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Insight

Applying AI in the Workplace: Tools, Decision-Making, Human Judgement and Decision Support

AI is often presented as a fast route to better decisions, smarter work and efficiency. The evidence is more cautious. Organisations may invest heavily but still report limited business gains, partly because implementation needs more than technology alone (Reim et al., 2020). AI can support knowledge management by speeding up information collection and interpretation, but it struggles with tacit knowledge and can amplify problems in decision-making rather than reduce them (Trunk et al., 2020). This means responsibility does not disappear when AI is introduced. It shifts. Leaders and teams need transparency about how outputs are produced, literacy to choose appropriate applications, and training to interpret results responsibly. Cultural alignment also matters, because AI changes work practices and can trigger resistance and ethical concerns.

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Jay Dehaan

Wed 15 Apr

The importance of theory in coaching: A lifelong journey, not just a skill
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The importance of theory in coaching: A lifelong journey, not just a skill

This question is understandable. Coaching is not just about acquiring a set of tools, it’s about developing a way of thinking, being, and relating to others. While practical application is essential, understanding the theoretical foundations of coaching is what sets truly transformational coaches apart.

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Abz Salloum

Thu 20 Feb

Using AI to Improve Productivity and Reduce Manual Effort
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Using AI to Improve Productivity and Reduce Manual Effort

Advances in artificial intelligence are transforming how work is performed across sectors, with growing interest in its ability to improve efficiency and productivity (Naqbi, Bahroun and Ahmed, 2024). Generative AI in particular enables the autonomous creation of content such as text, images, and data outputs, supporting a wide range of professional activities (Naqbi, Bahroun and Ahmed, 2024). Its use is associated with automating tasks, improving data analysis, and assisting decision-making processes (Naqbi, Bahroun and Ahmed, 2024). However, productivity gains are not automatic. Research shows that outcomes depend on how technologies are implemented and combined with existing workflows and systems (Bughin, 2026). While AI can improve task execution and reduce manual effort, its effectiveness is shaped by organisational design, supporting tools, and how work is structured (Bughin, 2026). Understanding these conditions is critical to using AI effectively in practice.

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Jay Dehaan

Mon 20 Apr

Prompt Engineering: Getting Better Results from AI
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Prompt Engineering: Getting Better Results from AI

AI tools are increasingly used across organisations to support tasks such as writing, research, planning, and analysis. While these tools offer significant efficiency gains, their outputs are highly dependent on how they are instructed. A key principle has emerged: the quality of output is shaped by the quality of input. Prompt engineering refers to the structured design of inputs that guide AI systems to produce relevant and accurate responses. Research into large language models demonstrates that even small variations in phrasing and structure can significantly influence outcomes (Brown et al., 2020). This highlights that effective use of AI is not solely a technical capability, but a reflection of how clearly users define problems, provide context, and communicate expectations. As AI becomes more embedded in everyday work, prompt engineering is increasingly recognised as a core skill that supports accuracy, consistency, and informed decision-making.

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Nawal Missi

Mon 20 Apr

Embedding AI into Organisational Ways of Working
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Embedding AI into Organisational Ways of Working

Artificial intelligence is increasingly shaping organisational work practices and culture. Research describes AI as a transformative force that is reshaping traditional work practices, automating routine tasks, enhancing decision-making, and driving innovation (Murire, 2024). These developments can improve efficiency, productivity, and the ability to focus on more value-added activities (Murire, 2024). However, embedding AI into ways of working is not just a technical task. Evidence suggests that AI adoption is also a behavioural and change-management challenge, and many initiatives focus too heavily on the systems themselves while assuming that people will simply adapt (Schweitzer et al., 2026). This can create resistance, uncertainty, and cultural misalignment if organisations do not manage the human side of change carefully (Murire, 2024). The research therefore points to a broader view of AI adoption: one that combines technology with leadership, communication, skills development, trust, and human involvement throughout implementation (Murire, 2024; Schweitzer et al., 2026; Xin et al., 2021).

