Our library features curated AI articles from expert voices, each with a summary and analysis of the key implications for AI strategy and training - so you can quickly grasp what matters and take action.
Ethan Mollick argues we have entered the age of managing AI rather than working with it – and that the choices organisations make right now will set precedents for everyone else.
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Ethan Mollick argues that AI has entered a new phase: rather than prompting AI back-and-forth, we now manage agents that can handle hours of human work autonomously. Exponential capability gains are enabling radical experimentation in how organisations work, illustrated by StrongDM's fully AI-coded Software Factory. A single week in February showed what rolling AI disruption feels like: sudden market reactions, job impacts and policy conflicts arriving together. With recursive self-improvement now on every major lab's roadmap, the window to shape how AI is used may not stay open for long.
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A veteran thought leader argues that AI has commoditised expert-sounding content – and that only original thinking and lived experience will remain credible.
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John Winsor, a self-described thought leader with six books and decades of experience, argues in this HBR essay that AI has commoditised the kind of expert-sounding content on which the category depended. As AI can now synthesise, summarise and package ideas at scale, generic insight faces rapid devaluation. What AI cannot replicate – original research, lived experience and genuine perspective – becomes the only defensible source of authority. The implication for organisations is clear: building AI capability must prioritise original thinking and judgment, not just the efficient production of polished AI content.
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Millions of people are already turning to chatbots to plan their retirement – and the financial advice industry is struggling to keep pace with consumer adoption.
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Millions of people are already using chatbots such as ChatGPT to plan their retirement, with an estimated 2.7 million UK adults now turning to AI for financial guidance and more than half willing to act on it. Among financial advice firms, AI adoption has more than doubled in a year – from 29% to 60% – yet advisers remain cautious about client-facing use, with average comfort scores of just 4.1 out of 10. Concerns centre on trust in outcomes and regulatory compliance, highlighting a growing gap between consumer adoption and professional readiness.
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A BCG study of 1,500 workers finds that intensive AI oversight is causing a new form of cognitive fatigue – but when AI offloads repetitive work, stress levels fall.
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A BCG study of around 1,500 workers, published in HBR, finds that AI is creating contradictory effects on employee wellbeing. When workers constantly supervise multiple AI systems or juggle several tools, cognitive fatigue – or "brain fry" – increases sharply: one in seven workers reports it, with associated rises in errors, decision fatigue and intention to quit. Yet when AI offloads repetitive tasks, stress drops around 15%. Productivity peaks at two to three tools simultaneously. The researchers argue that organisations must redesign work, rather than layer AI on top of existing processes.
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A 126-year market analysis co-authored by Cambridge Judge's Professor Elroy Dimson finds that new technologies do not always generate bubbles – and bubbles do not necessarily imply weak long-term returns.
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The UBS Global Investment Returns Yearbook 2026, co-authored by Professor Elroy Dimson of Cambridge Judge Business School, analyses 126 years of market data and challenges the assumption that hot new technologies inevitably produce bubbles. Railroads still outperform despite ceding dominance; technology has delivered 14.1% annualised returns over 29 years against 10.0% for the US market – even for investors who bought at the March 2000 dot-com peak. The Yearbook concludes that investors should shun neither new nor old industries: both overenthusiasm and excessive pessimism are persistent errors.
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Danone's COO Vikram Agarwal explains how AI and Industry 5.0 are transforming the food company by putting operations at the centre of growth.
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In this McKinsey interview, Danone COO Vikram Agarwal outlines the company's three-part operations transformation: digitalising planning processes, building production capacity with precision and investing in people and digital skills. AI now drives predictive maintenance, COGS forecasting and supplier partnerships, repositioning operations from a cost centre to a growth engine. Danone's Industry 5.0 Academy has already trained 20,000 of its 47,000-strong operations workforce since mid-2025. Agarwal's central lesson: digitisation must deliver measurable business value, and use cases must come bottom-up from the frontline – not simply be imposed from the top down.
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Six years of US job postings reveal AI cutting demand for automation-prone roles while boosting demand for work that humans and AI do together.
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Research by Harvard Business School's Suraj Srinivasan, analysing nearly all US job postings from 2019 to March 2025, finds that generative AI is reshaping the labour market in two directions. Since ChatGPT's launch, postings for automation-prone roles fell 13%, while demand for augmentation-prone roles – those requiring analytical, technical or creative work enhanced by AI – grew 20%. Skill requirements in automation-prone jobs are shrinking, while AI-related skills are rising in others. The researchers recommend investing in reskilling, continuous upskilling and treating AI as an augmentation tool rather than a cost-cutting measure.
