Last week’s Her Tech Circle Career Kickstarter in Sydney was sold out and buzzing. I spoke about using generative AI at work, why starting now matters, and how we’re asking everyone at DiUS to build practical literacy so we can guide clients with judgement, not hype.
From the moment you walked in, the signal was clear: this night was about practical help. People donated time for headshots and résumé reviews, interview practice, and guidance on career changes or re-entering the workforce after a break. That spirit set the tone for everything that followed.
It flowed into the talks. Her Tech Circle has a brilliant format: five minutes per speaker, with 60-second and 30-second warnings. If you run over, the whole room stands and gives you a standing ovation. Firm, kind, supportive and very on-brand for this community.
As a presenting partner, I put my hand up to give a quick glimpse into consulting at DiUS, less “tell and run,” more partnering: listening, advising, building together, and leaving teams stronger. And to share how generative AI is changing the way we work (sometimes brilliantly, sometimes… not so much). I wanted to match the community’s lean-in, get-stuff-done ethos, so also I focused on what to do with generative AI.
And the energy in the room proved the point, this was a community ready to take action, not just listen.
1. Start
If you haven’t dipped your toe into generative AI yet, that’s a miss. It won’t deliver every wild promise, but it is reshaping our industry, albeit unevenly. Some shifts will take ages (people and process get in the way); others will land overnight. Either way, it isn’t going away. If you wait until the hype dies down or the tools feel “finished,” you’ll already be behind. No one’s an expert yet. Start.
2. Learn (what using generative AI at work means for you)
At DiUS, we’ve asked every one of our consultants to get hands on, because our clients are looking to us for guidance, and you can’t lead from the sidelines. We know where AI works for us, but we also have the expertise to know where it doesn’t, yet. You need to understand what this looks like for you, your role and your industry and that only happens if you get hands-on.
First, treat AI like a collaborator, not an oracle; you’re still 100% responsible for the output. Next, learn to prompt well so the model has the best chance of giving you something useful.
Then, never accept the first draft; instead, iterate. Also, don’t be fooled by its certainty, confidence isn’t accuracy. Always check correctness, security and fit for purpose; and if you don’t understand why it wrote something, then pause and dig deeper before you ship.
Finally, be safe: check your organisation’s generative-AI policy and know what happens to your data.
3. Share
You build your AI muscle by creating feedback loops. for example, ask follow-ups, provide patterns, and push for explanations. Afterwards, share what you find, ideally in public channels or short demos. The real shift happens when teams adopt good habits together, when practice becomes shared practice.
Thanks to Her Tech Circle
The best part of the night? Conversations afterwards with people keen to upskill, curious about using AI well, and interested in consulting. It was a reminder that community beats hype every time: start, learn, share, and help the next person do the same.
