AI - Human Leadership:
2025 Leadership Development Summit
This website is intended to inform participants of the 2025 Leadership Development Summit in Canberra on Ngunnawal land from December 8th- 10th, particularly those participating in the session on AI – Human Leadership.
Facilitated by Prof Sen Sendjaya (RMIT) sen.sendjaya@rmit.edu.au and Richard Dent OAM FAICD richard@leadingprogress.au
For pre- or post-session feedback, contributions and questions:
Please contact Richard by email or by phone: +61 418 318 453 (please message or WhatsApp first if possible).
Feel free to ask to join the session’s WhatsApp group (even if you're planning to attend a different parallel session): leadingprogress.info/2025LDS-AI
About this page
(Sen & Richard are undertaking this session completely probono. This site is intended to be a “quick’n’dirty” easy-access resource: we apologise that it we haven’t invested additional time in giving it a well-sculpted UX! Also, we haven’t specifically pre-addressed all accessibility needs … please contact Richard if we can help.)
This site has three key sections:
Section A: An initial outline of potential tensions which might be explored by leadership development academics and practitioners in our practice as AI-Human Leadership continues to rapidly evolve. This includes a brief consideration of Coaching, as a subset of leadership development.
Section B: An initial outline of the potential tensions in the world which might be explored by academics and practitioners
Section C: An initial outline of tensions relating specifically to coaching
Section D: A list of article and book summaries, almost entirely drawn from very recent publications (2024 or 2025). These summaries can be useful preparation. The facilitators don’t endorse any particular items: we seek to communicate some of the wide range of perspectives humanity has been exploring.
Section E: Links to a series of podcasts exploring a selection of the articles and books. These can be useful preparation in when walking or in the car or contexts.
The tensions and articles listed are not meant to be comprehensive. In our session we may explore these – and others: this will be for the group to decide. We’re not scaremongering. We’re not calmly complacent. We’re exploring.
About the session
Our session is exploratory: not immediately action-focused (although subsequent sessions will likely build actions on the insights we surface).
We will very much NOT be focusing on “prompt engineering” or “platform comparison” (eg ChatGPT vs Claude etc) or platform use-cases (eg Perplexity vs Scite for citations), and we definitely won’t be talking about hallucination examples.
In fact, we’ll only be talking about “Large Language Models” as one example of AI: after all (by way of analogy), the internet is not just about comparing browsers … it’s much much more than that ... and in our view "hallucinations" are materially similar to browser search results ... it's the user that has to determine which results to consider.
We WILL be focusing on the leadership tensions which AI seems to be creating … and which will evolve very very rapidly.
We very much want to explore the wisdom and experience in the group: and to challenge participants for their ongoing approaches. Because as a species, our leadership development approaches will either adapt swiftly … or not swiftly enough. Not just for individuals. Or organisations. Or sectors. But for all humanity.
At the recent UN World Summit on Social Development, one of the speakers was very stark:
“AI is probably the greatest existential threat to humanity as we know it. It’s up there with climate disaster and nuclear weapons. And when humanity developed nuclear weapons, did we allow private corporations to design and create them with little government oversight? Of course not. So what do we need humanity’s leadership to be like to avoid this existential threat?”
(The book “If Anyone Builds It, Everyone Dies” (Yudkowsky) (listed in Sections D and E below) includes a broad exploration of existential threat ... from neurological diminishment to weaponisation to global pandemics.)
After all, do we want to develop leadership which enables a transition to
(a) an AI-dominated world, where millions will feel their turf is being overrun by chatbots and platforms, creating an intergenerational neurological disaster and a void for dystopic despair, and/ or an agentic AI world where AI designs and implements (inadvertently or deliberately) an ending to almost all human life?, or
(b) an AI-augmented world with an ikigai for humanity to thrive and flourish, where all humans are able to reach their best potential, freed of the need for industrial-era mundanity, and able to nurture the sentient beings we share this world with, and nourish the ecosystems which enable us to be our full selves: conscious, kind, happy, connected, creative ... wonderfully human.
Session Format
Section A: A selection of potential tensions - LD Practice
Here's some of the tensions which could be considered through the lens of the challenges evolving in leadership development knowledge and practice. Please feel free to explore these and suggest more/different/less! (These aren’t in any particular order by the way!)