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BCG argues that GenAI can dramatically improve day-to-day life for frontline and hourly workers – from smarter scheduling to in-the-moment training and troubleshooting.
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The article explains how GenAI can ease the daily pressures on frontline workers by simplifying scheduling, delivering instant training, troubleshooting issues in real time and centralising scattered technical and compliance information. Drawing on BCG's Frontline Ops AI tool and real-world examples from hospitals, quick-service restaurants and public-sector call centres, it shows GenAI reducing complexity, cutting call and wait times, and lowering burnout and attrition. The core message is that when organisations embed GenAI into the systems that govern how work actually gets done, AI becomes a decision support engine that makes frontline work more autonomous, humane and effective.
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AI-first hotels are already realising measurable gains in cost efficiency, customer experience and revenue growth – and the window for catching up is closing fast.
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This BCG report, produced with New York University hospitality and AI experts, examines how AI is transforming hotels across three dimensions: commercial and customer excellence, cost advantage through automation and robotics, and faster design and construction. AI-scaling companies are already seeing measurable gains in marketing, guest experience and staffing efficiency. Hotels that treat AI as an add-on will fall behind those that rewire fundamentals. Success requires people strategy, data integration and ongoing capability-building alongside technology – yet only 2.9% of hospitality workers currently have AI skills, compared with 21% in tech.
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Based on a YouGov survey of 500+ enterprise leaders, the report reveals how organisations are building data and AI skills – and why workforce readiness is a defining competitive advantage.
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Based on a YouGov survey of 517 enterprise leaders across the US and UK, the 2026 State of Data and AI Literacy Report finds that data and AI skills are now viewed as workplace fundamentals alongside writing and project management. However, most organisations face a readiness gap – not in advanced engineering, but in interpretation, judgement and practical application. Training remains fragmented, role-limited or too passive to build capability at scale. The report highlights that organisations seeing stronger returns from AI are those investing systematically in their people, and argues that workforce readiness is emerging as a defining competitive advantage.
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A curated collection of HBR's best management tips on building and sustaining trust within teams, a foundation increasingly tested by AI-driven change.
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HBR curates its best management tips on building trust within teams, drawing on practical advice for leaders navigating collaboration, motivation and team dynamics. Trust underpins every aspect of effective teamwork, from open communication and accountability to psychological safety and shared purpose. As AI reshapes roles, workflows and expectations, team trust becomes even more critical — and more easily eroded. Leaders who invest in trust-building behaviours create the conditions for teams to experiment with AI confidently, raise concerns openly, and adapt together rather than retreat into uncertainty.
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Board-level AI governance requires strategic focus, investment discipline and firsthand experience with the technology — not just awareness that AI matters.
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BCG argues that boards must move beyond awareness and into active AI governance across five areas: setting pace and priorities tied to competitive strategy; protecting strategic freedom by scrutinising technology and partner lock-in; managing AI investment as a deliberate portfolio balancing near-term returns with longer-horizon bets; aligning incentives and organisational readiness so ambition does not outrun delivery; and maintaining disciplined external communications as AI raises the stakes of every public statement. The article emphasises that boards do not need to become AI experts, but must gain firsthand experience with the tools to govern effectively rather than relying on secondhand briefings.
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An investigation of 30 leading AI agents finds that developers readily share capability data but withhold the safety disclosures needed to assess real-world risk.
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A research team led by the University of Cambridge, with collaborators from MIT, Stanford and other institutions, assessed the transparency and safety practices of 30 leading AI agents. The AI Agent Index found that while developers readily publicise what their agents can do, most withhold the safety evidence needed to assess risk. Only four agents have published formal safety documents covering the bot itself, 25 out of 30 do not disclose internal safety results, and browser-based agents — which operate at the highest levels of autonomy — have the worst safety reporting. The researchers warn of a 'transparency asymmetry' that amounts to a weaker form of safety washing, and call for governance frameworks that keep pace with increasingly autonomous AI systems acting in the real world.
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A survey of 10,000+ executives finds 75% of organisations struggling to build high-performance cultures, with rising productivity pressure and an AI readiness gap undermining results.
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McKinsey's 74-page report, based on input from over 10,000 executives across 15 countries and 16 industries, identifies nine shifts reshaping organisations, driven by AI acceleration, geopolitical disruption and evolving workforce expectations. A central finding is that 75% of organisations struggle to build lasting high-performance cultures, with limited career progression, poor incentives and disengaged employees as the top barriers. Critically, high-pressure environments are counterproductive — organisations that push hard on output without investing in people see lower willingness and commitment, while those balancing both are four times more likely to sustain top-tier financial performance. The report also highlights a major AI readiness gap and argues that the next productivity frontier lies not in restructuring but in improving how work flows across the organisation — simplifying workflows, reducing handoffs and clarifying decisions before automating.
