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This interactive guide helps professionals understand the AI development process without requiring technical expertise. Click on any step in the process above to explore detailed explanations for each phase.
Each step of this framework has been designed to clarify the roles throughout the AI development lifecycle, ensuring that business goals and values are properly integrated into AI systems.
"This is where you define the decision, prediction, or outcome you want AI to support — and make sure it's a valuable business problem worth solving."
Good Examples:
Too vague:
"AI success starts with how clearly you define the problem. If it's vague, misaligned, or unmeasurable, the rest of the project will suffer — no matter how good the data or model is."
| Responsibility | Example |
|---|---|
| Clarify Business Goal | "Reduce customer churn by 10% in Q3" |
| Frame as a Question | "Which customers are at risk before they cancel?" |
| Set Boundaries | "Focus on premium subscribers in the U.S. first" |
| Define Success | "80% recall on at-risk customers" |
| Ensure Strategic Fit | "Supports our broader retention strategy" |
| Business Goal | "Reduce customer churn" |
| Decision to Improve | "Retention outreach strategy" |
| Prediction Needed | "Which customers are at high risk?" |
| Data Needed | "Customer behavior, usage, support" |
| Success Metrics | "Catch 80% of churners 2 weeks early" |
| Watch For | Why It's a Problem |
|---|---|
| Problem is too vague | Model can't be focused or useful |
| Disconnected from goals | Even good predictions won't drive value |
| Technically interesting but not actionable | Wastes time and resources |
| No clear success criteria | Hard to measure effectiveness |
| No owner for the outcome | No accountability or follow-through |
Goal for Business Professionals: Help ensure the data being used is relevant, representative, ethical, and aligned with the real-world problem you're trying to solve.
"This is where we gather the raw materials for AI — the data that the model will learn from."
Data can come from:
Business Insight: "If the data is biased, missing key groups, or doesn't match what we care about — the AI model will be off base from the start."
"You're not just helping collect data — you're making sure the AI sees the right picture of your business. Your input ensures the data is accurate, complete, and meaningful."
| Role | What That Looks Like |
|---|---|
| Define What Matters | Clarify which metrics, attributes, or signals are relevant for your business decision |
| Spot Gaps | Identify what's missing (e.g., data from key customer segments, untracked behaviors) |
| Ask About Origins | Understand how data was collected and whether it reflects current conditions |
| Assess Representativeness | Ensure the data covers all relevant user groups, geographies, and time periods |
| Flag Risks | Call out data sources that might reflect historical bias, systemic inequality, or legacy decisions |
| Watch For | Why It Matters |
|---|---|
| Old or incomplete data | AI learns from the past — and that past may not reflect the present or future |
| Biased collection methods | E.g., only surveying loyal customers, or missing underserved segments |
| Overuse of proxy variables | ZIP codes often proxy for race/income, which can lead to unfair decisions |
| Data silos | Missing connections between marketing, operations, and support data |
| Lack of domain context | Data that's technically valid but doesn't match how the business operates |
Good data collection means bringing together business knowledge, technical expertise, and ethical considerations
Business Translation: Clean data leads to better predictions. Garbage in, garbage out.
Business Translation: AI models need data to be structured and consistent before they can learn from it.
When you DO label:
In supervised learning, you need examples of outcomes:
When you DON'T label:
Bias Alert: Labels often come from human decisions or subjective ratings, which may carry bias — and that bias gets learned by the model.
Creating new variables from existing ones:
Translation: Like giving the AI extra clues.
Dividing into sets:
Translation: Avoiding teaching to the test.
| Subtask | Explanation | When it Happens |
|---|---|---|
| Clean Data | Fix missing or inconsistent values | Always |
| Structure Data | Normalize formats, encode categories | Always |
| Label Data | Define correct outcomes | Only for supervised learning |
| Feature Engineering | Create meaningful variables | When needed |
| Split Data | Train/test/validate sets | End of this phase |
"In this stage, we clean and structure the data so AI can learn from it. If we're doing supervised learning, this is also where we label past outcomes so the AI knows what success looks like. We might also engineer smarter inputs to help the model predict better. Bias can sneak in here, so it's critical to question how the data was labeled and whether anything important is missing."
