Love, Leadership and AI

What this isWat dit is

A research-led essay arguing that AI makes love more structural in leadership: a disciplined commitment to the dignity, agency and flourishing of the people affected by technological decisions.

ContextContext

A 12-minute flagship essay in the AI & Human Agency series. It reframes AI transformation through human possibility, combining workplace research, moral traditions, an original decision framework and practical leadership commitments.

Role:Rol: Author

Format:Formaat: Interactive essay and LinkedIn edition

ImpactImpact

Created as a companion and counterpoint to Fear, Leadership and AI, moving the conversation from what technology may replace to what responsible leadership can help people become.

A different starting point

AI does not make love less relevant. It makes love more structural.

Fear asks what AI will replace. Love asks a harder question: what will our choices enable people to become? Fear has a legitimate place in leadership. It can alert us to harm, concentration of power, bias, surveillance and loss. But fear is a signal, not a complete operating system. If it becomes the sole lens through which we approach AI, every person starts to look like a cost, every task like a candidate for removal and every hesitation like resistance.

Love begins elsewhere: not with whether a machine can perform a task, but with what work is for; not with what can be extracted from people, but with what can be cultivated in them; not only with productivity, but with dignity, agency and belonging.

Love in leadership is the disciplined commitment to the dignity, agency and flourishing of the people affected by our decisions. This is not romantic love, softness or a licence to avoid standards or conflict. It is care made operational: attention before assumption, voice before imposition, boundaries before scale, and responsibility after deployment.

As machines mediate more of our writing, judgement, hiring, service and coordination, love cannot remain a private feeling. It must become a design principle.

• Not niceness: love can deliver an unwelcome truth and hold a demanding standard.
• Not consensus: love listens seriously, then accepts responsibility for the decision.
• Not sentiment: love changes power, process, time, incentives and who benefits.

The leadership question

We should not ask only, “What can AI do?” We should ask, “What becomes possible for people because it can?”

Capability tells us what is possible. Love asks what is worth making possible.

Five human qualities

The more work is mediated, the more leadership must notice what mediation hides. AI can extend capability. It cannot inherit our moral responsibility.

Five Human Qualities framework — Daphne Iris van Vliet, 2026.

These qualities matter not because machines can never imitate their surface, but because humans remain accountable for their meaning, use and consequences.

• Attention — See the person the system compresses. Ask what the aggregate conceals and make time to hear the outlier. Its counterfeit is personalisation without relationship.
• Discernment — Know when the answer is not the judgement. Weigh evidence, values, timing, uncertainty and who carries the cost. Its counterfeit is confidence mistaken for wisdom.
• Imagination — Ask what work could become. Use saved capacity to redesign the experience, not only increase volume. Its counterfeit is a faster version of inherited assumptions.
• Moral courage — Keep a boundary when scale rewards surrender. Define in advance what evidence would pause or end a use. Its counterfeit is ethics language without a stopping rule.
• Mercy — Leave room for context, repair and return. Create humane appeals, repair harm quickly and learn without humiliation. Its counterfeit is exception handling that still protects the system.

Evidence, carefully read

Technology changes work. Leadership shapes how the change is lived.

The evidence does not prove that “love improves AI.” It supports something more precise: participation, trust, skill and job design affect whether AI adoption improves human experience as well as output.

The ILO’s 2025 global index concludes that job transformation is the most likely broad effect of generative AI, while outcomes depend on policy and workplace choices.¹

OECD research on algorithmic management suggests that worker consultation and participation can support both organisational performance and job quality.² Research from Japan similarly identifies relationships between AI use, performance and aspects of job quality, while cautioning against broad generalisation from national evidence.³

In a field study of customer-support work, generative AI increased average productivity, with larger gains among less experienced workers—evidence that AI can expand capability, although the results should not be treated as universal.⁴

The World Economic Forum’s employer survey places leadership and social influence, empathy and active listening, and curiosity alongside fast-growing technical skills.⁵

These sources use different samples and methods. Employer expectations are not forecasts, national findings do not automatically generalise, and association is not causation. The case for love is ethical; evidence helps us see where humane practices can become operational.

Deep roots, living practice

Long before AI, we were warned not to confuse capability with purpose. Love is not a fashionable addition to technology ethics. It belongs to older accounts of what a person is and what power owes them.

In Islamic thought, niyyah asks what intention animates an action;⁶ ihsan calls for beautiful, excellent conduct beyond compliance;⁷ and the Qur’an’s honouring of the children of Adam grounds dignity before performance.⁸ Islamic ethical vocabulary also places mercy—rahmah—alongside intention, excellence and human dignity.

In Jewish thought, b’tzelem Elohim places worth before utility.⁹ Martin Buber’s I-Thou and I-It distinction warns how easily relation becomes use.¹⁰ Chesed is not mood but faithful action within relationship.¹¹

The ethics of care makes this organisational. Joan Tronto describes care as noticing need, taking responsibility, acting competently and responding to what happens.¹² Organisational research on compassion similarly treats care not merely as individual feeling but as something workplaces can notice, organise and enact.¹³ Erich Fromm treats love as care, responsibility, respect and knowledge.¹⁴ Love without power analysis becomes sentiment; power without love becomes optimisation.

• Ask not only whether the system works, but what purpose the work serves.
• Do not reduce excellence to speed when care requires patience.
• Design for the person with the least power to absorb an error.
• Never let the profile become more real than the person.
• Preserve encounters in which people can answer back.
• Judge systems by how they treat the stranger and the exception.