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As AI shifts from chatbots to agents, choosing the right combination of model, app and harness matters more than picking the smartest model alone.
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The article explains that using AI now means choosing between models, apps and harnesses rather than simply picking a chatbot. As AI shifts from conversation to autonomous action, tools like Claude Code, Claude Cowork and NotebookLM let AI complete multi-step tasks independently. The guide argues that the right harness matters more than the smartest model, and that learning to manage AI as a worker rather than prompting it as a tool is the key skill shift for professionals in the agentic era.
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A senior technology leader warns that AI could significantly reshape white-collar roles within 18 months, accelerating automation across knowledge work.
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The article reports comments from Mustafa Suleyman of Microsoft, who suggests AI could begin replacing significant elements of white-collar work within 18 months. Rather than a distant disruption, he frames the shift as imminent, particularly for routine knowledge tasks. While whole professions may not disappear, many roles will be redefined as AI systems take on analysis, drafting and administrative work, requiring organisations to rethink skills, structures and workforce strategy quickly.
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Consulting firms are racing to deploy AI agents, but rivals argue the number of agents matters less than the quality, efficiency and real business value they deliver.
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The article reports that McKinsey has deployed 25,000 AI agents in under two years, with plans to pair every employee with at least one agent. However, rivals EY and PwC argue that volume is the wrong measure of success. They focus instead on productivity, quality and cost outcomes, with EY noting that just a handful of agents deliver the most value. The piece highlights a broader debate about how to measure AI adoption maturity as consulting firms race to embed AI across their operations.
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An investor's viral essay arguing that AI's disruptive potential is under-appreciated drew over 80 million views, urging professionals to start experimenting with AI now.
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Investor Matt Shumer's essay 'Something Big Is Happening' went viral with over 80 million views, arguing that AI's capabilities are widely under-appreciated. He says AI can already perform the technical work of his job and expects professionals in law, finance, medicine and other fields to face similar disruption. While he clarified the essay wasn't meant to scare, his core message is clear: people should start using AI tools now to understand what is coming rather than assume their roles are protected.
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Anthropic's Cowork extends Claude Code's agentic capabilities to non-coding tasks, now available on Windows with file access, multi-step tasks, plugins and MCP connectors.
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Anthropic launched Cowork, a tool that brings Claude Code's agentic capabilities to non-technical tasks. Users give Claude access to a folder on their computer, and it can read, edit and create files autonomously — organising downloads, building spreadsheets from screenshots, or drafting reports from scattered notes. Now available on Windows with full feature parity, Cowork also introduces global and folder-specific instructions so users can tailor Claude's behaviour across sessions. Built on the same foundations as Claude Code, Cowork signals a broader shift from conversational AI towards sustained, agent-based work for all professionals.
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AI can help workers tackle complex tasks faster, but it does not turn novices into experts. Its impact depends on how it is used, understood, and integrated with human skill development.
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The article argues that while generative AI can reduce the time it takes novices to become competent at unfamiliar tasks, it does not automatically make them experts. Leaders often assume that access to powerful AI tools alone will upskill employees, but achieving expertise still requires human learning, context, judgement and feedback. Effective AI strategy should integrate AI with training, mentorship and real-world practice to truly enhance capability rather than create only a superficial sense of competence.
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Research suggests that rather than lightening workloads, AI adoption can intensify work demands by raising expectations, increasing complexity and shifting effort in unexpected ways.
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The article challenges the assumption that AI reduces work. While AI can automate routine tasks like drafting documents and summarising information, research suggests it often intensifies work by raising output expectations, increasing complexity and creating new demands. Rather than freeing time for high-value tasks, AI adoption can shift effort in ways organisations do not anticipate. The findings highlight the need for realistic planning around how AI changes work, not just whether it is adopted.
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Software stocks are sliding as investors question SaaS growth models amid AI disruption, pricing pressure and shifting enterprise spending patterns.
Read what our CEO Chris Hornby has to say on this topic
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The article examines the sharp decline in software stocks, dubbed the 'Saaspocalypse', as investors rethink the sustainability of traditional SaaS models. Slowing growth, higher interest rates and AI-driven disruption are compressing valuations. Generative AI is lowering barriers to entry, intensifying competition and challenging premium pricing. Companies that cannot clearly demonstrate durable differentiation, strong margins and meaningful AI integration face greater pressure in a more sceptical market environment.
