AI Project Risk Analysis
Validation Framework

A comprehensive methodology to identify, validate, and mitigate risks in AI initiatives

01

Business Threats

Value perception & market acceptance

02

Tech Threats

Data quality & model accuracy

03

Assumption Testing

Validate before implementation

04

Iterative Validation

Build confidence through experiments

RISK ANALYSIS

Two Fundamental Threat Categories

Understanding these threat categories is crucial for AI project success

1

Business Threats

Business threats are related to the value and acceptance of your AI-powered products or services. Even with attractive technology, people may not need or want to use it.

Key Business Threats:

  • Customer perception of value proposition
  • Trust in automated systems
  • Misalignment with actual customer needs
  • Unidentified "hidden" customers in the value chain

"Customers trust automated price estimates and are willing to base financial decisions on them, regardless of technical accuracy."

2

Technological Threats

Technological threats relate to the feasibility and performance of the AI system itself. These include data availability, model accuracy, and maintenance challenges.

Key Technological Threats:

  • Availability and quality of training data
  • Model accuracy and reliability
  • Data maintenance over time
  • Meeting stringent industry requirements

"We have the right data to build the model, and we can keep it up-to-date over time with sufficient accuracy."

Core Insight: Treat threats as testable assumptions
VALIDATION APPROACH

Assumption Testing Framework

Validate critical assumptions before full implementation

Business Assumption Testing

A

Wizard of Oz Testing

Simulate AI functionality with human workers to measure customer interest before building the actual system.

B

Fake Door Testing

Present a feature that appears functional but collects interest, revealing demand before development.

Real Estate Example:

Add "Instant Home Valuation" button that routes to human appraisers or shows "under maintenance" to gauge interest.

Benefits of Early Validation

Rapid Learning

Validate concepts in days instead of months with minimal budget

Risk Reduction

Identify non-viable ideas before significant investment

Evidence-Based Decisions

Make go/no-go decisions based on actual customer behavior

Resource Optimization

Focus development efforts on features with proven demand

"Taking a function isn't ideal, but it allows you to identify the benefits of your AI projects in days with basically no budget."

Validation Methodology Insight

"There is no fix for a product that isn't needed. Test business assumptions before writing code or collecting data."

ITERATIVE PROCESS

AI Vision Development Workflow

Successful AI emerges through experimentation and iteration

1
Identify Core Assumptions
2
Design Validation Experiments
3
Execute Lean Tests
4
Refine AI Vision
Key Insights from the Process

Start small with experiments rather than comprehensive surveys

Let projects inform the emerging AI vision organically

"Most successful AI companies didn't start with a complete solution, but rather connected the dots until their AI mission emerged."

AI
TRANSFORMATIVE AI

Building Your AI Future

To leverage AI as a transformative technology, organizations must develop their capability through dedicated experimentation.

Adopt a mindset of continuous learning, invest in AI functionality incrementally, and let each project inform your evolving vision.

Strategic Recommendations

Cultivate experimentation culture

View projects as building blocks

Let projects guide your AI roadmap

"If you're careful to listen, the projects themselves will tell you where your AI vision should lead."