AI Venture Velocity Challenge Submission Details

Problem Significance and Opportunity Quality (25%)

Problem Importance:

Does the venture address a meaningful, clearly defined problem with material business or societal relevance?

Customer Understanding:

Is there evidence that the problem is real and validated with target customers?

Market Opportunity:

Is the opportunity attractive in size and scope, with a clear target segment and value proposition?

Learning Velocity and Experimentation Rigor (30%)

Hypothesis Clarity:

Has the team identified the most important assumptions underlying the venture?

Experiment Design Quality:

Are experiments thoughtfully designed to test the highest-risk assumptions?

Evidence-Based Decisions:

Does the team use data and results to inform pivots, refinements, or strategic direction?

Speed of Iteration:

How quickly and systematically does the team test, learn, and iterate?

AI Integration & Leverage (20%)

AI as a Learning Accelerator:

How effectively are AI tools used to accelerate research, experimentation, prototyping, analysis, or discovery?

AI as a Solution Enabler (if applicable):

Does AI meaningfully enhance the product or service?

Intentionality and Appropriateness:

Is AI applied thoughtfully and strategically rather than superficially?

Evidence of Venture Progress (10%)

Progress Relative to Starting Point:

Has the venture made measurable progress during the competition window?

Uncertainty Reduction:

Have critical risks and assumptions been clarified or resolved?

Forward Momentum:

Is the venture demonstrating increasing clarity and direction over time?

Responsible Impact & Ethical Design (10%)

Societal Contribution:

Does the venture address important challenges and contribute positively to customers or communities?

Responsible AI Practices:

Does the team demonstrate awareness of ethical considerations such as privacy, bias mitigation, transparency, and governance?

Long-Term Impact Awareness:

Has the team considered potential unintended consequences and responsible scaling?

Adaptive Execution & Coachability (5%)

Responsiveness to Evidence:

Does the team adjust when data contradicts assumptions?

Decision Discipline:

Is decision-making clear and coherent?

Team Agility:

Does the team work cohesively and respond constructively to feedback?

Scoring Guidelines

Each criterion will be scored on a scale of 1 to 5:

1 – Poor: Element absent or inadequately addressed.
2 – Fair: Present but underdeveloped or lacking supporting evidence.
3 – Good: Adequately addressed with reasonable documentation.
4 – Very Good: Well-developed and evidence-based.
5 – Excellent: Exceptional, rigorously supported by documented experimentation and learning