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While AI has the potential to boost productivity, it will also disrupt existing jobs, industries and business models and gains from AI are unlikely to be evenly distributed.
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The article examines AI through the lens of creative destruction, arguing that while AI has the potential to boost productivity, it will also disrupt existing jobs, industries and business models. The piece challenges overly optimistic narratives, suggesting that gains from AI are unlikely to be evenly distributed and may take time to materialise. It highlights the risk that organisations and economies focus on technology adoption without sufficient attention to how work, skills and value creation are reshaped in practice. Learning and adaptation matter, as workers and firms will need to adjust roles, capabilities and expectations as AI-driven change unfolds.
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While leaders and employees may feel optimistic about AI's potential, many organisations lack the skills, governance and processes needed to deploy it responsibly and effectively.
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The article explores a growing paradox in AI adoption: confidence in AI is increasing faster than organisations' actual readiness to use it well. While leaders and employees may feel optimistic about AI's potential, many organisations lack the skills, governance and processes needed to deploy it responsibly and effectively. This gap creates risk, as misplaced trust can lead to poor decisions, over-reliance on AI outputs, or unaddressed failures. The article notes that trust must be earned through experience, transparency and understanding. Training and learning are essential to help people understand AI's limits, evaluate outputs critically and build informed confidence rather than blind trust.
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Also read this: AI in Insurance: Understanding the Implications for Investors
As AI assistants reshape how customers discover and choose insurance, insurers must rethink distribution, visibility and the role of human expertise.
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The article explains how insurers must adapt to a future where AI assistants increasingly mediate how customers research, compare and purchase insurance, fundamentally reshaping distribution and customer journeys. It describes three waves of AI-influenced distribution – augmented, assisted and autonomous – and argues that insurers need to remain visible within AI-driven ecosystems while redesigning digital touchpoints for AI agents as well as people. Rather than treating AI as a threat to intermediaries, the article shows how AI can enhance personalisation and efficiency, while human judgement remains critical for complex decisions and trust-based interactions. Success depends not just on technology, but on organisational readiness, workflow redesign and sustained investment in skills so teams can work effectively alongside AI.
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How AI, new work models and shifting expectations are redefining what work looks like – and what organisations must adapt to next.
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The article outlines nine interlinked trends that are reshaping how work is organised, experienced and valued in 2026 and beyond. AI features prominently as a force changing roles, performance expectations and collaboration, but the article stresses that technology alone does not determine outcomes. Instead, success depends on how organisations redesign work, develop skills and support people through ongoing change. Themes include the rise of human–AI collaboration, growing skills gaps, evolving career paths and increased pressure on leaders to balance productivity with wellbeing. Learning and adaptation emerge as central capabilities, as employees are expected to continuously update skills while organisations rethink how work gets done in an AI-enabled environment.
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AI agents will fundamentally reshape recruitment in 2026, shifting power and scale on both the candidate and employer side while raising new authenticity risks.
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The article argues that AI agents will fundamentally reshape recruitment in 2026, shifting power and scale on both the candidate and employer side. As AI tools become easier to use, candidates can deploy agents to search, match and apply for roles at scale, while employers use AI to screen and shortlist more efficiently. This increases speed and reach but also raises risks around authenticity, including AI-generated CVs, exaggerated experience and deepfakes. As a result, recruitment is moving away from CVs as proof and towards verification, skills assessment and human judgement.
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AI systems deliver uneven performance across teams and functions, making it essential to understand where AI works well and where human intervention is needed.
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The article examines how AI adoption often faces uneven performance across teams, projects and functions. AI systems can deliver impressive results in some areas while creating bottlenecks or inefficiencies in others. Understanding where AI works well and where human intervention is needed is key to maximising impact. The piece emphasises that training and skills development help employees recognise limitations, optimise AI outputs, and collaborate effectively with AI tools, turning potential bottlenecks into opportunities for learning and improvement.
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Effective AI experimentation requires structured, hypothesis-driven approaches linked to real business problems rather than ad-hoc pilots.
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The article argues that organisations should move from ad-hoc experimentation to a systematic approach. Effective experimentation is structured, hypothesis-driven and linked to real business problems. Teams learn faster when experiments test specific assumptions, compare human and AI performance, and capture reusable insights. Building learning loops sharing results and refining use cases develops internal capability. Skills and training are essential to design good experiments and interpret AI outputs critically.
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AI strategies fail when ambitions outpace organisational reality, requiring leaders to align goals with actual data quality, technical maturity and workforce capabilities.
