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Pilot limbo

From AI pilot to production.

AI pilots everywhere and value nowhere. Take your AI out of the pilot and into the line, and measure it against a number your plant already tracks.

What you see

  • Several proofs of concept running, none of them in daily production.
  • Use cases chosen because the technology was fascinating, not because of a business case.
  • Nobody owns the use case once the pilot team moves on.
  • No KPI agreed before the work started, so success is a matter of opinion.

What is really happening

Pilot limbo is rarely a technology problem. Use cases are picked by fascination instead of business impact, there is no path from proof of concept to production, and nobody is named to own adoption in operations. The pilot works. The organization around it was never set up to run it.

How we work on it

Four steps along the X.

THINK

Select by impact.

Rank running and planned use cases by business case and by feasibility on the shop floor. Continue the two that move the P&L, stop the rest.

MAKE

Build into the line.

From proof of value to MVP to production, with clear gates, on the IT/OT foundation the line actually runs on, brownfield included.

ADOPT

Name the owner.

Anchor the use case in operations: a named owner, trained users, governance and a fixed place in the operating model.

TRANSFORM

Scale what is proven.

Carry the proven use case from one line to the next plant and keep measuring it.

What you get

A list of use cases with business cases, the first one running in production, a named owner for adoption, and outcome KPIs defined in week one.

Typical KPIs: scrap rate, first-time-right rate, unplanned downtime, output per line and shift, use cases in production versus pilot, among others.

Form of engagement: Usually starts with an Executive Assessment (THINK) and continues as Build and Mobilize (MAKE and ADOPT).

All forms of engagement

From practice

Automotive supplier, 2,400 employees

Three AI pilots consolidated into two use cases with a named adoption owner.

Measured against the scrap rate the plant already tracked.

Read more

The pilot is not the problem. The missing owner for adoption is.

Most industrial AI pilots work. What fails is the handover into operations, and that has a name and a calendar.

Questions

Questions we are asked

Why do so many industrial AI pilots never reach production?

Because the pilot is set up to prove the technology, not the business case. Without a KPI agreed in advance, a path from proof of value to production and a named owner in operations, even a working pilot has nowhere to go. Axiva Industrial settles these three points before anything else is built.

How long does it take to bring an AI pilot into production?

That depends on the use case and on the IT/OT landscape. Scope and milestones are set per case, and the outcome KPIs are fixed in week one.

Which KPI should an AI use case be measured against?

Against a number the plant already tracks, such as scrap rate, downtime or output per shift. A metric invented for the pilot proves nothing to the P&L.

All six starting points

Find the one that describes your situation.

Pilot limbo AI pilot to production The strategy-execution gap From strategy to execution The IT/OT divide IT/OT convergence and IIoT Unmonetized software Industrial software pricing A hardware sales force in a software world Value selling for industrial software Transformation fatigue Measurable transformation
Andreas Geiss on stageLikeness: A. Geiss; AI image

The axiom is set.
Now prove
your case.

One conversation.
Your thesis. Our proof plan.

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