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Thinking on Data, AI & what comes next.
Practical perspectives from the team building production AI systems. No hype. No fluff. Just what’s actually working.
Latest articles
Skill Engineering: getting results on real tasks
A skill is knowledge plus tools wrapped around good prompting. One person building one for themselves is where this gets real. The moment it is meant to be shared, the requirements change, and knowing where that line falls saves a lot of wasted effort.
Prompt Engineering: how prompting still impacts results
The job title had a short life. The skill did not go with it. It is a practised skill that keeps changing as the models do, and the gap between someone who has put the time in and someone who has not is wide.
The skills that make or break AI impact
Whether you are hiring one person or planning how the whole organisation is resourced, the people who get value out of AI are not the ones you would expect. Three capabilities decide it, and none of them are technical.
Rethinking who does what: the changing role of humans in business decisions
AI isn't replacing business judgement. It's freeing it up to actually be used.
Why most AI agent systems fail in production, and what to do instead
Everyone's building agents. Almost nobody is running them reliably at scale. After deploying production multi-agent systems across a dozen client environments, we've found the same failure patterns appearing again and again.
Your data is ready. Your AI is ready. So why aren't you getting value yet?
Most businesses aren't short on data, and they're not short on AI tools. They're short on a clear, fast path from “we have all of this” to “we're actually doing something with it.” Here's where that path usually breaks, and how we've built Nova to close it.
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