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The article argues that AI strategies fail when ambitions outpace organisational reality. Leaders should align AI goals with the parts of the value chain they control and technologies they can manage. This means being honest about data quality, technical maturity and workforce capabilities. Progress comes from focusing on high-value use cases where AI can be embedded and scaled. Skills, learning and organisational readiness are critical without them, even well-funded initiatives struggle to deliver impact.
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Management skills like delegating, scoping problems and evaluating work are becoming the key to working effectively with AI agents.
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The article argues that management skills are becoming the key to working effectively with AI agents. MBA students created startups in four days using AI not because they were technical experts, but because they knew how to delegate, scope problems, and evaluate work. Traditional management frameworks like requirements documents and shot lists work remarkably well as AI prompts. The skills often dismissed as "soft" – giving clear instructions, providing feedback, recognizing quality work – are now the hard skills that matter. Success with AI depends less on clever prompting and more on knowing what you want and explaining it clearly.
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Many organisations struggle to move beyond AI pilots because the challenge is aligning data, processes, people and decision-making, not the technology itself.
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Drawing on insights shared at the World Economic Forum's Davos Summit, the article explains why many organisations struggle to move beyond AI pilots and achieve impact at scale. The challenge is rarely the technology itself, but the difficulty of aligning data, processes, people and decision-making across the organisation. The article highlights that without the right skills, incentives and organisational support, AI initiatives stall. Training and learning play a critical role in building confidence, reducing friction and enabling teams to integrate AI into everyday workflows.
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Hundreds of AI agents coordinated autonomously for a week to build a working web browser, signalling a shift from single-task AI towards sustained, team-based autonomous work.
Read what happened when our Learning Director Philippa Cameron tried her hand at using Cursor...
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The article reports on Cursor's experiment in which hundreds of AI agents, powered by OpenAI, autonomously built a web browser over a week with no human intervention. Agents were organised into planners, workers and judges, coordinating across millions of lines of code. While the result was incomplete and not production-ready, the experiment demonstrates that AI can now sustain complex, open-ended work far longer than before, pointing towards a future where autonomous AI teams take on entire projects.
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Leaders should explain their impact in terms of direction-setting to build trust.
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The article argues that senior leaders often underestimate the importance of clearly articulating their own contributions, especially when their work is less visible and more strategic. It outlines how leaders can explain their impact by linking decisions, trade-offs and long-term thinking to tangible outcomes for the organisation. Rather than listing activities, effective leaders frame their contribution in terms of direction-setting, enabling others, and managing complexity. In periods of change – including AI-driven transformation – this clarity helps teams understand priorities, reduces uncertainty, and builds trust in leadership judgement.
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Leading CEOs treat AI as a catalyst to reimagine processes and organisational design, with impact driven by business transformation rather than technology alone.
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This podcast transcript frames AI as a defining leadership moment, arguing that its impact is driven far more by business transformation than by technology alone. CEOs who are making progress treat AI as a catalyst to reimagine processes, decision-making and organisational design, rather than something to be bolted on. Agentic AI is accelerating change, flattening hierarchies and shifting value towards judgement, learning and adaptability. A recurring theme is fluency – leaders and employees alike must actively learn through hands-on use, experimentation and curiosity. Training, access to tools and shared learning spaces are critical to moving from scattered experimentation to sustained value creation, while governance and responsible AI practices must scale alongside adoption.
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AI-first companies redesign end-to-end workflows around AI capabilities from the outset, rather than layering AI onto existing processes.
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The article explores what it really means to be an AI-first organisation, using life insurance as a concrete example. Rather than layering AI onto existing processes, AI-first companies redesign end-to-end workflows around AI capabilities from the outset. This enables faster decisions, more personalised products, and lower operating costs, while shifting human effort toward judgement, exceptions, and customer relationships.
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Fear of AI often stems from uncertainty about job security and changing roles, and leaders who avoid these conversations make anxiety worse.
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The article explores why anxiety about AI is widespread among employees and how leaders can address it constructively. Fear often stems from uncertainty about job security, changing roles and a lack of understanding about how AI will be used. The article argues that avoiding these conversations makes anxiety worse. Instead, leaders should talk openly about what AI will and will not do, acknowledge legitimate concerns, and involve teams in shaping how AI is adopted. Training and learning are central to reducing fear, helping employees build confidence, develop new skills and see how AI can support their work rather than replace it.
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CEOs' confidence in revenue growth has hit a five-year low – and uneven AI returns are emerging as a defining divide between leaders and laggards.
