I'm Nicholas Babb. For 17 years I've worked across design, marketing, and engineering. Today I lead a design, engineering, and data science team at McKesson that modernizes how the business runs, and I still build the tools myself. I automate what clear rules can handle and use AI only where it beats a simpler solution.

$6M+ Annual cost savings driven
25% Reduction in customer friction
30% Lift in user satisfaction
20+ Led across design, engineering & data science
4 Production AI systems built solo
01 · What I actually do

Most leaders talk about AI adoption.
I run it on the floor.

At McKesson I lead cross-functional teams across UI/UX design, frontend and full-stack engineering, and data science. We replace manual, error-prone work with automation and scalable cloud tools, and turn leadership priorities into shipped, measurable outcomes: over $6M in annual savings, customer friction down 25%, satisfaction up 30%. The lever behind those numbers isn't a single tool. It's getting an entire cross-functional workforce productive with AI.

That means setting the design vision and craft standards, establishing the workflows and guardrails, and building a culture of thoughtful critique and accountability, so designers, engineers, and product people move from "curious about AI" to "shipping with it every day." I build the patterns, prove them on real work, then scale them across teams.

Claude → Figma via MCP

Wired Claude directly into the design pipeline through the Model Context Protocol, turning prompts and product intent into structured Figma output. Designers go from blank canvas to working layouts in minutes, not days.

AI-assisted building

Coached non-traditional builders to ship real software with Claude Code, Cursor, Antigravity, and VS Code: agentic editing, spec-first prompting, and review discipline, so design and product can prototype production code, not just mockups.

Responsible adoption

Adoption without governance is a liability. I pair every rollout with guardrails: review gates, data-handling rules, and quality standards suited to a regulated healthcare environment, so velocity never comes at the cost of trust.

Scale & change management

I run the workshops, build the playbooks, and mentor the practitioners and senior stakeholders. The goal is durable capability: teams that keep compounding after I've moved to the next problem.

02 · The playbook

Not a theory of AI adoption.
A playbook that's already running.

At McKesson I built and deployed a complete AI enablement program from the ground up. Not a slide deck, but a living system of programs, rituals, and reusable assets that move people from "curious" to "shipping." Here's what comes with me.

Founding member

The AI Council

I'm a founding member of the company's AI Council, the cross-functional body that sets AI adoption strategy, standards, and governance. I help decide how a Fortune 10 enterprise adopts AI responsibly, then carry those decisions down to the teams doing the work.

Deployed

Lunch & Learns

A recurring series that demystifies AI for the whole org: live demos, real use cases, and open Q&A. Low-pressure entry points that turn skeptics into participants and keep the adoption funnel full.

Deployed

Hands-on build clinics

Working sessions where people actually build: Claude-to-Figma via MCP, AI-assisted coding in Claude Code and Cursor, prompt design. Attendees leave having shipped something real, not just watched a slide.

Deployed

Stakeholders in the build

I pull business stakeholders directly into rapid-prototyping sprints. They vibe-code alongside the team, so requirements become a working prototype in the same room, in the same session. No requirements telephone game.

Deployed

Reusable AI asset library

A governed, shared library so nobody starts from a blank page. Each asset encodes the org's best practice and compounds as the library grows.

Prompt templatesMarkdown specsCrystal memory storesStarter kits
Deployed

Governance & guardrails

Review gates, data-handling rules, and quality standards suited to a regulated healthcare environment, baked into every program, so adoption scales without creating risk. Velocity and trust, advancing together.

You wouldn't be starting from zero.

The curriculum, the reusable assets, the governance model, and the council structure already exist, built and proven inside a Fortune 10 enterprise. Hire me and the playbook arrives on day one.

03 · Proof, not slides

Four production systems.
Built solo, with AI tooling.

These aren't prototypes. They're enterprise-grade applications I architected and shipped, using exactly the AI-accelerated workflows I teach. They're how I know what "good" looks like when you put AI in the hands of a builder.

