The AI in Business Podcast - AI Governance and Strategy for Long-Term Impact - with Steven Eliuk of IBM
In this episode, Stephen Eliuk from IBM discusses the barriers organizations face when scaling AI projects from pilot to operational stages. He emphasizes the importance of due diligence, which involves rigorous validation at every stage of AI deployment, from development to production. This process ensures that AI models are scalable, reliable, and deliver a return on investment. Eliuk also highlights the need for executive AI fluency, which goes beyond technical skills to include understanding how to align AI initiatives with business strategy and manage costs effectively. He points out that many organizations lack a unified process for AI deployment, which can lead to inefficiencies and increased costs. Additionally, Eliuk stresses the importance of questioning assumptions about compute requirements and infrastructure choices to avoid unnecessary expenses and technical debt. He suggests that organizations should have a formal due diligence process that includes periodic reviews to ensure that AI deployments remain effective and aligned with strategic goals. This approach helps in identifying potential issues early and adapting to changes in the business environment or technology landscape.
Key Points:
- Conduct thorough due diligence at every stage of AI deployment to ensure scalability and ROI.
- Develop executive AI fluency to align AI projects with business strategy and manage costs.
- Implement a formal due diligence process with periodic reviews to maintain effectiveness.
- Question assumptions about compute and infrastructure to avoid unnecessary costs.
- Ensure that AI initiatives are not just for innovation but align with long-term business goals.
Details:
1. 🎙️ Introduction to AI in Business
1.1. AI's Role in Business Governance
1.2. AI in Product Development
2. 🤝 Welcoming Stephen Eliuk
- Organizations face critical barriers in operationalizing AI, which include the need for thorough due diligence and enhancing executive AI fluency.
- Aligning technical decisions with business strategy is crucial for successful AI implementation.
- For example, a company improved its AI project success rate by 30% after executives underwent AI fluency training, highlighting the importance of informed leadership.
- Another organization reduced AI deployment time by 40% by integrating business strategy into technical planning, demonstrating the effectiveness of strategic alignment.
3. 🚧 Barriers and Strategies for Scaling AI
3.1. Challenges in Scaling AI
3.2. Strategies for Overcoming AI Scaling Barriers
4. 🔍 Due Diligence and Executive AI Fluency
4.1. 🔍 Due Diligence in AI Implementation
4.2. 🔍 Executive AI Fluency for Strategic Advantage
5. 🔄 Continuous Validation and Lifecycle Management
5.1. Financial Metrics and Unexpected Costs
5.2. Due Diligence and Cross-Department Collaboration
5.3. Data Privacy and Retention Policies
5.4. Technical Expertise and Project Operationalization
5.5. Domain-Specific Knowledge and Strategy
5.6. Involving Technical Experts in Planning
6. 💾 Data Infrastructure and Cost Management
- Apple sets a high standard in redundancy and product validation, serving as a model for other companies to follow.
- Conducting independent project analysis and audits is essential for verifying project claims and ensuring their accuracy.
- Implementing code and data instrumentation in a dashboard format is crucial for effective analysis and validation of claims.
- Continuous validation of data and predictions is necessary to ensure that practical applications align with training outcomes.
- Recognizing the implications of errors in specific domains is often neglected but is critical for maintaining accuracy.
- Establishing a formal due diligence process is vital for deployment, ensuring that results are both valid and scalable.
- Due diligence should be an ongoing process, not a one-time task, requiring continuous analysis and validation.