Paciolan — Building the Design Org for a Platform at Scale

THE BUSINESS STAKES

A 40-Year Platform at an AI Inflection Point

Paciolan has powered ticketing, fundraising, and fan engagement for collegiate and live entertainment organizations for more than 40 years. The platform is foundational infrastructure: 500+ client organizations, 120M+ tickets transacted annually, millions of fans whose experience of a game, concert, or season ticket starts in a Paciolan-powered flow.

Two pressures converged at the same moment. Consumer technology — Apple, DoorDash, OpenTable, Spotify — reshaped what fans expect from any digital experience, and a 40-year platform's information architecture, interaction patterns, and visual language had not kept pace. At the same time, AI moved from a future capability to an immediate strategic question: not whether to use it, but how it should change what we build, for whom, and what we hold the line on.

My mandate was to build the design organization that could answer those questions — while shipping product against them.

STARTING POINT

The Org I Inherited

Coming into the role, I focused first on understanding three things: where the existing team and individual designers were strongest, the actual state of the design system across product lines, and the working relationship between Design, Product, and Engineering. The platform's 40-year history meant that decades of decisions — some still serving customers well, some no longer fit-for-purpose — were carried forward in the experience. The honest starting picture: a team with real product knowledge that hadn't been resourced to operate at the scope the moment now demanded.

A more detailed version of that starting-state assessment — team shape, design-system maturity, research practice, and the specific structural gaps I identified — is the kind of detail best discussed in conversation rather than published.

BUILDING THE ORG

What I Built

Team. The shape of the design organization is the lever that compounds. I'm building a team structured around the way Paciolan actually delivers product — with clear ownership across ticketing, fundraising, marketing, and fan engagement, paired with shared craft standards that travel across product lines. Specific structural decisions, hires, and team-shape changes are best discussed in conversation.

Design System. The unification effort is anchored in a single source of truth across the product portfolio — a token-based architecture, a clear governance model, and an adoption strategy that prioritizes the highest-traffic surfaces first so the system earns its keep before it asks for buy-in. Specific adoption metrics and rollout milestones are available on request.

Process & Culture. The rituals I've introduced — design critiques, research-in-the-room cadences, and cross-functional reviews early in product cycles — are designed to do one thing: get design upstream of decisions, not downstream of delivery. The culture I'm building is bias-to-evidence over bias-to-opinion, with feedback that's direct, specific, and tied to what the work is trying to achieve.

AI STRATEGY

Designing in the Age of AI

The AI work is the most differentiating part of this role. AI compresses the distance between idea and artifact — and that's not a threat to design, it's a clarifying lens on what design actually is. When execution gets cheaper, the premium shifts entirely to judgment: knowing what to make, for whom, why it matters, and what it costs to get it wrong. At Paciolan, my responsibility is leading both sides of that equation: how AI changes our product, and how it changes how we design.

Product side. The work focuses on UX patterns for AI-assisted personalization, intelligent recommendations, and dynamic interfaces — with particular attention to trust, transparency, override paths, and graceful behavior when the model is wrong. The fan-experience surface is where these patterns matter most: a seat recommendation, a price suggestion, a personalized offer carry weight that a generic e-commerce moment does not. Specific shipped features and product roadmap detail are NDA-sensitive and available in conversation.

Team side. Inside the design team, AI shows up as generative exploration in early concepting, AI-assisted accessibility auditing, and prompt craft treated as a core design skill alongside typography, interaction, and research synthesis. The skills I'm protecting most fiercely are research framing and judgment about what to make — the work AI is least good at and the work that matters most.

The skill I protect most fiercely on the team: knowing which question to ask before reaching for a solution. AI is exceptional at answering questions. It's not good at knowing which questions are worth asking. That remains a human job — and a design job.

OUTCOMES SO FAR

Early Results

The most meaningful early shift has been qualitative: design entering product conversations earlier — in discovery and strategy rather than execution. That's the leading indicator that everything else compounds from. Team metrics, design system adoption data, and specific shipped outcomes are NDA-sensitive while I'm in the role and available in conversation for serious opportunities.

WHAT'S NEXT

Building Forward

Three things I'm focused on next:

1. Translating the three AI design principles from doctrine into shipped patterns — the override paths, the explanation surfaces, the failure-mode interactions that show up in real fan experiences.

2. Continuing the design system rollout across remaining product surfaces, with the goal of consolidating decades of fragmented experience patterns under a single, governed source of truth.

3. Building leadership development pathways for senior ICs ready for staff and principal-level scope — the next generation of design leaders inside the platform.