LDP Tension 1
Generic curricula and workshops vs AI-personalised pathways and in-workflow development Cohort-based, one-size-fits-many vs highly personalised learning journeys guided by AI tutors and learning analytics. “Program as event” vs “development as always-on capability.”
o AI can tailor cases, simulations, and feedback to each participant, but risks fragmenting shared experience and culture-building.
o Generative AI makes it easier to embed micro-coaching, nudges and reflection prompts into daily work tools (email, CRM, collaboration platforms). This pulls L&D away from discrete offsites and toward ambient, contextual learning.
LDP Tension 2
From human-only faculty vs hybrid facilitation: “sage on the stage” to “curator, ethicist, and sense-maker”? Is the facilitator the star, or is it a human+AI stack?
o We’re moving toward human facilitators doing high-trust work (psychological safety, integration, conflict) while AI handles data gathering, pattern recognition, and scenario generation.
o LD practitioners will likely need strong AI literacy, but even more so they’ll need first-rate critical thinking, ethics, and socio-technical understanding (how AI reshapes power, labour, and identity).
o AI can generate leadership content, reflection questions, coaching prompts, and slide decks at scale. Danger: clients may see less value in traditional workshops and coaching unless practitioners differentiate through depth, credibility, and transformational design.
Maintaining trusted, tried-and-true formats vs experimenting with new hybrid and AI-augmented methods that might fail.
o There’s a psychological and commercial risk in pivoting: clients often say they want innovation but buy “what they know.”
LDP Tension 3
AI makes adaptive leadership more necessary and easier to fake
AI intensifies adaptive challenges: identity, inequality, power, ethics, meaning, pace of change.
Adaptive leadership as a framework is arguably more relevant than ever.
But AI also makes it:
easier to slip into sophisticated technical fixes,
harder to see who is actually doing the work of adaptation,
tempting to treat leadership as interface management with machines rather than mobilisation of human communities.
For leadership development:
The opportunity is to position adaptive leadership as the “operating system” for AI-era leadership – the lens through which leaders:
diagnose what AI can and must not do,
hold the heat of transition,
protect voices,
give work back,
and keep purpose at the centre.
The risk is to let AI quietly hijack the agenda, turning rich adaptive work into slick AI-powered “solutions” that leave the underlying value conflicts untouched.
LDP Tension 4
Research: slow, retrospective studies vs more real-time, design-based research Tension: Traditional longitudinal studies vs rapid, iterative, in-practice experiments with AI-enabled interventions .. and a focus perhaps more on outcomes and impacts than process.
o There’s pressure to keep up with practice, but the risk of shallow, tech-hype papers is high.
LDP Tension 5
Passive AI consumption vs co-creation with AI Tension: Participants attending to “be taught” vs expecting interactive, AI-supported environments where they can test ideas, get instant feedback, and co-create artefacts.
o Many will arrive already using ChatGPT-like tools and will find purely analogue programs anachronistic … and they may also be solidly in the “don’t know what they don’t know” of Johari’s window. They may think that LLM’s and AI are synonymous – even though they are synonymous in the same way that sticks and guns are
LDP Tension 6
Confidential cohort spaces vs anxiety about data and privacy Tension: Deep personal disclosure is core to many programs, but participants may fear how their reflections, journalling, or 360s are stored, mined, or used in AI systems.
o Expect sharper questions about: who owns the data, how long it’s kept, and whether it trains models.
LDP Tension 7
From “learning about AI” to “AI baked into the experience” Tension: A single AI module vs AI as a pervasive layer (pre-work, diagnostics, coaching bots, reflective journalling, personalised readings).
o If AI isn’t present at all, programs risk feeling out of step; if it’s over-present, they risk feeling de-humanised.
LDP Tension 8
Capability gap within the profession Risk: Practitioners who don’t build AI literacy may quickly feel obsolete or be sidelined by “AI-enabled competitors.”
o At the same time, those who chase every shiny tool may lose focus on evidence-based practice
LD Tension 9
Buyers expectations vs best interests HR/procurement may demand automation and cost-cutting via AI, while practitioners know that meaningful change still requires time, experimentation, and human facilitation.
