AI Workforce Enablement · Experience Design Leadership
I'm Nicholas Babb. For 20+ years I've led experience and design at the enterprise level. Today I pair that with hands-on AI engineering. I build production-grade systems and teach teams to build alongside me. I don't just adopt AI tools; I turn entire workforces into people who ship with them.
At McKesson I lead an AI-driven CX practice that converts annual operating plan and MBO targets into shipped, measurable outcomes: over $120M 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 establishing the workflows, the guardrails, and the coaching that let 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.
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.
Coached non-traditional builders to ship real software in VS Code with AI pair-programming: agentic editing, spec-first prompting, and review discipline, so design and product can prototype production code, not just mockups.
Adoption without governance is a liability. I pair every rollout with guardrails: review gates, data-handling rules, and bank-grade quality standards, so velocity never comes at the cost of trust.
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.
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.
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.
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.
Working sessions where people actually build: Claude-to-Figma via MCP, vibe-coding in VS Code, prompt design. Attendees leave having shipped something real, not just watched a slide.
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.
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.
Review gates, data-handling rules, and bank-grade quality standards baked into every program, so adoption scales without creating risk. Velocity and trust, advancing together.
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.
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.
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.
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.
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.
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.
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.
Map the real work, the friction, and where AI moves the needle. No theater. Find the workflows where adoption pays back fastest.
Prove it on live work with a small group. Build the patterns and guardrails on real stakes, not in a sandbox.
Workshops, playbooks, and pairing that turn the pilot into a transferable skill. People learn by shipping.
Bake in review gates, data rules, and quality standards so speed and trust scale together.
Roll the proven pattern across teams, measure the lift against business KPIs, and hand off durable capability.
Engineered AI-integrated UI solutions and led UX overhauls with lean UX methods and rapid prototyping, boosting satisfaction 30% and accessibility compliance, and aligning technical trade-offs with user and business goals.
Led end-to-end enterprise UX of large scope: design systems, journey mapping, and accessibility from inception, plus mentoring designers across product, engineering, and research.
UI/UX consulting, marketing analytics and KPI ownership, and creative/art direction across enterprise clients, the operating and storytelling foundation under everything since.
I started as a graphic designer (BFA, Metro State University of Denver) and spent two decades growing into enterprise experience leadership. Along the way I 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.
Tell me what you're trying to move, and I'll tell you exactly how I'd approach it.