Why AI is harder in a business that already works
The constraints an established business arrives with — the process, the systems, the exceptions nobody wrote down — are not obstacles between you and the AI. They are the specification for it.
Insights
Practical thinking for building AI-native businesses.
AI is moving quickly. The harder question is how to apply it effectively inside a real business.
Our focus is not AI news or hype — it is the operating, product, technical, and organizational questions that determine whether AI actually works.
Inory Insights focuses on
- Where AI agents create real business value
- How to redesign workflows around humans and AI
- How to move from prototypes to production
- How to evaluate AI reliability and business impact
- How product and engineering teams should build with agents
- How organizations and professionals evolve as AI takes on more execution
Featured
Featured insights.
How to choose your first AI workflow
The usual selection method produces forty ideas scored on two axes nobody can estimate before building. Four questions predict a first workflow better, and every one of them is answerable in a week.
How to measure AI value without misleading the organization
Most AI metrics are true and useless: activity counted as outcome, projection reported as result, a pilot cohort extrapolated to a department. Five parts fix all three.
Human-in-the-loop is an operating model, not a checkbox
Saying a person reviews the output is not a control. A control specifies who reviews, against what standard, with what authority, and what happens when they disagree.
Why most AI pilots never become operating capabilities
A pilot proves a model can do something. Production proves an organization can rely on it. The gap between the two is not technical, and it is where most programmes stop.
Topics we explore
What we write about.
AI strategy and opportunity
Choosing where AI belongs in the business and where it does not.
AI agents and workflow design
Designing workflows across agents, tools, data, and people.
AI-native product engineering
Building products where AI is part of the core experience.
Evaluation and reliability
Testing quality, failure modes, business impact, and human-review requirements.
AI-native organizations
How roles, management practices, workflows, and professional capabilities evolve with AI.
Put the ideas into practice.
Have a workflow, product, or business problem you are considering applying AI to?