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Over the years, my work spread across many platforms. Blog posts, articles, talks, technical notes, experiments. Some published under company banners, some on personal sites, some simply gone. That fragmentation started to bother me. Not emotionally — architecturally. Platforms change. Companies rebrand. Services shut down. Links decay. If your work depends on someone else’s platform, it is fragile by default. So I chose a different approach. This site is not “another blog”. It is my personal digital archive, intentionally built to remain accessible beyond platforms, trends, and tooling choices. ...

Data and AI platforms are at the heart of digital transformation. But too often, they are built on proprietary formats and closed models that create silos, raise costs, and limit flexibility. In a world where organizations need interoperability, sovereignty, and speed, this approach is no longer sustainable. Open standards offer a better path forward. From Lock-In to Openness Data and AI platforms used to be closed worlds. Proprietary formats, vendor-specific connectors, and black-box models accelerated adoption, but at a cost: lock-in, high switching barriers, and reduced sovereignty. Once data was stored in a proprietary format or models were trained in a closed environment, moving them became difficult and costly. Enterprises often found themselves redesigning workflows, rewriting code, or even discarding valuable assets when switching vendors. ...

We’ve professionalized technology to the point where we’ve lost touch with it. In many organizations, technology leadership has turned into governance, reporting, and compliance. The intent was good: control risk, manage complexity, scale globally. But in doing so, many organizations created a gap between leadership and the actual technology. Decisions are now abstracted behind PowerPoint decks and KPIs instead of grounded in code, infrastructure, or architecture. When governance replaces guidance, you lose speed, context, and credibility. The result is a leadership layer that knows how to measure delivery but not how to drive it. ...

The role of the architect is being rewritten. Not by new frameworks or methodologies, but by intelligent systems that can design, optimize, and learn on their own. As AI agents, autonomous workflows, and generative tools become part of daily operations, architecture itself becomes dynamic. It’s no longer a static blueprint. It’s a living system that continuously adapts and improves. From Control to Coordination For years, architecture has centered on control: standards, reviews, governance. But in an agentic world, control alone is not enough. AI systems generate code, detect anomalies, and make real-time adjustments faster than any process can document. ...

For years, data platforms were designed around people. The main goal was to provide humans with dashboards, reports, and analytics so they could make better decisions. But this model is being reshaped. With the rise of AI agents, data platforms are entering a new era—one where the consumers of data are not only people but also autonomous systems. And here’s the important part: these agents don’t just consume. They also produce. They generate enriched datasets, annotations, and continuous insights. This dual role changes how we need to think about architecture, governance, and collaboration. ...

Over the past few years, we’ve seen organizations experiment with the concept of an AI Factory—a structured way to industrialize the development and deployment of AI. At the same time, data leaders have embraced the idea of data products and data fabrics to make data more reusable, governed, and available at scale. It’s time to bring these two together! From Projects to Production Lines Think of how manufacturing evolved: from craft workshops to assembly lines, where processes were standardized and repeatable. We are witnessing a similar shift in data and AI. Instead of bespoke projects—each with its own tools, pipelines, and governance—organizations are moving toward industrialized AI + data factories. ...

We’re entering a phase where engineering is less about writing every instruction and more about teaching systems how to think. The shift is clear: from procedural to declarative. From “write every line” to “explain the outcome.” From “code it” to “coach the model.” This isn’t just about GenAI. It changes how we design, build, and operate systems when AI becomes part of the team. Code Is No Longer the Core Output Most engineering teams still measure productivity by the amount of code shipped. But with AI-generated code, low-code platforms, and autonomous agents, raw output is no longer the right metric. ...

Artificial Intelligence isn’t slowing down. In fact, it’s speeding up — reshaping how we work, how decisions are made, and how businesses compete. But while the technology keeps evolving, leadership often struggles to keep up. And that’s where the real risk lies. If you’re in a leadership role today — especially in tech, strategy, or operations — you can’t afford to treat AI as a distant future trend or a side experiment. It’s already here. The question is: are you moving fast enough to lead with it? ...

Every company wants to move fast with AI. The real challenge isn’t adoption—it’s making sure innovation is guided by responsibility and strong governance. The question is not whether to adopt AI — it’s about doing it right. AI governance helps organizations find the balance between driving innovation and managing risk. It gives you the structure to build solutions that serve your business and your customers, without losing control. The Innovation Challenge Speed matters. Companies that apply AI to predictive analytics, automation, and personalization are gaining an edge — whether it’s a hospital improving diagnosis or a bank detecting fraud in real time. ...

Many organizations still treat AI like it’s experimental. A side project. A shiny object in a lab that only a few experts are allowed to touch. That mindset is holding them back. If you want AI to drive real impact, it needs to move out of the lab and into the engine room of your organization. That’s what the AI Factory is about. Not experiments, but execution. Not proofs of concept, but products. ...

