Innovating at the Pace of AI
Executive Technology Board | Toronto, March 31, 2026
The pace of change today is the slowest it will ever be. Competitive advantage increasingly depends on scaling AI-driven innovation and leveraging the broader ecosystem — yet execution remains the hard part: integrating models into real workflows, data, platforms, and controls at enterprise speed.
The hype cycle has matured into something more complicated: real deployments, real disappointments, and open questions about what sustainable value actually looks like at scale. The closed sessions are designed to surface what peers are genuinely experiencing — not the polished version.
Outcomes for the day
- Enterprise lessons learned: repeatable patterns that increase AI innovation velocity inside large organizations—what is working, what is not, and what operating model changes enable safe scale.
- External signals: outside-in perspectives on what is emerging in real deployments, informing where to partner ahead.
The goal is not consensus; it is collective intelligence.
Pre-work reflection
- One AI initiative delivering measurable business value — and why succeeded.
- One AI initiative that stalled — and the root cause.
- One architectural or organizational decision currently being wrestled with.
- One “most dangerous assumption” embedded in our current AI strategy.
Closed Session 1 | AI: What’s Actually Working
Core question: What AI programs are producing measurable outcomes — and what separated them from everything else?
Tensions to pressure-test
- Central platform vs federated execution: leverage/reuse and risk reduction vs speed/context/adoption.
- Concentrated bets vs portfolio experimentation: credibility and funding focus vs learning rate and step-change discovery.
Closed Session 2 | Architecture, Stack, and Concentration Risk
Core question: Where is AI stack investment concentrating over the next 12–18 months — and what strategic risks are being designed around?
Tensions to pressure-test
- AI apps/workflows vs orchestration/foundations: differentiation and integration vs reliability, controls, and scalability.
- Standardize on a few providers vs design for optionality: speed and simplicity vs resilience, leverage, and long-term cost.
Closed Session 3 | Ecosystem, Partnerships, and What’s Next
Core question: Where does ecosystem leverage accelerate the strategy — and where does it create dependencies that cannot be afforded?
Tension to pressure-test
- Build vs partner: differentiation and control vs time-to-advantage and ecosystem leverage.
Open Session | External Perspectives: What’s Coming Next
A curated conversation drawing on Toronto’s AI research and startup ecosystem, with research leadership, venture investors, and founders on:
- Where the frontier is moving and what will matter next
- What is being built now—and what is being seen in real deployments
- What effective enterprise–ecosystem collaboration looks like in practice: where to partner, what to build, and how to avoid brittle dependencies
Executive Technology Board (c)