Thinking Through AI 🤖

Here's a common scenario in government work: a team designs a new online service. The logic is sound. The interface looks clean. It passes internal review. Then it launches, and the feedback arrives. The timeout is too short. Error messages confuse people. There's no way to save progress when internet connection drops. What seemed straightforward to the design team creates real barriers for the people who need to use it. This isn't about bad design. It's about a natural human tendency—we build things that work for people like us. When you have reliable internet, you forget others don't. When government terminology is your daily language, you forget it's confusing to most people. Your policy might be well-reasoned. Your service might address a genuine need. But if it unintentionally excludes people you're trying to reach, it's not working. Think about who uses government services in BC: the senior in Victoria who's comfortable with her phone but has n...

You're a policy analyst with a deadline. You've just closed a public survey and have data from 2,500 residents about housing affordability. Now you need to turn it into a briefing note. So you do what makes sense: you upload your Excel file to an AI tool and ask for help. "Can you give me step-by-step instructions to calculate response rates by age group and identify the top 3 concerns for each region?" The AI responds with clear, confident instructions. You follow them. You build your briefing note. You send it up the chain. There's a good chance those numbers are wrong. Why This Matters in Government Work A small analytical error in the private sector costs money. In government, it misdirects policy, misallocates budgets, and undermines public trust. Your results inform ministerial briefings, shape budget decisions across health authorities, and determine which communities get priority for affordable housing programs. If your response rate calculations are off b...