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The report from PwC's 29th Global CEO Survey reveals that CEOs increasingly see technological change, including AI adoption, as a central challenge; 42% cite keeping pace with tech transformation as a top concern. Despite heavy AI investment, only a small minority of organisations report that AI has delivered both cost savings and revenue gains, and more than half say they have seen no significant financial benefit from AI to date. The findings highlight a widening gap between companies that have built strong AI foundations and embedded AI across products, services and decision-making and those still struggling to scale.
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Generative AI is transforming education, but its impact depends on purposeful integration and pedagogical guidance.
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The OECD Digital Education Outlook 2026 examines how GenAI is reshaping teaching, learning, assessment and educational administration worldwide. While GenAI tools are increasingly accessible and can produce high-quality outputs, the report finds that without guidance, students risk offloading cognitive effort, reducing engagement and long-term skill acquisition. The report emphasises that teachers remain central, with AI serving as an augmenting tool rather than a replacement. Education systems must invest in human-centred policies, teacher training, adaptive infrastructure, and inclusive access to ensure AI supports meaningful learning and equity.
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Professionals underestimate AI's impact on their own roles, creating a perception gap that slows upskilling and leaves organisations unprepared.
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The article argues that professionals underestimate AI's impact on their own roles, creating a perception gap that slows upskilling. Workers often misjudge their skills and delay learning, while organisations fail to provide structured, personalised training. Optimism bias leads employees to assume their roles are safe from disruption. Well-designed, purpose-driven AI training drives high engagement. Success requires balancing technical AI fluency with soft, adaptive skills like communication and critical thinking. Proactive upskilling in both technical and soft skills is essential for employees and organisations to thrive.
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AI pioneer Yann LeCun is leaving Meta to develop next-generation AI systems that understand the physical world through reasoning, planning and persistent memory.
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The article discusses AI pioneer Yann LeCun's decision to leave Meta and launch an independent AI start‑up focused on next‑generation AI systems that understand the physical world. LeCun argues that current large language model approaches are limited in reasoning and real‑world understanding. His venture will develop world models capable of reasoning, planning and persistent memory for industrial, robotics and decision‑making applications. LeCun emphasises openness and diversity in AI research, pushing back against short‑term product strategies and advocating long‑term foundational work that can redefine how AI is built and trained.
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As AI investment accelerates, leadership ownership becomes a decisive factor in whether organisations see real returns.
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The article argues that as AI investment accelerates, leadership ownership becomes a decisive factor in whether organisations see real returns. AI can no longer be treated as a purely technical or IT-led initiative. Instead, senior leaders are stepping in to set direction, prioritise use cases, and ensure AI efforts are aligned with business strategy. The article highlights that many AI programmes still struggle because organisations lack the skills, structures and confidence to scale. Training, learning and capability-building are essential to help leaders and teams move from experimentation to sustained value creation.
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The EU is investing over €307 million in AI to strengthen Europe's ecosystem, with skills development positioned as essential to translating investment into impact.
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The article outlines the European Union's decision to invest more than €307 million in artificial intelligence and related technologies as part of its broader digital strategy. The funding is aimed at strengthening Europe's AI ecosystem, supporting research, innovation, and the adoption of AI across sectors. A key focus is ensuring that organisations and workers are equipped to use AI responsibly and effectively, alongside investments in infrastructure and governance. Skills development, training and capability-building are positioned as essential to translating public investment into real economic and societal impact.
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AI strengthens security but also introduces new vulnerabilities, requiring organisations to manage human-AI interactions and build workforce trust frameworks.
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The article argues that as AI becomes embedded in enterprise workflows, it creates a paradox – it strengthens security but also introduces new vulnerabilities. Attackers can exploit AI agents, manipulate data, or target over-reliance on AI outputs, while human behaviour remains a central risk. Organisations must manage human–AI interactions, not just systems. Workforce trust frameworks – focusing on reliability, accountability, transparency and ethical alignment – are essential. Training and AI literacy are critical so employees can evaluate AI outputs, detect manipulation, and apply cybersecurity principles in AI-augmented environments.
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AI systems will inevitably make errors, and organisations must prepare employees to detect mistakes, evaluate outputs critically and respond appropriately.
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The article explains that AI systems will inevitably make errors, and organisations must prepare to manage them effectively. Mistakes often stem not from flawed algorithms, but from gaps in oversight, process design, or user understanding. The piece emphasises that training, skill development and capability-building are essential so employees can detect errors, evaluate AI outputs critically, and respond appropriately. By combining human judgement with AI systems, companies can minimise risk and maximise the value of AI adoption.
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As AI becomes more autonomous, human purpose becomes more important for setting goals, supervising behaviour and intervening when systems act unexpectedly.
