Rapid AI Use-Case Verification Workshops

This step-by-step approach is based on insights from real AI PoC projects. It cuts risk and leads you to successful AI implementations. We’ll guide each use case through four essential quality gates:

1. Business value – What is the clear impact?
2. Usable data – Is the data available and ready to use?
3. AI operating model – How can the solution be integrated into operations?
4. Prototype validation – Validate it with a prototype implementation.

Only validated use cases move forward, ensuring you invest in use cases ready to scale.

Introduction

Your input: A suggestion for how AI could improve a specific step in your business workflow

Introduction from PRODYNA:

• Expectation management
• Overview: How GenAI works
• Introduction to AI design
• Overview of today's scenarios
  

Business workflow

Split into workgroups:

• Draw your business workflow or process steps on the pinboard
• Highlight the workflow steps (where time is lost, that are repetitive, where errors occur)
• Prioritize the problematic workflow steps
  

AI card mapping

• Explore the Microsoft AI cards
• Map the features on the AI cards to the steps in your business workflow
• Use the clusters of AI cards to identify the workflow steps that can be best automated with AI services
• Prioritise the clusters according to your perceived business value
  

Review & feedback

Group discussion: Present the findings

Output: Hypothesis for using AI in the most relevant parts of your business workflow: e.g., If we can automate text extraction from the customer forms, we can save c.a. 8000 human hours of effort/year.

Data relevance

Your input: Concrete use-case suggestion for automation using AI Services

Clarification of:

• Contextual fit: Alignment with the specific goals of the project
• Timeliness: Does the data reflect the current state of the subject matter?
• Applicability: Is the data applicable to the specific context in which it is being used?
  

Data quality

Clarification of:

• Accuracy: Does the data represent the real-world values it is intended to model?
• Completeness: Does the data include all information without missing values or gaps?
• Consistency: Is the data uniform across different data sets and times? Are data formats, definitions, and values consistent?
• Reliability: Is the data dependable and stable over time?
  

Data security

Clarification of:

• Confidentiality: Is the data accessible only to authorized individuals?
• Integrity: Can the accuracy and completeness of data over its lifecycle be maintained?
• Availability: Are the remeasures to protect against data loss and ensure resiliency?
• Compliance: Adherence to relevant laws that govern data protection, like GDPR or HIPAA.
  

Data usability

Clarification of:

• Reachability: Is the data properly documented and in an appropriate format?
• Understandability: Is the data structure, meaning, and context clear?

Output:

• Findings and recommendations regarding all aspects of the data required for the use case
• Clear ‘Go’, or ‘No Go’ for further development of the AI use-case.
  

Platform landing zone

Your input: Concrete use-case suggestion for automation using AI Services.

Clarification of:

• Infrastructure: Are necessary compute, storage, and networking resources provided?
• Compliance: Does the environment adhere to security and regulatory requirements?
• Data management: Are services for data ingestion, storage, and governance provided?
  

Platform operating model

Clarification of:

• Responsible AI: Does a Responsible AI policy exist and what are the ramifications for this workload?
• Deployment & ML-OPs: What are the guidelines for model and artefact deployment?
• Monitoring: How will the model performance be tracked and issues such as model drift detected and addressed?
• FinOps: Can centralized services be used for cost optimization –e.g., TPU.
  

App integration

Clarification of:

• Backend Integration: Is the integration of other backend systems e.g., SAP per API required?
• Data Synchronization: Do potential data sync problems need to be anticipated and mitigated?
• Security: Which authentication, authorization, and encryption measures need to be implemented?
• Resilience: How should errors be detected and handled?
  

App scalability

Clarification of:

• Application Architecture: Is the suggested architecture suited to the performance requirements?
• Cloud Services: Do the suggested cloud services fit the performance requirements?

Output:

• Findings and recommendations
• Clear ‘Go’ or ‘No Go’ for prototypic implementation of the AI use case.
  

Define

Your input: Concrete use-case for automation using AI Services

Clarification of:

• Implementation team
• Environment onboarding
• Write user stories & create a Kanban board
• Responsibilities
  

Develop

Agile development:

• Implementation of the user stories
• Create deployment pipelines
• Iterative deployment to the target environments
  

Evaluate

Iterative testing:

• Does the prototype fulfill the technical requirements?
• Does the prototype fulfill the business requirements?
• Does the prototype fulfill the performance requirements, and how does it scale?
  

Document

Documentation of:

• Technical & business functionality
• Business value fulfillment

Result: Clear ‘Go’ or ‘No Go’ for further FULL product development of the AI use-case to production readiness.