Careers, in their own words

Clients tell me I found them a great candidate. Candidates tell me I kept them updated. Both are true, but neither is really it. The difference is that they felt understood — I could tell them how I was going to get to the result, not just promise it.

It's the symptom. The placement, the shortlist, the speed — those are all outcomes. What sits underneath is asking enough questions to understand the business, the hiring manager and what the candidate actually wants, before anyone else has clocked the risks. Recruitment isn't really about matching CVs. It's about reducing uncertainty for everyone involved.

It usually doesn't. A CV tells you what someone has done. It rarely tells you how they think, how they influence a decision or how they change the outcome. That only really shows up in conversation — which is why I still back interviews and references over keyword matching.

More often than not, honestly. A client wanted a senior infrastructure engineer with a long list of technologies and certifications. A couple of meetings in, it was clear the real problem wasn't infrastructure at all — they needed someone who could communicate, influence people and bring structure to a growing team. We rewrote the brief and hired someone with a different technical background but far stronger leadership. Recruit against the written brief and you find exactly what was asked for. Recruit against the actual problem and you find what they needed.

AI's been brilliant at taking the admin out of recruitment — summarising interviews, drafting adverts, tidying up job descriptions. What it still can't do is judgement. And interview prep is the one thing I'd never hand over. The insights and tips that get a candidate genuinely ready are the recruiter's job, and it's in my interest to do it well, because how they perform reflects on me.

Early on I was focused on the craft — the detail, the execution, making something people would look at and think that’s impressive. Now I can step into almost any problem space, however messy, and trust that I’ll understand it and make good calls. That didn’t come from getting things right. It came from years of experimenting, failing and shipping real products. The shift was going from needing to prove I could design something, to knowing I create value through the decisions I make.

Customer research has changed dramatically. It used to be slow and expensive, a lot of investment before you got to anything worth acting on. AI tools can now gather and synthesise insight at a scale and speed we didn’t have. What they don’t replace is the interpretation — the judgement and empathy to work out what any of it actually means. The point isn’t removing people from the process. It’s removing the repetitive part so we can spend our time on the harder problems.

The feedback I value most is when someone says I saw something they hadn’t considered. Most of my work has involved tangled customer journeys, multiple systems and competing priorities, and the rewarding part is connecting those pieces and finding the opportunity that was hidden in the complexity. Taking something that feels unclear and making it simple and actionable — that’s where design does the most.

I start with facts. When people disagree it’s usually because they’re coming at it from different experience and assumptions, so we establish what we actually know first, then what we believe, then what we still need to test. And a lot of disagreement isn’t really disagreement — it’s people wanting to be heard. Once they feel part of it, alignment gets much easier. Healthy debate is a sign of a strong team, not a broken one.

The invisible parts of leadership. A CV shows outcomes and deliverables, but not the judgement and influence it took to get there — how you brought clarity to something ambiguous, or kept people motivated through uncertainty. It doesn’t capture the bit outside your own role either: unblocking colleagues, mentoring, building trust across teams. The biggest value a leader brings is often not the things they personally create, but the conditions they create for others to succeed. That’s incredibly difficult to put on a CV.

Usually that I made something complicated feel clearer and more manageable. And that they felt heard, especially when things were difficult or emotionally charged. I try to understand not just what someone's saying, but what's driving it and what they need to move forward.

That's the outcome, not the underlying thing. The real difference is probably that I connect things across disciplines — I've worked across commercial, operations, technology, people and strategy, so I don't look at a problem through only one lens. What looks like clarity is usually just having asked what the goal is, what it'll take to deliver, what could go wrong and who it affects — and caring that everyone can see a path forward, even if they don't all agree.

Listening for what isn't being said. Watching who's speaking and who isn't, and whether someone's point is being dismissed for how they said it rather than what it was. I try to make sure the person closest to the work is heard — seniority and insight aren't the same thing. Mostly I'm helping a group get from discussion to a decision they'll actually act on, rather than one that's been imposed on them.

That's exactly the problem. A CV can say I led operations, technology or transformation. It's much harder to show that a lot of my value is in connecting those areas, bringing clarity to ambiguity and helping people make better decisions. CVs describe what you were responsible for. They're not good at how you actually operate — and in senior roles that's most of the job.