AI Cost Governance

The control plane for enterprise AI spend.

Centinel governs how organizations consume AI: budgets, rate limits, intelligent model routing, PII scanning, and internal policy enforcement, so finance, security, and engineering finally share one source of truth. Built to a strict charter: zero external dependencies, nothing leaves the customer's infrastructure, runs on a $5/mo VPS, fully async.

  • Bank-grade data discipline: exact numerics, validated at every boundary
  • Local-first license validation via signed JWT / JWKS
  • Hardened with a deep automated test suite
PythonFastAPIPostgreSQLstdlib-only
AI Regulatory Intelligence

Regulatory applicability, on autopilot.

Guardian monitors regulations, scans codebases for applicability, generates compliance attestations, and even predicts upcoming legislation. It turns a slow, manual, expert-only process into a continuous, auditable system, with multi-LLM model benchmarking baked in.

  • 37 data tables, ~90 API endpoints, 28 dashboard experiences
  • Codebase scanner that maps real code to regulatory obligations
  • Scout: forward-looking legislative prediction
TypeScriptHonoReact 19SQLite + DrizzleMulti-LLM
Codebase Intelligence

Give any AI agent real context, without your code leaving the building.

Geodesic scans a repository entirely on your machine and produces three artifacts every run: a full architecture map, an agent-ready skill file (machine-readable context for Cursor, Claude Code, and Copilot), and a scored gap report with exact file:line findings. A mandatory PII/PHI intercept scrubs every value before any AI call and writes a tamper-evident, SHA-256-linked attestation chain, so regulated teams can point AI at their code without a BAA.

  • Local-first engine: source never leaves the environment
  • HIPAA-grade PII scrubbing + tamper-evident attestation chain
  • "Crystal Store" learning layer: ~70% token reduction on cache hits
  • Bring your own AI: Anthropic, OpenAI, Gemini, Azure, or local Ollama
TypeScriptVS Code / Cursor / JetBrainsCLIMulti-provider AI
AI Dev Orchestration

AI writes the code. HyperPace keeps the sprint honest.

HyperPace is a local-first orchestration layer between engineering teams and Atlassian. It turns PRDs into fully-pointed Jira stories, watches the IDE as code is written, catching AI-authored bursts and edit collisions in real time. It auto-assigns tickets on save and pushes SonarQube quality and Figma drift back to the right Jira issue. PII is redacted before any AI call, and everything runs on the developer's machine.

  • Document Shredder: PRD → Jira stories with AI-assigned story points
  • Real-time burst & collision detection from inside the IDE
  • Encrypted (AES-256-GCM), provider-agnostic across seven LLMs
  • Free IDE extension acquires devs; paid dashboard pulls in their leaders
TypeScriptNext.jsVS Code extensionTauriJira / Confluence

Every one of these was designed, built, and hardened by one person moving at the speed of a team, because the AI workflow is the multiplier. That's exactly the capability I'd bring to your teams.

04 · How I enable a workforce

A repeatable path from
curiosity to capability.

  1. 01

    Assess

    Map the real work, the friction, and where AI moves the needle. No theater. Find the workflows where adoption pays back fastest.

  2. 02

    Pilot

    Prove it on live work with a small group. Build the patterns and guardrails on real stakes, not in a sandbox.

  3. 03

    Enable

    Workshops, playbooks, and pairing that turn the pilot into a transferable skill. People learn by shipping.

  4. 04

    Govern

    Bake in review gates, data rules, and quality standards so speed and trust scale together.

  5. 05

    Scale

    Roll the proven pattern across teams, measure the lift against business KPIs, and hand off durable capability.

05 · Track record

From design leadership
to AI in production.

