Part 1 of a Series. AI Adoption Is Not the Problem. Thinking Is. Last week, I sat in on a discussion that, at first glance, looked like things were working exactly as they should. A team had just reviewed a summary of customer feedback generated using AI, and the output was everything you would expect from a well-trained professional. It was structured, balanced, and written in a tone that felt measured and appropriate, the kind of summary that makes meetings move faster beca

What the numbers show us Look at the chart and the story seems straightforward: Singaporeans eat a lot of chicken — far more than pork, and dramatically more than beef. Consumption has also been trending upward over the decades. Did you know that Singapore consumes an average of 40kg of chicken per person each year? 23 kg of pork per person each year 7 kg of beef per person each year Key insight #1 - Data tells us the "What" and "How much" The chart gives us a clear picture o

I started this on Friday. It’s now Tuesday, 4pm. All I needed to do was extract 849 records from a 53-page PDF. No analysis. No modelling. Just getting structured data into Excel. It sounded like the perfect use case for AI, so naturally I used it. My report was about claims so accuracy is of upmost importance. At the beginning, it actually felt promising. The outputs looked structured, almost usable. It felt like I was just one step away from getting this fully automated. If

A lot of the loudest commentary about AI and work starts with the same assumption: AI will replace jobs. A more useful starting point is simpler (and less dramatic): AI replaces (or reshapes) tasks — not whole jobs. Most roles are bundles of tasks, stitched together into a workflow. When technology changes, the bundle changes. This is exactly why task-based research often finds that “automation risk” is frequently overstated when we treat occupations as all-or-nothing. So the

Recently, a study on overqualification in Singapore made its way across headlines, reports, and social media. At first glance, it seemed straightforward: Singapore has a highly educated workforce Some workers are “overqualified” for their jobs And depending on who you read, this is either a concern… or not But here’s where it gets interesting. Three different sources — the Ministry of Manpower (MOM) , NTUC , and Channel News Asia — all reported on the same topic. Yet, they t

Part 7 of a Series: From Messy Problems to Clear Definitions When Everything Looks Important… Nothing Moves You fixed the problem. You built the solution. You improved the process. And yet, things still feel slow. This is where many teams find themselves. Not because they lack tools, or data, or effort, but because everything seems equally important. Problems appear from different directions, each one urgent, each one worth solving. So work begins. Sometimes with dashboards.

Part 6 of a Series: From Messy Problems to Clear Definitions When Everything Is Right… But Progress Is Still Slow You fixed the problem. You built the solution. People are using it. And still, progress is slow. By now, we have worked through multiple layers. The problem is clear, the KPI is structured, the breakdown point is identified, the work is aligned, and the solution is designed for use. At this stage, things should move. But in many organisations, they don’t. The issu

Part 5 of a Series: From Messy Problems to Clear Definitions When the Work Is Aligned… But Still Ignored In the previous article, we looked at a common issue in analytics work. The problem is clear. The work is aligned. The objective is defined. And yet, nothing changes. OKR → Data Product thinking helps connect what we build to meaningful business outcomes. That already improves the quality of the work. But even when that alignment is in place, another issue often emerges. T