Judgement. You can delegate activity, analysis, even big areas of decision-making, and you can use technology to make all of it faster. What comes back is the call you have to make when there's no perfect answer, or when the technically correct answer isn't the right one for the people involved. That, and accountability — you can give people ownership, but you can't outsource creating the conditions for them to succeed.

That's changed in the last couple of years. Five years ago I think they'd have told you I was diligent about understanding the problems and the drivers of a client's business. Twice in the last twelve months, two different clients have told me that what they admire is fearlessness. Both times it was a surprise. Both times they said it was something they wished they saw more of.

I think they're actually the same thing. In both cases I was pitching ideas that were unlikely to succeed, but that were hard to deny the value of. My thinking was that even though they'd probably say no, the way they said no would be really valuable. With one of them it led to a subsequent deal, because the place the no came from was their own internal way of solving the problem.

Putting decks together has got so much easier with AI. Account planning, territory breakdowns — there's still a big advantage to experience in those, but the tools available now let anyone do them.

Execution. Balancing time against activity, having the human conversation with a client or a prospect. Although — filtering all of that into next steps is something I now lean heavily on AI for. So I can imagine that all these things are only a matter of time until the human is not necessary.

Great question. Don't know how to answer it.

I was handed a project that was bleeding red. We had a serious budget overrun, and my job was to bring it back on track financially. Part of the issue was poor time recording on work already delivered; another part was broad assumptions about the work still to come. Between these issues, it was almost impossible to get executive leadership to approve budget for anything outstanding. So I meticulously analysed the root cause of the overruns. I fixed the time recording process, built a tighter estimation model so effort and resource allocation matched what was actually needed, then put in a weekly review of the burn rate and the forecast. There was still about a third of the project to deliver. If it hadn’t been brought under control, there was a real risk the whole project would have been canned.

I mention this as an accomplishment for effective budget management within a specific project.

In a matrixed organisation that’s most of the job. The hardest was a programme with multiple streams running across in-house teams and several vendors, where every team’s deliverable depended on someone else’s. What it came down to was being explicit: articulating what I needed from each of them and what a delay from one team did to the critical path for everyone else. And constantly reminding people what was in it for them to get it done, and what the repercussions were if they didn’t. A structured, regular cadence with the right stakeholders is what actually held it together.

What I choose to photograph hasn’t changed. I use the generative tools to assist the process rather than replace it. I have been able to fix images with them that I’d otherwise have rejected, or wouldn’t have photographed at all. Generative tools have helped me overcome a few limitations I had with my equipment or environment.

Generally the beneficiary of my work is a stakeholder, a customer or a CEO, who has had some burden eliminated. That might come through refining a product’s user interface, or through managing a critical business operation. They’ll inevitably say that improving the process is what made the difference.

They’re describing the benefit, the outcome. What really makes the difference is organisational focus, ownership and enablement. Without a clear mandate that understanding and improving customer experience matters, you don’t get the resourcing or the processes that enable it. Without ownership, a team won’t take the initiative to work through complex multi-party problems, and the stakeholder’s pain persists unnoticed. They report the resolution of their symptom, but the seed of the solution was laid far earlier by the organisation’s culture.

We engaged an external firm to redesign the mobile purchase path for our flight booking product. After the first few iterations it was clear they didn’t understand the nuance of the product or the constraints we were working within. Rather than let it run on, I pushed to bring the project in-house and applied a prototype and user-feedback loop until we had something robust, de-risked and ready to go to development. Without that intervention I believe the project would have incurred significant costs and delivered a high-risk, unvalidated solution, leading to a significant loss in sales. Identifying the risk early and applying sound process is what made the outcome work.

This is essentially the binding thread of my multi-faceted career, and it sits at the top of my CV: the ability to identify risk early and mitigate it with best practice. It spans web development framework selection, design process methodology and the establishment of startup financial operations.

I documented one case on my blog where I tried handing off the build of a complex feature of Web3Connect to AI. It ultimately broke the site, and I had to roll back days’ worth of work, break it down into smaller chunks and try again. It was a good lesson in working within the constraints of the team you have, whether that’s an AI agent or a human.

Ownership. Historically I’d have focused primarily on validating skills and simply assumed ownership, but after leading multiple startup teams it’s clear how damaging a lack of it is. Beyond that, being AI-native is now a must-have, although it wouldn’t have appeared on resumes ten years ago!