Loading
Thoughts on technology leadership, strategy, and digital transformation
The productivity asymmetry between senior and junior engineers under AI tooling creates team dynamics most engineering managers weren't trained for. This is primarily a management question, not a tooling one.
AI coding tools genuinely shift what engineers can do. But the productivity gains aren't free — they move costs rather than eliminate them. Here's an honest accounting.
Board-level AI questions have shifted from capability to accountability. Most technology leaders aren't ready for them. Here's what the questions actually are and what a credible answer sounds like.
The main barrier to AI adoption isn't the models or the tooling. It's the data infrastructure underneath. How you sequence that work determines what's actually buildable.
Agentic AI is changing how infrastructure incidents get handled — from reactive alerts to autonomous remediation. What this shift actually means in practice, and what it doesn't solve.
AI agents that respond to incidents autonomously are moving from experimental to mainstream. This post looks at what actually changes for operations teams — and what doesn't.
AI tools have made developers faster. That is mostly a good thing. But with more code shipping faster, the operational surface area expands in ways that most organisations are not ready for.
DevOps was built for deterministic code. LLMs are probabilistic. AI agents are autonomous. The same playbooks that worked for CI/CD are not sufficient for what is being built now.
DevOps was built for deterministic code. AI agents are probabilistic and autonomous. The same playbooks that got teams from waterfall to CI/CD won't get them from CI/CD to agentic — and it's worth understanding why.
When deployment velocity doubles, traditional compliance gates become the bottleneck — or they get skipped. Policy as Code is the pattern that resolves this tension, but it requires more than a tool change.
When AI tools increase development velocity, traditional compliance gates struggle to keep up. The more durable answer is shifting compliance left — making non-compliant deployments technically difficult rather than policy-prohibited.
As AI tools make it easier to write and ship code, the operational surface area grows faster than most teams expect. Platform engineering is how you stay ahead of it — but it's often deprioritised until the cost becomes obvious.
Companies often have the right ideas about what to build. Where things go wrong is the order — addressing the wrong layer first, or solving a future problem before the present one is settled.
What does effective technology leadership look like today? An exploration of the evolving CTO role and how strategic advisors help companies navigate critical technology decisions.