The Machine Learning Framing Record: Your Bridge from Business Ideas to Technical Success

You’ve heard it a thousand times: “We need to use AI for this.” But when you ask for specifics—what exactly should the AI do, how will we measure success, what data do we need—the room goes quiet.

This gap between business ambition and technical implementation kills more ML projects than any algorithm ever could. According to S&P Global Market Intelligence, 42% of companies are now abandoning most of their AI initiatives—up from just 17% last year—with the average organization scrapping 46% of AI proof-of-concepts before reaching production. Poor problem framing remains a leading cause of these failures.

Enter the Machine Learning Framing Record: your systematic approach to transforming fuzzy business ideas into concrete, measurable ML projects.

The Million-Dollar Question Nobody Asks

Before spending months and millions on ML development, successful organizations answer one critical question: “How will we know if this ML solution is actually solving our business problem?”

Most teams can’t answer this because they jump straight from “we need AI” to “let’s build a model” without the crucial translation step in between.

The Machine Learning Framing Record fills this gap by systematically converting business ideas into technical specifications with clear evaluation criteria.

What is a Machine Learning Framing Record?

Based on principles from the AWS Well-Architected Machine Learning Lens, the ML Framing Record is a structured document that bridges business understanding and technical problem framing.

It transforms:

• Vague business ideas → Specific ML problems
• Wishful thinking → Measurable objectives
• “AI will solve this” → “Here’s exactly how we’ll measure success”

Think of it as your ML project’s birth certificate—establishing its identity, purpose, and success criteria before a single line of code is written.

The Two-Phase Foundation

The ML Framing Record builds on two critical phases from the AWS Well-Architected ML Lens:

Phase 1: Business Understanding

This phase identifies and prioritizes the business problem, along with the people, process, and technology changes required to deliver value. As AWS notes, “An organization considering ML should have a clear idea of the problem, and the business value to be gained by solving that problem.”

Phase 2: Technical Problem Framing

Here, the business problem transforms into an ML problem—determining what to observe, what to predict, and how to optimize performance. According to the ML Problem Framing documentation, this involves “characterizing the problem as an ML task, such as classification, regression, or clustering.”

The Anatomy of an ML Framing Record

1. Business Context Section

Business Problem Statement

• What specific business challenge are we solving?
• Why does this problem matter to the organization?
• What’s the cost of not solving it?

Example: “Customer service receives 10,000 emails daily, taking 48 hours average response time. This delays result in 23% customer churn and $2.3M annual revenue loss.”

Success Vision

• What does success look like in business terms?
• How will stakeholders know the problem is solved?
• What business metrics will improve?

2. Technical Translation Section

ML Problem Type Based on your business problem, identify the ML task:

• Classification: Categorize inputs (spam/not spam, fraud/legitimate)
• Regression: Predict continuous values (sales forecast, price estimation)
• Clustering: Group similar items (customer segmentation)
• Recommendation: Suggest relevant items (product recommendations)
• Anomaly Detection: Identify outliers (fraud detection, system failures)

Generative AI opens up entirely new task categories:

• Text Generation: Create human-like content (copywriting, code generation, creative writing)
• Summarization: Condense long documents into key points (meeting notes, research papers, legal documents)
• Question Answering: Provide contextual answers from knowledge bases (customer support, internal wikis)
• Translation: Convert between languages while preserving meaning and context
• Text-to-Text Transformation: Rewrite content for different audiences, tones, or formats
• Conversational AI: Multi-turn dialogue systems (virtual assistants, tutoring systems)
• Information Extraction: Pull structured data from unstructured text (invoice processing, resume parsing)
• Reasoning and Analysis: Complex problem-solving and analytical tasks (code review, medical diagnosis support)

Input-Output Mapping

• Inputs: What data will the model observe?
• Outputs: What should the model predict?
• Constraints: Latency requirements, accuracy needs, regulatory limits

3. Evaluation Criteria Section

This is where most ML projects fail—they don’t establish clear, measurable success criteria upfront.

The emergence of generative AI tasks has motivated entirely new evaluation frameworks. While traditional ML focused on accuracy and error rates, generative AI requires metrics that assess qualities like faithfulness, coherence, and semantic preservation—leading to the development of specialized frameworks like RAGAS and human-aligned evaluation methods.

