Commentary Quick Take
Quick Take: OpenAI's Enterprise Play Is Smarter Than You Think
Everyone's focused on the model improvements. The real story is the data residency and compliance infrastructure they're quietly building.
Most AI advice is expensive fiction.
Strategy and implementation guidance for people who'd rather ship than speculate. What actually works, what doesn't, and how to tell the difference.
Why Most AI Strategies Fail Before They Start
The problem is rarely the technology. It's that most companies skip the boring, essential work of defining what success actually looks like before they start buying tools.
Strategy
Read the piece
AI projects fail because companies optimize for impressive demos instead of measurable business outcomes. Start with the problem, not the technology.
Fresh from the field
Implementation
RAG Is Not a Product Strategy
Everyone is building RAG applications. Most of them shouldn't be. Here's how to tell if retrieval-augmented generation is actually the right approach for your use case.
Tactical Quick Take
Three Questions to Ask Before Setting an AI Budget
If you can't answer these clearly, you're not ready to spend money on AI yet.
Strategy
Stop Evaluating AI Vendors on Their Demos
A great demo tells you almost nothing about whether a product will work in your environment. Here's what to look at instead.
Your boss just said "RAG."
Plain-English AI definitions. No PhD required.
Embeddings
A way to convert text (or images, or other data) into lists of numbers that capture meaning. Similar concepts end up with similar numbers, which lets computers find related content efficiently.
AI Agent
An AI system that can take actions autonomously, not just generate text. It can use tools, make decisions, and execute multi-step workflows with minimal human intervention.
Fine-tuning
Taking a pre-trained AI model and training it further on your specific data to make it better at a particular task. Like teaching a generally knowledgeable person the specifics of your industry.
Hallucination
When an AI model generates information that sounds confident and plausible but is factually incorrect. The model isn't "lying." It's generating statistically likely text that happens to be wrong.
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