Case Study: Using AI to Accelerate a High-Stakes Hiring Project.
Something a bit different this week….and to be honest, I wasn’t sure about doing it. But, here we are.
There are approximately 3,457 ads online every day, telling us how we’re falling behind if we’re not AI literate by the end of the year. So it’s hard not to feel like you’re underpowered in the AI stakes.
But then I remember something that I posted in a very early edition of this series 👇🏻
“Right now, no one has a 10-year headstart in AI”
So, whilst there are a whole bunch of people who I’m sure are way more advanced in their daily use of AI, I’m equally sure that this might be helpful for some.
So, where to start?
When I came on board to help a new venture build its engineering team in Melbourne, I knew this wasn’t going to be a case of posting a job ad and waiting for luck. They need very specific people, in terms of skills, but also attitude, drive and curiosity. We’re also doing this in an industry where the local talent pool is already quite crowded.
The CTO is still hands-on with the codebase and acting as the hiring manager. His time is precious, and I see my role as not only to source and close the best people possible but also to protect his time. That means only putting candidates in front of him and the hiring team who have a high chance of progressing. In turn, this means that the role has to have been well calibrated.
To do that, I needed to start with planning and alignment, not diving straight into sourcing.
Building the Knowledge Centre
Before I touched a single candidate-facing asset, I built out a knowledge base.
- I pulled together as much information as I could find about the business and the founders. That included press coverage, vendor case studies, and details of past projects.
- Using Fathom to record all the early discussions with the CTO, hiring team, chief of staff and other early team members, I added the transcripts of these discussions to the knowledge base. The transcripts were invaluable. They captured the off-hand remarks about culture, expectations, and hiring practices that never make it into public documents.
- I consolidated all of this into a knowledge centre that captured the company’s story, engineering culture, growth direction and the type of person who would actually thrive there.
From this, I created the internal scaffolding:
- A position description to start with.
- A hiring rubric that defined what good looked like in this specific context.
- A hiring guide so that everyone involved in interviews was aligned on requirements, preferences and deal breakers.
- Documentation to keep the initial team consistent when talking about the role and what success would mean.
AI was a force multiplier here. Instead of spending hours turning notes and transcripts into structured documents, I used ChatGPT to accelerate the process.
It helped me organise messy information into usable hiring rubrics and guides extremely quickly. Surprisingly, it also surfaced some things that I’d missed in some of the conversations and in the press releases.
Moving External
Only once the internal alignment was clear did I start building the candidate-facing assets.
- Job ads written in plain language, clear about both the challenge and the opportunity.
- A candidate pack so
- Social posts in my own voice, designed to reach the kinds of engineers we actually wanted to attract.
- Sourcing channels mapped across LinkedIn, GitHub, Discord and Slack communities.
Because the knowledge base was so strong, the external content was sharper. Every job ad, post and candidate pack was rooted in the real culture and trajectory of the business, not just a generic description of an engineering role.
Early Reflections
This is still very early days. I’m only a week into the project, so the outcomes will take time to show. But already I can see the value.
By leaning on AI to take care of the time-heavy groundwork, I’ve been able to spend more time on the things that matter most, which have been direct sourcing, having good quality conversations with potential candidates, and making sure every interaction has value.
For me, that is the real advantage. AI doesn’t replace the recruiter. It gives me more bandwidth to focus on the high-value, human parts of the job.
How are you using AI in your day-to-day?
This issue is sponsored by the team at Taly.
In a recent issue, I spent some time talking with Peter Treloar, the founder of Tally, linked here. So that if you’re one of the few that hasn’t seen it, you now can.
We see a lot of talk online about the responsibility of leaders and how great teams thrive. For me this is all made earlier when you better understand people. And as Peter says “If you don’t know someone, you can’t lead them. If you don’t know what’s going on inside them, how to connect with them, then you can’t lead them. You just keep throwing stuff out and hope something sticks.”
So, if you’re thinking more deeply about how to engage, attract, and retain your people, speak to Peter and the team at Taly. I’m sure they’ll be able to give you some insight and some answers through the platform.
The Australian HR Institute’s Quarterly Labour Outlook
The latest report reveals a labour market wrestling with competing pressures, and if you’re running a startup, the numbers deserve attention.
At first glance, the data looks promising. Employment intentions reached their highest level since the survey began, with 69% of organisations planning to hire this quarter. The Net Employment Intentions Index rose to +48 from +38 in the previous quarter, indicating that employers are feeling optimistic about growth.
But that’s not the complete picture. While hiring intentions are up, so are redundancies, with 27% of employers planning cuts this quarter – the second-highest level on record. This isn’t a typical economic downturn pattern. Instead, we’re seeing what AHRI terms the “5Rs effect” – organisations juggling recruitment, retention, reorganisation, reskilling, and redundancy simultaneously.
The driver appears to be rapid technological change and AI adoption, forcing structural shifts across workplaces. Nearly a third of organisations believe more than 20% of their roles face significant change within three to five years. Companies aren’t simply cutting costs – they’re fundamentally reshaping operations.
For startups, this creates both opportunity and uncertainty.
The tight labour market means talent competition remains intense, particularly for the roles startups need most. Skilled trades top the difficulty list at 33% of employers struggling to fill them, followed by professionals and associate professionals at 29%. For tech startups, this translates to fierce competition for technical talent.
Yet established companies are creating openings as they restructure. The data shows 47% of organisations are using redeployment as a strategy to manage workforce changes, suggesting a pool of experienced workers being shifted around the market and potentially available to startups offering the right opportunities.
Wage expectations offer some relief – pay increase projections have moderated to 2.9% from 3.3% last quarter. That’s useful for cash-strapped startups, though competition for specialised skills will likely keep certain roles expensive.
The strategic workforce planning trend reveals a significant gap that startups can exploit. While 57% of organisations now maintain formal workforce plans extending beyond 12 months, 32% expect significant role changes within 3-5 years. This disconnect suggests many companies are flying blind into workforce transformation.
The AI element presents another opportunity window. While 73% of organisations expect AI to assist their workforce planning within the next few years, current adoption remains limited. That gap presents an opportunity for startups to get ahead of established competitors who are still planning rather than implementing.
The broader story isn’t recession or boom but seems to be a transformation. Companies are reconfiguring for a different operating environment, creating equal measures of churn and opportunity. The public sector shows particularly strong hiring confidence with a +71 employment intentions index versus +44 for the private sector, suggesting government contracts or partnerships might be more readily available.
For startups, traditional employment market signals might prove less reliable than usual.
The sensible approach is probably focusing on skills and sectors where this transformation is creating clear demand, rather than betting on broad market trends. The companies that can navigate this period of structural change most effectively will likely emerge stronger once the dust settles.
Before you go, can you help me out?
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Thanks for being here. Have a brilliant week ahead.
Weekly newsletter drop: Wednesday 1:30pm – The Main Issue: Usually a comprehensive guide with advice for teams & talent, plus the latest podcast
Need hiring help?
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Simon