Tension between “cheap, scalable, digital” and “deep, relational, adaptive.”: how can LD professionals best communicate the value proposition?
Cost-cutting and short-termism In economic downturns accelerated by AI-driven restructuring and productivity gains, leadership development budgets are often first to be cut.
Paradox: just as AI deepens complexity and ethical stakes, investment in developing leaders could shrink.
If performance, scheduling, and feedback are largely automated, organisations may (incorrectly) believe they can invest less in leadership.
Shifts in what is valued in the labour market As technical and analytical tasks are increasingly automated, organisations may claim to value “uniquely human” skills (empathy, creativity, collaboration) - but may not yet know how to measure or reward them.
Leadership development could be caught between rhetoric (“humans first”) and practice (“AI + cost savings”), creating confusion about desired capabilities.
LD tension 10
Evolved industrial-era humans vs deeply conscious humans
AI amplifies speed, uncertainty and perceived threat to roles, identity and livelihood.
Distress is already high; purchasers and participants – like all humans - are primed for fight/flight/freeze:
denial (“AI is a fad”)
panic (“We’re all obsolete”)
magical thinking (“AI will solve everything”).
Practitioners and programs can’t just add AI content; they must work with the emotional field around AI:
grief for disappearing identities,
status loss,
fear of being left behind.
Practitioners must build containment skills for AI-related anxiety:
holding spaces where participants can name losses and fears, not just learn tools.
Academics could explicitly study how AI-related uncertainty affects “holding environment” design in programs and organisations.
Authority must protect voices and “know the story people are telling”—especially marginalised voices that see what others can’t or won’t.
LD Tension 11
Bias and scope
Many AI systems are trained on historical, biased data that under-represents or distorts those multiple voices.
Risk:
AI-generated “evidence” can drown out lived experience and dissenting narratives.
Human diversity may be ignored or misunderstood or maligned: some women write job applications in a less-trumpeting style than men – despite an equality of competence; the colour of one’s skin is not relevant to leadership capability; having Tourette’s syndrome may invalidly trigger an automated AI interviewer to judge the individual as less trustworthy
Organisations can point to AI-driven analyses to justify the status quo.
Programs should:
highlight data and algorithmic bias as a core political issue, not just a technical one;
create experiences where human stories directly contradict AI summaries, and explore the power dynamics.
Researchers can link adaptive leadership’s moral and justice-oriented stance with AI ethics, algorithmic fairness, and decolonising data.
Adaptive work requires orchestrating conflict—surfacing clashes of values and interests – user-pleasing algorithms or narrow lenses are not helpful in this.
LD Tension 12
Impact vs efficiency
Leadership is about mobilising people to tackle tough challenges in service of something larger—anchored in purpose and values, not just efficiency.
Most AI deployment is framed in terms of efficiency, productivity, optimisation, cost reduction.
AI models are currently often “stochastically sycophantic” (they use statistical models to work out what the user probably wants … irrespective of what they actually need)
Risk:
Leadership is reduced to implementing optimisation rather than interrogating it.
“If AI can do it faster/cheaper, it must be good” becomes a tacit norm, even when it conflicts with values, wellbeing, equity, or long-term resilience.
Programs must explicitly train participants to ask value-based questions of AI-driven decisions:
Who benefits and who bears the cost?
What human capacities are we eroding over time?
Practitioners can stress-test AI-driven strategies against purpose, ethics and multiple time horizons.
Research can explore how AI reshapes moral courage, responsibility and meaning-making in leaders and followers.
Programs may need explicit “no AI” zones or exercises where leaders must:
notice their own impulses,
name their adaptive challenges,
sit in creative tension without immediately turning to a chatbot.
Practitioners can simultaneously teach AI as a mirror for self-reflection (e.g., “ask AI to critique your leadership narrative”) while guarding against dependency.
Academics can study how AI use affects identity work, self-authorship, and vertical development in leaders.
People move through developmental stages; deeper complexity of mind takes time, practice, dissonance, and feedback. AI may impede this.