(Owners of the problem)
(Owners of data pipelines)
| Task | Who Leads? | Who Supports? |
|---|---|---|
| Define useful labels | Business team | Data team (validates feasibility) |
| Identify bias or missing context | Business team | Data team (flags statistical signs) |
| Clean & format data | Data team | Business team (explains anomalies) |
| Engineer new features | Shared effort | Both (ideas from business, built by data team) |
| Validate output quality | Data team | Business team (judges usefulness) |
This is where the data team selects the type of AI model or algorithm that will learn from your data and make predictions.
Classification
e.g., churn yes/no
Regression
e.g., forecast sales
Clustering
e.g., group similar customers
Recommendation
e.g., next best action or product
Business takeaway: "Different problems need different models. It's not one-size-fits-all."
| Model Type | Strength | Weakness | Use Case Example |
|---|---|---|---|
| Simple models | Transparent, explainable | May lack power on complex data | HR, finance, regulated industries |
| Complex models | Very accurate on big data | Harder to explain, risk of black-box | Image recognition, product recs |
| Rule-based systems | Fully controlled | Rigid, can't learn from data | Compliance, decision logic trees |
Business takeaway: "Sometimes it's better to trade a little accuracy for more explainability."
Simple Model (Speedometer)
Complex Model (Jet Engine)
These empower business stakeholders to collaborate on model selection:
Business takeaway: "You don't need to choose the algorithm, but you do help define what a good model looks like for the business."
Choosing a model isn't just technical — it reflects:
"The best model isn't always the most complex one. It's the one that balances technical performance with business requirements for transparency, ethics, and usability."
"We're showing the AI a bunch of past examples so it can learn to spot patterns and make predictions about new situations."
Business decisions shape how the model is trained:
Example: In fraud detection, is it worse to miss a fraud case or to flag a legitimate customer?
Business professionals define performance goals and tolerances.
| Risk | Explanation | Business Impact |
|---|---|---|
| Overfitting | The model memorizes the training data too closely — works great on past data, fails on new | Looks smart in testing but fails in real life |
| Underfitting | The model is too simple to learn anything useful | Predictions are inaccurate or vague |
| Bias Reinforcement | If training data contains bias, the model will learn and repeat it | Can lead to unfair or unethical outcomes |
Business input can help detect overfitting or bias by asking:
"This is where we test the model on new data it hasn't seen before to make sure it works — and works fairly."
Business takeaway: "This step helps us avoid surprises when the model goes live."
| Metric | What It Means | Example |
|---|---|---|
| Accuracy | How often the model gets the prediction right | 90% of churn predictions were correct |
| Precision & Recall | Does it find the right things without too many false alarms? | Spam filter: flags real spam and not your boss |
| Fairness | Does it work equally well for different groups? | Hiring model works across age/gender |
| Robustness | Will it still work if things change a little? | Seasonality, new customer behaviors |
Business takeaway: "It's not just about how good the model is — it's where it works well and who it works well for."
Business takeaway: "If we can't explain or trust the results in this phase, we're not ready to use the model."
"AI is being tested — but humans still decide if the performance is acceptable."
"Validation is where business professionals say: Does this meet our standards? Not just statistically, but ethically, reputationally, and strategically."
"Deployment is when the model leaves the lab and becomes part of a live decision-making process."
Examples:
Business takeaway: "This is when the AI begins influencing real decisions and outcomes."
"AI doesn't end at launch. It needs regular checkups — just like a new hire."
Key monitoring focuses:
Business takeaway: "What works today may fail tomorrow if we're not watching."
You don't just plant the seed (train it) and walk away.
You watch it, weed it, and adjust when the environment changes.
Neglect = decay or drift.
| Responsibility | What It Looks Like in Practice |
|---|---|
| Define success criteria post-launch | "If this model increases churn accuracy by 10%, that's a win." |
| Watch for real-world side effects | "Are customers opting out more since this model went live?" |
| Track adoption and feedback | "Are frontline staff using the AI? Do they trust its outputs?" |
| Raise red flags early | "This score looks off — has something changed in the inputs?" |
| Participate in regular model reviews | "Quarterly check-ins on fairness and accuracy metrics." |
Business takeaway: "Owning the business impact of the AI system doesn't stop after go-live."
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