About KAIVANT · The measurement platform for AI nativeness

Built to measure what actually matters in the AI era.

KAIVANT was built because the right question was not being asked: whether your AI integration is building or depleting the human capability that durable performance depends on. It still isn't. This is our attempt to change that.

The name
kai (改, continuous change)  ·  avant (ahead of the curve)

The discipline of staying in front of AI, so it compounds your advantage instead of quietly hollowing it out.

I The Problem We Set Out to Solve

The gap that existing frameworks cannot close.

Organisations are deploying AI at scale. Most are not measuring what that deployment is doing to the capabilities they depend on. When researchers ask whether AI investment is producing measurable performance outcomes, the answer is consistent across studies, sectors, and geographies: it mostly isn't.

The problem is not implementation. It is measurement. Every major framework in use today measures activity: adoption rates, deployment velocity, tool penetration, automation percentages. None asks the foundational strategic question: is your organisation genuinely AI-native, or only AI-enabled?

The distinction between an organisation that has deployed AI and one that has reorganised itself around AI is the central strategic question of the decade. The difference in long-term performance between those two trajectories is large. And it is not being measured.

KAIVANT was built to measure it.

The adoption gap
69% of organisations actively using AI. 89% report no measurable impact on performance.
The agent gap
97% have deployed AI agents. 29% report measurable ROI.
The measurement problem
Existing frameworks measure activity. None measure whether AI integration is building or depleting organisational capability.
II What KAIVANT Is

A measurement platform with two instruments and a commitment to honest validation.

Organisational Instrument

Kaivant-O

Measures whether an organisation is genuinely AI-native: where its leverage gains are real, where they are brittle, and what changes will produce compound improvement. Nine dimensions across Leverage Architecture, Organisational Capital, and Adaptation Architecture. Non-compensatory scoring means an organisation cannot average its way to a high result.

Individual Instrument

Kaivant-I

Measures whether an individual's AI integration is augmenting their human capability or substituting for it. It is the central question no existing assessment asks. Private by design: the instrument produces a development profile for the individual, not a performance report for their employer. Genuine engagement requires genuine psychological safety.

Conceptual layer

Stable · 10–20 year horizon

Three claims: the two-axis compounding thesis with its non-compensatory scoring, the augmentation-substitution distinction, and the managed loop of governing, finding new value, changing the way the work is done and keeping the gains. It is grounded in established research on how organisations coordinate, develop human capability and sustain motivation, and in years of practice. This layer is designed to remain stable as AI technology evolves.

Indicator layer

Versioned · Reviewed annually

The specific dimensions and indicators through which the conceptual layer is measured. Reviewed annually in dialogue with the practitioner community, with a published version log. The framework can evolve without the intellectual foundation shifting.

KAIVANT is in operational use, in its first phase. The method is practice-proven and research-grounded. Statistical validation from KAIVANT's own data grows with every assessment. We publish what we know and what we don't. See the full validation programme →

III What We Are Building Towards

A field standard. Built openly. Through the people who use it.

The long-term ambition is not a proprietary tool. It is a field standard: the measurement framework that HR leaders, OD professionals, coaches, and management consultants reach for when they need to assess AI nativeness with rigour and honesty.

Field standards are not declared. They are earned through evidence, through practice, and through the community of professionals who test them against real organisations and real people. That is the model KAIVANT is built on.

The Foundation Paper is the intellectual anchor: the research case for why AI-native organisations are structurally distinct from AI-enabled ones, why the difference cannot be measured with existing frameworks, and how the Kaivant Score addresses it. It is published in full and updated as the framework develops.

The practitioner network is the vehicle for applying the framework. The founding cohort are HR leaders, OD professionals, executive coaches, and management consultants. They are the first people to apply the framework in real conditions, challenge what does not hold, and add to the evidence base with every assessment they run.

This is an open process. Where KAIVANT stands is published, and so is the indicator review process. This is what a measurement standard that earns trust looks like.

The intellectual anchor
Foundation Paper (October 2026, free PDF on the foundation page): the research case for the AI-native and AI-enabled distinction and the measurement architecture that resolves it.
The operational vehicle
A founding practitioner cohort building the evidence base through real assessments, structured feedback, and progress published openly.
The commitment
No claim on this site goes beyond the evidence. The method is practice-proven and research-grounded. Statistical validation from KAIVANT's own data grows with every assessment.
IV Who Built It

Designed and built by its Chief Architect.

KAIVANT was designed and built by Michiel R. de Boer, its Chief Architect. His experience spans portfolio, programme, project and risk management, governance, organisational change and transformation, and measuring individual and organisational performance in ways that are both effective and developmental. He has worked internationally in these roles since the early 1990s.

KAIVANT grew out of this mix of practice and study. As AI moved into everyday work, a gap became clear: organisations could show how much AI they were using, but not whether it was making them stronger, or whether it was building their people's abilities or quietly replacing them. KAIVANT was built to close that gap. Its core idea is that being truly AI-native takes two things growing together: what AI is already delivering, and the human capability being built for the future. Strength in one cannot make up for weakness in the other. From this came two assessment instruments, one for organisations and one for individuals, and a validation programme that widens what KAIVANT may claim only as real-world evidence grows and evolves. The thinking behind it is set out in the KAIVANT Foundation Paper.

He is also co-owner of MindMagine, a consulting, coaching and training firm, and founder of Human Essential, an assessment instrument that measures how people think and learn. It shows where their natural strengths become more valuable as AI takes on more of the work around them.

LinkedIn

The team

Jean Yong, Chief Engagement. Jean leads the growth of an international community of partners and Certified Kaivant Practitioners (CKP). Together, they help organisations discover how AI-native they really are and turn that insight into a clear path for AI adoption. Jean brings more than 20 years of building channel partnerships in professional certification training.

LinkedIn

Vicks Kanagasingam, Chief Coach. Vicks designed KAIVANT's go-to-market strategy around one simple belief: technology should always serve human potential. Vicks helps organisations scale their business and empowers transformation practitioners to lead human-centric AI adoption, drawing on decades as a seasoned executive and transformation strategist.

LinkedIn

KAIVANT is a registered company, KAIVANT Sdn. Bhd., based in Kuala Lumpur, Malaysia. KAIVANT® is a registered trademark.

Enquiries, research partnerships & press.

For media enquiries, research partnerships, speaking invitations, or general questions about KAIVANT, reach us directly. We respond to every substantive enquiry.

Start with the Foundation Paper.

The research case for why existing frameworks cannot measure what matters in the AI era, and what the Kaivant Score does differently.