| Business Objective |
What problem are you trying to solve with AI? |
We want to reduce manual ticket triage time. |
Understand the core use case and business value. |
| AI Use Case Type |
Is this a predictive, generative, or classification use case? |
We want to predict equipment failure before it happens. |
Helps determine the AI model type and architecture. |
| Data Availability |
What kind of data do you have access to? |
We have historical logs, sensor data, and incident reports. |
Assesses data readiness and integration needs. |
| Data Location |
Where is your data stored (cloud, on-prem, hybrid)? |
Most of our data is in Azure Data Lake. |
Determines data pipeline and access strategy. |
| Real-Time vs Batch |
Do you need real-time insights or is batch processing sufficient? |
Real-time alerts are critical for us. |
Influences infrastructure and model deployment strategy. |
| Integration Points |
What systems will this AI solution need to integrate with? |
ServiceNow, Jira, and our internal monitoring tools. |
Identifies APIs, connectors, and integration complexity. |
| User Interaction |
Will end users interact with the AI directly (e.g., chatbot) or indirectly? |
It will be embedded in our internal dashboard. |
Helps define UI/UX and delivery method. |
| Security & Compliance |
Are there any compliance or data privacy requirements (e.g., HIPAA, GDPR)? |
Yes, we must comply with SOC 2 and GDPR. |
Determines constraints on data handling and model training. |
| Preferred Cloud |
Do you have a preferred cloud provider or existing cloud contracts? |
We’re primarily an Azure shop. |
Guides service selection (e.g., Azure ML, AWS SageMaker, GCP Vertex AI). |
| AI Maturity |
Have you used AI/ML in production before? |
We’ve done some POCs but nothing in production. |
Assesses readiness and need for foundational support. |
| Model Ownership |
Do you plan to build your own models or use prebuilt ones (e.g., OpenAI, Azure AI)? |
We’d prefer to fine-tune a prebuilt model. |
Helps scope the project and choose between custom vs. managed services. |
| Monitoring Needs |
How will you monitor and evaluate model performance? |
We’ll need dashboards and alerts for drift and accuracy. |
Ensures observability and governance are planned. |
| Scalability |
How many users or transactions do you expect the AI to handle? |
We expect 10,000+ daily interactions. |
Determines infrastructure sizing and cost implications. |
| Budget & Timeline |
What’s your budget and timeline for this initiative? |
We have a 3-month window and a $50K budget. |
Helps prioritize scope and feasibility. |
| Success Metrics |
How will you measure the success of this AI solution? |
Reduction in ticket resolution time by 30%. |
Aligns technical goals with business KPIs. |