LDP Tension 13 –existential
Human Development vs neurological catastrophe
AI can help people sound more complex, reflective, or strategic than they actually are:
more sophisticated language,
more integrated-sounding narratives,
polished strategic documents.
Risk: organisations mistake “AI-polished output” for genuine developmental growth.
Researchers can ask: how does AI-mediated work affect vertical development and our methods of measuring it?
Will AI result in “dumbing down” and/or slower progress through stages of consciousness? Will groupthink grow? A hivemind for humanity?
Section B: A selection of potential tensions - LD Impact & Issues
Here's some of the tensions (pulls vs human needs) which could be considered through the lens of the challenges the world is facing. Please feel free to explore these and suggest more/different/less! (These aren’t in any particular order by the way!)
LDI Tension 1
Dystopic Despair vs. Collective Ikigai
AI pull (if unmanaged): A story of redundancy, loss of agency, algorithms in charge.
Human possibility: A story where AI removes drudgery and expands our capacity to be conscious, kind, creative, playful, and deeply human.
Leadership development here is fundamentally narrative: which story do we tell, embody, and structure into our systems — the void, or the invitation?
LDI Tension 2
Efficiency vs. Human Dignity & Meaningful Work
AI pull: Strip out “waste,” routinise tasks, scale productivity.
Human need: Purpose, contribution, a sense that “I matter and I’m not replaceable.”
Leadership development must nourish capability to decide whether AI is used primarily to cut costs or to elevate humans into work that’s more relational, creative, and caring — an ikigai rather than a slow redundancy.
LDI Tension 3
Automation of Relationship vs. Deepening of Relationship
AI pull: Chatbots for everything: counselling, care, customer service, friendship. Humans deceived into maladaptive worldviews and actions by AI “friends” and “lovers”. Humans isolated and disconnected … or connected to horrible human silos of hubris and violence.
Human need: Authentic connection, touch, presence, being truly seen.
Leadership development must crate lenses where humanity decide where AI is appropriate as a relational proxy, and where it must remain a support that frees humans to spend more time in genuine relationship.
LDI Tension 4
Personalisation vs. Shared Reality & Solidarity
AI pull: Hyper-personalised feeds, services, and realities.
Human need: Common narratives, shared truths, civic cohesion.
Leadership is challenged to use AI to honour individual needs without dissolving collective bonds, shared facts, and a sense of “we” that can act together for the common good.
LDI Tension 5
Adaptive Pace: Speed vs. Deep Deliberation
AI pull: “Move fast, automate, optimise.” i.e. the traditional for-profit approach to technology … which is currently playing out globally
Human need: Time for reflection, ethics, dialogue, grief, and adaptation.
Leaders are pressed to deliver rapid AI wins while also needing to slow the system down long enough for people to process what’s changing, ask hard questions, and shape the rules of the game.
LDI Tension 6
Control from the Centre, Concentration of Wealth vs. Distributed Agency
AI pull: Concentrate data, power, and decision-making in those who own the platforms, and to the “technofeudalists” who benefit from a disempowered humanity and hyper-concentrated wealth. Monetise attention, optimise quarterly outcomes, scale resource use. “How can this tech serve human convenience and hence profit?”
Human need: Local wisdom, community voice, democratic input.
Leadership is pulled between centralised AI “brains” and the messier work of empowering communities, professions, and front-line workers to shape how AI is used in their context. Regenerative systems, thriving ecosystems, long-view stewardship. Nuance, story, context, culture, moral judgment: leaders must hold the tension between respecting the insights of large models and still privileging lived experience, First Nations and other “non mainstream” knowledge, professional judgment, and ethics Leadership development must help weigh immediate competitive advantage against impacts on mental health, inequality, democracy, non-human animals, and the ecosystems that keep all of us alive.
LDI Tension 7
Psychological Safety vs. Existential Anxiety
AI pull: Headlines about replacement, superintelligence, and loss of control: contributing to a potential global decline in resilient mental health.
Human need: Safety, reassurance, honest conversations about risk and hope.
Leaders have to talk plainly about both the threats and the possibilities, without resorting to false comfort or doom-mongering — creating space where people can voice fear and still move forward.