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The article explores agentic AI systems that act autonomously and make decisions with limited human input. As AI becomes more agentic, human purpose becomes more important, not less. Without clear intent, values and direction, organisations risk deploying systems that optimise for wrong outcomes. Guiding agentic AI requires more than technical controls it depends on human judgement, ethical clarity and organisational capability. Training is critical so people can set goals, supervise AI behaviour, and intervene when systems act unexpectedly.
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Modern AI supports extended problem-solving and iterative building, breaking work into smaller testable steps and encouraging rapid learning.
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The article argues that todays AI systems can do real, sustained work beyond one-off prompts. Modern AI supports extended problem-solving, experimentation and iterative building particularly powerful for programmers and those who are programming-adjacent. AI is changing how tasks are approached, breaking work into smaller, testable steps and encouraging rapid learning. Skills development and hands-on exploration are essential to harness this new mode of working effectively.
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Organisations that excel at strategic foresight systematically scan for weak signals, consider multiple futures and embed foresight into decision-making.
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The article explores what organisations that excel at strategic foresight do differently when navigating uncertainty. Instead of relying on single forecasts, they systematically scan for weak signals, consider multiple plausible futures and embed foresight into decision-making. AI can support this work by detecting emerging patterns earlier, while human judgement interprets what those signals mean. Strong foresight is as much about mindset as process seeing uncertainty as something to engage with, not avoid.
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AI will fundamentally reshape jobs and skills, making workforce readiness - not automation - the critical factor for organisational success.
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Gartner argues that AI's biggest workforce impact will come from job redesign and accelerated skills change rather than widespread job loss. As AI takes on routine and analytical work, human capabilities such as judgment, creativity and leadership become more valuable. The article stresses that organisations must prioritise continuous learning, proactive workforce planning and clear governance to ensure AI delivers sustainable value while supporting employees through rapid transformation.
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Progress with AI will depend on how well humans guide and govern powerful technologies, with judgement, values and responsibility shaping positive outcomes.
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The article looks ahead to 2026, exploring how technological change including AI is likely to shape organisations, decision-making and society. It highlights both optimism and caution, stressing that progress will depend on how well humans guide and govern powerful technologies. Rather than focusing only on technical capability, the article emphasises the role of judgement, values and responsibility in shaping positive outcomes. Learning and capability-building matter, as leaders and employees alike will need to adapt their thinking, skills and behaviours to keep pace with change.
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As AI moves from adoption to transformation, HR must lead how organisations hire, develop and retain talent, with AI fluency becoming a baseline enterprise skill.
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The article argues that as AI moves from adoption to transformation, HR must lead how organisations hire, develop and retain talent. AI fluency is now a baseline enterprise skill, embedded into recruiting, performance evaluation and operations. Companies are screening for AI skills and redesigning roles. Training on how to collaborate with AI as a team member is critical. Leaders must address employee fear of becoming obsolete (FOBO) through clear strategy and learning pathways.
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AI increases creativity for employees who use metacognition to reflect on problems, question outputs and deliberately adjust their approach, while passive users see little benefit.
Read about our microlearning course on AI and Metacognition
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The article explains why AI increases creativity for some employees but not for others. The difference lies less in access to AI tools and more in how people think about and manage their own thinking. Employees who reflect on problems, question AI outputs and deliberately adjust their approach tend to use AI in more creative and exploratory ways. Others use AI passively and see little benefit. The article argues that training in metacognition – learning how to plan, monitor and evaluate one's thinking – can significantly improve creative outcomes when working with AI.
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Many companies struggle to find employees with the right AI capabilities, highlighting the need for targeted reskilling aligned with business priorities.
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The article examines the growing AI skills gap and how organisations can address it strategically. Many companies struggle to find employees with the right capabilities to implement and scale AI initiatives. The report highlights the need for targeted reskilling, upskilling and learning programmes, combined with workforce planning and recruitment strategies. Organisations that take a proactive approach aligning skill development with business priorities and providing structured training are better positioned to extract value from AI investments and sustain long-term competitive advantage.
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Technology functions are realising tangible AI benefits by integrating it into workflows and ensuring employees have the skills to use it effectively.
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The article explores how technology functions are realising tangible benefits from AI, from automating routine tasks to improving decision-making and product development. Success depends on integrating AI into workflows, aligning teams around clear goals, and ensuring employees have the skills to use AI effectively. The piece highlights that training, reskilling and capability-building are critical to scaling AI impact, enabling tech teams to move from operational efficiency to strategic innovation. Organisations that invest in both technology and people see faster adoption and higher returns from AI initiatives.
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AI enhances sales performance when product and sales teams work together to refine models with context, feedback and human oversight.
