Dear friends,
In Week 1, I shared the basics I was learning about AI — just the facts, and how AI already shows up in our daily lives.
In Week 2, we went one step further. I played with prompts and showed how the way we phrase a question changes the answer AI gives back. That was my way of experimenting with how to talk with AI.
Now, in Week 3, I’ve been reflecting on what that means for us as finance professionals. If AI can generate numbers and respond to prompts, what’s left for us to do?
From what I’ve seen so far, it’s the language that makes the difference. According to McKinsey, up to 42% of finance tasks are already automatable, with another 19% mostly automatable. Gartner even predicts that by 2027, 90% of descriptive and diagnostic analytics will be automated. The machines will keep getting better at doing the “what happened” part.
But numbers on their own don’t drive decisions. It’s the way we frame them that matters. For those of us mid-career, this actually feels like an opportunity rather than a threat. We’ve seen cycles, not just spreadsheets. We’ve learned how to judge what’s material, and how to explain the difference between a blip and a trend. And the more I look at executive commentary, the more I realise: it’s the words that turn raw data into something useful.
So, this week I want to share something small I’ve been building: a kind of mini financial language check. Think of it as a cheat sheet for financial storytelling — especially in the industries I know best: property development, real estate funds, hospitality, and retirement living.
The pattern is simple:
- Start with the metric (occupancy, sales, margins)
- Add the context (market trends, cost pressures, demand)
- Close with the implication (resilience, risk, opportunity)
Here are some examples:
🏗 Property Development
- “Pre-sales momentum at X% has de-risked the project pipeline ahead of schedule.”
- “Construction cost inflation of Y% continues to pressure margins, partly offset by disciplined procurement.”
🏢 Real Estate Funds / REITs
- “Portfolio occupancy remained resilient at Z%, underpinned by strong demand in logistics assets.”
- “Cap rate expansion of X bps drove a valuation decline, but rental reversions provided partial offset.”
🏨 Hotels
- “RevPAR growth of X% reflects both ADR uplift and occupancy recovery.”
- “Operating leverage remains strong, with GOP margin expanding Z bps.”
🏡 Retirement Living
- “Sales velocity in independent living units slowed, reflecting broader housing affordability pressures.”
- “Care occupancy remained stable at X%, with ongoing demand for higher-acuity services.”
I’m continuing to build out a fuller financial language cheat sheet across industries. If you find this useful, or if there’s a particular sector you’d like me to explore, feel free to subscribe to the blog or message me directly.
For me, the lesson of this week is simple: AI is speeding up the basics, but that just makes our human edge clearer. The real question isn’t “what will AI replace?” — it’s: are we building the skills that last beyond automation?
Until next time —
Lydia
P.S. If this letter found you at just the right moment, I’d love to hear about it. Join my weekly letter list and let’s figure it out together — one AI-shaped step at a time. Join the weekly letter list.40+ and Figuring It Out
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