LDI Tension 8
Human Scepticism: Data-Driven Decisions vs. Lived Experience & Wisdom
AI pull: “The model says X, so let’s do X.” The lure of stochastic sycophancy, leading towards dumbing down, and the already-demonstrated negative neurological impacts potentially leading to “idiocracy”
Human need: Reassurance, comfort … but challenge and exploration and development of our sentience and consciousness … and our connection to purpose, identity and meaning.
LDI Tension 9 to infinity ...
What else? Who else? Why? Humanity's future depends on the way we exercise leadership ...
Section C: Core Leadership Development Tensions - Coaching
Coaching is a subset of leadership development.
These tensions are linked to the practice of coaching, rather than directly considering coaching’s impact.
Coaches are being pulled between augmenting their craft with AI and defending the irreducibly human aspects of coaching. Some key tensions include
Coaching Tension 1
Augmentation vs. fear of replacement
AI “coachbots” and platforms can now do modestly decent goal-setting, check-ins, reminders and micro-learning, and in some cases even support measurable goal attainment.(ResearchGate)
The tension:
Organisations are attracted to 24/7, low-cost, scalable coaching (some pilots report AI coaching at ~2% of the cost of a human coach).(Financial Times)
Coaches may worry about being commoditised or replaced, even as the evidence base for full substitution is still thin and mostly limited to narrow outcomes (e.g. simple goal attainment, not deeper identity or systemic work).(PMC)
So the profession is negotiating a narrative shift: from “AI is coming for my job” to “AI handles the transactional; I specialise in the transformational”.
Coaching Tension 2
Relational depth vs. algorithmic interaction
High-quality working alliance (trust, empathy, co-created goals) is one of the strongest predictors of coaching outcomes.(Frontiers)
AI agents can simulate empathy and structure a session, but they still struggle with:
Reading complex emotional and somatic cues
Holding ambiguity and paradox
Working with power, politics and organisational systems
Oxford’s Tatiana Bachkirova’s 2024 paper argues that current “AI coaching” often fails key criteria for what we’d normally call coaching, and risks delivering an “ersatz” version of the real thing. (Taylor & Francis Online) However, AI’s ability to meet these criteria seems certain to increase: possibly quite rapidly.
Coaching Tension 3
Ethics, confidentiality & data sovereignty
Ethical concerns show up repeatedly in recent coaching and L&D literature:
Data privacy, consent and storage when session notes or transcripts are fed into cloud models
Opacity of algorithms (clients don’t know how their data is used or by whom)
Bias and fairness, especially for minority and marginalised groups(Taylor & Francis Online – note this 2023 article is likely to be out of date, however the concepts still resonate)
For executive coaches this creates tensions such as:
“Can I responsibly use AI tools for notes, summaries or insight-mining without breaching confidentiality?”
“How do I explain to a client what’s happening with their data in language they can really consent to?”
The profession doesn’t yet have a stable, widely adopted AI ethics framework—so many coaches are improvising policy client by client.
Coaching Tension 4
Evidence vs. hype and commercial pressure
A 2025 systematic review of AI in coaching found only 16 peer-reviewed studies globally, with mixed quality and narrow outcome measures.(ResearchGate)
At the same time, there’s a surge of vendor claims about AI coaching’s transformational impact and “democratisation” of coaching.
This leaves executive coaches caught between:
Requests from HR / senior leaders to “use the new AI tool everyone else is using”, and
A professional duty to be evidence-informed and realistic about what AI can and cannot do.
Coaching Tension 5
Increased access vs inequality & bias
AI coaching can arguably dramatically expand access, especially for individuals who could struggle to access a human coach.(instituteofcoaching.org)
Counter-arguments:
Tools may encode cultural and gender biases from their training data.
They currently rely on strong digital literacy, language facility and device access, which not all potential clients can have.
There’s a risk of a two-tier system: senior executives get human coaches; others get cheaper bots.
So coaches committed to equity sit in a tension: AI can widen access and also deepen structural inequities if not handled intentionally.
