Outcreating Oracle AI Innovation: Building a Gen AI-Powered Center of Excellence for a Global Digital Infrastructure Leader

Our Client

The client is a global digital infrastructure leader with a complex Oracle ecosystem supporting critical enterprise processes. As the organization expanded its AI agenda, it sought to improve productivity across development and operations while making enterprise data more accessible to business users. It also needed a scalable approach to identify, prioritize, and deploy AI use cases across its Oracle landscape.

Industry Trends

Enterprises are moving beyond isolated AI pilots toward centralized operating models that can scale adoption across technology and business functions. Within complex Oracle environments, AI is increasingly being applied to software development, testing, documentation, data access, and operational monitoring. AI Centers of Excellence help organizations establish governance, prioritize investments, reuse capabilities, and translate experimentation into measurable enterprise value.

Challenges

The client's growing Oracle environment created several barriers to scaling AI:

• Fragmented AI adoption: Without a centralized function, identifying and prioritizing Oracle-focused AI opportunities was difficult.
• Manual development lifecycle: Manual documentation, test creation, code development, and validation activities slowed software delivery and consumed valuable resources.
• Limited data accessibility: Business users depended on technical teams to query enterprise data and generate routine reports.
• Operational complexity: Integration monitoring, EDI transactions, and exception management required substantial manual intervention.
• Inconsistent innovation pipeline: The organization lacked a repeatable framework to continuously evaluate, develop, and scale high-value AI use cases across Oracle ERP environments.

The client needed to turn AI from a collection of opportunities into a structured, continuously managed enterprise capability across its Oracle ecosystem.

LTM Solution

LTM established a centralized Oracle AI Center of Excellence (CoE) with a POD-based operating model to identify, develop, and scale AI opportunities across the enterprise.

The engagement began with business process analysis, process mining, and AI opportunity assessments. These activities helped identify high-impact use cases and establish a roadmap focused on improving productivity across development, testing, reporting, and operational functions.

The CoE then deployed Oracle Generative AI capabilities across the Oracle ecosystem, including:

• AI-assisted development: Code generation, user story creation, documentation, and test-case generation reduced repetitive development activities.
• Intelligent testing: AI-supported test-result analysis and validation improved quality assurance and reduced manual effort.
• Natural-language data access: Natural language-to-SQL enabled business users to query enterprise data and generate insights without specialized technical support.
• Intelligent operations: AI-assisted monitoring and resolution recommendations improved EDI transaction management and operational exception handling.
• Continuous innovation: The CoE created a repeatable process for identifying, prioritizing, developing, and deploying new Oracle AI use cases.

This approach shifted AI adoption from isolated experimentation to a structured enterprise innovation engine, enabling the organization to continuously expand AI across its Oracle landscape.

Business Outcomes

• AI innovation at scale: Established a centralized Oracle AI CoE and launched 12+ AI use cases across business and IT functions.
• Faster software delivery: Reduced manual effort across development, testing, documentation, and validation.
• Greater business self-service: Natural language-to-SQL improved access to enterprise data and reduced dependency on technical teams.
• Stronger operational efficiency: Automated documentation, EDI monitoring, exception management, and resolution recommendations improved process responsiveness.
• Higher developer productivity: AI-assisted coding, testing, and documentation freed teams to focus on higher-value activities.
• Scalable innovation model: Created a structured framework for continuously evaluating and deploying Oracle-focused AI opportunities.

Conclusion

LTM helped the client establish an Oracle AI Center of Excellence that transformed AI from an experimental initiative into a strategic business capability. By embedding Generative AI across development, testing, reporting, and operational processes, the organization improved productivity while creating a repeatable model for innovation. With a growing portfolio of AI-powered use cases and a centralized governance model, the organization now has a strong foundation for sustained innovation, operational excellence, and competitive differentiation.