Career Evolution:
Stephen's journey started in bioinformatics, merging biology and computing. His path evolved from software development to management consulting and later to pre-sales roles in supply chain technologies before joining IBM. He emphasizes the importance of combining technical expertise with commercial acumen in client-facing roles.

IBM's Legacy and Differentiation:
IBM's 100+ year legacy in technology innovation spans mainframes, AI, and quantum computing. While not always top-of-mind in today's tech scene, IBM’s enterprise-focused innovations underpin critical infrastructure like banking transactions and AI governance.

Enterprise AI Adoption Challenges:
Only 9% of generative AI projects are in production. Stephen highlights hurdles such as cost optimization, scalable governance frameworks, and ensuring trust and transparency. He emphasizes the importance of managing "hallucinations" (erroneous AI outputs) and meeting global regulatory standards like Australia’s AI guardrails.

Ethical AI Practices:
IBM prioritizes responsible AI use through initiatives like the AI Ethics Board and promotes collaboration between risk management and technical teams to ensure ethical deployments. Transparency in training data and using clear, trustworthy models are foundational.

Empowering Productivity:
Stephen illustrates how AI enhances workflows, from generating emails in minutes to automating repetitive tasks. He envisions AI shifting from a rigid chatbot approach to autonomous "agentic workflows," optimizing planning, reasoning, and execution for businesses.

Actionable Insights for Enterprises:
Fine-tuning AI models with corporate data remains key, yet it requires significant effort. IBM's InstructLab democratizes this process, allowing non-technical business users to shape AI outputs, unlocking value without massive resource investments.