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StrucTime

AI at work

We put AI to work on your real tasks.

The work is as much about processes and people as it is about the tool.

« We are told we should be doing AI. We have no idea where to start. »

Why it often fails

Failure almost always comes from the organisation.

The chosen task cannot be checked

If nobody can say whether the result is right, nobody will trust the machine, and the work will be redone by hand.

Teams meet the tool on a Monday morning

A tool imposed without explanation becomes a tool worked around. The people doing the job know why a step exists.

Nothing changes around it

One step gets automated, but the form before it and the approval after it stay the same. The time saved is lost again right next door.

How we work

We start by watching people work.

Before talking about models, we spend time with the people doing the task. That is where you see the back-and-forth, the copy-pasting and the double-checks nobody ever wrote down.

We train teams on what concerns them, and we tell them what the tool cannot do. A machine whose limits are known gets corrected.

How it goes

From the task to a result you can measure.

  1. Step 1

    Spotting

    We look for tasks that repeat, take time and produce a checkable result. All three conditions must hold at once.

  2. Step 2

    Clarifying

    A task nobody can call right or wrong gets clarified before it gets automated. This is often where the gain turns up.

  3. Step 3

    A supervised trial

    We test on real cases, alongside the usual work. We compare both outputs and keep a record of the gaps.

  4. Step 4

    Going live

    The tool goes live with the process adapted around it: what goes in, who approves, and what happens when the result looks doubtful.

  5. Six months on

    Measuring

    We come back to see whether the gain still exists. If it has gone, we say so and look for why.

What you are spared

What your teams will not have to do.

  • Learn one more tool without knowing what it is for.
  • Check by hand a result nobody is monitoring.
  • Feed two systems while the new one is being tried.
  • Defend a headline gain nobody ever measured.

What we do not do.

  • We do not plug a model into data the company does not control.
  • We do not promise a gain we would not be able to measure six months later.

Let us start from your tasks.

Describe what repeats in your company. We will tell you what can be automated, what needs clarifying first, and what is not worth it.

Contact us