• To succeed, businesses must validate their AI
ideas, reduce risks, and build solutions that
are desirable, viable, and feasible.
Three Key Benefits
• Extract & analyze key assumptions behind your AI idea • Identify & prioritize the riskiest challenges before investing resources • Develop structured experiments to validate AI-driven business concepts
Duration: —| Format: Live Online Interactive Workshop |
DAVID J. BLAND
David Bland is a leading expert in business experimentation and innovation strategy. As the co-author of ‘Testing Business Ideas,’ - Internationally Best Selling Book -David Bland has helped top companies worldwide implement structured validation methods.
Take the Next Step in AI-Driven
Experimentation
What You’ll Gain from Attending This Workshop
Agenda
Date: August 2, 2025
Location: Online (via Zoom)
4:00 PM – 5:30 PM
Open Dialogue with Global Innovation Experts A dynamic discussion addressing the most pressing challenges in
innovation environments, featuring insights, solutions, and inspiring
experiences from renowned speakers in the field.
6:00 PM – 8:00 PM
Interactive Session with David Bland An engaging, hands-on session where David will present the latest
methodologies for testing and developing innovative ideas, with
real-world business examples and actionable tools.
8:00 PM – 9:30 PM
Practical Applications with Assistant Consultants Interactive workshops led by expert consultants, offering participants the chance to apply innovation tools and techniques in real time, with open discussions and direct feedback.
Know how to extract and prioritize assumptions for AI solutions Design AI-driven experiments to
reduce risk and maximize impact Apply a structured framework to validate AI business ideas
Learn from a global expert in business experimentation
Secure Your Spot Today!
Validate. Innovate. Succeed
WHY DO MANY AI PROJECTS FAIL ?
Because businesses invest in untested ideas without validation—wasting
time, money, and resources. The Testing Business Ideas framework helps companies avoid these costly
mistakes by using structured, data-driven experiments.