The Human Possibility Test

Choose a familiar AI decision, then assess its effect on the people living with it. The goal is not a perfect number. It is a better conversation before deployment.

The Human Possibility Test — original framework by Daphne Iris van Vliet, 2026.

Use the test for scenarios such as AI meeting summaries, performance recommendations or customer-response drafting. Score each dimension from reducing human possibility to expanding it. The result should guide inquiry, not create a verdict.

• Agency — Can people shape, contest or opt out?
• Dignity — Are people treated as persons, not inputs?
• Attention — Does the design create presence or merely more throughput?
• Growth — Does it deepen judgement and capability?
• Voice — Were affected people involved?
• Shared benefit — Who receives the time or value created?

Care becomes credible when it changes the decision

A human in the loop is not enough if that human has no time, authority or safety to disagree.¹⁵

Leaders do not prove love by describing their intentions. They make it visible in who is consulted, what is measured, where discretion remains, how errors are repaired and whether a system is stopped when its human cost is too high.

• Listen before you automate — Map invisible expertise, workarounds and vulnerabilities with the people who do the work.
• Return some of the gain — Decide whether time saved becomes recovery, learning, better service or simply more demand.
• Protect the right to question — Provide a legible route to challenge recommendations, correct data and reach a responsible human without penalty.
• Keep judgement alive — Design roles so people still practise discernment when the system is uncertain, novel or wrong.
• Measure human consequences — Track workload, autonomy, skill, trust and distribution, not only adoption, speed and cost.
• Refuse some uses — Some decisions should not be automated, some data should not be collected and some efficiencies are not worth their price.

Why I believe this

AI has expanded my own field of possibility. It has helped me write, work across limitations and connect ideas that matter to me, including a family legacy I wanted to carry forward.

That experience makes me hopeful. It also makes me unwilling to accept the thin version of AI transformation: one in which capability rises while people shrink.

The best technology does not make us less necessary to one another. It can give us more room to notice, imagine, learn, repair and create. Whether it does so is not a property of the model. It is a choice expressed through leadership.

AI cannot read a prayer, measure mercy or take moral responsibility for what happens next. We can.

Build systems that leave us more human

The measure of AI leadership is not simply how much work the technology can do. It is whether people retain the dignity, agency and capacity to become more fully themselves because of the way we chose to use it.

The more capable our systems become, the more valuable attention, discernment, imagination, moral courage and mercy become.

AI can extend our capability. It cannot inherit our responsibility.

LinkedIn edition

What human qualities become more important as work becomes more AI-mediated?

Fear asks what AI will replace. Love asks a harder question: what will our choices enable people to become?

By love, I do not mean sentiment, softness or the avoidance of difficult decisions. I mean the disciplined commitment to the dignity, agency and flourishing of the people affected by our choices.

As AI mediates more of our writing, judgement, hiring, service and coordination, that commitment must become structural.

The more capable our systems become, the more valuable attention, discernment, imagination, moral courage and mercy become.

AI can extend our capability. It cannot inherit our responsibility.

The question is not simply how much work technology can do. It is whether people retain the dignity, agency and capacity to become more fully themselves because of the way we chose to use it.

Capability tells us what is possible. Love asks what is worth making possible.

• Listen before automating.
• Preserve people’s ability to question and contest.
• Return some of the time and value technology creates.
• Keep human judgement alive.
• Measure autonomy, trust and skill—not only speed and cost.
• Refuse uses that diminish dignity, even when they are technically possible.

Sources and limitsBronnen en grenzen

1. Global labour research: ILO — Generative AI and Jobs: A Refined Global Index of Occupational Exposure — 2025; exposure is distinguished from replacement. ↩
2. Workplace research: OECD — Exploring win-win outcomes of algorithmic management — 2025; participation, productivity and job quality. ↩
3. Workplace research: OECD — AI, performance and job quality in Japan — 2025; national findings should not be generalised automatically. ↩
4. Field study: Brynjolfsson, Li and Raymond — Generative AI at Work — Context-specific evidence on generative AI and worker performance. ↩
5. Employer survey: World Economic Forum — Future of Jobs Report 2025 — Directional employer expectations, not a forecast. ↩
6. Islamic source: Sahih al-Bukhari 1 — Actions and intentions — The ethical significance of intention. ↩
7. Qur’an: Qur’an 16:90 — Justice, excellence, generosity and restraint. ↩
8. Qur’an: Qur’an 17:70 — Human dignity and the honouring of the children of Adam. ↩
9. Jewish source: Genesis 1:27 — B’tzelem Elohim, the human being in the divine image. ↩
10. Philosophy: Stanford Encyclopedia of Philosophy — Martin Buber — I-Thou and I-It relations. ↩
11. Jewish source: Micah 6:8 — Justice, chesed and humble walking. ↩
12. Ethics of care: Joan C. Tronto — Caring Democracy: Markets, Equality, and Justice — 2013. ↩
13. Compassion at work: Monica C. Worline and Jane E. Dutton — Awakening Compassion at Work — 2017. ↩
14. Philosophy of love: Erich Fromm — The Art of Loving — 1956. ↩
15. AI governance: European Commission High-Level Expert Group on AI — Ethics Guidelines for Trustworthy AI — Human agency and oversight as requirements for trustworthy AI. ↩

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Use the Human Possibility Test before an AI deployment: assess its effect on agency, dignity, attention, growth, voice and shared benefit.

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