Technical Metrics for Traditional ML

• Accuracy: Overall correctness (but beware of imbalanced datasets)
• Precision: Of predicted positives, how many are actually positive?
• Recall: Of actual positives, how many did we catch?
• F1 Score: Balanced measure of precision and recall
• AUC-ROC: Performance across all classification thresholds
• RMSE: For regression problems, average prediction error

Technical Metrics for Generative AI

Evaluation Framework Options:

1. RAGAS Framework (RAG-specific) (arXiv:2309.15217)
   • Faithfulness: Measures factual accuracy of generated answers against provided context
   • Answer Relevancy: Evaluates how relevant the generated response is to the query
   • Context Precision: Measures signal-to-noise ratio in retrieved documents
   • Context Recall: Assesses completeness - whether all relevant information was retrieved
2. Traditional NLG Metrics
   • ROUGE: Recall-based metric comparing n-gram overlap (mainly for summarization) (Lin, 2004)
   • BLEU: Precision-based metric for translation and generation quality (Papineni et al., 2002)
   • METEOR: Combines precision, recall, and semantic similarity
   • BERTScore: Uses contextual embeddings for semantic similarity (arXiv:1904.09675)
3. Task-Specific Metrics
   • Coverage: Proportion of key information points addressed (summarization)
   • Fidelity: Accuracy of information preservation (summarization)
   • Hallucination Rate: Percentage of generated content not grounded in source
   • Latency: Response time for real-time applications
4. Human-Aligned Metrics
   • LLM as a Judge: Using strong LLMs to evaluate model outputs, matching human preferences with 80%+ agreement (arXiv:2306.05685)
   • G-Eval: GPT-based evaluation for fluency, coherence, consistency (arXiv:2303.16634)
   • Human Eval: Direct human assessment scores
   • User Satisfaction: Task completion rates and feedback

Business Metrics

• Revenue impact
• Cost reduction
• Time savings
• Customer satisfaction scores
• Process efficiency gains

Evaluation Thresholds

• Minimum acceptable performance: “The model must achieve at least…”
• Target performance: “We aim to achieve…”
• Stretch goals: “Exceptional performance would be…”

4. Risk and Constraints Section

Data Considerations

• Data availability and quality
• Privacy and compliance requirements
• Bias and fairness concerns

Operational Constraints

• Inference time requirements
• Infrastructure limitations
• Maintenance capabilities

Business Constraints

• Budget limitations
• Timeline requirements
• Change management needs

The Power of Proper Framing: Real Examples

Example 1: Manufacturing Quality Control

Poor Framing: “AI should detect defects”

Proper Framing via ML Record:

• Business Problem: 3% defect rate costs $800K/year in returns and reputation damage
• ML Problem: Binary classification of product images
• Inputs: High-resolution product images from 3 angles
• Output: Defect/No-defect classification with confidence score
• Success Metrics:
  • Technical: Recall > 0.95 (catch 95% of defects)
  • Business: Reduce defect escape rate to <0.5%
  • Evaluation: Parallel run for 30 days before cutover

Example 2: Enterprise Knowledge Search (RAG)

Poor Framing: “Use AI to improve search”

Proper Framing via ML Record:

• Business Problem: Employees spend 2.5 hours/day searching for information across 15+ systems
• ML Problem: Retrieval-Augmented Generation for enterprise knowledge discovery
• Inputs: User query, document corpus, user context/role
• Output: Synthesized answer with source citations
• Success Metrics:
  • Technical (RAGAS):
    • Faithfulness > 0.85 (answers grounded in sources)
    • Context Recall > 0.80 (retrieves all relevant docs)
    • Answer Relevancy > 0.90
  • Business: 50% reduction in time-to-information
  • Evaluation: User satisfaction surveys + query resolution rates

Example 3: Document Summarization System

Poor Framing: “AI should summarize documents”

Proper Framing via ML Record:

• Business Problem: Legal team reviews 500+ pages/day, missing critical clauses costs $1.2M annually
• ML Problem: Abstractive summarization with key clause extraction
• Inputs: Legal documents, clause taxonomy, risk templates
• Output: 2-page summary with highlighted risk clauses
• Success Metrics:
  • Technical:
    • ROUGE-L > 0.65 (content overlap)
    • Coverage > 0.95 (all critical clauses identified)
    • Fidelity > 0.90 (no hallucinated information)
  • Business: 70% reduction in review time, zero missed critical clauses
  • Evaluation: Expert review of 100 document sample

