Three problems. One structural gap. One week to act.
When AI-powered development tools rolled out to every designer across our 230-member chapter, output multiplied. But without shared infrastructure, all of it had nowhere to land. I identified three compounding problems that nobody had yet named as a single systemic failure.
No single source of truth for design intelligence
Research, insights, project status, and team output were scattered across SharePoint, Confluence, Teams channels, Figma, and personal folders. Product Managers, BAs, and engineering leads had no way to pull live design context without hunting across five tools. The same questions were asked week after week. The same research was duplicated quarter after quarter. Stakeholder decisions were made without access to knowledge that already existed.
AI outputs had nowhere to go, creating governance exposure
With every designer receiving a Cursor licence, the volume of AI-generated output exploded. HTML prototypes, tools, research pages, presentations, all being created faster than ever. Without a shared home, all of it ended up in personal GitHub repos, Teams messages, and email threads outside approved channels. Good Cursor commands were passed around informally with no library, no attribution, no reuse. Some designers held back from using AI at all.
Designers needed AI for high-friction tasks but had no structured access
Nobody was asking AI to replace design thinking. They were asking it to clear the path so they could spend more time on work that actually moves the needle. The demand was unambiguous. The infrastructure to answer it did not exist yet.
This is not an AI problem. It is an infrastructure problem. AI tools make it worse before they make it better. Designers are now producing more output than ever, with no shared home, no governance layer, and no audit trail.
I ran a poll to validate demand before building anything.
Before proposing a solution, I ran a team poll to surface where the real demand was. The results clustered around two categories: quality assurance tasks where AI acts as a first-pass checker, and speed-to-explore tasks where AI reduces time from idea to something shareable.
The same questions asked week after week. The same research duplicated quarter after quarter. Stakeholder decisions made without access to knowledge that already existed.
The pitch nobody asked for. The mandate secured in a day.
The DesignOps Principal responsible for the organisation's GitHub Enterprise had been granted a new org but had no governance strategy or adoption model for it. I recognised this immediately as the opening I needed.
"I got this GitHub now, and I am still figuring out how to set it up."
DesignOps Principal, NABI drafted and presented a proposal the same day. One repository per design chapter, GitHub Pages for zero-infrastructure publishing, and a central Intelligence Hub to aggregate everything into an org-wide view. Designers would never touch a terminal. The system would handle everything through natural language commands in Cursor.
I was granted org owner rights the same day. By day five, I had a live proof of concept on GitHub Pages: demo-ready, running without a single developer, a single line of manually written code, or a single infrastructure dependency. Pure AI-first development. Idea to shipped internal SaaS in five days.
The architecture: three layers, one command, zero overhead.
I designed the system to solve all three problems simultaneously and to do it invisibly, with no added workflow burden on the people using it. That constraint was not a limitation. It was the design brief.
The designer
They create output in Cursor and say "publish this." The publish command I built handles everything: quality checks, meta tag generation, correct sub-team folder placement, commit, push. The page is live on GitHub Pages immediately. A permanent, shareable URL is ready before the designer closes their laptop. Automated WCAG 2.2 accessibility checks and design token compliance run on every publish, embedding governance at the point of creation rather than after review.
The team repo
Each chapter runs a single sync command in Cursor. The system scrapes all published pages, extracts structured metadata from tags and git history, and generates the data file that powers the team hub dashboard. No manual data entry. No reporting cycles. No maintenance overhead.
The central hub
The org-level hub I built aggregates every chapter's data, pulls in external sources including the research library and people directory, and surfaces a unified view across all 10 chapters. Product Managers, engineering leads, and executive stakeholders can pull live prototypes, user journeys, and research findings directly, without waiting for a design handoff. The standard design-to-product friction loop is removed entirely.
Three-layer intelligence flow: designer publish command, team hub sync, central org-wide Intelligence Hub. Every layer runs from a single natural language instruction in Cursor.
The designer never touches git, terminal, or config files. They work in Cursor, say "publish", and it is live. The governance layer runs invisibly. The intelligence layer builds itself. The system is designed to disappear.
I designed for the least technical person in the room. That gave me 100%.
Technical architecture is the easier half. The harder half is activation: driving a 100% onboarding rate across designers with varying technical confidence, many of whom felt genuine anxiety about tools that felt developer-adjacent. This is a classic self-serve product activation problem. I treated it exactly like one.
