drafting job descriptions, and automating service workflows. In legal and procurement, it is accelerating contract review, cross-referencing policies, and improving statement-of-work drafting and triage. In IT, it is supporting code testing, data modelling, and decisions around third-party tooling. In client-facing environments, leaders cited AI’s role in fraud prevention, claims handling, customer account analysis, and summarising conversations to reduce handling time. Even in highly regulated or high-risk settings, organisations are beginning to use AI to support operational monitoring and knowledge retrieval.

“What are people doing now that they were not doing a year ago? Increasingly, they are prompting AI, checking AI, and redesigning work around AI — acting as curators rather than creators.”

That shift is especially visible in entry-level and highly structured work, where some of yesterday’s apprenticeship tasks are now automated. A McKinsey survey from 2025 found that 51% of organisations reported that generative AI was reducing their need for entry-level roles — a trend with significant downstream consequences for talent pipelines. Deloitte’s 2025 Human Capital Trends similarly found that two-thirds of hiring managers and executives already believe entry-level hires are underprepared, with lack of experience cited as the primary weakness. The implication is significant: the disappearance of some tasks does not eliminate the need for people, but it does change where people create value — and creates an urgent imperative to rethink how early careers are designed and developed.

Sources: McKinsey State of AI Survey 2025; Deloitte Human Capital Trends 2025; McKinsey “How AI Is — and Isn’t — Changing the Future of Work” (April 2026)

2. The Premium Is Shifting From Technical Know-How Alone to AI Fluency and Human Judgment

If AI is reshaping work, it is also reshaping the skills portfolio organisations need. Roundtable participants were consistent on this point: broad AI awareness is becoming a baseline expectation, but it is not sufficient on its own. The skills gaining value are a combination of AI literacy, prompt writing, data awareness, process analysis, adaptability, and change leadership. Yet the strongest theme was not technical expertise in isolation. It was the growing importance of critical thinking: knowing when an output is good enough, when it is wrong, what is missing, and what should never be delegated to a machine.

The market data is unambiguous on the value of AI fluency. PwC’s 2025 Global AI Jobs Barometer — which analysed close to one billion job advertisements across six continents — found that workers with AI skills command a 56% wage premium over peers in equivalent roles without those skills, more than doubling the 25% premium recorded just one year earlier. That acceleration represents one of the fastest skill premium increases in modern labour market history. Critically, the premium applies across all industries and roles analysed, including those considered most automatable — suggesting that AI fluency enhances human value rather than simply protecting workers in ‘safe’ roles.

What does AI fluency mean in practice? It means more than knowing which tools exist. In most organisations, AI fluency increasingly combines four capabilities:

• Know the limits. Understanding what AI can and cannot do, and where human oversight is non-negotiable.
• Prompt effectively. Asking better questions through structured prompting — communicating a problem, context, and constraints clearly to an AI system.
• Verify critically. Validating outputs against context, policy, and evidence, and knowing when machine-generated content requires challenge.
• Apply with judgment. Applying AI responsibly to improve outcomes within ethical and governance frameworks.

Research from Microsoft and Carnegie Mellon reinforces that critical thinking does not disappear as AI embeds itself in knowledge work; it shifts toward verification, synthesis, and stewardship. In that environment, human value sits less in producing the first draft and more in directing, challenging, and improving it. Human skills add the most value where ambiguity is high and trust matters: ethical decision-making, relationship-building, contextual interpretation, coaching, negotiation, and the ability to see second-order consequences. As AI expands, these are not soft extras. They are increasingly hard requirements. The World Economic Forum projects that 94% of business leaders today face AI-critical skill shortages, with one in three reporting gaps of 40% or more — a dual challenge the WEF calls “workforce overcapacity and talent scarcity occurring simultaneously.”

Sources: PwC Global AI Jobs Barometer 2025; WEF “Balancing AI Overcapacity and Talent Shortages” October 2025; Microsoft/Carnegie Mellon research on critical thinking and AI-augmented work

3. Talent Strategy Must Move From Filling Roles to Building Capability

The third implication from the roundtables is strategic. If work is being redesigned and skills are shifting faster than job descriptions can keep up, then workforce strategy also has to change. Participants spoke about looking across permanent and contingent talent together, broadening sourcing geographically, and adopting a build-borrow-buy mindset for AI capability. They also highlighted the need for real-time skills mapping, stronger compliance, and faster hiring processes supported by AI. Beneath all of that sits a larger shift: talent strategy is becoming less about filling static roles and more about assembling the capabilities needed to deliver outcomes.

