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AI assistant: why two teams with the same tool do not get the same result

Same licence, same machine, very different results. The gap is not the model or the workstation. Where it is really decided, and what it means for a CIO.

Premaccess30 July 20264 min read

Two teams, the same tool, the same subscription, equivalent machines. Six months later, one of them has removed several days of manual work per month, the other has mostly consumed budget. We see this gap at almost all of our clients, and it is explained neither by the model chosen nor by the power of the workstations.

Where the gap is really decided

It is decided by usage discipline, that is to say by working habits that settle in within a few days and which, once acquired, are never lost again. That is good news for a CIO: this is not an investment matter, it is a matter of framing and support.

It is also bad news if nobody takes care of it, because nothing spontaneously pushes a user to adopt these habits. They are counter-intuitive, and the tool does not impose them.

The three symptoms that should alert you

  • The bill goes up without output following. A classic sign of unframed usage: requests that are too broad, sessions that drag on, the same work redone every month.
  • Nobody knows how to replay what worked. A good result obtained once and never recorded is a lost result.
  • A single member of staff is “the one who knows”. Know-how does not spread on its own, and you have just created a single point of failure.

What has to be set down on the organisational side

Three decisions, independent of the tool chosen:

  1. Accounts in the company's name, not personal accounts. That is the condition for then sharing methods between staff, and for not losing the work when someone leaves.
  2. A common place where validated methods land. Without a shared repository, each person reinvents on their own, and you pay several times over for the same learning.
  3. A regular sharing session. What one person works out must serve the others. It is the only mechanism that really raises the average level.

The hardware is not the issue

One question always comes up in session: do the workstations need to be re-equipped? In almost every case, no. The workstation accounts for a very small share of the difference in results between two users. The budget is far better placed in the framing and the support of the first few weeks.

We install these habits on your own files, over three days on site then two days of follow-up. See the AI training and the AI PILOT track for technical teams.

Going further

Does this concern you? Have a look at our AI and Data services, or talk to an expert (reply within 24 business hours).

Frequently asked questions
Do workstations need to be re-equipped to use AI in a company?

In almost every case, no. The workstation configuration accounts for a very small share of the difference in results between two users. The budget is better placed in framing usage and in the support of the first few weeks.

Why does the bill go up without any visible gain?

It is the symptom of unframed usage: requests that are too broad, sessions that drag on, and above all the same work redone from scratch every month for want of having recorded what was working.

How can you stop the skill staying with a single person?

Accounts in the company's name rather than personal ones, a common repository where validated methods land, and a regular sharing session. These three decisions are organisational, not technical.

P
PremaccessCloud experts · Franco-Swiss since 2007
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