013 Field Notes
Useful ideas for people actually running the business.
Plain-language guidance for choosing a useful first project, understanding the risk, asking better vendor questions, and making sense of what the tools can really do.
Start with the business
Decide what the work should do before you learn every term.
A useful AI decision starts with a real point of friction: something people copy, chase, compare, retype, explain, or wait to find out. Then ask whether the best fix is AI, automation, a process change, or nothing at all.
Will this change a decision or move the work?
If the answer is no, it is probably a demonstration—not an operating improvement. Name the owner, the source, the action, the exception, and the proof before you buy the tool.
Start here
Make one good decision before learning every term.
Picking your first AI project
A practical filter for finding a bounded workflow with a visible owner and measurable result.
Seeing the opportunityAI can do more than answer questions
Where reading, comparing, drafting, routing, and exception-finding fit into ordinary work.
Choosing the mechanismAutomation, workflow, or agent?
A business-first explanation of when simple rules are enough and when adaptive tools may help.
Protect the business
Know what the tool sees, decides, and costs.
What happens to information you put into an AI tool?
The retention, training, logging, and contract questions to ask before sharing business data.
BuyingHow to evaluate an AI vendor’s claims
Move from polished demonstration to evidence, limits, ownership, security, and exit terms.
CostWhat will this actually cost to run?
Understand subscriptions, usage pricing, implementation effort, and the cost of oversight.
ControlWhat guardrails can—and cannot—do
Why permissions, review, monitoring, and rollback matter more than a single safety setting.
ReliabilityWhy AI sometimes makes things up
How to design work so unsupported answers are caught before they matter.
ToolsWhen free is enough—and when it is not
A decision guide for sensitive data, team controls, reliability, support, and integrations.
The 013 promise
No daily AI theater.
We publish when there is a useful operating lesson, a concrete workflow, a meaningful tool change, or a decision an owner can make. Technical detail earns its place only when it changes that decision.