Ramy Nassar

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
• 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, 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 framing of “AI Now, Next and Never” is designed for boards that need to commit capital and set governance lines at the same time, and his strategic foresight work gives leaders a structured way to test decisions against multiple long-range scenarios.

Faculty roles at McMaster University, Toronto Metropolitan University 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