Generative AI Center of Excellence

Prompt

Create a new document describing the process for an generative AI center of excellence (GenAI CoE). The core of this process is to create a "funnel" of generative AI ideas.

A one hour online training program is offered to help people generate grate ideas that are both practical and have a good ROI.

Everyone in the organization can submit their ideas in a web form.

The ideas are reviewed by a GenAI expert group and a review is sent to the submitters and their managers.

Ideas are categorized and scored based on factors such as feasibility, risk and benefits.

Awards are given out to individuals or teams with the best ideas.

Each month the expert group team reviews new ideas and selects a few to be funded by a development team.

The GenAI development teams report to the GenAI CoE on their progress.

Each quarter, the GenAI CoE reports to executives on the status of their idea funnel. The funnel reports are similar to a traditional "pipeline" report that shows how many ideas are in various stages.

The GenAI team has a taxonomy of the types of problems solved and a taxonomy of the ways they are solved (tools and frameworks).

The GenAI team encourages teams to work together that have similar problems and to build a shared code repository for the entire organization to use.

Return this document as a Markdown document. Suggest places where charts an figures should be placed. Make sure there are two newlines before each list.

Response

Generative AI Center of Excellence: Idea Funnel Process

Executive Summary

The Generative AI Center of Excellence (GenAI CoE) operates on a systematic funnel approach to identify, evaluate, develop, and deploy generative AI solutions across the organization. This process ensures that AI initiatives are strategically aligned, technically feasible, and deliver measurable business value while fostering organization-wide innovation and collaboration.

[Suggested Figure: High-level GenAI CoE Idea Funnel Overview - showing the flow from idea submission through deployment]

Infographic Prompt

Please create a new p5.js program that contains an infographic for the High-level GenAI CoE Idea Funnel Overview - showing the flow from idea submission through deployment. The infographic has colored regions with a short label. When the user hovers over the region an infobox appears that has the details of what happens at each step. New ideas come from a "New GenAI Idea Form" on the left side of this infographic. "Summary Dashboard " reports with KPIs appears on the right side of the infographic. Generate the full HTML file, but do not indent the JavaScript code so the JavaScript code is easy to paste into the p5.js editor. Show a preview of the infographic on the right side of your canvas.

1. Foundation: AI Literacy Training Program

1.1 One-Hour Online Training Module

The GenAI CoE provides a comprehensive one-hour online training program designed to educate employees on:

Fundamentals of generative AI capabilities and limitations
Identifying high-value use cases within their domain
Understanding feasibility constraints and technical requirements
ROI calculation frameworks for AI projects
Best practices for idea formulation and submission

1.2 Training Objectives

The training program ensures participants can:

Recognize opportunities where generative AI can add value
Distinguish between feasible and unfeasible AI applications
Articulate business benefits and success metrics
Understand implementation complexity and resource requirements

[Suggested Chart: Training Module Completion Rates by Department]

2. Idea Submission Process

2.1 Universal Access Web Portal

All employees have access to a standardized web form for submitting GenAI ideas that captures:

Problem statement and current process description
Proposed AI solution approach
Expected benefits and success metrics
Resource requirements and timeline estimates
Stakeholder impact assessment
Risk considerations

2.2 Submission Guidelines

Clear criteria help employees submit high-quality ideas:

Focus on specific, measurable business problems
Include baseline performance metrics where possible
Identify key stakeholders and decision makers
Provide realistic implementation timelines
Consider data availability and quality requirements

[Suggested Figure: Idea Submission Web Form Interface Mockup]

3. Expert Review and Feedback Process

3.1 GenAI Expert Review Panel

A dedicated expert group consisting of:

AI/ML technical specialists
Business analysts with domain expertise
Risk management representatives
IT infrastructure specialists
Change management professionals

3.2 Review Process

Each submitted idea undergoes:

Technical feasibility assessment
Business value analysis
Risk evaluation
Resource requirement validation
Strategic alignment review

3.3 Feedback Delivery

Within two weeks of submission:

Detailed written feedback provided to submitters
Copy sent to submitter's direct manager
Recommendations for next steps or improvements
Connection to similar ongoing initiatives when applicable

4. Categorization and Scoring Framework

4.1 Evaluation Criteria

Ideas are scored across multiple dimensions:

Feasibility (1-5 scale)

Technical complexity
Data availability and quality
Integration requirements
Timeline realism

Risk Assessment (1-5 scale)

Technical risk
Business continuity risk
Regulatory compliance risk
Change management risk

Benefits Analysis (1-5 scale)

Cost savings potential
Revenue generation opportunity
Productivity improvements
Strategic competitive advantage

[Suggested Chart: Scoring Matrix with Feasibility vs. Benefits Quadrant Analysis]

4.2 Categorization System

Ideas are classified by:

Problem Type: Process automation, content generation, decision support, customer service, etc.
Complexity Level: Simple automation, moderate integration, complex transformation
Department/Function: HR, Finance, Marketing, Operations, etc.
Implementation Timeframe: Quick wins (0-3 months), Medium-term (3-12 months), Long-term (12+ months)

