Feasibility Assessment

Feasibility Assessment is a critical checkpoint that determines whether an ML project should proceed to development. It evaluates the project across multiple dimensions to identify potential blockers, gaps, and risks before significant resources are invested.

This process answers the fundamental question: "Can we actually build this?" A thorough feasibility assessment prevents costly failures by identifying showstoppers early and ensuring that the organization has the necessary ingredients for success.

The assessment results in a Go / Conditional Go / No-Go decision that guides the project's next steps.

A comprehensive feasibility assessment evaluates four key dimensions. Each dimension must meet minimum thresholds for the project to proceed:

• 1
  Data Audit
  Inventory available data sources, assess quality, identify gaps, and verify accessibility. Conduct sample analysis to validate data assumptions.
• 2
  Technical Spike
  Conduct proof-of-concept experiments to validate technical approach. Test baseline models, evaluate infrastructure requirements, and identify technical risks.
• 3
  Cost-Benefit Analysis
  Estimate total project costs (development, infrastructure, maintenance) and quantify expected business benefits. Calculate ROI and payback period.
• 4
  Resource Assessment
  Evaluate team capabilities, identify skill gaps, assess resource availability, and determine if external support is needed.
• 5
  Compliance Review
  Verify regulatory requirements, data privacy obligations, and ethical considerations. Ensure the project can comply with all applicable regulations.
• 6
  Scoring & Decision
  Score each feasibility dimension, aggregate results, and make a Go/Conditional/No-Go recommendation with supporting rationale.

Each dimension is scored on a scale of 1-5. The overall feasibility score guides the project decision:

Based on the feasibility scores and identified gaps, make one of three decisions:

Use this checklist to ensure all critical feasibility factors are evaluated:

• ✓
  Be Objective, Not Optimistic
  Resist the temptation to inflate scores. Honest assessment prevents costly surprises later. Bad news early is better than failure late.
• ✓
  Validate with Data
  Don't rely on assumptions. Actually examine the data, run experiments, and verify claims. Sample data early to avoid surprises.
• ✓
  Involve Stakeholders
  Include business, technical, and operational perspectives in the assessment. Diverse viewpoints identify gaps that individuals miss.
• ✓
  Document Assumptions
  Every feasibility score is based on assumptions. Document them clearly so they can be revisited if conditions change.
• ✓
  Consider Alternatives
  If ML feasibility is low, explore alternative approaches. Sometimes simpler solutions (rules, heuristics) can deliver value faster.