Article
A few words on moving from personal AI use to enterprise AI use
What works beautifully for one person falls apart the moment it has to work for a whole organisation.
Most people's mental model of AI comes from personal use: open a chat, ask, judge the answer, move on. It is fast, forgiving and genuinely impressive.
Enterprise use looks similar on the surface and is a different discipline underneath. The gap between the two is where a great deal of budget is currently being lost.
What changes when the user is an organisation
- The person judging the answer is no longer the person who asked — so quality has to be measurable, not felt.
- The data is not yours alone: access control, privacy and retention become part of the design.
- Volume turns a small per-answer cost into a budget line that needs attribution.
- One-off answers become processes that must be repeatable, monitored and auditable.
- Errors have consequences beyond the user: a customer, a regulator, a payment.
What has to be added
Enterprise use needs grounding in the organisation's own knowledge, evaluation to know whether output is good, guardrails to constrain what the system may do, and a control layer that sees every model call for cost, policy and audit purposes.
None of that is exciting, and all of it is the reason a working demo becomes a working service.
How to close the gap
Pick one process rather than one tool. Define what a good answer is and how you will measure it. Put governance in place before scale, not after. Then expand to the next process using the same foundation.
Personal enthusiasm is a useful starting point — it just is not an operating model.
Personal AI use shows you what is possible. Enterprise AI use is the work of making it dependable, and that work is where the return actually comes from.
This article is the English version of a post originally published in Hebrew on our LinkedIn page.