Example 4: Customer Sentiment Analysis

Poor Framing: “Analyze customer feedback”

Proper Framing via ML Record:

• Business Problem: 10K daily reviews, manual analysis misses 60% of actionable insights
• ML Problem: Multi-aspect sentiment analysis with trend detection
• Inputs: Customer reviews, product metadata, historical trends
• Output: Sentiment scores by aspect, emerging issues alerts
• Success Metrics:
  • Technical:
    • Aspect detection F1 > 0.85
    • Sentiment accuracy > 0.88
    • Completeness > 0.90 (all aspects captured)
  • Business: 48-hour → 2-hour issue detection time
  • Evaluation: Correlation with customer churn + NPS scores

Building Your ML Framing Record: The Process

Step 1: Stakeholder Discovery Session (2-4 hours)

Gather business stakeholders, technical team, and end users:

• Map the current process and pain points
• Quantify the business impact
• Define success in business terms
• Identify available data sources

Step 2: Technical Translation Workshop (2-3 hours)

With ML practitioners:

• Convert business problem to ML problem type
• Map inputs and outputs
• Identify technical constraints
• Propose initial evaluation metrics

Step 3: Alignment Review (1-2 hours)

Bring both groups together:

• Validate technical approach solves business problem
• Agree on success metrics (both technical and business)
• Establish evaluation methodology
• Set go/no-go criteria

Step 4: Documentation and Approval

Create the formal ML Framing Record:

• One document, 3-5 pages maximum
• Clear enough for executives to understand
• Detailed enough for engineers to implement
• Specific enough to evaluate success

The Reality of Metric Uncertainty: Start Anyway

Here’s a truth most ML frameworks won’t tell you: You won’t know the right metric thresholds at the beginning, and that’s perfectly fine.

Many leaders get paralyzed trying to determine if they need 85% accuracy or 92%, whether 100ms or 200ms latency is acceptable, or if 70% cost reduction is realistic. Or worse yet—they demand the impossible and ask for 100% accuracy, setting their teams up for guaranteed failure. This uncertainty and unrealistic expectations stop projects before they start.

The Iterative Metric Refinement Process

Step 1: Make Your Best Guess Based on business needs and industry benchmarks, set initial targets:

• “We think we need 90% accuracy”
• “Response time should probably be under 2 seconds”
• “We’d like to reduce costs by at least 30%”

These are hypotheses, not contracts.

Step 2: Measure Everything Even with uncertain targets, measurement provides critical insights:

• Your “90% accuracy” might achieve only 82%—but users might be thrilled
• Your “2-second response” might take 3.5 seconds—but still transform the workflow
• Your “30% cost reduction” might hit 60%—revealing bigger opportunities

Step 3: Gather Context Through Feedback Numbers without context are meaningless. After initial deployment:

• User Feedback: “The 82% accuracy is fine for routine cases, but we need 95% for rush orders”
• Stakeholder Input: “The 3.5-second response is acceptable if it’s this accurate”
• Business Impact: “Even 20% cost reduction would be game-changing”

Step 4: Refine Your Metrics Based on real-world learning:

• Add Segmentation: Different accuracy targets for different order types
• Adjust Thresholds: Relax response time requirements, tighten accuracy
• Introduce New Metrics: Discover that “user trust score” matters more than raw accuracy
• Deprecate Others: Remove metrics that don’t correlate with business value

Example: The Evolution of Success Metrics

Initial Framing (Month 0):

• Accuracy: 95% (guess based on competitor claims)
• Speed: <1 second (seemed reasonable)
• Cost savings: 50% (hopeful target)

First Measurement (Month 1):

• Accuracy: 87% achieved
• Speed: 2.3 seconds average
• Cost savings: 35%
• Discovery: Users care more about “completeness” than “accuracy”

Refined Metrics (Month 2):

• Completeness: 95% of all action items captured (new primary metric)
• Accuracy: 85% acceptable for non-critical items, 95% for financial data
• Speed: <5 seconds acceptable given completeness improvement
• Cost savings: 35% exceeds ROI requirements
• New Metric: Confidence scores to flag uncertain extractions

Mature Metrics (Month 6):