I applied the same product thinking I would to any user onboarding funnel. Design the activation experience around the least technically confident user in the room. Every friction point removed at that end of the curve means 100% activation, not 80%.
I built a step-by-step onboarding wizard that runs entirely inside Cursor. One URL, one paste, and the wizard takes over. Prerequisites checked, authentication handled with a single browser click, chapter selected, repo cloned, all rules and commands installed. The designer never opens a terminal.
The onboarding wizard I built: a guided self-serve activation flow inside Cursor. The designer never touches a terminal at any stage.
I ran alpha testing with three volunteer designers in week two. It surfaced anxiety spikes and drop-off points, which I addressed through real-time iteration of the wizard, commands, and supporting guides. Week three: 25 designers onboarded. 100% activation. No support tickets. No dependency on a technical champion within each team.
Every designer who started the wizard completed it. The system scaled without adding coordination overhead or dedicated support resource. That is the activation bar I set for myself from the beginning.
One demo. One week. Embedded in a new team.
While scaling the Design Intelligence Hub across remaining design chapters, I demoed the system to the Personal Banking Enablement Manager, responsible for scaling Cursor adoption across 800+ staff in the Personal Banking domain: Product Managers, Business Analysts, Engineers, Solutions Architects, and Executive General Managers.
He had been hitting a wall. His developers were telling him the only way to build the context library he needed was to ask every member of staff to manually upload their knowledge into it. He had three fears blocking progress entirely. I had already solved all three.
| The fear blocking progress | How the hub eliminates it |
|---|---|
| Nobody has time to maintain a context library manually | The context library populates itself as a side effect of normal work. Publishing an output automatically generates structured metadata. No separate maintenance step exists. |
| Nobody will use it if it adds friction to their workflow | The system is embedded in Cursor, the tool people are already using. Publishing is a single natural language command. There is no new tool, no new login, no new habit to form. |
| Non-technical roles cannot understand Cursor well enough to contribute | The onboarding wizard handles setup end-to-end. The 100% activation rate in the design chapter proved this works for non-technical users. The same approach applies at scale. |
The demo did not just impress him. It removed every blocker that had been stopping him for months. He requested my capacity immediately. I was embedded in his team within a week of that conversation.
This was the moment the Design Intelligence Hub became something larger. The proof of concept had validated the architecture. Now I was scaling it into a cross-functional enterprise platform for the entire Personal Banking domain.
The Personal Banking Intelligence Hub. An enterprise context engine for 800 people.
I designed the Personal Banking Intelligence Hub as the scaled version of the same architecture, built for an 800-person cross-functional domain spanning five distinct disciplines. The central insight I brought to this phase: the hub is not just a publishing platform. It is a machine-readable context library that every member of the domain can feed their Cursor agents from directly, for research, market studies, board presentations, product pitches, technical specs, and strategic planning.
The key architectural decision that makes this possible: outputs published to the hub generate structured, machine-readable metadata automatically. A Product Manager drafting a strategy document can point their Cursor agent at the hub and pull live user research, journey maps, and design decisions in context, without asking a designer, without searching Confluence, without waiting for a handoff meeting.
The Personal Banking Intelligence Hub. Proof of Concept, 2026.
Traditional enterprise structures isolate product requirements, technical architecture, and user experience into separate siloed tools: Jira, Confluence, SharePoint, Figma. Each holds a fragment of the picture. The hub I built creates a single AI-indexed interface where a Product Manager or Executive can trace a feature from initial business requirement down to its live design component and user research finding, in one natural language query.
Growing people, not just building systems.
My most important output here is not the platform. It is the capability of the people using it. Building infrastructure that nobody knows how to use is just expensive shelf-ware. The real work is the change management loop that turns non-technical creators (BAs, PMs, and engineers) into high-velocity AI operators who reach for these tools instinctively.
Working with the PB Enablement Manager and an Agile Coach, I designed a persona-based, level-based learning programme running across two parallel tracks.
Self-guided course
Structured modules I designed around each of the five personas, allowing individuals to onboard at their own pace without blocking their team. Non-technical roles get a different entry point from engineers, but both arrive at the same outcome: confident, independent Cursor use embedded in their daily workflow.
Masterclass series
Deeper capability sessions I run as cohort learning across disciplines. Designers, PMs, BAs, and engineers in the same room, learning to use the Intelligence Hub and Cursor agents together across the full SDLC. Cross-functional understanding built through shared practice, not slide decks.
