Artificial Intelligence

For several years now, our transformation projects have brought us face to face with a recurring question from our clients: how can we integrate innovation, regulatory compliance, and transformation management into a single system? When it comes to agent-based AI, meetings vary depending on the context, whether with the risk director of a regional bank, the CIO of an insurance group, or the head of ethical AI at a research lab, but they often highlight the same blind spot: ag

How Gabriel Greenfield Achieved CyberVadis Certification and Transformed Itself into a Trusted Digital Third Party. A few weeks ago, a major client sent us a request that we were familiar with in form but had never fully addressed before: to provide a CyberVadis attestation to continue our business relationship. A questionnaire with 32 checks covering governance, data protection, technical security, incident management, and business continuity. For a consulting firm of 15 peo

At Gabriel Greenfield, we work with organizations that have invested years in their data governance. And today, many of them are asking the same question: “Does our data governance prepare us to govern AI?” Yes, but not automatically. The foundations are there. The transition, however, must be built consciously. Here are the 10 pillars of this transition, along with what we’re seeing in practice. ① Data quality → Reliability of production models In traditional data governance

Last week, I attended a training session at the National University of Singapore led by Akina Ho, an executive coach and associate professor at the NUS School of Computing. The topic: how major consulting firms are now incorporating AI interaction tests into their recruitment processes. What I heard really struck a chord with me. It was surprising and made perfect sense. AI hides nothing. It reveals everything. Before AI entered the recruitment process, an interviewer had to

A few weeks ago, I asked Claude a question: “Give me a sector benchmark analysis on the digital maturity of asset management firms in Hong Kong.” Within four minutes, I had forty pages of well-structured, properly cited content, complete with recommendations. This work would have kept a junior consultant busy for a week. And honestly? The result was usable and actionable. I run a consulting firm specializing in AI transformation. I could have found this threatening. Instead,

A financial firm decides to transform its back office using agent-based artificial intelligence. The use cases pile up on the slides: automatic data extraction from documents, automated process execution, intelligent business controls, anomaly detection in data flows, algorithmic prioritization... The stated goal is ambitious, and it would be tempting to view compliance as a millstone we can drag along tomorrow (but not right now, please). This is a strategic mistake, as perf

80% of executives believe that agent-based AI will be critical to their company’s survival by 2027 (Cisco/Omdia, 2025). However, Deloitte reports that only 11% of organizations are actually using it in production, and Gartner predicts that more than 40% of agent-based projects will be abandoned by the end of 2027. The gap is not technological. It is one of governance. Agent-based AI: A Paradigm Shift That Demands a Commensurate Response AI agents are systems capable of planni

Let’s not build AI applications. Let’s build applications designed for the journey toward AI. Rome wasn’t built in a day, and neither is the deployment of AI in business. We are paralyzed by a sense of awe that promises us the next big wave, and we run around like headless chickens waiting for the ultimate disaster: the takeover of the workforce by AI-driven automation. In this way, we think in terms of disruption, when there is frankly no reason not to think in terms of tran

Not everything is AI. And that’s precisely what makes AI so powerful. AI doesn’t replace your applications. It enhances them. For the past two years, there has been a persistent misconception among senior management and CIOs: the idea that artificial intelligence is a replacement technology. That applications need to be rebuilt, systems reimagined, and everything started over from scratch to “make the switch to AI.” That’s not what we’re seeing. That’s not what we’re building

There is a ceiling built into every self-improving AI system ever designed. A philosophical one. The mechanism that drives the improvement is written by humans. It is fixed, does not evolve, and no matter how far the system climbs, it climbs within a cage designed by its own architects. A research team from Meta FAIR, the University of British Columbia, NYU, the University of Edinburgh, and the Vector Institute just published a paper that tries to break that cage. They call t