Tagged: ai
12 posts
When the Architect Picks Up the Hammer
Something shifts when a senior technologist stops directing software to be built and starts building it himself, with AI as the pair. After four decades in software and twenty years away from the keyboard, the distance between intent and implementation finally collapsed. This is what that felt like, and what it revealed about experience, craft, and the future of building
The Decline of the Org Chart
For most of the last century, the challenge of organizing large groups of people around shared work produced a consistent answer: the hierarchy. Not because anyone designed it to be optimal, but because it solved five distinct coordination problems at once, and nothing simpler could do the same. Those problems are now separable. The structure built to solve them all at once is beginning to come apart, unevenly and in ways most commentary misses.
[Podcast] WBSP482: Grow Your Business by Learning Why Data Consolidation is Critical for AI and ML Initiatives w/ Claus Jepsen
AI is changing the world. You have an infusion of AI at every step in the process, whether you talk about the first mile or the last mile. Whether you talk about AI being used to intelligently and automatically capture paper-based invoices or to enrich, augment, and predict incomplete data.
The Return of the Builder
For decades, the software field grew by scaling the work of converting specifications into code. AI has now moved into that role. What remains, and what the field is reorganizing around, is the capacity that was always harder to scale: knowing what to build and why.
When Coding Becomes Cheap, Experience Becomes Rational Again
For decades, software teams faced a trade-off: build the best experience, or build something affordable. Native applications offered quality and performance, but they were expensive. So the industry optimized for economics. The web and cross-platform frameworks were good enough, and good enough won. That constraint is now shifting. With AI-assisted development, implementation is becoming cheap. Machines can generate working software quickly. What they cannot generate is clarity. Intent, structure, domain models, and behavior are now the real bottlenecks.
The Why Layer: Why Intent Is the Missing Infrastructure of Enterprise Software
Code tells us how a problem is solved. It rarely tells us why. As AI-assisted development accelerates, the gap between behavior and intent is becoming an architectural risk, and closing it requires treating shared intent as infrastructure.
AI and the post-modern, modern data stack
of AI-powered applications and agentic AI is dramatically redefining the demands placed on data stacks. Rather than becoming irrelevant, the modern data stack is a major consideration as organisations…
Want to become a CTO? Here’s the hard truth
For anyone aiming to become a Chief Technology Officer (CTO) that means working out how to build the technical, strategic, and leadership skills to stay relevant and in demand throughout their career.…
AI For Business: Focus On The Steak, Not The Sizzle
Many of us love sharing amusing AI-generated images with our friends. But are you willing to trust AI with your core business? More people, I've observed, are waking up to the costs and risks. Whoops…
Ambient ERP – breaking free of the enterprise software triad
Imagine a future where your back-office applications are context-aware, operating in the background, only surfacing what matters when it matters. Thanks to progress being made in artificial intelligence (AI) this is no longer a fanciful dream.
Self-driving ERP systems will end digital drudgery, halving the number of tasks that need manual interventions
Agentic AI is attracting a lot of attention for its ability to behave autonomously and render complex, multi-step processes into seamless wholes by joining up data from multiple sources.
The Philosophy of Self-Driving Enterprise Software – Rethinking How We Build Software
Enterprise systems should support people, not the other way around. Technology should extend human capability, not consume it. For too long, software has demanded that people conform to rigid structures, turning productivity tools into digital bureaucracy. AI-supported systems can reverse this. By operating on context rather than sequence, and relevance rather than rigid workflows, they reduce cognitive load and return time, focus, and agency to the people doing the work. This is not just about efficiency. It is about respecting human judgment, preserving meaning, and building systems that adapt to real work rather than forcing work to adapt to systems