Robert Vaughn Elliott
Strategy · Agentic AI · Design Science · Data Science
Strategy for complex problems in an agentic AI era.
I research how agentic AI can augment strategy design—combining design science and data science with decades of experience turning complex problems into business outcomes.
About
The work has changed. The problem has not.
Much of my career has been spent where technology, economics, organizations, and difficult decisions meet.
At IBM, Intria-HP, and TELUS, that meant helping shape managed-services capabilities, outsourcing arrangements, large complex deals, strategy, innovation, and roadmaps. The technologies changed. The recurring challenge was translating complexity into something an organization could understand, decide on, and execute.
Today, I am bringing that practitioner experience into a new research question:
How can agentic AI augment the way organizations design strategy?
My work explores that question through agentic artificial intelligence, data science, and design science research— particularly where problems are ambiguous, interconnected, and resistant to a single “right” answer.
The goal is not AI for its own sake. It is better ways to frame problems, explore alternatives, make decisions, and turn strategy into action.
Current research
What I’m exploring now
Agentic AI and strategy design
How might AI agents augment—not merely automate—the work involved in developing strategy? I am interested in how people and agents might work together to frame complex situations, develop alternatives, interrogate assumptions, synthesize evidence, and carry learning forward.
Design science and wicked problems
Strategy problems are rarely clean optimization exercises. They involve incomplete information, competing perspectives, changing constraints, and choices whose consequences can alter the problem itself. Design science provides a disciplined way to create and evaluate artifacts intended to improve these problem situations.
Data science as evidence
Data science provides an analytical layer for working systematically with evidence, models, uncertainty, and learning.
human judgment + organizational context + data + agentic systems
Foundations
Experience that shaped the work
IBM
Building managed-services capability
Part of teams developing IBM Global Services' data-centre and desktop outsourcing capabilities, including work associated with the IBM/Bell strategic alliance.
Intria-HP
Turning infrastructure into a services model
Worked on large data-centre outsourcing opportunities as CIBC's internal technology environment evolved into a long-term HP outsourcing relationship, with a focus on service and solution development.
TELUS
From complex deals to strategy and innovation
Worked on a series of large, complex business opportunities and later supported strategy, innovation, and roadmap development for TELUS Business.
Education
A career built on learning across disciplines
My formal education has followed the same questions as my work—from computing, to the economics of decisions, to data and artificial intelligence.
- 1984
Bachelor of Science, Computer Science — Distinction
University of Regina
- 2000
Master of Science, Financial Management
University of London
- 2025
Master of Science, Data Science & Artificial Intelligence — Distinction
University of London / Goldsmiths
Capabilities
What I bring to the problem
- Strategy & innovation
- Large complex deals
- Roadmap development
- Managed-services development
- Solution development
- Information & communications technology
Current focus
What I’m researching now
- Agentic artificial intelligence
- AI-augmented strategy design
- Design science research
- Data science
Contact
Interested in the same questions?
I am interested in conversations at the intersection of strategy, agentic AI, design science, data, and complex organizational problems—particularly where emerging technology has to become something practical.