Jan 2026 – Present Remote

Senior Project Manager · McKesson

  • Lead cross-functional teams across UI/UX design, frontend and full-stack engineering, and data science, setting direction, standards, and a culture of critique and accountability.
  • Modernize core operational processes, replacing manual work and workarounds with scalable cloud-based tools that improve speed, accuracy, and visibility for the teams who depend on them.
  • Match the solution to the problem: automate what clear rules can handle and reserve AI for work that genuinely requires judgment, keeping tools simpler, lower-cost, and easier to govern and maintain.
  • Size new initiatives by business impact, feasibility, and risk, then build the business case and align product, engineering, compliance, and business leadership to move them from concept to production.
  • Established governance for responsible AI adoption, including review gates, data-handling rules, and quality standards suited to a regulated healthcare environment.
  • Built production AI workflows, including a Claude-to-Figma pipeline over Model Context Protocol and AI-assisted building in Claude Code, Cursor, and Antigravity, shortening concept-to-prototype cycles.
  • Evaluate and select AI tooling, and lead enablement through workshops, playbooks, and hands-on coaching that put technical capabilities in plain language.
  • Delivered measurable outcomes: $6M+ in annual cost savings, a 25% reduction in customer friction, and a 30% lift in user satisfaction.
Oct 2023 – Jan 2026

Senior User Interface Engineer · McKesson

Built the UI and design foundation the current practice runs on, and was promoted as the approach scaled. Improved user satisfaction through data-informed design and accessibility-first engineering, automated repetitive work and simplified interfaces to cut manual effort, and aligned technical trade-offs with product roadmaps to keep launches on schedule.

Sep 2019 – Oct 2023 Arlington, VA

Senior UI/UX Designer · Enterprise Knowledge, LLC

  • Design lead for the U.S. Department of State: modernized how a team that monitors world events and prepares reporting for the Secretary of State and senior Department leadership tracked events, research, and communications, a process that had run on email for decades.
  • Led design of a portal that ingests reporting from wire services, operational desks, embassies, and vetted news sources and links each event to past Department responses and protocols, creating a single “golden thread” for analysts.
  • Led end-to-end design engagements for other enterprise clients with ambiguous scope, translating broad executive concepts into requirements and shipped products.
  • Established design system standards, versioning workflows, and journey-mapping methods that became team-wide playbooks, and mentored designers from concept through launch.
Oct 2018 – Sep 2019 Lake Worth, FL

UI/UX Consultant · Previlent Media

Advised small and mid-market businesses on web strategy, custom builds, and integrated marketing, owning work from wireframe through deployment and ongoing optimization.

Sep 2014 – Oct 2018 Boca Raton, FL

Marketing Director & Art Director · Dana Parham Holdings

As Marketing Director (2016–2018), led commercial strategy across sales, creative, and analytics, built the KPI, dashboard, and ROMI reporting that tied marketing spend to pipeline, and owned RFP responses and brand growth programs. As Art Director (2014–2016), ran daily creative operations, directed internal teams and outside partners, and managed departmental budgets.

Feb 2009 – Jul 2014 Vero Beach, FL

Senior Graphic Designer · Boston Barricade Co., Inc.

Designed scale-accurate barricades, point-of-purchase displays, and fixtures from architectural drawings for retail and mall environments, and qualified vendors and managed production quality across print and fabrication partners.

06 · About

Design taste. Engineering hands.
A rare combination.

Based
Richmond, Virginia area
Currently
Senior Project Manager, McKesson
Leading
20+ across design, engineering & data science
Background
Marketing leadership & physical product design

I started as a graphic designer, turning architectural drawings into retail fixtures and displays, then ran marketing and creative teams, owning budgets, RFPs, and vendor relationships. From there I moved into enterprise UX, and did something most design leaders never do: I learned to build.

That hybrid is the whole point. I can sit with executives and frame the business case, run the design-thinking workshop, and open the editor and ship the system. When I tell a team AI can change how they work, I'm not speculating. I've done it, in production, this week.

Based in the Richmond, Virginia area. Happiest when I'm turning a vague, high-stakes problem into something people can actually use.

07 · Let's talk

If you're trying to make your
workforce AI-capable, that's my work.

Tell me what you're trying to move, and I'll tell you exactly how I'd approach it.