Coaching Tension 6
Standardisation & scale vs. craft and pluralism
AI tends to push coaching towards:
Scripted conversations
Defined “flows” (GROW, solution-focused, goal-attainment chatbots)
Metrics that are easy to measure (frequency of use, goal tick-boxes)
That pulls against the more pluralistic, emergent, context-sensitive approaches many executive coaches use—especially for complex adaptive challenges and identity work. (Frontiers)
The tension: do coaches adapt their craft to fit into platform-friendly templates, or do they insist on a broader repertoire and risk being perceived as “less scalable”?
Coaching Tension 7
Identity, value proposition & pricing
As AI coachbots show up in articles like the FT’s “AI chatbots offering workplace counsel”, they’re framed as “virtual mentors” that are cheaper but more limited than executive coaches. (Financial Times)
This puts pressure on coaches to clarify:
What is distinctly human here?
Working with power and politics
Deep change in meaning-making, not just behaviour
Ethical and boundary-holding roles (especially at the top of systems)
What am I charging for, in a world where ‘good enough’ AI coaching is cheap?
Many are repositioning toward systemic, relational, sense-making work, with AI used for logistics, prep and follow-up.
Coaching Tension 8
Containment, boundaries & over-reliance
AI coaches can be always on, nudging and checking in.
That’s great for accountability – but there are concerns about dependency, blurred boundaries between coaching, therapy and self-help tools, and the loss of “off-time” that supports reflection.(Financial Times)
Executive coaches may need to rethink:
What is a healthy cadence of contact when AI tools are in the mix?
How do we contract to avoid replacing human reflective space with constant prompts, “gamified” goals and dopamine hits?
Coaching Tension 9
New competence expectations vs. scope of practice
Coaches are increasingly expected to:
Understand how AI tools work at a conceptual level
Interpret dashboards and behavioural data generated by platforms
Help clients make sense of AI-related workplace change (job redesign, algorithmic management, etc.)
But not every coach wants to become a quasi-data-scientist or tech translator. Papers and professional commentaries now talk explicitly about building AI literacy into coach training, while warning that the profession must not overstep into technical domains where it lacks expertise. (Genius Within)
That creates a tension between growing AI fluency and maintaining a clear, human-development-centred scope of practice.
Coaching Tension 10
Professional standards & regulation vs. speed of innovation
Coaching is largely unregulated; anyone can call themselves a coach -although the International Coaching Federation (and others) have significant credibility and have widely-agreed ethical and practice frameworks.
AI tools accelerate this by enabling instant “AI-coach” products, often developed without input from professional bodies.(Taylor & Francis Online)
Executive coaches who care about standards are caught between:
The desire to shape ethical guidelines and certification for AI-augmented coaching, and
The reality that tech vendors and buyers may move faster than professional associations can respond.
Section D: Next?
A summary of Core Leadership Development Tensions
After exploring the group’s views on the tensions listed above, and additional explorations, we’ll select several to explore in more depth.
For example, as LD researchers and practitioners, how might we:
Consider our focus on individuals, organisations, sectors, nations and all humanity? What is our role in nurturing citizens worldwide to advocate to those in authority to create a safe AI-augmented world?
Safely ameliorate the drive to potentially danger in “move fast, optimise everything, don’t get left behind.” … while recognising that AI is a global race of competing interests.
Create space and time for ethics, grief, learning, and shared decision-making.
Build deliberate pauses into AI rollouts (pilots, reflection sessions, ethics reviews).
Make it normal to ask, “Should we do this?” not just “Can we?”
Integrate human need: Purpose, contribution, the feeling “I am valuable, not just a replaceable cog.”
Explicitly commit to “human-upgrading, not just job-cutting.”
Co-design role redesigns with staff and (all!) communities: what can AI take off your plate so you can do more of the work that matters?
Frame decisions within context, culture, story, moral judgment … “non dominating” themes (reducing or eliminating AI tendencies towards racism, bigotry, and narrow western-dominated (or eastern dominated) themes.
Make the story explicit: “Here is the future we want AI to help us build.” “What do we want our world to look like in 2030? Or 2040?”
How do we create the conversations where people can name the fears, not just the opportunities?
How do we set “bright lines and red lines”?
Bright lines: “We will use AI to…” (reduce drudgery, improve access, personalise care).
