The MIT Center for Information Systems Research (MIT CISR), led by Peter Weill and Stephanie Woerner, recently released a major update to its renowned digital business model framework. Based on an analysis of 2,378 companies studied between 2013 and 2025, this new framework is no longer “digital”: it is AI-native. It is based on two simple axes: action along the value chain (assisting vs. representing the customer) and business execution (structured vs. adaptive), which defin

The role of the leader has never been more paradoxal. On the one hand, generative artificial intelligence, autonomous agents, and real-time data platforms are automating entire areas of cognitive work. On the other, McKinsey noted in January 2026 that aspiration, judgment, and creativity remain “only human” traits, and that an organization’s sustainable competitive advantage will depend less on its algorithms than on the quality, authenticity, and accountability of its leader

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,

Today, the average large enterprise runs 2,191 applications and has no idea what half of them do (Torii, 2026). Forty-eight percent of enterprise applications are unmanaged (Productiv, 2024). Shadow IT accounts for between 30% and 40% of IT spending (Gartner). This is not a governance crisis. It is a strategic opportunity that few CIOs have yet seized. The application portfolio: the blind spot of IT performance For years, the priority was delivery. Delivering projects, deploy

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

88% of business transformations fail to meet their initial goals (Bain, 2024). Organizations invest millions, mobilize teams, hire consulting firms… and end up back where they started. What if the problem isn’t the transformation itself, but the way it’s designed? Transformation is no longer an option Any company that waits for ideal conditions to transform is waiting for its own obsolescence. Markets no longer give advance warning. AI is reshuffling the deck in a matter of q

In 2025, 78% of enterprise architects considered AI to be their top strategic priority. A year later, it is no longer a priority. Welcome to the era of learning architecture. Traditional enterprise architecture is dead. Long live intelligent architecture. In 2026, organizations are adopting AI-driven architectures capable of adapting, learning, and evolving autonomously. Next-generation EA platforms, including that of our partner Ardoq, now natively integrate predictive analy

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

At a time when every organization wants to “get into AI”, the question is no longer about having access to the right tools. It’s about having the structure in place to turn that access into lasting value. A simple observation Most organizations embarking on AI transformation share the same starting point: a patchwork of initiatives with no common direction. An LLM project here, an RPA initiative there, experiments underway in several different directions at once. Each team mo

Transformation is no longer a project; it is an ongoing process Leading consulting firms are unanimous: organizational transformation is no longer a one-off project that you start and then wrap up. It is a constant requirement of the modern business. PwC puts it plainly: when people, processes, and infrastructure are aligned, growth, ROI, and agility follow naturally. But when this triad is out of sync, the cost of a failed transformation is measured in lost market share, ero

The National University of Singapore (NUS) has developed a framework to identify the four major waves of transformation and gain a clear understanding of technological evolution in businesses. It follows a logical progression: from tools to processes, then to raw data, and finally to autonomous intelligence. Here is an explanation of these waves and an analysis of why certain critical concepts, such as sovereignty or cybersecurity, are not classified as “waves” of transformat

What economist and Nobel laureate Philippe Aghion teaches us about creative destruction in the age of artificial intelligence explains why organizations need to appoint a Chief Transformation Officer. Philippe Aghion is one of the most influential economists of our time. A professor at the Collège de France, the London School of Economics, and INSERM, he has devoted thirty years of research to an idea as simple as it is unsettling: prosperity arises from destruction. His work

Context As part of a Data Science/Artificial Intelligence project, Gabriel Greenfield is supporting a client on renewable energy projects. To date, our work has focused on solar energy (photovoltaic) projects and purely technical projects (statistics, software development) within the client's organization. Our Achievements Automated daily calculation of metrics (e.g., average sensor temperature) at a photovoltaic site Creation of a module for detecting outliers in solar data