2.4. Reflection Checkpoint

Key Takeaways

• Separate training (occasional, costly, defines what the model knows) from inference (constant, per-use cost); models are snapshots and do not improve without deliberate retraining or updating.
• Place problems in the right circle: predictive ML for scores and forecasts from structured history, generative AI for drafting, summarizing, and understanding unstructured content.
• Assess data type and quality first: quality and representativeness beat volume, and generative AI can unlock unstructured data that was previously invisible.
• Know the standards by purpose: ISO/IEC 22989 and 23053 align vocabulary and system descriptions; ISO/IEC 42001 is a certifiable AI management system for governing AI across the organization.
• Choose the least flexible solution that works: rules for fixed, auditable logic; chatbots and RAG assistants for answers; agents only when multi-step actions across systems are required, with limits and escalation.
• Budget for monitoring and retraining from day one; drift is silent and inevitable.
• Contain shadow AI with transparent classification (approved, under evaluation, blocked), a fast intake process, sanctioned alternatives, and enforcement, not bans alone.
• Close generative AI gaps in order of cost: prompt first, RAG for knowledge, fine-tuning for behavior; treat tokens and context as a budget.

Connecting Forward

With the vocabulary in place, Phase 3 turns to the largest domain: deciding which AI initiatives to pursue and proving they are worth it. You will use what you learned here constantly: build-buy-partner decisions depend on understanding foundation models and adaptation costs, ROI depends on token-based and instance-based pricing, and knowing when AI is not appropriate builds directly on the rules-versus-AI test.

Self-Check Questions

• A vendor proposes fine-tuning a model on your knowledge base so that support agents get accurate answers about products that change monthly. What would you challenge, and what would you propose instead?
• Why might a company that bans all public generative AI tools end up with more data-leakage risk than a company that classifies tools and offers an approved alternative?
• A customer-service process requires checking order status, choosing between refund and replacement, and executing the choice. Why is a RAG assistant insufficient, and what controls must accompany the alternative?
• A model's performance has declined steadily since a major product launch. Which concept explains this, and what governance elements should already have been in place?