Building an Organization for Scaling AI - The AI CoE Model for Gaming Organizations

Artificial intelligence (AI) is reshaping industries, driving efficiencies, and enabling innovation at scale. However, its success depends not just on the technology itself but also on how organizations structure and implement their AI strategies. For medium to large organizations, a critical component for achieving AI scalability and alignment is tahe Center of Excellence (CoE) model.

An AI CoE serves as a centralized hub where expertise, tools, and resources are pooled to drive AI initiatives efficiently. It establishes best practices, enables cross-functional collaboration, and ensures that AI efforts align with broader organizational objectives. By combining central oversight with decentralized execution through business units, the CoE model provides the structure necessary to scale AI effectively.

Two main types of AI organizations leverage CoEs in different ways:

• AI-driven organizations, where AI is the core business enabler, use CoEs to lead disruptive innovation and product development.
• AI-enabled organizations integrate AI into existing operations, relying on CoEs to enhance efficiency, optimize processes, and improve decision-making.

Without an effective AI CoE, organizations risk duplication of efforts, inefficient resource allocation, and disconnected use cases. By establishing a CoE as part of a broader AI strategy, organizations can overcome these challenges and unlock the transformative potential of AI.

In this article, I would like to present an AI CoE model for a gaming company. The proposed model will serve as a high-level strategy for the AI/ML department, encompassing a vision, mission, organizational structure, and a phased approach to implementing the CoE. It is assumed that data teams already exist within the organization. The AI CoE will act as a unifying structure to democratize AI and ML across product development teams, accelerating delivery, fostering domain expertise, and scaling operations effectively.

An AI CoE for an Organisation

Vision Statement

To be a leading force in AI-driven innovation, delivering exceptional value and competitive advantage.

Mission Statement

To establish and scale AI initiatives that enhance products, optimize operations, and drive strategic growth across an organization.

The AI CoE: A Strategic Imperative

The AI CoE serves as a centralized hub for AI strategy, governance, and innovation. Its primary objectives include:

• Enhancing Customer Experience: Leveraging AI to improve user interactions and engagement.
• Optimizing Productivity: Automating processes and reducing costs.
• Driving Innovation: Staying ahead of market trends to maintain leadership in the gaming industry.

The Key Pillars of the AI CoE can be summarized as:

1. Strategy Development: Align AI initiatives with organisational goals.
2. Governance: Establish ethical, secure, and compliant AI practices.
3. Innovation: Develop cutting-edge AI solutions to address critical business challenges.

Core Tasks:

1. Develop a High-Level Strategy:
   • Define a clear vision and mission for the AI/ML Department.
   • Design an organizational chart specifying roles, responsibilities, and reporting structures within the CoE.
2. Phased Implementation Plan:
   • Outline and execute a multi-phase approach with key activities, objectives, and measurable outcomes for each phase.
3. Promote Collaboration and Democratization:
   • Foster cross-functional collaboration to share AI/ML knowledge and tools.
   • Create resources and frameworks for teams to adopt AI efficiently.
4. Enhance Gaming Innovation:
   • Leverage AI to improve player experience, inform game feature development, and offer data-driven insights into user engagement.

Phased Approach to Implementation

Phase 1: Preparation and Data Contracts

Key Activities:

• Infrastructure and data assessment to evaluate existing capabilities.
• Define data contracts and schemas for real-time ingestion.
• Establish a strategic roadmap for AI initiatives.

Outcomes:

• Comprehensive infrastructure plan.
• Data contracts ensuring consistency and accuracy.
• A well-aligned AI strategy and roadmap.

Phase 2: Building the AI CoE Platform

Key Activities:

• Integrate user, game, and payment data into the AI platform.
• Develop scalable infrastructure and robust workflows.

Outcomes:

• Unified, real-time data integration for accurate AI modeling.
• Fully operational AI infrastructure to support innovation.

Phase 3: AI-Solution MVP Development

Key Activities:

• Evaluate and integrate existing AI/ML proofs of concept (PoCs).
• Deploy automated processes for operational improvement.
• Establish a closed-loop feedback system for continuous refinement.

Outcomes:

• AI/ML models embedded within the CoE framework.
• Enhanced operational efficiency and process automation.
• Data-driven impact assessment and iteration.

Phase 4: Exposure and AI Democratization

Key Activities:

• Seamlessly integrate AI solutions into user-facing systems.
• Foster collaboration and knowledge-sharing across teams.
• Define success metrics for AI initiatives.

Outcomes:

• AI solutions embedded into key platforms like PAM and CMS.
• Improved stakeholder engagement and cross-team collaboration.

Phase 5: Expansion

Key Activities:

• Scale AI solutions across departments.
• Develop new AI models and enhance governance practices.
• Optimize AI performance through continuous monitoring.

Outcomes:

• Broader organizational adoption of AI solutions.
• Expanded capabilities to address complex business challenges.
• Strengthened governance and sustained AI effectiveness.

Organizational Structure of the AI CoE

Proposed AI CoE Org Chart

Role	Responsibilities
Engineering	Manage AI infrastructure, tools, and robust MLOps practices.
Data Science	Develop AI models and solutions; conduct advanced research.
Project Management	Oversee project delivery, timelines, and stakeholder alignment.
Analytics & BI	Provide insights, dashboards, and data-driven decision-making.
Product Ownership	Ensure alignment between AI products and business needs; manage product lifecycles.

Potential AI Offerings

Service Categories and Examples

• Churn Prevention: Real-time streaming models to retain customers.
• Personalized Marketing: Generative AI for content creation.
• Game Recommendations: AI-powered recommendations to enhance player engagement.
• Fraud Detection: Advanced models to mitigate financial risks.

AI Platform: Key Features and Benefits

1. Consistency Across Training and Serving: Ensures uniformity in feature engineering.
2. Advanced Feature Engineering: Enables complex transformations for reproducible workflows.
3. Collaboration and Reuse: Promotes feature sharing, reducing duplication.
4. Scalability and Performance: Supports high-throughput, low-latency environments.
5. Governance and Compliance: Implements robust access controls and ethical standards.

AI Governance Best Practices

• Code Quality: Enforce standards with tools like pylint and pre-commit hooks.
• Performance Monitoring: Use MLflow to track and alert on model performance degradation.
• Bias and Fairness: Regular re-training to address drift and ensure compliance.
• Documentation: Comprehensive templates for transparency and audit readiness.

Conclusion

By establishing a robust AI CoE, Organisation Group can position itself as a leader in gaming innovation, delivering superior products, driving efficiency, and ensuring sustainable growth. The phased approach ensures scalability, stakeholder alignment, and continuous improvement—laying the groundwork for long-term success in an AI-driven world.

For further inquiries or collaboration opportunities, feel free to reach out at zerafachris@gmail.com.

Let’s transform the future of gaming with AI!