Advanced command authoring, prototype generation, publishing workflows. From individual contributor to chapter AI champion.
Querying the hub for live design context via Cursor agents. AI to accelerate briefs, specs, analysis, and cross-functional documentation.
High-level access to live E2E service blueprints and product status. Board-ready context from a single natural language query.
The training is not just about tool adoption. It is about shifting how 800 people across five disciplines think about knowledge sharing, cross-functional handoff, and AI-assisted work. The hub is the infrastructure. The enablement programme is the culture change. Both are required for either to work.
I built the foundation solo. Then the team assembled around it.
The proof of concept was entirely my own work. The architecture, the publish commands, the onboarding wizard, the hub itself. Once the system was validated, a cross-functional team formed around the platform organically. That is product-led growth operating as an internal organisational dynamic.
Initial collaborators
Principal
Tooling
I identified his problem before he had articulated it, and pitched the solution directly. He granted org owner rights the same day and championed the initiative to DesignOps leadership.
Principal
Design Systems
Validated the hub as a potential replacement for existing project management tooling and provided strategic direction on governance and org-wide adoption.
Developer
Architecture validation
Collaborated with me on validating the architecture, framework, and Cursor commands. We updated shared Cursor guides together to reflect the new system.
Designers
Alpha testing and onboarding
Chapter designers were end users and feedback partners through alpha testing and onboarding. Their friction points shaped every iteration of the publish command, quality checks, and onboarding wizard.
The team that assembled after the demo
Manager
PB Enablement Manager
Requested my capacity immediately after the demo. We have been presenting together to executives and teams across the domain, co-leading the expansion strategy and cross-functional programme governance.
Engineer
Distinguished Engineer
Technical architecture partner for the Personal Banking Intelligence Hub infrastructure, context library schema design, and integration with enterprise data sources.
Full-Stack Developer
Building and extending the context library and hub infrastructure alongside me as the platform scales to the full 800-person domain.
Coach
Agile Coach
Co-designing the training strategy and masterclass programme with me, ensuring the learning approach works across all five discipline personas and seniority levels.
What it delivered.
Executive endorsement and expansion mandate
DesignOps leadership identified the system as a potential replacement for existing project management tooling. The PB Enablement Manager requested my capacity within a week of the demo, expanding the mandate from 230 designers to 800+ cross-functional staff.
Governance automated
Automated WCAG 2.2 accessibility checks and design token compliance run on every publish. Governance embedded at the point of creation, eliminating compliance review cycles that previously added manual overhead to every release.
Context library problem solved
The context library that developers said required manual uploads from 800 staff now populates itself as a side effect of normal work. Machine-readable, Cursor-queryable, zero maintenance burden.
Cross-functional velocity unlocked
Product Managers, BAs, and engineers can now pull live design context, user research, and journey maps directly via Cursor agents, removing the standard design-to-product handoff friction loop entirely.
What changed
| Before | After |
|---|---|
| Design outputs buried in personal repos and email | Every output has a permanent URL, indexed and discoverable org-wide |
| Cross-chapter knowledge invisible to stakeholders | Product Managers and engineers pull live design context without asking |
| Duplicate research across chapters every quarter | 600+ outputs indexed, searchable by keyword, author, type, or team |
| Governance exposure from untracked AI outputs | Automated WCAG 2.2 and token compliance on every publish, zero manual oversight |
| Context library requiring manual maintenance by 800 staff | Machine-readable context builds itself as a side effect of normal work |
| Tool adoption stalled by technical complexity | 100% activation rate via self-serve wizard, zero support dependency |
| AI capability siloed in individual experts | Cursor command library shared, attributed, and inherited by every new joiner |
What I'd do differently.
The system scaled faster than the change management. I would invest in the enablement programme in parallel with the technical build rather than sequentially, which would have compressed the timeline between POC and enterprise expansion. The infrastructure was ready before the culture was. A more deliberate communication strategy from week one would have made the difference.
What I am most proud of is not the technology. It is that I treated an internal operational problem as a product problem, validated it through a proof of concept, and let the results earn the mandate for scale. The cross-functional team that now delivers this programme assembled organically around the platform. That is the outcome I was engineering for from day one.
Five days to POC. Eight hundred people in the expansion. No developers. AI-first from day one.