Ryan C. Burke, PhD / Enterprise AI leadership

I build AI that makes it into use.

From building a global team to deploying AI products, I connect strategy, engineering and adoption so better decisions become part of everyday work.

See selected work Start a conversation
Ryan C. Burke, PhDGlobal Data Science Lead at Royal Canin (Mars)
Biomedical scientist turned enterprise AI leader
Based in France · Open to international opportunities
0 → ~15Built and scaled a global AI and Data Science team of employees and partners
~€300KDemonstrated energy savings at the first manufacturing site
Multiple productsShared GenAI and agentic AI foundation now used across Royal Canin and available to Mars Petcare

01 / Selected work

Build the capability. Prove the value. Make it repeatable.

Three examples of my work across organizational design, production AI and reusable platforms.

01

Building a global AI function

The need
Move from scattered initiatives to a coordinated capability that could support different business domains.
My role
Built the team, product aligned operating model and discovery framework for testing feasibility and value before major investment.
Result
Scaled to approximately 15 employees and partners supporting manufacturing, supply chain, commercial and R&D work across regions.
02

Manufacturing intelligence at scale

The need
Help sites make better operational decisions and move beyond a promising prototype.
My role
Led the AI Factory Optimiser from architecture through production deployment, combining predictive models, optimization, virtual sensors and digital twins.
Result
Demonstrated approximately €300K in energy savings at the first site, with broader rollout being evaluated.
03

A reusable foundation for GenAI

The need
Give teams a way to build useful applications without recreating evaluation, monitoring and governance each time.
My role
Led a shared GenAI and agentic AI platform with enterprise connections, LLM-as-judge evaluation and usage monitoring.
Result
A common foundation powering multiple Royal Canin products and made available across Mars Petcare.

02 / How I lead

The model is only part of the work.

Enterprise AI succeeds when technical design, product ownership and adoption reinforce one another.

01

Start with a decision

Frame the business problem and the decision to improve before choosing a technology.

02

Test before scaling

Use discovery, feasibility and prototypes to decide what deserves a product investment.

03

Build for use

Align data science, engineering and product around ownership from MVP through operation.

04

Earn trust

Make evaluation, responsible AI and practical literacy part of delivery and adoption.

03 / Foundation

Scientific rigor, product judgment, global leadership.

My career began in neuroscience and biomedical research, where careful measurement and difficult questions were everyday work. I brought that discipline into clinical data science, consulting and product teams, then into building an enterprise AI function.

Today I lead across strategy, product delivery, platform choices and organizational design. I work with executives and practitioners alike to make AI useful, understandable and accountable.

04 / Speaking & writing

Thinking in public.

I write and speak about decision intelligence, AI strategy and the organizational work required to turn technology into impact.

Writing / The Decision LayerEssays on AI and the decisions it should improveRead on Substack ↗Speaking / CDO Retail Exchange 2026Context as the Missing Ingredient for AIConnect and ask about the talk ↗

05 / Career

A path from research to enterprise AI.

Royal Canin (Mars)

Global Data Science Lead

Jellysmack

Senior Data Scientist · product ML

Novo Senso & Limoges University Hospital

Animal health technology and clinical data science

Independent practice

AI and data science consulting across healthcare, biotech and finance