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richardsonchrisj/README.md

Chris Richardson

AI Learning Strategy · People Science · Workforce Transformation

I design learning systems for organizations entering the AI-native era — systems that help people move from awareness to fluency, from experimentation to adoption, and from tool usage to measurable changes in how work gets done.

My work sits at the intersection of enterprise AI adoption, behavioral science, learning design, and operating-model strategy. The throughline is simple: advanced tools only matter when people can use them fluently, safely, and meaningfully in the flow of real work.

At Genentech, my work has focused on AI-enabled learning design strategy: translating emerging technology into scalable capability, practical adoption systems, and measurable behavior change in a high-complexity biotech environment.

I. Current Work

Primary framing I build the learning architecture that helps people and organizations move from AI awareness to AI fluency.

Core questions

  • How do we turn AI adoption into durable capability?
  • What does effective human–AI collaboration require from a learning system?
  • How should organizations measure behavior change instead of completion?
  • What operating models help learning teams move with product-level speed and scientific discipline?
  • How do we make complex systems feel usable without making them shallow?

Working thesis The future of learning is not more content. It is better infrastructure: adaptive systems, useful feedback loops, intelligent workflows, and learning experiences that change how people perform.

II. Strategic Lanes

Lane Focus
AI Learning Strategy Enterprise AI adoption, capability-building, learning ecosystems
People Science Behavior change, motivation, cognitive load, trust, transfer
Agentic Learning Systems AI coaches, tutors, copilots, reflective agents, workflow partners
Measurement & Feedback Learning analytics, adoption signals, performance data, experimentation
Operating Model Design Governance, workflows, team rituals, scalable enablement systems
Experience Design Human-centered tools, prototypes, learning journeys, interface clarity

III. What I Build

Learning systems AI-enabled learning journeys, adoption playbooks, simulations, workshops, toolkits, and reusable frameworks.

Agentic experiences Prototypes and workflows where AI supports coaching, reflection, analysis, practice, and performance support.

Measurement systems Feedback loops that make learning visible: what people try, where they stall, what changes, and what value emerges.

Strategic artifacts Executive narratives, adoption strategies, operating models, enablement plans, and systems that help teams move with clarity.

IV. Public Work Index

Repo What it is
ScenarioChoiceHandler Capture learner choices from Rise 360 scenarios as xAPI data for learning analytics
agentic-learning-patterns Pattern library for AI coaches, tutors, copilots, and reflective agents
learning-measurement-playbook Templates for measuring learning transfer, AI adoption, and behavior change
ai-learning-systems-lab Working notes and prototypes behind the repos above

V. Selected Tools

AI / Agents        Claude · GPT-4/5 · prompt systems · agent workflows · evaluation
Design             Figma · Miro · Gamma · design systems · rapid prototyping
Web / Technical    Next.js · TypeScript · Node.js · Tailwind · Python · GitHub
Learning           Articulate Rise · Storyline · Canvas · LMS/LXP ecosystems
Data / Ops         Tableau · Airtable · Notion · Tana · workflow automation

VI. Point of View

AI adoption is a behavior-change problem. Most organizations frame AI adoption as a tooling challenge. The harder work is human: trust, relevance, confidence, incentives, cognitive load, and the fear that lives just beneath the surface of “enablement.”

Learning needs product discipline. Modern learning teams need to think less like content factories and more like product, research, and systems teams: define the behavior, ship the intervention, measure the signal, and improve what survives contact with reality.

The interface is part of the strategy. If a system is too complex to use, it is not yet intelligent enough. Good design makes advanced capability feel natural without hiding the rigor underneath.

Measurement changes the conversation. Completion data can tell you that something happened. Better systems help reveal whether capability changed, whether behavior transferred, and whether the work improved.

VII. Portfolio

Projects, experiments, and writing: chrisrichardson.dev

VIII. Background Signal

Before working in biotech and AI learning strategy, I studied media theory, cultural systems, and the way stories shape behavior. That still informs my work: technologies are never just tools. They are meaning systems, power systems, and behavior systems.

I am also a longtime Batman reader and a published author on the Joker’s role in pop culture. Great villains — like great learning systems — reveal the hidden architecture underneath people’s choices.

IX. Open To

Conversations about:

  • AI learning strategy
  • People science
  • Workforce transformation
  • Agentic coaching
  • Enterprise AI adoption
  • Learning measurement
  • Behavioral design
  • Future-of-work systems

Design the system. Measure the behavior. Make the future usable.

Pinned Loading

  1. ScenarioChoiceHandler ScenarioChoiceHandler Public

    Capture learner choices from Articulate Rise 360 scenarios and send structured xAPI-style data for learning analytics.

    HTML 3

  2. agentic-learning-patterns agentic-learning-patterns Public

    A pattern library for designing AI coaches, tutors, copilots, and reflective agents for workplace learning.

  3. ai-learning-systems-lab ai-learning-systems-lab Public

    Prototypes, patterns, and research notes for AI-enabled learning systems and workforce capability design.

    Python

  4. learning-measurement-playbook learning-measurement-playbook Public

    Templates and models for measuring learning transfer, AI adoption, and behavior change.