Building the prototype that wins the contract, before a line of code gets written

Situation

Federal agencies don't sign a contract on a pitch deck. Every engagement starts the same way: prove the idea works before anyone commits real budget to it, on a clock measured in days, not quarters. Years later, the company faced a different version of the same problem internally, "make the company AI-native" is a slogan until 1,500 employees have an actual path to act on it.

Approach

Pre-Discovery is the company's answer to the first problem: a 10-day sprint, stakeholder interviews, and a modified Scrum process that ends in a working concept prototype. Ten days isn't enough time for real end-user access, so we design around proxy users from the start. I helped design that process with my team, workshopping it in Mural, and I help run it. A federal customer can see and use a working prototype before competitors have written a proposal, and these sprints have helped win federal contracts. Once a customer signs, a longer Discovery program, 30, 60 or 90 days with real users and data, takes the research further. I've contributed to four of those prototypes, each one a full concept dashboard built in Figma and Mural for a different federal problem.

Leadership & AI Strategy

The AI problem needed a different kind of prototype: a curriculum. I’m the project manager for Agile Defense University, built on the company's own AI Learning → AI Leaning → AI Leading adoption framework, co-authored by our Chief Experience Officer and President. My team and I draft storyboards, write and validate scripts, then update the storyboards to match before anything moves into production, the same repeatable pattern we're now using to build out the rest of ADU. ADU's goal is for every employee to know exactly where they stand on their career path and to have something they can act on immediately, at whatever speed makes sense for them. It helps employees, and it scales the company's AI fluency without a training department that grows headcount every time the curriculum does.

The clearest example is AI Foundations, which we just released to the whole company. It explains AI so anyone can follow it: where it came from, what it is, how to use it well, and what can and can’t go into an AI system depending on someone’s clearance and the classification of the information. Every employee now takes it before using our internal AI tools. I helped storyboard, script, and build it. Make the problem simple, then solve the simple problem: that course is the rule in practice.

My first client-facing project management effort was a 16-week, 8-sprint pilot of a defense data fabric ecosystem, starting in April 2025. I project-managed a 13-person team of strategists, architects, data engineers, developers and governance advisors, with five government stakeholders, and supported discovery along the way. It went live as a working system on schedule. The client asked for more work, and other branches of the military asked for something similar.

I’m also the project manager for Experience Studio, the four-person design team behind this work, co-led the Labs project management office for six months during a post-acquisition reorg (reorgs are never as clean as the org chart makes them look), and created Fast Plays. I built them from our internal methods wiki as my contribution to Pre-Discovery’s design: about 15 templated workshop methods that let me set up a discovery workshop in minutes, even before every stakeholder is known, and that any employee can run without deep facilitation experience.

Solutions Architects had the same problem in miniature: a multi-phase process with a lot of tribal knowledge nobody had written down. I helped organize and design a Jira board that auto-populates each phase's subtasks, linked to the OneDrive folder holding that phase's artifacts and a reference for how to fill each one out. A new solutions architect can join an effort already in progress, at any phase, and know immediately what to do and where to find it, without tracking someone down to ask.

Outcome

Four concept prototypes gave federal clients something to react to before signing anything, real screens, realistic synthetic data, real decisions about what leadership needs to see first. ADU is now teaching around 1,500 employees the same discovery-first thinking, at company scale.

• Project-managed a 16-week defense data fabric pilot that went live on schedule and drew requests for similar systems
• Help design and run 10-day Pre-Discovery sprints, which have helped win federal contracts
• Contributed to 4 federal concept dashboards through Pre-Discovery
• AI Foundations: required for every employee before using our internal AI tools
• Around 1,500 employees reached through Agile Defense University
• Project manager for Experience Studio, a 4-person design team
• Fast Plays: about 15 templated workshop methods that set up a discovery workshop in minutes
• TASK Agents: AI agents that turn interview notes and stakeholder feedback into personas, affinity maps and themes, which I helped build
• Jira board built for the Solutions Architect team, so a new SA can join an effort at any phase and know immediately what to do