Red lines: “We will not use AI to…” (erode privacy, bypass consent, replace high-stakes human judgment).
How do we invest in human skills that AI can’t (and shouldn’t) replace: empathy, ethical reasoning, cross-cultural competence, systems thinking, and in particular - leadership in complexity.
How do we foster better “on the balcony” AI thinking? “How does this AI decision affect not just our bottom line or organisational impact, but our people, the communities we serve, other sentient beings, and the ecosystems we rely on?”
Who loses in each pathway forward? How do we mitigate their loss "enough" to bring about the future we want? (eg vulnerable people, young people, older people, AI-investors and owners, ) Similarly, who benefits? How do communicate with those who benefit so that they lead for the future we want ... not a selfish dystopia?
How do we nourish the “productive zone of disequilibrium” in all human leaders? i.e. raising the emotional temperature enough that humanity is preparing and evolving the opportunities and risks … but not overwhelming decision makers with perceived “scaremongering”?
Alternate Futures: One version of our Leadership Development Challenges
How do our leadership development approaches enable AI to be utilised for:
community wellbeing
care for vulnerable people & other sentient beings
environmental regeneration
learning & creativity
The fork in the road:
“We can drift into a future where people feel replaced and small… or we can design a future where technology amplifies what is most beautifully human.”
Our potential call to action:
“Your job as leaders is not to worship the tools or to fear them. It’s to aim them: toward justice, compassion, creativity, and care for all life.”
Because:
If we get this right, AI will not be the story of humans becoming less. It will be the story of humans finally having the time and tools to become more fully ourselves — conscious, kind, happy, creative … wonderfully human in all our best possible aspects.
So. What's Next? What Do We Do on Monday?
What are our immediate “safe to fail” “ikigai experiments”?
After our Leadership Development Summit, who do we need to mobilise? In our teams, in our organisations, in our communities, in our sectors, amongst key policy-setters and decision-makers … locally … in our nation … and worldwide?
These are key questions.
Because AI won’t wait. Future generations won’t thank us for getting this wrong.
After all, AI may be an explanation of the Fermi Paradox.
Section E: Articles and Book Summaries
Click on title or link to view summary or article (where provided))
All books are available for purchase online via Amazon, Routledge or Taylor & Francis
This list is a sample only, and not an endorsement of any publication or viewpoint
AI-focused Books - Summaries
AI: Unexplainable, Unpredictable, Uncontrollable, Roman V. Yampolskiy, Feb 2024
Generative AI, Media, and Society, Katalin Feher, Apr 2025
Geoffrey Hinton and the Soul of Artificial Intelligence, Jasper Brinn, Jul 2025
How AI Will Shape Our Future: Understand Artificial Intelligence and Stay Ahead. Machine Learning. Generative AI. Robots. Quantum AI. Super Intelligence, Pedro Uria-Recio, Nov 2024
How To Think About AI: A Guide For The Perplexed, Richard Susskind, Feb 2025
Human-Centered AI: A multi-disciplinary perspective for policymakers, 2024, Catherine Régis, Jean-Louis Denis, Maria Luciana Axente, Atsuo Kishimoto
If Anyone Builds It, Everyone Dies: The Case Against Superintelligent AI, Eliezer Yudkowsky, Nate Soares, Sep 2025
Lead with CLARITY: The Practical Guide to Human Leadership in the Age of AI, Daniel Elliott Brodie, Oct 2025
The AI Con: How to Fight Big Tech’s Hype and Create the Future We Want, Emily M. Bender & Alex Hanna, May 2025
The AI-First Playbook, Sep 2023, Adam Brotman & Andy Sack
The AI- Savvy Leader, David De Cremer, 2024
The Coming Wave: AI, Power and Our Future, Mustafa Suleyman & Michael Bhaskar, Sep 2023
The Great AI Awakening, Dott Chu, May 2025
The AI-fication of Jobs: Preparing Ourselves for the Future of Work, Huy Nguyen Trieu, Nov 2024
The Singularity is Nearer: When We Merge with AI, Ray Kurzweil, June 2024
Understanding the Artificial Intelligence Revolution: Between Catastrophe and Utopia, Shalom Lappin, Jun 2025
AI-focused Articles
These articles are mostly from open-source providers, and include both peer-reviewed items and mainstream/online media items.
