Free Applied AI Resource
AI Readiness Scorecard for Business Leaders
Evaluate whether your organization is ready to implement AI solutions. Covers data infrastructure, use case clarity, team capability, ethical considerations, and ROI expectations.
Use Ctrl+P (or Cmd+P on Mac) to save as PDF
AI is powerful, but most businesses aren't ready for it — not because they lack ambition, but because the foundations aren't in place. This scorecard helps you identify whether AI will solve your problem or become an expensive distraction.
1. Data Foundation
Relevant data exists and is accessible
The data you'd need to train or fine-tune a model actually exists in a usable format.
Data volume is sufficient
For ML/AI, you typically need thousands to millions of examples. Small datasets need different approaches.
Data quality is known
You've assessed accuracy, completeness, and bias in your training data.
Data pipeline exists
Data flows from source systems to where it's needed without manual CSV exports.
Historical data is preserved
Past data isn't deleted — it's available for model training and validation.
2. Use Case Clarity
Problem is well-defined
You can articulate exactly what decision or task the AI would improve — not just 'use AI somewhere'.
Current process baseline exists
You know how the task is done today and can measure improvement against it.
Human fallback planned
For cases where AI confidence is low, there's a human review process.
Success criteria are measurable
Specific accuracy, speed, or cost targets that determine whether the AI solution is working.
Build vs. buy decision made
You've evaluated whether an off-the-shelf AI product solves this, or if custom development is needed.
3. Team & Capability
Technical talent available
Someone on your team (or a trusted partner) understands ML fundamentals — not just AI buzzwords.
Domain expertise captured
Subject matter experts are available to validate AI outputs and provide training feedback.
Maintenance plan realistic
AI models degrade over time. Someone is responsible for monitoring and retraining.
Infrastructure ready
Compute resources for training/inference are available or budgeted.
Cross-functional alignment
Business, tech, and legal teams are aligned on goals, constraints, and timelines.
4. Ethics & Governance
Bias assessment planned
You'll test for demographic, geographic, or historical biases in model outputs.
Transparency requirements defined
Users will know when they're interacting with AI, and can understand why decisions were made.
Data privacy addressed
Training data handling complies with DPDP Act / GDPR requirements.
Accountability structure clear
Someone is responsible for AI decisions — not 'the algorithm decided'.
Regular audit planned
AI outputs will be reviewed periodically for accuracy, fairness, and drift.
5. ROI & Expectations
Realistic timeline set
AI projects typically take 3-6 months to show results. Quick wins are possible but not guaranteed.
Total cost understood
Budget includes data preparation, development, compute, maintenance, and iteration — not just the initial build.
Incremental approach planned
Starting with a pilot/proof-of-concept before committing to full-scale deployment.
Failure criteria defined
You know when to stop investing if results don't materialize — not just success criteria.
Alternative solutions considered
You've confirmed that simpler approaches (rules, dashboards, better processes) don't solve this already.
Need Help Implementing This?
Want expert help evaluating AI opportunities for your business? SenseSys provides AI strategy assessments, proof-of-concept development, and enterprise RAG/LLM implementations — with honest advice about when AI is the right answer and when it isn't.