A structured approach to translate business ideas into machine learning projects using the Framing Canvas methodology.
Why is this project helpful?
How do we measure success?
What will ML deliver?
What data is needed?
Where will data come from?
Acceptance thresholds
"We'll use internet data to predict what people will buy."
"We'll use customer data to find the best ones."
No clear definition of what "success" looks like
Clear specification of what the AI will produce
Data connecting inputs to desired outputs
Quantifiable criteria for evaluating performance
Successful AI projects require translating business needs into ML-specific terms
AI Leader:
"We tested a house-price predictor where users input property info and get immediate price estimates. People loved it. Now we want an AI to do it in seconds."
Data Scientist (John):
"What variable are we predicting? What features should we use? Do we have past transaction data?"
AI Leader:
"We're predicting sale price. We have expert broker insights on features and 6 years of transaction data (hundreds of thousands of records). Users accept predictions within +3% of actual price."
Business Goal
Immediate home price estimates
KPI
Prediction within +3% of actual price
ML Output
Predicted home sale price
ML Input (Features)
Square meters, bedrooms, location factors, neighborhood reputation
Data Sources
Past transaction records (6 years)
The Framing Canvas is designed to guide non-technical stakeholders through the process of converting business projects into well-defined ML projects.
Define why this project is helpful for your organization. What problem does it solve?
Establish how you will define and measure success. What quantitative metric will track ROI?
Determine what ML will deliver to achieve your goal. What exactly will the algorithm produce?
Identify what data the model needs. What information would you need to do the algorithm's job?
Locate where you will get this data. What systems or datasets contain this information?
For each KPI, identify a value that represents the minimum accepted performance.
Starting Point:
Measure current solution's performance on your KPI
New Products:
Run financial benefit calculations to determine needed performance
False Positives
Cost of incorrect predictions (e.g., pursuing unnecessary retention actions)
False Negatives
Cost of missed opportunities (e.g., failing to retain at-risk customers)
Identifying customers about to cancel subscription services
Business Goal
Reduce subscription cancellations
KPI
Customer churn rate (monthly)
Success Metric
Recall > 90%
ML Output
Churn probability per customer
ML Input (Features)
Product usage, demographics, subscription type
Data Sources
CRM data, previous unsubscribed customers
Optimizing cooling systems to reduce energy consumption
Business Goal
Reduce energy costs
KPI
PUE (Power Usage Effectiveness)
Success Metric
Prediction within +3% of real PUE 95% of time
ML Output
Future performance of cooling systems
ML Input (Features)
Outside temperature, humidity, water pressure, data center load
Data Sources
Data center appliance logs, weather data
Offering next-day loans to eligible small businesses
Business Goal
Expand into small business loan market
KPI
% of eligible businesses identified
Success Metric
Precision > 95%
ML Output
Loan eligibility per customer
ML Input (Features)
Cash flow, business size, location, industry
Data Sources
CRM data, transaction data, financial markets data
The Framing Canvas produces clearly defined ML specifications
that can be directly implemented by technical teams
Clearly articulate why the project matters to the organization before diving into technical details.
Define measurable KPIs and establish acceptance thresholds upfront.
Understand the business impact of false positives vs. false negatives.
Ensures business and technical teams share the same understanding of project goals.
Helps identify data gaps and technical challenges early in the process.
Provides clear specifications that accelerate the ML development process.
The Framing Canvas bridges the gap between business vision and ML implementation
By translating AI projects into ML-friendly terms, organizations can significantly increase their success rate with AI initiatives.