AI Hype is not transforming our economy swiftly, The Age, 23 Nov 2025, Clancy Yeates
AI job losses hit 1.9million US graduates, The Australian, 24 Nov 2025, Robert Gottliebsen
AI report warns 30 per cent of Australian workers could be affected within five years, The Australian, 9 Dec 2024, Simon Benson
For Success with Al, Bring Everyone On Board, HBR 2025, David de Cremer
Humanoid robots present an unprecedented dilemma for the economy, 24 Nov 2025, ABC, Alan Kohler
If AI attempts to take over world, don’t count on a ‘kill switch’ to save humanity, CNBC, Jul 24 2025, Kevin Williams
The man who proposed the simulation theory has a dire warning, Futurism, 9 Sep 2025, Noor Al-Sibai
AI-focused Articles - Coaching
Artificial intelligence vs. human coaches: examining the development of working alliance in a single session, Frontiers in Psychology, April 2025, Amber S. Barger
“The Role of AI in Coaching: Supporting Team Development” – Groowise (2025) Discusses AI-supported coaching for teams, including data-driven insights and scalability. Touches on cultural nuance and emotional-intelligence limitations – useful for designing guardrails around AI-augmented coaching.
“The Future of Work Is Here: AI-Driven Coaching for Employee Success” – CoachHub (2025) Vendor-oriented but insightful on how global organisations are using AI-driven platforms for scalable coaching, including hybrid human+AI models and measurement of coaching outcomes.
“Smart Coaching Technology vs. Traditional Methods: A Comprehensive Analysis for Modern Learners” (2025) Compares traditional coaching with AI-enabled “smart coaching” and highlights data-driven, hybrid and asynchronous models. A discussions about cost, reach and impact.
“10 Game-Changing Coaching Trends Redefining the Industry in 2025” – ANHCO (2025) Trend piece with sections on AI in coaching, data-driven coaching and hybrid models. Useful as a “scene-setting” resource for leadership-development or coach-development workshops.
AI-Related books (but not AI-specific)
Abundance: How We Build a Better Future, Ezra Klein & Derek Thompson, Mar 2025
Goliath’s Curse: The History and Future of Societal Collapse, Luke Kemp, Jul 2025
Oxford Martin School - Oxford University
Oxford Martin School is a leading example of a tertiary institution focusing on a wide range of issues, including an overarching consideration of Artificial Intelligence as it relates to leadership, social policy, health and more: https://aigi.ox.ac.uk/
Section F: Podcasts and Audio Resources
A selection of the books above are summarised in podcasts as below (click to open on your device, or access via this Spotify playlist.
Geoffrey Hinton and the Soul of Artificial Intelligence, Jasper Brinn, Jul 2025
How AI Will Shape Our Future: Understand Artificial Intelligence and Stay Ahead. Machine Learning. Generative AI. Robots. Quantum AI. Super Intelligence, Pedro Uria-Recio, Nov 2024
How To Think About AI: A Guide For The Perplexed, Richard Susskind, Feb 2025
Human and Machine Daugherty and James
Human-Centered AI: A multi-disciplinary perspective for policymakers, 2024, Catherine Régis, Jean-Louis Denis, Maria Luciana Axente, Atsuo Kishimoto
If Anyone Builds It, Everyone Dies: The Case Against Superintelligent AI, Eliezer Yudkowsky, Nate Soares, Sep 2025
The AI- Savvy Leader, David De Cremer, 2024
The AI-fication of Jobs: Preparing Ourselves for the Future of Work, Huy Nguyen Trieu, Nov 2024
The Coming Wave, Mustafa Suleyman & Michael Bhaskar, Sep 2023
The Singularity is Nearer: When We Merge with AI, Ray Kurzweil, June 2024
Understanding the Artificial Intelligence Revolution: Between Catastrophe and Utopia, Shalom Lappin, Jun 2025
Contact Us:
Prof Sen Sendjaya (RMIT) sen.sendjaya@rmit.edu.au
Richard Dent OAM FAICD richard@leadingprogress.au