The interview
Before Elmtrail, how did you feel about bringing AI into your team’s work?
I was definitely hesitant, as I think any team new to AI would be. Most of my doubts came from not yet seeing how automating mundane, repetitive tasks could free up space for personal growth and innovation. Letting go of a familiar routine wasn’t easy either.
What was the moment you realised it was actually working?
At the start, the setup was handled for us. We’d share feedback and the Elmtrail team would make the changes, which got us off the ground. But no matter how good the communication, it was hard to fully translate the details of a workflow to someone who wasn’t doing it day to day, and that made it harder for us to feel ownership of the change.
I warmed up to the idea quickly once I was trusted to give input directly, and eventually to redesign the workflow around what the team actually needs. After I’d learnt the ropes of prompting logic and deployed a few fixes, our stats and productivity went up significantly. That was when I could see beyond simply replicating what we’d always done and start building new approaches that were faster, more accurate and needed far less fault-finding.
Seeing the impact of my own updates has made me want to dig deeper, and I’m now much more involved in conversations about how we scale these solutions to other teams.
How does a typical day look different now?
My day no longer consists of hours spent checking lists of information, drafting repetitive emails or clicking buttons to send them to multiple leads one by one. Using the input I’ve mapped out and refined, AI starts conversations with users at scale, so I can focus on the highly engaged ones who need extra help with the final nudge.
Instead of executing mundane tasks, I now have ownership over the workflow end to end: building it, working through feedback from the team and improving it so it keeps up with our needs and with fast-moving changes in the organisation.
What has Elmtrail made possible that wasn’t possible before?
Time, mostly. Because the workflow runs faster and needs much less correcting, I’ve got back hours that used to go into checking and fixing. I’ve reinvested that time into further experimenting and extending the solution to areas outside my original scope, which has opened up growth opportunities that simply weren’t on the table before.
What would you say to a team on the fence?
It’s daunting at first, and nobody starts out as an AI guru. But the better you understand how it works, the more you can shape it around what you and your organisation actually need. Start small: talk openly about what’s stopping you from doing the parts of the job you enjoy, and expect plenty of trial and error.
It’s easy to assume AI does the thinking for you, but in my experience it’s the opposite. Building a meaningful process that works takes real critical thinking, not simply copying the old way of doing things. Build it with the people who use the tool every day and the end user at the centre, and keep questioning every step of the way. It’s a bit like learning to use the internet when you were 10 all over again!
