Brief

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, what to build, and what to avoid.

The goal is not consensus; it is collective intelligence.

Pre-work reflection

One AI initiative delivering measurable business value; and the specific reason it worked.
One AI initiative that stalled - and the root cause.
One decision currently being wrestled with (architecture, operating model, risk posture, or vendor strategy).
One “most dangerous assumption” embedded in current AI strategy.

Closed Working Session | Corporate Innovation in the Age of AI

1) AI: What’s Actually Working

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:

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.

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.

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?

Tensions 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 set of external perspectives from research leadership, venture investors, and founders, and a conversation on frontier signals, deployment learnings, and collaboration patterns.

The evolution of the AI stack
The future of models
Vertical vs horizontal AI
Individual vs institutional AI

Executive Technology Board (c)

Innovating at the Pace of AI

Executive Technology Board | Toronto, March 31, 2026

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, what to build, and what to avoid.

The goal is not consensus; it is collective intelligence.

Pre-work reflection

One AI initiative delivering measurable business value; and the specific reason it worked.
One AI initiative that stalled - and the root cause.
One decision currently being wrestled with (architecture, operating model, risk posture, or vendor strategy).
One “most dangerous assumption” embedded in current AI strategy.

Closed Working Session | Corporate Innovation in the Age of AI

1) AI: What’s Actually Working

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:

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.

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.

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?

Tensions 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 set of external perspectives from research leadership, venture investors, and founders, and a conversation on frontier signals, deployment learnings, and collaboration patterns.

The evolution of the AI stack
The future of models
Vertical vs horizontal AI
Individual vs institutional AI

Executive Technology Board (c)