How to cut through AI hype and choose solutions with confidence

Summary

Teams evaluating AI solutions can waste time, money, and trust when they lack technical fluency, choose unsuitable vendors, underestimate internal requirements, or expect immediate perfection. The post introduces Retrieval-Augmented Generation (RAG), vector search, agentic AI, and MCP as concepts that help teams distinguish static or rules-based tools from systems that retrieve current information, understand intent, take actions, and operate within governed boundaries. It argues that building an AI Agent in-house requires substantial work across embeddings, retrieval, prompt chaining, governance, security, maintenance, and model upgrades. Vendor evaluation should consider ongoing R&D, product roadmaps, adaptability, and maintenance, while implementation depends on structured content, documentation, process visibility, and APIs. Fin is cited as typically resolving over 76% of support questions out of the box, with resolution expected to improve through ongoing optimization and measurement.

Takeaways

1. 1

   Building an AI Agent internally involves data chunking, embeddings, relevance tuning, prompt chaining, RAG pipelines, model upgrades, fallback orchestration, security, permissions, audit logs, and governance.

2. 2

   Vendor evaluations should examine AI R&D investment, the improvement roadmap, workflow adaptability without constant engineering support, and ongoing maintenance requirements.

3. 3

   Resolution rate should be baselined, measured consistently, and treated as a growth metric; the post states that Fin typically resolves over 76% of support questions out of the box.