5. Recognition and Awards Program

5.1 Award Categories

Innovation Excellence: Most creative and impactful ideas
Business Impact: Highest ROI potential
Technical Innovation: Novel application of AI capabilities
Cross-Functional Collaboration: Ideas spanning multiple departments

5.2 Recognition Process

Monthly recognition for top-scoring submissions
Quarterly awards ceremony with executive participation
Internal newsletter features and case studies
Professional development opportunities for award recipients

[Suggested Figure: Awards Dashboard showing monthly and quarterly recognition metrics]

6. Monthly Funding and Selection Process

6.1 Expert Panel Review Meetings

Monthly meetings where the expert group:

Reviews all submitted and scored ideas
Considers available development resources
Evaluates strategic priorities and timing
Selects 2-5 ideas for active development funding

6.2 Selection Criteria

Priority given to ideas that:

Score highest on combined feasibility and benefits metrics
Align with current organizational strategic objectives
Leverage existing technical infrastructure
Demonstrate clear success measurement criteria
Have engaged stakeholder support

6.3 Resource Allocation

Selected projects receive:

Dedicated development team assignment
Budget allocation for tools and infrastructure
Project management support
Regular progress monitoring and support

[Suggested Chart: Monthly Funding Pipeline showing ideas selected, in development, and completed]

7. Development Team Reporting Structure

7.1 Progress Tracking

Development teams provide bi-weekly reports to the GenAI CoE including:

Milestone achievement status
Technical challenges and solutions
Resource utilization and budget status
Stakeholder feedback and engagement
Timeline adjustments and risk mitigation

7.2 Support and Escalation

The GenAI CoE provides:

Technical guidance and best practices
Resource reallocation when needed
Escalation paths for significant blockers
Cross-team collaboration facilitation

8. Quarterly Executive Reporting

8.1 Pipeline Report Structure

Quarterly executive reports include:

Funnel Metrics

Total ideas submitted
Ideas in review phase
Ideas approved for development
Projects in active development
Successfully deployed solutions
Projects discontinued and lessons learned

Performance Analytics

ROI achieved from deployed solutions
Resource utilization efficiency
Time-to-deployment metrics
User adoption and satisfaction rates

Strategic Insights

Emerging trends in AI application areas
Capability gaps and investment recommendations
Competitive intelligence and market opportunities
Risk assessment and mitigation strategies

[Suggested Figure: Executive Dashboard with Funnel Visualization and Key Performance Indicators]

8.2 Success Stories and Case Studies

Each quarterly report features:

Detailed case studies of successful implementations
Quantified business impact and lessons learned
Best practices and replicable frameworks
Recommendations for scaling successful approaches

9. Taxonomy and Knowledge Management

9.1 Problem Taxonomy

The GenAI CoE maintains a structured classification of:

Problem Categories

Content creation and generation
Process automation and optimization
Data analysis and insights generation
Customer interaction and support
Decision support and recommendation systems
Quality assurance and testing

Business Functions

Human Resources
Finance and Accounting
Marketing and Sales
Operations and Supply Chain
Customer Service
Research and Development

9.2 Solution Taxonomy

Tools and Frameworks

Large Language Models (LLMs) and applications
Image and video generation tools
Code generation and development assistants
Document processing and analysis platforms
Conversational AI and chatbot frameworks
Integration and workflow automation tools

Implementation Patterns

API integration approaches
User interface design patterns
Data pipeline architectures
Security and compliance frameworks
Testing and validation methodologies

[Suggested Figure: Interactive Taxonomy Visualization showing relationships between problems and solutions]

10. Collaboration and Shared Resources

10.1 Cross-Team Collaboration Framework

The GenAI CoE facilitates collaboration by:

Identifying teams working on similar problem domains
Organizing regular cross-functional working groups
Sharing technical approaches and lessons learned
Coordinating resource sharing and knowledge transfer

10.2 Shared Code Repository

Repository Structure

Reusable components and libraries
Template solutions for common use cases
Integration utilities and connectors
Testing frameworks and validation tools
Documentation and implementation guides

Governance and Maintenance

Code review and quality standards
Version control and release management
Security scanning and compliance validation
Community contribution guidelines and recognition

Monthly technical lunch-and-learn sessions
Quarterly cross-departmental showcases
Annual GenAI innovation conference
External community engagement and thought leadership

[Suggested Figure: Collaboration Network Diagram showing connections between teams and shared resources]

Conclusion

The GenAI Center of Excellence idea funnel process creates a systematic approach to harnessing organizational creativity while ensuring strategic alignment and technical feasibility. By combining structured evaluation with collaborative development and knowledge sharing, organizations can maximize the value of their generative AI investments while building sustainable innovation capabilities.

This framework enables continuous learning, reduces implementation risks, and accelerates the adoption of proven AI solutions across the enterprise. Success depends on sustained executive commitment, adequate resource allocation, and a culture that encourages experimentation and cross-functional collaboration.

[Suggested Figure: Success Metrics Dashboard showing overall program impact and ROI]