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.
Vol. 62, No. 1
Go to Digital EditionThis 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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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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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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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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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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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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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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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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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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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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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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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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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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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 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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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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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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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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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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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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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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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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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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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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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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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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