About
Arjen Hendrikse
Founder, Aivance Consulting · Singapore
Founder Perspective
My entry point into AI was not a boardroom or a consulting project. It was a wheelchair.
In the 1990s, during an internship at the University of Adelaide in Australia, I worked on research exploring how neural networks could be used for voice recognition to control wheelchairs. The objective was to give people with physical disabilities greater autonomy over their mobility. Neural networks in the 1990s were a research frontier, not an engineering tool. Getting the system to function at all was uncertain. Getting it to function reliably enough to hand control of a wheelchair to someone with a physical disability was a different problem entirely. When technology controls something as fundamental as movement, reliability and accountability are essential.
That experience shaped how I think about technology. When a system controls something consequential — mobility, financial decisions, access to services — reliability is not a feature. It is the non-negotiable. The system must be capable of stopping. It must have a hard limit that cannot be overridden by optimistic assumptions about what will probably happen. It must be designed to fail safely, not just to succeed frequently.
That principle has stayed with me throughout more than thirty years in technology. It is also the principle that most AI governance programmes are currently missing.
Career
Most of my career has been spent working with enterprise platforms and large-scale digital infrastructure. From 2015 to 2024, I served as Director of Advanced Consulting Services for the Asia-Pacific and Japan region at Akamai Technologies.
In that role I worked closely with enterprises across the region on complex technology challenges involving security, performance, and operational risk. Large digital systems carry significant responsibility. Decisions about architecture, governance, and operational controls often determine whether technology strengthens an organisation or exposes it to new risks.
Earlier in my career I worked at Ericsson, Nagravision, and Nokia/Alcatel across the full lifecycle of enterprise technology in the region, from sales engineering and delivery through to operations and strategic advisory.
Why I Founded Aivance
Over the last few years I kept seeing the same pattern across enterprises deploying AI in Singapore and Southeast Asia.
Organisations were investing seriously in AI adoption and taking governance seriously in the way they knew how: they hired consultants, built policy documents, established oversight committees, and referenced the right frameworks. The documentation was often good. What was almost always missing was the enforcement layer underneath it.
Policies that describe what humans should do are not controls. Dashboards that show you what happened after execution are not oversight. Committees that review reports are not the same as mechanisms that halt decisions. Most AI governance programmes I encountered had the language of governance without the architecture of governance. The documentation would satisfy a compliance question. It would not survive a real incident.
I founded Aivance in the first half of 2025 to build something different: a firm that operates at both the policy layer and the enforcement layer. The technical background matters here. Most governance consultants come from legal, risk, or policy backgrounds. I come from enterprise infrastructure. I understand what it means to build a control that is technically real, not just procedurally described. That is the specific gap Aivance is built to close.
Credentials and certifications
Community
I mentor founders through the NUS National GRIP programme, where I work with early-stage companies navigating the intersection of technology, regulation, and scale.
Why Aivance
The name comes from "AI" and "advance" — the belief that AI governance is not a constraint on progress but a precondition for it. Organisations that build trustworthy AI infrastructure are not slower than those that skip it. They are more defensible, more credible, and better positioned for the regulatory environment that is coming.
Most governance consultants operate at the policy layer. Aivance operates across both. The technical background means enforcement controls can be specified, assessed, and built — not just described.
AI adoption structured around regulatory readiness, risk management, and enforcement architecture from the outset. Governance retrofitted after deployment is harder, slower, and more expensive.
Nine years at Akamai across APAC, plus an earlier senior role at Ericsson. An understanding of how large organisations actually build and operate technical controls under operational pressure.
Every engagement produces specific, auditable deliverables — enforcement architecture maps, override authority matrices, Suspended Handoff State definitions. Not slide decks of principles.
The AI Governance Gap
The governance gap most organisations face is not a documentation problem. They have policies. They have oversight committees. They reference the right frameworks. The gap is between what their governance documents say will happen and what their systems are actually capable of enforcing. When an AI makes a consequential decision in flight, is there a mechanism that can halt it? When a risk threshold is breached, does the system demand human ratification before execution clears? For most organisations, the honest answer is no. That is the specific problem Aivance was built to solve.
Who We Work With
Thirty years of building systems that have to work.
The lesson from that work is that governance without enforcement is just documentation. If you are looking for a partner who operates at both the policy layer and the enforcement layer, the AI Governance Review is the right starting point.
Book Your Free Governance Review