All field notes

An agent demo is cheap; a production agent isn't. What drives AI agent development cost—integrations, guardrails, evaluation—and how to budget for the real thing.

Not sure if it's time for AI? Seven clear signals your business is ready—and the signs you should fix data and process fundamentals before you start.

Before you invest in AI, assess readiness honestly. The five dimensions—data, use case, skills, infrastructure, governance—and what to fix before you start.

The first AI project sets the tone for everything after. How to start—use case, data, success metric, a small pilot that ships—so you build momentum, not regret.

A POC proves it can work; an MVP is the smallest thing real users can use. Which to build for your AI project—and why the distinction decides whether it ships.

AI projects don't run like traditional software projects. The differences—uncertainty, data, evaluation gates, adoption—and how to manage them without nasty surprises.

You don't need a giant AI team—you need the right roles. Which roles actually matter, when to hire vs partner, and why one accountable owner beats headcount.

AI isn't just for giants. A practical strategy for SMBs—start focused, use a partner, pick use cases where small gains compound—to win without a big budget.

LLMs power the current AI wave, but few leaders can say what one actually is. A plain-English explainer—what an LLM is, what it's good and bad at, and where it fits.

Embeddings are the quiet engine behind AI search, recommendations, and RAG. A plain-English guide to what they are and why they matter for your business.

Vector databases are the backbone of RAG and semantic search. What they are, how they differ from a normal database, and when your AI project needs one.

Prompt engineering is how you instruct AI to get useful output. What it is, the techniques that work, and why context engineering matters even more in production.

RAG is how you make AI answer from YOUR data, with citations. A plain-English explainer of retrieval-augmented generation—what it is, why it matters, and its limits.

Machine learning underpins most 'AI'—but what is it, really? A business guide: how systems learn from data, the main types, and where ML actually pays off.

Deep learning powers LLMs, vision, and speech. What it is, how it differs from classic machine learning, and when your problem actually needs it.

Foundation models are the base layer of modern AI—big, general models adapted to many tasks. What they are, why they changed AI economics, and what it means for you.

The transformer is the architecture behind every modern LLM. A no-math explainer of what it is, why 'attention' changed AI, and what it means for you.

Semantic search finds by meaning, not keywords—so users find what they mean, not just what they type. What it is, how it works, and where it changes everything.

AI can multiply a sales team's output—or fill inboxes with generic spam. Where AI genuinely helps sales, and where the human relationship must stay human.

Finance can't tolerate confident wrong numbers. Where AI safely helps finance teams—reconciliation, reporting, forecasting—and the controls that keep it trustworthy.

Operations is where repetitive volume and firefighting collide—prime territory for AI. Where AI helps ops teams handle more with fewer errors, and how to start.

Procurement drowns in documents, suppliers, and spend data. Where AI helps procurement teams move faster and see more—without losing human judgment on decisions.

Compliance is drowning in documents and rules—and can't afford wrong answers. Where AI safely helps compliance teams, and the guardrails that keep it defensible.

IT helpdesks handle the same tickets endlessly. How AI deflects the routine, guides troubleshooting, and routes the hard cases—freeing engineers for real problems.