A strategic guide for executives navigating the AI transformation
For Executive Teams: Review terms quarterly with your technology leaders. Use the boardroom questions in strategy sessions to pressure-test your AI initiatives and identify blind spots.
For Board Members: Focus on business model innovation and regulatory readiness terms. These represent the strategic and fiduciary risks that demand board-level oversight.
For Project Sponsors: Master the automation and infrastructure terms. You'll need this vocabulary to evaluate vendor proposals and internal roadmaps effectively.
For All Leaders: Don't aim to become technical experts. Aim to ask smarter questions, identify bullshit, and make decisions that position your organization to win in an AI-native future.
The leaders who thrive in 2026 won't be those who understand every technical detail—they'll be those who ask the right strategic questions and recognise the business implications of technical choices.
Last Updated: November 2025
Definition: A technique that connects large language models to your company's specific data sources, allowing AI to answer questions using current, proprietary information rather than just its training data.
Why It Matters: RAG turns generic AI into a competitive advantage by grounding responses in your actual customer data, product specs, and institutional knowledge—without the cost of retraining entire models.
Boardroom Question: "How are we using RAG to ensure our AI tools cite accurate company data rather than hallucinate answers?"
Definition: AI systems that can process and generate multiple types of content simultaneously—text, images, video, audio, and code—rather than specializing in just one format.