Leadership Insights

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Insight

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

What is Artificial Intelligence?
Insight

What is Artificial Intelligence?

Leaders are expected to make decisions about AI, even when the term is used inconsistently. One reason is simple: “There is no generally accepted definition” of AI, and the variety of definitions can create confusion (Sheikh, Prins and Schrijvers, 2023). If AI is defined too broadly as “algorithms”, it can include everything from calculators to cookbooks, which is not useful (Sheikh, Prins and Schrijvers, 2023). If it is defined too strictly as full imitation of human intelligence, it can imply AI does not exist at present (Sheikh, Prins and Schrijvers, 2023). This hot topic clarifies AI using explicit definitions from the sources, including Abbass’s “automation of cognition” definition (Abbass, 2021) and the European Commission AI High-Level Expert Group (HLEG) definition adopted by (Sheikh, Prins and Schrijvers, 2023).

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Jay Dehaan

Wed 15 Apr

Expert Viewpoint: Rethinking Leadership - Unlocking the Power of Diverse Thinking
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Expert Viewpoint: Rethinking Leadership - Unlocking the Power of Diverse Thinking

With lived experience at the heart of her work, Emily helps leaders understand their responsibilities under the Equality Act, navigate reasonable adjustments with confidence, and create environments where neurodivergent people can perform at their best. She works across sectors to bridge the gap between awareness and practical leadership action.

Emily Banks

Tue 17 Mar

New Data-Powered Leader Apprenticeship Launched
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New Data-Powered Leader Apprenticeship Launched

As organisations generate more data than ever before, the challenge is no longer access to information. It is capability. Many teams rely on manual reporting, inconsistent validation, or limited analysis. The result? Missed insight and avoidable risk. Our new Data-Powered Leader apprenticeship has been designed to close that gap.

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Jay Dehaan

Fri 20 Feb

Is your apprenticeship investment paying off? Measuring the tangible and intangible returns
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Is your apprenticeship investment paying off? Measuring the tangible and intangible returns

Progress of apprenticeship content can easily be tracked but understanding the real impact the learning is having, may be more difficult to quantify. The true value of an apprenticeship can be seen through everyday decision making, the conversations being held and behaviours displayed. By observing how people perform, how they solve problems and take responsibility, you can start to see whether the learning is being converted into real life improvements, both for the individual and across the business.

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Jane Savage

Thu 19 Feb

Expert Viewpoint: Leading with Judgement - Building Responsible AI Governance That People Trust
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Expert Viewpoint: Leading with Judgement - Building Responsible AI Governance That People Trust

As an HR professional since 2004, Mimie has a wealth of expertise, seamlessly combining a personal touch with in-depth knowledge of HR and employment law.

Mimie Wallace

Tue 10 Feb

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