Building and mentoring teams, aligning stakeholders, shaping product strategy, and translating complex business challenges into products customers value.
“I've never believed that leadership means stepping back from the work. The best work comes from being close enough to the problem to feel it — and close enough to the team to help them solve it."
Example"The most common reason products fail isn't bad design — it's designing for the wrong person, or the right person at the wrong moment. I've spent 25 years wokring on getting that bit right first.
Example"I've worked in startups and multinationals. Both taught me something — the best work happens when you understand the whole system, not just your corner of it."
Example"I was an early adopter of AI in my design practice. I always think carefully about where it adds real value and where it flattens things that should have texture."
ExampleI'm long enough in the tooth now to have understood a few things along my path. Here's three for free:
Collaboration
I'm at my best when I'm working in cross-functional teams to create real bottom-line value. Collaborating across teams, not working in silos, is key.
Making good products is one thing, but ensuring business teams have everything they need to market, sell, support, track, and grow the product is another thing.
Empathy
Context and framing is everything. Understanding who our users are is at the centre of being able to create meaningful products.
When people are on multiple devices, we need to appreciate that we're just part of their day, not the whole thing.
Experience
A considered approach is key. AI is fast-tracking our ability to go from 0–60%, but balancing that speed with taking a moment to know what the right thing to build is, when, and who for can save time, energy (and tokens!).
...and many more.
Customer research revealed that financial research creation is a fragmented, trust-dependent process — spanning multiple tools, involving heavy reliance on analyst judgement, and offering little transparency to end recipients.
Macrobond needed to solve for that trust gap while also finding a way to extend its footprint beyond existing customers and into the hands of their customers' customers.
I led the full design effort from 0 to 1 — hands-on across user research, UX, UI and engineering collaboration. Recognising that Macrobond's unique position as both a data provider and analysis tool was the key differentiator, I shaped a platform that used existing data structures to make the analysis behind research transparent and interactive.
Alongside the product design, I created and now manage a brand new design system built to support Amplify and scale with future product development.
Working cross-functionally with Marketing on GTM strategy, Sales and Support on enablement, Finance on pricing, and Engineering on delivery, I brought the first iteration of Amplify to market from nothing. Early sales numbers are positive and customer feedback has been strong, with the team continuing to iterate.
Amplify represents a new product category for Macrobond — one that creates value not just for existing customers but for the audiences those customers serve.
Research showed that financial professionals were increasingly turning to LLMs to support their research and analytics workflows. LSEG had a significant opportunity to lean into that behaviour using their extensive dataset library — but the challenge was designing an AI experience that felt trustworthy in a regulated, high-stakes environment.
The question wasn't just how to build a chat interface, but how to make AI-generated financial insight feel verifiable and credible enough for professional use.
Working hands-on alongside Engineering and Data Science, I designed LSEG's first LLM interface — creating a familiar chat-based experience that allowed users to interact naturally with datasets and research documents. Beyond the interface itself, I defined the tone of voice for the AI and led the work of training the model to produce outputs that gave clear data lineage.
That transparency — showing users exactly where the insight came from so they could validate it themselves — was the central design challenge and the key to building the trust the product needed to succeed.
The prototype was well received by both the Analytics and Platform teams internally, and performed strongly in customer beta testing. Findings were synthesised, socialised across the business, and used as the foundation for rolling AI capability into the wider LSEG platform.
Critically, the project didn't stop there — it directly catalysed a broader wave of AI initiatives across the organisation, demonstrating the strategic value of getting the first experience right.
Customer research uncovered a near-universal behavioural pattern: people think about their short-term and long-term finances in fundamentally different ways, with a strong head-in-the-sand tendency when it comes to retirement.
With a wide range of financial tools already available — for day-to-day management, investments, and pensions — the problem wasn't a lack of product. It was the mental disconnect between them. Aviva wanted a design-led solution that could bridge that gap within their customer app ecosystem.
I led the project from concept through to working prototype, designing a unified financial view that placed everyday finances and long-term planning on the same timeline. The experience brought together salary, bills, pension payments, savings and forward projections — with dynamic modelling showing users where different choices would take them.
Working hands-on across UX, UI and interaction design, I collaborated closely with the engineering team throughout to validate feasibility and ensure the concept could realistically be built.
The prototype was tested with Aviva's beta programme customers. 85% of test subjects said the concept gave them an everyday view of their longer-term finances — a strong validation of the core design hypothesis.
Aviva elected to build the concept into their customer app as part of a wider platform rebuild, taking the prototype and engineering feasibility work as the foundation for that development.
The US personal finance market is saturated with apps that do one thing well. Unifimoney's ambition was to bring a European-style PFM approach to that fragmented landscape — pulling best-in-class services together into one unified experience, with the goal of becoming the go-to B2C app for personal financial management.
Partway through the build, customer research delivered a significant market signal: the US market wasn't ready for that model. The business needed to pivot, and the design had to move with it.
I built and led the design team, taking full ownership of design direction from day one. Working hands-on, I designed the core product experience — an app that aggregated data across multiple APIs and services and presented it in a way that layered insight on top of raw data, centred around a growing net wealth figure. I also established the design system from scratch, bringing structure and consistency to the work and creating the foundation for everything that followed.
When research confirmed the B2C pivot to a white-label B2B model for mid-tier banks, I led the redesign of both the system and the UX to suit — ensuring we could move quickly on B2B demos while keeping the product genuinely aligned to end-user needs.
The pivot enabled Unifimoney to reposition as a white-label service provider for mid-tier banks — a more viable near-term commercial model given the market conditions.
The flexible design system we had built meant the team could iterate rapidly and provide tailored demos for new B2B targets without starting from scratch, while the underlying UX remained grounded in real user research rather than assumptions about what banks wanted to show their customers.
As Macrobond worked through a significant business restructure, it became clear that there was no shared understanding of who the target customers actually were. Different teams were operating from different assumptions: Sales needed clear conversation frameworks for outreach, Marketing needed to understand how to speak to the wider community, and Product needed to understand how customers actually used their tools in order to optimise the experience.
Without a single source of truth, alignment across the business was difficult.
I led a structured three-phase programme to fix that. Phase one used AI to generate a comprehensive set of baseline personas and segments at scale — creating a foundation we could test and iterate on quickly.
Phase two went deeper, using AI again to enrich and interrogate those personas in detail. Phase three was about validation — pressure-testing the documentation with internal subject matter experts, then going out to a wide range of real customers to surface nuance and find where the assumptions broke down.
I was hands-on throughout: leading interviews, synthesising findings and shaping the final outputs into a full, rounded persona and segmentation suite.
The resulting documents are now actively used across Sales, Marketing and Product — informing everything from outreach talking points to product decisions. Taking the work further, I converted the persona outputs into an LLM-powered internal agent tool, giving teams the ability to query customer insight dynamically rather than just reference a static document.
This led to sharper Sales decks, better customer conversations in Support, and a stronger evidence base for the Product team.