• Completeness by category (orders: 99%, questions: 92%, complaints: 95%)
• Accuracy with confidence bands
• Processing time vs. complexity correlation
• Cost per transaction type
• User trust score
• Downstream impact metrics

The Learning Loop Framework

1. FRAME → Set initial metrics (best guess)
    ↓
2. BUILD → Implement with measurement built in
    ↓
3. MEASURE → Collect quantitative data
    ↓
4. CONTEXTUALIZE → Gather qualitative feedback
    ↓
5. PRIORITIZE → Identify which metrics matter most
    ↓
6. REFINE → Adjust targets and add/remove metrics
    ↓
(Return to BUILD with better understanding)

Key Principles for Metric Evolution

1. Perfect is the Enemy of Good: Starting with imperfect metrics beats not starting
2. Feedback Provides Context: Numbers need stories to become insights
3. Metrics Aren’t Sacred: Be willing to completely change your success definition
4. Segmentation Often Emerges: One-size-fits-all metrics rarely survive reality
5. Business Value Trumps Technical Precision: A “worse” model that users love beats a “better” one they don’t

Common Pitfalls and How to Avoid Them

Pitfall 1: Solution-First Thinking

Wrong: “We need a neural network for customer service” Right: “We need to reduce response time from 48 to 4 hours”

Pitfall 2: Vague Success Criteria

Wrong: “The model should be accurate” Right: “The model should achieve 90% precision with 85% recall on the test set”

Pitfall 3: Ignoring Business Constraints

Wrong: “The model achieved 99.9% accuracy” Right: “The model meets accuracy targets while satisfying <100ms latency requirement”

Pitfall 4: Technical Metrics Without Business Impact

Wrong: “Our F1 score improved by 0.15” Right: “Our F1 score improvement translates to $1.2M annual savings”

Pitfall 5: Waiting for Perfect Metrics

Wrong: “We can’t start until we know if we need 87% or 93% accuracy” Right: “Let’s target 90% accuracy, measure actual performance and user satisfaction, then refine based on what we learn”

The Evaluation Framework That Ensures Success

Your ML Framing Record should establish a comprehensive evaluation framework:

1. Offline Evaluation

• Performance on historical test data
• Cross-validation results
• Error analysis by segment

2. Online Evaluation

• A/B testing framework
• Canary deployment strategy
• Rollback criteria

3. Business Impact Evaluation

• Pre/post business metric comparison
• ROI calculation methodology
• Stakeholder satisfaction measurement

4. Continuous Monitoring

• Model performance degradation alerts
• Business metric tracking
• Feedback loop implementation

Your ML Framing Record Template

Download our comprehensive ML Framing Record Worksheet to systematically work through:

1. Business Understanding Section
   • Problem statement
   • Impact quantification
   • Success vision
   • Stakeholder mapping
2. Technical Problem Framing Section
   • ML problem type selection
   • Input/output specification
   • Constraint identification
   • Initial approach hypothesis
3. Evaluation Criteria Section
   • Technical metric selection
   • Business metric mapping
   • Threshold establishment
   • Testing methodology
4. Risk Assessment Section
   • Data quality risks
   • Technical risks
   • Business risks
   • Mitigation strategies

The Bottom Line: Frame First, Build Second

The difference between ML projects that deliver value and those that don’t often comes down to the quality of initial problem framing. The ML Framing Record ensures you:

• Start with clear business objectives
• Translate them into measurable technical goals
• Establish evaluation criteria before building
• Create alignment between stakeholders
• Set realistic expectations
• Enable objective go/no-go decisions

As the AWS Well-Architected ML Lens emphasizes: “Determining what to predict and how performance must be optimized is a key step in ML.”

Don’t let your ML project become another statistic. Use the Machine Learning Framing Record to bridge the gap between business vision and technical reality.

Take Action

1. Download our ML Framing Record Worksheet
2. Schedule a 2-hour framing session for your next ML initiative
3. Complete the record before writing any code
4. Align all stakeholders on success criteria
5. Evaluate against your predetermined metrics

Remember: A well-framed ML problem is already half-solved. A poorly framed one is guaranteed to fail, regardless of how sophisticated your algorithms are.

Start with the right frame. Build with confidence. Measure what matters.

Ready to transform your ML initiatives from wishful thinking to measurable success? Download our ML Framing Record Worksheet and join our community of strategic ML practitioners who frame first and build with purpose.