External research points in the same direction. Workday’s 2025 skills research found that 55% of organisations have already begun shifting toward skills-based talent models, driven in part by uncertainty about future talent shortages and the speed of AI-led change. Meanwhile, the WEF’s Future of Jobs Report 2025 found that 77% of businesses are prioritising reskilling and upskilling their existing workforce — above even hiring new AI-ready talent (69%) — as the primary response to AI disruption. KPMG similarly argues that workforce planning now needs to encompass both human and digital workers, with leaders determining which tasks should be done by people, which by AI, and which through collaboration between the two. That is a profound change for recruitment leaders: job architecture, career paths, and workforce planning can no longer assume the workforce is made up only of employees and contractors. Increasingly, it also includes digital tools, copilots, and agents.

“The winners over the next few years are unlikely to be those that simply deploy the most AI. They will be those that most clearly define where human judgment creates advantage, how AI extends that judgment, and how talent practices evolve quickly enough to keep both aligned.”

McKinsey’s most recent analysis underlines the urgency: AI adoption in organisations has jumped from 78% in 2024 to 88% in 2025, yet the transition from pilots to scaled impact remains a work in progress at most organisations. The gap between enthusiasm and execution is widest in workforce and talent strategy. Gartner reports that the share of HR leaders actively planning or deploying generative AI jumped from 19% in mid-2023 to 61% by early 2025 — yet organisational talent architectures have not kept pace. McKinsey also notes a striking early signal: among early-career employees (tenure under one year), intent to quit dropped from 37% in 2023 to 32% in 2025 — not because opportunities have improved, but because the market has tightened. For talent leaders, this compresses the window to invest in development before aspiration turns to disengagement.

Roundtable Straw Poll: How Are You Hiring Today?

That gap matters. It suggests many organisations understand the direction of travel but have not yet redesigned the underlying systems of hiring, performance, progression, and learning. Workday’s research showing 55% of organisations have begun shifting toward skills-based models suggests momentum is building — but the roundtable data suggests execution remains patchy. The organisations that close this gap fastest will be best positioned to assemble the capabilities they need, when they need them.

Sources: Workday Skills Report 2025; McKinsey State of AI 2025; Gartner HR Leader Survey January 2025; WEF Future of Jobs Report 2025

Closing Remarks

The message from these conversations was both pragmatic and provocative. AI is not only changing efficiency; it is changing the logic of workforce design. Roles are being broken into tasks. Entry pathways are being reconsidered. Human contribution is being re-weighted toward judgment, creativity, risk management, and relationship value.

PwC’s finding that AI-exposed industries now see three times higher revenue growth per employee than the least exposed is a powerful signal: this is no longer primarily a risk to be managed but an opportunity to compete differently. The organisations pulling ahead are not simply deploying more AI. They are redesigning work around it, developing the human capabilities that make AI outputs better, and building talent architectures flexible enough to keep pace with change that is accelerating in real time.

For leaders in talent, recruitment, and workforce strategy, the real question is no longer whether AI belongs in the organisation. It is whether the organisation is ready to redesign work, skills, and career structures around a future in which humans and AI operate as an integrated workforce. Those that do this well will not just work faster. They will compete differently.

Sources & Further Reading

• PwC 2025 Global AI Jobs Barometer — analysis of close to 1 billion job ads from six continents (pwc.com)
• World Economic Forum Future of Jobs Report 2025 — 78 million new opportunities, 39% skill change by 2030
• McKinsey “State of AI 2025” — 88% of organisations using AI in at least one business function
• McKinsey “How AI Is — and Isn’t — Changing the Future of Work” (April 2026) — entry-level role dynamics
• McKinsey “Building a Talent Pipeline for the AI Era” (October 2025)
• Deloitte Human Capital Trends 2025 — entry-level preparedness and AI workforce evolution
• WEF “Balancing AI Overcapacity and Talent Shortages” (October 2025) — dual skills challenge
• Gartner HR Leader Survey (January 2025) — GenAI adoption in HR from 19% to 61%
• LinkedIn 2025 Workplace Learning Report — skills crisis and AI capability gaps
• Workday 2025 Skills Research — 55% of organisations shifting to skills-based talent models