Article
AI readiness unlocked: Oracle’s blueprint for enterprise transformation
July 30, 2025 · Authored by Dave DuVarney, Daniel Jensen
As artificial intelligence (AI) transforms the business landscape, corporations face a vital question: How do we prepare for and successfully implement AI technologies? The answer lies not in rushing to adopt the latest AI tools, but in understanding and building organizational readiness across multiple dimensions.
With AI-powered capabilities already deployed across their application suite and a comprehensive cloud infrastructure supporting major language models, Oracle is demonstrating how enterprise AI should work in practice. Recent discussions in an AI webinar with Baker Tilly’s Dave DuVarney, Principal; Daniel Jensen, Senior Manager and Oracle’s Jody Clayton, Group Vice President, revealed that most organizations recognize AI as essential for future competitiveness, but many struggle with where to begin. The key is establishing a structured framework that addresses the fundamental building blocks of AI success.
The five dimensions of AI readiness
The foundation of AI readiness begins with identifying the right use cases. The most successful organizations approach this as an innovation process, generating multiple ideas before narrowing focus to high-impact, manageable projects. Businesses must balance three critical factors:
- Organization value: What business problems will AI solve?
- Complexity: How difficult will implementation be?
- Strategic impact: Which initiatives will deliver meaningful results?
Quality data remains the cornerstone of effective AI implementation. Without solid data foundations, even the most sophisticated AI tools will produce unreliable results. Organizations should evaluate both structured and unstructured data assets, ensuring:
- Strong data governance frameworks
- Well-categorized and organized information
- Clean, reliable data sources
- Proper data architecture for AI applications
Modern AI implementations require robust technical infrastructure with appropriate security measures. Evaluating infrastructure’s ability better supports AI initiatives while maintaining security standards and regulatory compliance. Key considerations include:
- Scalable computing resources for AI workloads
- Secure data handling and privacy protection
- Integration capabilities with existing systems
- Compliance with regulatory requirements
AI adoption can introduce new categories of risk that require careful management. Many businesses extend their existing data privacy policies to cover AI governance, addressing both opportunities and risks. It’s crucial to have:
- Clear AI governance policies
- Risk assessment frameworks specific to AI use cases
- Data privacy protections
- Compliance with emerging AI regulations
Perhaps the most important dimension, successful AI adoption requires a fundamentally different approach than traditional technology implementations. Instead of mandating usage, organizations must:
- Identify and empower AI enthusiasts throughout the organization
- Create communities for sharing experiences and best practices
- Develop evangelization strategies
- Implement detailed learning and change processes
