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How to Structure AI Use Cases: A Strategic Framework for AI Solutions Managers
Many AI initiatives struggle or fail—not due to poor models, but due to vague problem statements, poor data understanding, or lack of business alignment. This framework ensures you're solving the right problem the right way.
March 30, 20254 min read min read
How to Structure AI Use Cases: A Strategic Framework for AI Solutions Managers
Introduction Many AI initiatives struggle or fail—not due to poor models, but due to vague problem statements, poor data understanding, […]
Introduction
Many AI initiatives struggle or fail—not due to poor models, but due to vague problem statements, poor data understanding, or lack of business alignment. Without a clear structure, teams risk building technically sound models that never deliver value. This article presents a strategic, six-step framework to help AI Solutions Managers structure AI use cases that align with business goals, ensure feasibility, and drive adoption. Whether you're building a readmission predictor or a compliance automation tool, this framework ensures you're solving the right problem the right way.
1. Problem Framing
Strategic RoleProblem framing is the foundation. It defines the business goal, identifies the decision point, and sets the success criteria. Poor framing leads to misaligned solutions, regardless of technical excellence.
Key Questions
What's the business goal or pain point?
What KPI are we trying to optimize?
What decisions will the model inform?
ExampleHealthcare: Reducing 30-day readmission rates.KPI: Readmission %.Decision: Should a patient be flagged for post-discharge intervention?
Pitfalls to Avoid
Vague objectives like "use AI to improve care."
No measurable KPI.
Confusing correlation with causation.
Tips/Tools
Use problem-framing canvases like AI Canvas (IDEO, Microsoft).
Align with line-of-business stakeholders early.
2. Data Evaluation
Strategic RoleData shapes feasibility and scope. Evaluate data quality, availability, and labeling needs before modeling begins.
Key Questions
What data exists today?
Structured, unstructured, or both?
Is it labeled, clean, and complete enough?
ExampleRetail: Predicting next-week demand per SKU-store.Data: Transaction logs (structured), promo ads (unstructured), weather feeds.
Pitfalls to Avoid
Assuming more data = better data.
Ignoring data freshness or granularity.
Misjudging labeling effort for supervised learning.
Tips/Tools
Run quick EDA (Exploratory Data Analysis) using Pandas, SQL, or notebooks.
Leverage data profiling tools like Great Expectations or Monte Carlo.
3. Modeling Feasibility
Strategic RoleThis step aligns the problem with the right learning paradigm—supervised, unsupervised, NLP, etc.—and sets realistic expectations.
Key Questions
What type of model fits best?
Are labels available or feasible to obtain?
Do we need embeddings, annotations, or domain models?
ExampleCompliance: Automating fraud detection in expense claims.Fit: Supervised + NLP.Need: Annotated historical claims + OCR pipeline.
Pitfalls to Avoid
Overengineering with deep learning when simpler models work.
Choosing models without understanding business constraints (e.g., interpretability).
Tips/Tools
Start with model cards to evaluate trade-offs.
Use low-code AutoML tools for prototyping (H2O.ai, Azure AutoML).
4. Value Estimation
Strategic RoleQuantifies business impact to prioritize high-ROI projects and justify investments.
Key Questions
What is the business impact if solved?
Will it reduce costs, increase revenue, or lower risk?
What's the time to value?
ExampleLogistics: Route optimization to reduce fuel use.Projected Impact: $1M/year savings per 5% efficiency gain.Time to Value: 10-week pilot.
Pitfalls to Avoid
No baseline for comparison.
ROI framed only in technical terms.
Ignoring TCO (Total Cost of Ownership).
Tips/Tools
Use a business case calculator.
Partner with finance to validate estimates.
5. Deployment Path
Strategic RoleTranslates models into operational systems. Deployment must align with user workflows and latency needs.
Key Questions
Real-time or batch?
Cloud, on-prem, or edge?
What UI/workflow changes are needed?
ExampleInsurance: Underwriting risk scoring model.Deployment: Batch scoring via API, integrated with internal CRM.
Pitfalls to Avoid
"Throwing models over the wall" to engineering.
No clarity on where or how predictions are consumed.
Tips/Tools
Build MLOps pipelines using MLflow, FastAPI, or Docker.
Conduct user journey mapping to ensure integration into decision loops.
6. Change Management
Strategic RoleDrives user adoption by aligning people, processes, and policies. Even the best model fails if no one uses it.
Key Questions
Who needs buy-in?
What training or process changes are required?
How will feedback and iteration work?
ExampleFinance: Automating invoice classification.Required: Finance team training, policy revision on automation QA, feedback loop to update model monthly.
Pitfalls to Avoid
No champion in the business unit.
Ignoring frontline resistance.
Assuming change happens on its own.
Tips/Tools
Engage change management leads early.
Use RACI charts, stakeholder maps, and pilot feedback loops.
Conclusion
A structured framework transforms AI from experimentation to enterprise impact. By rigorously addressing problem framing, data readiness, modeling feasibility, and deployment planning—while managing change—you maximize both technical success and business ROI. Use this six-step framework as a strategic checklist for every AI use case to ensure alignment, feasibility, and adoption. In AI, structure isn't just helpful—it's essential.