Ramy Nassar
Most AI initiatives stall between the pilot and the operating line. Boards have approved spend, teams have shipped demos, and nothing in the actual product, process, or P&L has changed. The pressure now is to move from curiosity to deployed advantage, with governance that holds up to scrutiny and design choices that customers will actually use.
Ramy Nassar is a former Mattel Head of Innovation and author of the AI Product Design Handbook who helps senior leaders move AI initiatives out of pilot purgatory into operational, governed, customer-facing products.
Full Profile
Why organisations work with Ramy Nassar
- He brings a published, structured method for AI product design from idea to launch, set out in the AI Product Design Handbook and used by enterprise teams who need a repeatable process rather than another inspiration deck.
- He has run innovation inside Mattel and led design and strategy at Architech for Apple, Air Canada, CIBC, Facebook and Rogers, so the operational reality of shipping new products inside large organisations is in the room.
- His work separates “AI Now, Next and Never,” giving boards a defensible position on what to deploy, what to wait on, and where to draw governance lines.
- He teaches design thinking and innovation at McMaster University and Toronto Metropolitan University, and executive programmes at Schulich ExecEd, which keeps the frameworks tested against working leaders.
- Client engagements span Apple, TD Bank, TELUS, Verizon, New Balance and the Government of Canada, covering both regulated and consumer environments.
Biography highlights
- Author, AI Product Design Handbook, and the forthcoming The World After AI (early 2027)
- Former Head of Innovation, Mattel, where he launched the company’s first innovation lab
- Former Managing Director, Design and Strategy, Architech
- Founder, 1000 Days Out, advisory practice on AI and emerging technology
- Faculty: McMaster University, Toronto Metropolitan University, the Norwegian University of Science and Technology, Schulich ExecEd at York University
- Featured by CPA Canada at The ONE national conference; speaker at FITC, IxDA, FWD50 and World Usability Congress
Biography
Most AI projects inside large organisations have the same shape. A pilot ships, attention moves on, and the operating model does not change. Boards then have to decide whether the next round of spend should go into more experiments or into the harder work of getting AI into the product and into the workflow. That is the decision Ramy Nassar is built to inform.
The AI Product Design Handbook sets out a repeatable process for taking AI products from idea to launch, with explicit attention to user trust, transparency and governance. It is used by product and design leaders who need a method, not a manifesto. The argument is grounded in his time as Head of Innovation at Mattel, where he launched the company’s first innovation lab and led emerging-technology work for Barbie and Hot Wheels, and earlier as Managing Director of Design and Strategy at Architech, where he ran client work for Apple, Air Canada, CIBC, Facebook and Rogers.
Through 1000 Days Out he advises senior teams at Apple, TD Bank, TELUS, Verizon, New Balance and the Government of Canada on AI adoption, strategic foresight, and product strategy. His forthcoming book, The World After AI, extends that foresight work, with the argument that the decisive variable in any AI future is the set of choices leaders make, not the technology itself. His framing of “AI Now, Next and Never” is built for boards that need to commit capital and set governance lines at the same time.
Faculty roles at McMaster University, Toronto Metropolitan University, the Norwegian University of Science and Technology, and Schulich ExecEd at York University keep the frameworks tested against working executives. CPA Canada featured him at The ONE national conference on turning insights into measurable impact, and his international speaking includes FITC, IxDA, FWD50, and the World Usability Congress.
Key speaking topics
- Artificial intelligence adoption and product design
- Responsible AI and governance
- Strategic foresight and scenario planning
- Innovation strategy
- Emerging technology and exponential change
- Digital product and customer experience design
Ideal for
- Boards and C-suite teams setting AI investment and governance policy
- Chief Product Officers, Chief Design Officers and Chief Innovation Officers moving from pilots to deployed AI
- Heads of Strategy and corporate development running scenario planning under technology uncertainty
- Public-sector leaders shaping responsible AI policy and procurement
Audience outcomes
- A defensible “now, next, never” view of where to deploy AI, where to wait, and where to refuse
- A structured method for taking AI features from concept to launch with governance built in
- Sharper foresight tools for testing strategy against multiple long-range scenarios
- Concrete language for communicating AI risk, ethics and product trade-offs to non-technical executives and customers
Talks
What AI can do in the enterprise today, what is realistic over the next five to ten years, and where governance should set hard limits.
Key takeaways:
- A working view of current AI capability, separated from vendor narrative
- A capital-allocation lens on which investments scale and which stay in pilot
- Governance lines for responsible enterprise use, including where to walk away
A working foresight toolkit for anticipating disruption and testing strategy against multiple long-range scenarios.
Key takeaways:
- Methods for spotting weak signals and trend convergence before competitors act
- Scenario-planning techniques, including horizon scanning and Janus Cones, applied to live decisions
- A move from reactive crisis management to proactive direction-setting
How AI, quantum, and blockchain are converging, and how leaders decide when to experiment, when to scale, and when to wait.
Key takeaways:
- A jargon-free read on AI, quantum, and blockchain, framed by strategic impact
- A timing framework for emerging-technology bets
- Strategies that hold up as multiple technologies converge
How large organisations build the speed of a startup into enterprise structures that were built to stay the same.
Key takeaways:
- What slows enterprise innovation in practice: silos, legacy process, and risk aversion, and where to intervene
- Innovation as a managed system rather than a culture campaign, drawn from the innovation lab he launched at Mattel
- How to use AI to compress the cycle from idea to working prototype
How to deploy AI across an enterprise without the accuracy and governance failures that erode customer and regulator trust.
Key takeaways:
- The specific failure modes that derail adoption, including bias, hallucination, and weak explainability, and how to design against them
- What an AI governance function actually contains, based on the policies and governance committees he has built inside regulated organisations
- How to set governance lines that hold up to board and regulator scrutiny while keeping deployment moving