Start with the workflow, not the model
The most common AI product mistake is starting with the LLM selection. The real question is: which workflow in your product is bottlenecked by speed, consistency, or context — and can AI measurably improve it?
AI works best when it is assisting a defined task with a structured input and a reviewable output. It works worst when it is asked to generate open-ended content, make high-stakes decisions without a human reviewer, or operate on data it was not designed to interpret.
The five-question product strategy test
- What specific task does AI assist in your product?
- Who reviews the output before it reaches the end user or triggers an action?
- What data does AI see, and have you written down what it is not allowed to see?
- How will you know in 30 days whether this is actually working?
- What happens when the AI is wrong — who catches it, and what is the recovery path?
Where AI creates durable product value
- Structured extraction: turning unstructured inputs (text, audio, forms) into structured data
- Personalized generation: drafts, summaries, recommendations grounded in user-specific context
- Intelligent routing: directing requests, tickets, or decisions to the right queue or owner
- Pattern detection: surfacing anomalies, trends, or signals in data that would take a human analyst hours
- Operating memory: preserving decision context so the next person or AI session does not start from zero
Data strategy is the real moat
Most AI model capabilities will commoditize. The durable competitive advantage is the data you collect, the structure you impose on it, and the workflows you build around it.
This is why The Brain and the IQ suite are not just AI wrappers — they are structured knowledge systems that give AI something worth working with.
Productizing AI: from feature to offering
An AI feature is a GPT button in your existing product. A productized AI offering is a complete workflow: intake, AI-assisted processing, human review, delivery, and a repeatable outcome.
To productize AI: define the input format (what does the user give you?), the AI-assisted step (what does the model do?), the review layer (who approves the output?), the delivery format (what does the user receive?), and the feedback loop (how does the product improve over time?).
Common failure modes to avoid
- Launching AI features without a human review gate in customer-facing flows
- Measuring prompts run or tokens consumed instead of outcomes improved
- Using consumer AI endpoints for sensitive business data without a data agreement
- Building on a single LLM provider without an abstraction layer
- Skipping the data policy — what AI can and cannot see should be a written document
- Automating before you understand the workflow well enough to catch when automation fails
Where B. PM Consulting focuses
B.'s work on AI product strategy focuses on the intersection of product design, operating infrastructure, and AI-native tooling. The deliverables are usually: a written AI product strategy, a workflow map with review gates, a data policy, and a measurement framework — not just a technology recommendation.