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The article explains how AI can enhance sales performance when product and sales teams work together to refine and guide AI systems. Rather than relying solely on AI recommendations, teams can iteratively improve models by providing context, feedback and human oversight. This collaboration ensures AI agents deliver more accurate, actionable insights while aligning with business goals. The piece highlights that training and skill development are essential, enabling employees to interpret AI outputs, make informed decisions, and continuously improve AI-driven processes.
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Earlier articles that remain highly relevant
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The article explores how people work with AI on what is described as the “jagged frontier” of capability – where AI performs extremely well at some tasks and poorly at others. It distinguishes between two collaboration models: centaurs, where humans and AI divide tasks, and cyborgs, where work is tightly interwoven. Performance gains depend less on the tool itself and more on how tasks are designed and how well people understand AI’s strengths and limits. The article highlights that without the right judgement, users can be misled by AI’s uneven performance. Learning and capability-building are essential so individuals can choose the right collaboration model, adapt workflows and use AI in ways that genuinely improve outcomes.
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Wes Kao argues that standing out in a noisy world requires developing a "spiky point of view" – a perspective you feel strongly about and will advocate for, even if others disagree. Unlike generic insight, a spiky POV is rooted in lived experience, conviction and authentic voice, making it almost impossible to imitate. It should challenge the audience to think differently, be defensible rather than universally agreed and reflect genuine belief rather than safe consensus. As AI commoditises generic content, a spiky POV becomes an increasingly rare and distinctive competitive advantage.
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Tiago Forte's Building a Second Brain is a widely adopted personal knowledge management system for capturing, organising and using information more effectively. Its CODE framework – Capture, Organise, Distil, Express – argues that the human brain is ill-suited to storing everything we need to know, and that we should externalise memory into a trusted digital system instead. The result: ideas compound over time, creative output improves and cognitive load falls. In an era of information overload and accelerating AI change, a reliable personal knowledge system has never mattered more to knowledge workers.
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Vol. 62, No. 1, February, 2026
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This article describes the emergence of the "agentic organization," where humans work alongside autonomous AI agents to deliver end-to-end outcomes. Rather than using AI as a support tool, early adopters are redesigning operating models, decision rights, governance, and workflows around AI agents. The shift is positioned as the most significant organizational transformation since the industrial and digital revolutions, requiring new structures, skills, and leadership mindsets.
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The article argues that traditional change management approaches are no longer sufficient in an era of continuous disruption. Organizations must move from episodic transformation programmes to ongoing reinvention. This requires new leadership capabilities, faster decision making, greater adaptability, and the ability to integrate technological change – especially AI – into the core of how change happens.
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This article highlights that competitive advantage from AI comes less from technology itself and more from leaders who can connect business problems to AI possibilities. Many organisations underinvest in developing leaders' AI literacy, leaving a gap between technical teams and strategic decision makers. Building this "AI muscle" is framed as a core leadership responsibility.
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This article outlines the new risks introduced by agentic AI systems, including autonomy, escalation, and unintended behaviour. It argues that traditional risk frameworks are insufficient and proposes a proactive approach combining technical safeguards, governance, human oversight, and organisational readiness. Security and safety are positioned as enablers of scale, not blockers.
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In this interview, Delta's CEO reflects on leadership through uncertainty, learning, and long-term thinking. The discussion reinforces the importance of humility, adaptability, and openness to change. These qualities are increasingly essential as AI reshapes industries and decision making.
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This article examines why only a minority of organisations believe they have high-quality strategy. Successful "strategy champions" excel not only at bold strategic design but also at execution and mobilisation. The article stresses clarity, alignment, and sustained focus – capabilities increasingly challenged by rapid technological change.
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This article explores why many operating model transformations fail and identifies six common pitfalls. Success depends on clear outcomes, disciplined execution, and alignment between structure, processes and capabilities, which are often stressed by AI-driven change.
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The article examines the rapid progress of humanoid robots and the remaining barriers to large-scale commercial deployment. It argues that cost, reliability, integration, and workforce acceptance will determine adoption, rather than technological novelty alone.
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This AI briefing explains the "jagged frontier" of AI capability: models can perform extraordinarily well in some tasks while failing unexpectedly in others. By examining model and system cards, the article highlights risks such as hallucinations, deception, and misalignment, reinforcing the need for informed and critical AI use.
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This article analyses the global healthcare workforce shortage and argues that solving it requires rethinking training, retention, and care delivery models. AI is presented as a potential enabler in reducing administrative burden, supporting diagnostics, and empowering patients, but not a substitute for systemic reform.
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