All field notes

Beyond the hype, a few durable shifts define AI in 2026: agents that act, a hard focus on production and ROI, and governance moving from optional to expected.

Enterprises have moved past AI curiosity into AI accountability. The trends defining enterprise AI now—production, governance, cost, and capability.

AI agents are moving from novelty to infrastructure. Where they're headed, what will hold them back, and how to adopt them without getting burned.

Agentic AI is real but oversold. What agents genuinely do well in 2026, where they still break, and how to capture value without betting on full autonomy.

AI writes code well—so will it replace engineers? The honest answer: it changes the job more than it ends it. What shifts, and what stays human.

AI coding tools are impressive—and imperfect. Whether they replace developers, what they actually change, and how to stay valuable in an AI-native world.

AI doesn't just speed up coding—it reshapes the whole development process. What changes, what new disciplines emerge, and how teams should adapt.

An AI-native company doesn't 'add AI'—it's built around it. What that actually means in products, operations, and culture, and how to get there.

Bigger isn't always better. Small language models are winning on cost, speed, and privacy for many tasks. When to reach for one instead of a frontier model.

Multimodal AI understands more than text—images, audio, documents, video. What it is, what it unlocks, and the practical business wins available now.

Beyond the hype, AI agents deliver real value in specific, well-scoped jobs. The use cases that work today, and how to scope an agent that ships value.

Generative AI is more than chatbots. The business use cases with real ROI—drafting, summarization, support, search—and how to pick where to start.

Enterprises don't need more AI ideas—they need ones that pay back. The use cases with proven ROI, and how to prioritize where to start.

AI copilots put an expert assistant beside every worker. Where they deliver real productivity, and how to deploy one your team actually uses.

AI automation goes beyond rules—handling judgment and unstructured inputs. The use cases that remove real manual work, and how to measure the payback.

Most AI is measured by the wrong things. How to define success before you build, connect model metrics to business impact, and prove real ROI.

Landing one AI win is the easy part. Scaling it across the enterprise—without a graveyard of pilots—takes platform, governance, and capability. Here's how.

An AI Center of Excellence can accelerate adoption—or become a bottleneck. What it should do, what to avoid, and how to structure one that actually helps.

Tokens are how language models read and price text. What they are, why they drive cost and context limits, and what that means for building with LLMs.

A context window is the model's working memory—finite and token-measured. What it is, why it constrains your app, and how RAG works around its limits.

Fine-tuning trains a model on your examples to change its behavior. What it is, when it helps, and why to try prompting and RAG first.

An AI hallucination is a confident, plausible, wrong answer. Why models do it, why it's dangerous, and how grounding and evaluation keep it in check.

RLHF is how raw language models learn to be helpful and aligned. What it is, how it works at a high level, and why it shapes the AI you use every day.

Temperature is the dial between predictable and creative AI output. What it does, and how to set it right for reliability or for ideation.