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Jay Dehaan

Thu 16 Apr

Designing and Adopting AI-Supported and Automated Workflows
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Designing and Adopting AI-Supported and Automated Workflows

Artificial intelligence is transforming organisational processes by automating routine tasks, improving data analysis, and supporting decision-making (Al Naqbi et al., 2024; Sabah et al., 2024). These capabilities allow organisations to streamline workflows, reduce errors, and improve operational efficiency. AI-driven systems can handle repetitive and time-consuming activities such as data entry, scheduling, and customer interactions, enabling a shift towards more efficient and scalable processes (Sabah et al., 2024). However, the benefits of AI are not realised through automation alone. Evidence shows that performance depends on how automation is combined with human involvement and embedded into structured workflows (Hasan, 2025). AI systems must function within coordinated processes where human oversight, collaboration, and governance remain visible. Without this integration, AI risks becoming disconnected from actual work practices. Designing AI-supported workflows therefore, requires a focus on both technology and how work is organised, ensuring that automation improves efficiency while maintaining clarity, coordination, and control.

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Jay Dehaan

Thu 16 Apr

Sustainable AI: Smarter Decisions, Responsible Impact
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Sustainable AI: Smarter Decisions, Responsible Impact

Artificial intelligence has rapidly reshaped how organisations operate, analyse information and make decisions. Its ability to process vast amounts of data and generate insights offers significant advantages in efficiency, innovation and strategic clarity. However, this progress comes with increasing complexity and cost. As models grow in scale and capability, so too do their energy demands, environmental impact and ethical risks. Sustainable AI emerges as a response to this tension. It recognises that AI is not only a tool for solving problems but also a system that must be designed, deployed and governed responsibly. This includes understanding its environmental footprint, ensuring transparency in decision-making and aligning outputs with human values and societal expectations. At its core, sustainable AI is about balance. It requires organisations to move beyond performance alone and consider efficiency, accountability and long-term impact. When applied effectively, it enables better decisions, stronger trust and more resilient outcomes in an increasingly data-driven world.

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Jay Dehaan

Thu 16 Apr

Responsible AI Leadership: Governance, Risk and Accountability
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Responsible AI Leadership: Governance, Risk and Accountability

AI is increasingly used to support decisions, automate routine work, and generate insights. But the same shift raises legal, ethical and governance questions, especially when AI influences corporate reporting, risk management, or strategic change. Ustahaliloğlu (2025) describes both the opportunities, and the “complex legal questions” AI introduces into corporate governance, and notes that adoption is still early-stage with open questions. London (2024) argues that responsible AI depends on how people organise work, because AI systems sit inside an organisational “ecosystem” of norms, structures and division of labour. Hossain, Fernando and Akter (2025) add that leaders need technical, adaptive and transformational capabilities to lead responsibly in AI-driven contexts. (Hossain, Fernando and Akter, 2025)

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Jay Dehaan

Thu 16 Apr

Ethical and Responsible AI in Leadership Practice
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Ethical and Responsible AI in Leadership Practice

Artificial intelligence is rapidly changing how organisations operate, reshaping leadership practices and decision-making processes (Kandasamy, 2024; Madanchian et al., 2024). AI can improve efficiency, enhance decision-making, and drive innovation, but it also introduces challenges, including bias, privacy concerns, and job displacement (Kandasamy, 2024). As AI becomes more embedded in organisational systems, ethical leadership becomes increasingly important. Ethical leadership involves fostering fairness, integrity, transparency, and accountability while ensuring organisational changes align with societal values and norms (Kandasamy, 2024). The success of AI systems depends not only on technology but also on how they are integrated into organisational practices and human behaviour (London, 2024). Trust is central to this process. AI can deliver value only if people trust the outputs it produces, particularly regarding unbiased results and credible decision-making (Abbu, Mugge and Gudergan, 2022). Ethical and responsible AI is therefore not just a technical issue but a leadership responsibility shaping how technology is used and trusted.

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Jay Dehaan

Thu 16 Apr

AI in Organisational Practice: Applications, Benefits and Limitations
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AI in Organisational Practice: Applications, Benefits and Limitations

Artificial intelligence is increasingly positioned as a major technological development with significant organisational implications. It has been described as systems that “apply advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions” (Neumann, Guirguis and Steiner, 2024). Similarly, AI can be understood as “intelligent systems created to use data, analysis, and observations to perform certain tasks without needing to be programmed to do so” (Reim, Åström and Eriksson, 2020). Organisations invest in AI because it offers “enormous potential for adding value and competitive advantage” (Reim et al., 2020) and is associated with cost reduction, improved productivity and faster decision-making. However, the advantages of AI “cannot be achieved without successful implementation” (Merhi, 2023), and many organisations struggle to adopt it effectively (Neumann et al., 2024).

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Jay Dehaan

Thu 16 Apr

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