An assessment framework for evaluating market demand, competition, regulatory landscape, and resource requirements for AI solutions

MARKET DEMAND ANALYSIS

Problem Validation

• Define the specific business problem your AI solution addresses
• Quantify the size and cost of this problem for target customers
• Validate that customers actively seek solutions for this problem
• Confirm customers currently spend money trying to solve this problem
• Identify how customers solve this problem today (workarounds, manual processes, competitors)
• Document customer pain points with existing solutions
• Assess urgency level: is this a “nice-to-have” or “must-have” solution?

Target Market Sizing

• Calculate the Total Addressable Market (TAM) for your AI solution
• Determine Serviceable Addressable Market (SAM) based on your capabilities
• Estimate Serviceable Obtainable Market (SOM) within 3-5 years
• Segment market by industry, company size, and use case
• Identify geographic markets with the highest demand concentration
• Analyze market growth rate and future expansion potential
• Validate market size assumptions with industry reports and expert interviews

Customer Discovery

• Conduct 50+ customer interviews across target segments
• Map customer decision-making processes for AI purchases
• Identify key stakeholders and influencers in buying decisions
• Document customer budget allocation for AI/technology solutions
• Understand customer procurement cycles and timing
• Assess customer technical sophistication and AI readiness
• Validate willingness to pay at proposed price points

COMPETITIVE LANDSCAPE ASSESSMENT

Direct Competition Analysis

• Identify all direct AI competitors solving the same problem
• Analyze competitor pricing models and market positioning
• Evaluate competitor technical capabilities and performance claims
• Assess competitor customer base size and growth trajectory
• Review competitor funding levels and financial backing
• Analyze competitor go-to-market strategies and sales channels
• Document competitor strengths and weaknesses

Indirect Competition & Alternatives

• Map non-AI solutions customers use for the same problem
• Evaluate traditional software alternatives and their limitations
• Assess manual processes and their associated costs
• Identify consulting services addressing similar challenges
• Analyze the “do nothing” option and its implications for customers
• Document switching costs from existing solutions

Competitive Positioning

• Define your unique value proposition versus all alternatives
• Identify sustainable competitive advantages (data, algorithms, partnerships)
• Assess barriers to entry for new competitors
• Evaluate the potential for incumbent players to build similar solutions
• Determine your differentiation strategy beyond technical features
• Plan response strategies for competitive threats

REGULATORY & COMPLIANCE LANDSCAPE

Current Regulatory Environment

• Research AI-specific regulations in target markets
• Identify industry-specific compliance requirements (healthcare, finance, etc.)
• Assess data privacy regulations (GDPR, CCPA, sector-specific)
• Understand algorithmic bias and fairness requirements
• Review export control restrictions for AI technology
• Evaluate intellectual property protection requirements
• Document required certifications and audit processes

Future Regulatory Risks

• Monitor proposed AI legislation and regulatory changes
• Assess the potential impact of algorithmic transparency requirements
• Evaluate risks from AI liability and accountability measures
• Plan for potential data localization requirements
• Consider the implications of AI governance frameworks
• Develop a compliance roadmap for anticipated regulations

TECHNICAL FEASIBILITY & RESOURCE REQUIREMENTS

Technical Validation

• Validate that core AI algorithms work at the proof-of-concept level
• Test solution performance with production data sets
• Assess data quality and availability requirements
• Evaluate computational resource needs and costs
• Validate integration capabilities with customer systems
• Test solution scalability across different customer sizes
• Benchmark performance against existing solutions

Development Resources

• Calculate the total engineering effort required (person-months)
• Identify required AI/ML expertise and availability
• Assess the need for specialized hardware or infrastructure
• Evaluate third-party dependencies and licensing costs
• Plan for ongoing model maintenance and improvement
• Estimate time-to-market for the minimum viable product
• Budget for continuous R&D investment requirements

Data Strategy

• Identify data sources needed for model training and operation
• Assess data acquisition costs and partnerships required
• Evaluate data quality, completeness, and bias issues
• Plan data labeling and annotation requirements
• Design data privacy and security protocols
• Establish data governance and retention policies
• Create data partnership and licensing strategies

BUSINESS MODEL & FINANCIAL VIABILITY

Revenue Model Validation

• Test pricing models with target customers (subscription, usage-based, license)
• Validate customer lifetime value (LTV) assumptions
• Calculate customer acquisition cost (CAC) across channels
• Assess pricing sensitivity through customer interviews
• Evaluate revenue predictability and recurring revenue potential
• Plan pricing strategy for different customer segments
• Model revenue scaling with customer growth

Cost Structure Analysis

• Calculate unit economics for serving customers
• Project infrastructure costs at different scales
• Estimate sales and marketing expenses
• Budget for regulatory compliance and legal costs
• Plan for customer success and support expenses
• Account for ongoing R&D and product development
• Calculate gross margins and path to profitability

Funding Requirements

• Estimate total capital needed to reach cash flow positive
• Plan funding milestones and use of capital
• Assess investor appetite for your AI market category
• Evaluate alternative funding sources (grants, partnerships, revenue-based)
• Calculate the runway needed between funding rounds
• Plan for capital efficiency and burn rate optimization

GO-TO-MARKET READINESS

Channel Strategy

• Identify optimal sales channels (direct, partner, marketplace)
• Evaluate partner ecosystem opportunities
• Assess distribution partnerships and requirements
• Plan digital marketing and lead generation strategies
• Design customer onboarding and implementation processes
• Develop customer success and retention programs

Market Entry Strategy

• Select initial target segments and geographic markets
• Plan pilot customer acquisition and case study development
• Design pricing and packaging for market entry
• Prepare competitive differentiation messaging
• Plan PR and thought leadership strategy
• Schedule industry conference and event participation

RISK ASSESSMENT & MITIGATION

Market Risks

• Assess the risk of market timing (too early/too late)
• Evaluate customer adoption rate risks
• Plan for economic downturn impact on AI spending
• Consider technology displacement risks
• Assess regulatory change impacts on market viability

Execution Risks

• Identify key technical development risks
• Plan for talent acquisition and retention challenges
• Assess partnership and vendor dependency risks
• Evaluate competitive response scenarios
• Plan contingency strategies for major setbacks

HOW TO CONDUCT THE VIABILITY ASSESSMENT

• Score each category (1-10) and calculate the overall feasibility rating
• Identify the top 3 risks and mitigation strategies
• Create a go/no-go decision framework with clear criteria
• Document assumptions requiring validation in the next 90 days
• Plan a quarterly feasibility reassessment process