Insights
A weekly reading of the research and professional thinking behind board, executive and talent decisions. Written for directors and senior leaders who want the argument and the evidence, in one place.
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25 of 25 latest editions
Human judgement remains a leadership capability in an AI-enabled workplace
As AI use spreads, leaders still need judgement to question assumptions, weigh trade-offs and keep decisions anchored in context. Research from Stanford, Harvard and MIT suggests the task is not to choose between people and tools, but to strengthen the human capabilities that shape how tools are used.
The hidden leadership work behind AI-enabled productivity
AI can lift personal output quickly, but sustainable productivity depends on leadership choices that shape standards, trust, and judgement. Research from Stanford, Harvard and MIT suggests the real work is not merely adopting tools, but setting boundaries, redesigning routines and creating disciplined learning loops.
From AI ambition to oversight: a practical agenda for the next board meeting
Boards do not need to master every model, but they do need a disciplined way to govern AI. Recent university research suggests a practical agenda: clarify use cases, test leadership assumptions, set guardrails, and keep human judgment central while adoption, risk and responsibility move in parallel.
Building trust when AI changes how decisions are made
As AI starts shaping more workplace decisions, trust depends less on the tool itself and more on how leaders explain, govern and challenge its use. Research suggests people respond better when leaders keep human judgement visible, ask better questions, and set clear guardrails.
Measuring coaching progress without reducing people to scores
Coaching progress is often easier to describe than to capture. Useful measurement blends reflection, behavioural evidence and agreed outcomes, while avoiding crude rankings. The aim is not perfect precision, but a fairer picture of change that respects context, trust and the person behind the metrics.
Why talent strategy needs to be part of the AI business case
AI business cases often focus on tools, cost and governance, but the people side is not optional. Evidence from university research suggests leaders, operating model choices and capability building shape whether AI translates into usable value, making talent strategy a core investment rather than an afterthought.
The director’s role in a human-centred AI strategy
Boards do not need to become technical specialists, but they do need to shape how AI is governed, explained and used. The director’s role is to balance experimentation with accountability, ensuring AI serves people, supports decisions and stays aligned to strategy, risk and culture.
From prompts to practice: making leadership development meaningful
AI can help leaders practise the mechanics of coordination, questioning and decision-making, but prompts alone do not build judgement. The useful shift is from casual experimentation to deliberate practice, with governance, reflection and human feedback shaping how leaders learn, transfer and apply those lessons.
What AI experimentation reveals about organisational culture
AI pilots do not just test technology; they expose how organisations handle uncertainty, governance and learning. Research from Stanford, Harvard and MIT suggests experimentation works best where leaders set clear boundaries, invite questioning, and treat AI as a disciplined management practice rather than an unstructured novelty.
Executive succession when roles are changing faster than job descriptions
Succession planning now has to work in shifting conditions, where the next executive role may not yet be fully defined. Research from Stanford, Harvard and MIT suggests boards should test learning agility, systems thinking and AI-era judgment, rather than relying on static role profiles alone.
AI risk reporting directors can actually use
Boards do not need more AI detail; they need clearer risk reporting that supports judgement, challenge and prioritisation. Research points to uneven responsible-AI reporting, jagged capability, and the value of structured leadership. The practical task is to convert technical noise into board-ready decisions.
A responsible boundary between AI feedback and human assessment
AI can help people practise, reflect and prepare, but it should not be mistaken for a full assessment of performance, potential or judgement. The responsible boundary is clear: use AI for structured feedback and human expertise for high-stakes decisions, interpretation and care.
The hidden leadership work behind AI-enabled productivity
AI can speed up work, but the real gains depend on leadership choices that are easy to overlook. The task is less about buying tools and more about setting guardrails, designing habits, and helping teams judge outputs critically while keeping trust intact.
Why human judgement remains a leadership capability
AI can widen a leader’s options, but it does not remove the need for judgement. Research from Stanford, Harvard and MIT suggests leaders still need to ask better questions, weigh context, and decide when a machine’s output should be challenged, adapted or set aside.
Why AI oversight belongs in the board skills conversation
Boards are no longer only asking whether the organisation uses AI; they must also ask who can oversee it wisely. The evidence suggests capability is advancing faster than responsible practice, while effective leadership depends on questioning, judgement and governance habits that boards can strengthen.
Leading Across Australia, New Zealand and Asia with AI: What Leaders Need to Do Now
AI is moving quickly, but leadership responses often lag behind. For leaders working across Australia, New Zealand and Asia, the practical task is not to chase every tool; it is to set guardrails, build shared habits and use AI to support clearer decisions and better coordination.
Using AI simulations to practise difficult leadership conversations
AI simulations can help leaders rehearse difficult conversations in a low-stakes setting, but they work best as practice, not proof. The useful question is not whether the model sounds convincing, but whether the simulation helps leaders ask better questions, notice blind spots and prepare for human reactions.
Who should own generative AI across the organisation?
Generative AI works best when ownership is shared but clearly assigned. Research suggests executive sponsorship, cross-functional governance and business-unit execution each matter. The practical question is not who “owns” AI alone, but how leaders coordinate risk, value and learning across the organisation.
Board succession in an age of artificial intelligence
AI is changing what boards need from future directors, but succession should not become a race for technical novelty. The practical task is to identify leaders who can govern AI risk, ask disciplined questions, and stay grounded in human judgment while building a board with varied oversight capability.
Why psychological safety matters when teams experiment with AI
Teams experimenting with AI need more than access to tools. Psychological safety helps people surface errors, question outputs and share uncertainty early. Research suggests leaders set the tone through curiosity, transparency and structured conversation, while clear guardrails reduce risk without shutting down experimentation.
Protecting Confidentiality in AI-Assisted Leadership Coaching
AI can support leadership coaching, but confidentiality does not come for free. The safest use starts with clear boundaries, minimal data sharing, approved tools, and human oversight. The practical task is to gain usefulness without turning reflective coaching into an unmanaged data trail.
The questions every board should ask before approving an AI investment
Boards should treat AI proposals as strategic change programmes, not software purchases. The right questions probe use case clarity, governance, risk, capability and operating discipline. Research suggests AI value depends on deliberate management, while capability and responsible-use practices are advancing at different speeds.
How executives can distinguish AI activity from business value
Executives are being urged to look past visible AI activity and ask whether it changes decisions, workflows and outcomes. Research from Stanford, Harvard and MIT suggests value comes from disciplined use cases, clear governance, and leadership that tests whether AI improves judgment rather than simply increasing output.
Using AI for executive reflection, without turning it into a coach
Executives can use AI to test ideas, surface blind spots and rehearse decisions, but it remains a tool for preparation rather than judgement. The safest value comes from bounded use, clear prompts and human coaching for context, challenge and confidential reflection.
Leading AI teams starts with asking better questions
AI leadership is less about having all the answers than creating conditions for better ones. Recent research suggests stronger leaders question more, listen longer and shape clearer turn-taking. For managers, the practical task is to govern AI use, probe assumptions and build disciplined team dialogue.
Further professional reading
External professional resources, separate from the numbered sources cited in each edition. These are not university studies, and listing them does not indicate any collaboration, certification, affiliation or endorsement.
- Global Institute of Directors (opens in a new window)External professional resource · governance and directorship
- World Coaching Institute (opens in a new window)External professional resource · coaching practice
How each edition is made
Synthesis, then verification.
Editions are drafted with AI-assisted synthesis of published university research and professional resources, and every cited source is checked for availability before publication. Citations are numbered in the text and listed in full at the end of each edition.
Insights is general information. It is not advice for any particular organisation, board or individual.