AI and Web3 are often discussed as separate trends. But the way they’re starting to influence each other is where the real potential lies. For organizations building digital strategies, understanding how these technologies interact is becoming more important by the day. This isn’t about chasing hype — it’s about practical ways these two technologies can address each other’s current limitations and open up new business models. Web3 Can Help Solve AI’s Trust Problem Trust is one of the biggest blockers for AI adoption. How can we prove that an AI model works as intended? How do we know decisions haven’t been tampered with? ...

We’re entering the agent era. Is your team ready? Large Language Models have already changed how we interact with data. But the rise of agentic AI — systems that reason, take action, and adapt — goes even further. These agents don’t just answer questions. They perform tasks, orchestrate workflows, and make real-time decisions. That changes everything for data & AI teams. If you’re still focused on dashboards, isolated models, or batch pipelines, you’re building for the past. ...

The pace of technology change hasn’t slowed down. In fact, it’s accelerating. Every week, new solutions are pitched that promise to transform your business, disrupt your industry, or unlock new revenue streams. For CxOs, this creates both opportunity and risk. It’s easy to get caught up in the latest buzzwords, but with budgets tighter and delivery pressure higher, making smart choices is critical. So what’s real in 2025 — and what’s still more noise than substance? Here’s my radar view, based on what I’m seeing across industries. ...

Automated Data Integration and the Future of ETL Data is one of the most valuable assets for any organization. But turning raw data into useful insights is still a challenge. Traditional ETL (Extract, Transform, Load) processes are slow, rigid, and resource intensive. They require manual coding, monitoring, and maintenance. That’s not scalable in today’s data-driven world—especially with the speed and complexity of modern applications. This is where generative AI steps in. ...

In today’s data and AI era, organizations are facing a lot of complexities to become data driven. They are facing issues such as data silos, lack of collaboration between business and IT, high maintenance costs, increasing data privacy concerns and governance needs. However, in today’s digital economy, data is the new oil. For organizations to remain competitive, relevant, and capable of rapid innovation, it is key to effectively use this extremely valuable resource. It helps to better understand the market and the competition, it improves processes, it helps to give more insights into performance, and ultimately make better decisions. Data is a vital asset that shapes business strategies, drives innovation, and provides a competitive edge in today’s dynamic economy. ...

Digital organisations work in new ways that require us all to adapt and develop new skills and framing and making sense of the world. How to tackle this is one of the more difficult challenges requiring addressing in a digital transformation. Dave, Sjoukje & Rob talk with Micheal Hamman, Teacher and Coach, about different styles of organisation and about how our beliefs can impact how we act. He also talks about the over emphasis on the engineering mindset, how we need to consider the human technologies and how all of our "human operating systems" may require some new apps. ...

Conversations around the short and long term risks and potentially significant unintended consequences of AI are increasing in volume. This has culminated recently in a controversial open letter, coordinated by Future of Life institute, from hundreds of leading figures in this space, including Elon Musk, asking for development and AI learning to be paused, until it is better understood. “Advanced AI could represent a profound change in the history of life on Earth, and should be planned for and managed with commensurate care and resources,”the letter says.“Unfortunately, this level of planning and management is not happening, even though recent months have seen AI labs locked in an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control.” ...

Digitalisation in an inherently complex activity, with unknowns being very high and experimentation required on an ongoing basis - there is no ‘once and done’. The changes in leadership framing, decision making and ways of working required are hugely under estimated in discussions and planning of ’transformation’ and failure to engage with that could risk your success. Dave, Sjoukje & Rob talk with Dave Snowden, Director and Founder of the Cynefin Centre about his work on making sense of complexity, they discuss Dave’s seminal work, the Cynefin Framework, how he has subsequently built on that thinking and how you apply that it in the digitalisation process of your organisation. His insights are not to be missed. ...

AI usability and adoption continues to rapidly accelerate in 2023, but it is important to understand the risks to your business from mis-using AI and best practices for evaluating the right AI tools for the right jobs. Dave, Sjoukje & Rob talk with Michelle Zhou, Founder and CEO of Juji, about her history and take on the progression of AI over the last 20 years, why democratisation of AI is both increases the safety and precision of AI, as well as likely driving the right and best human experiences. Also, how do you nurture your AI?! ...

Platform engineering is a core part of modern digital operating models. The discipline has been developing over the last 10 years and provides underlying safe, secure and optimised platforms, essential for creative and customer centric innovation. Dave, Sjoukje & Rob talk with Jennifer Riggins, Tech Culture Author about what good platform engineering looks like, why conditions today could lead to good platform engineering practices, how platforms help organisations innovate and deal with technical complexity, how it leads to strong DevOps implementation and how it helps deal with a major issue in tech today, developer cognitive overload. ...