PIPELINEGuide
AI Advisory: Measure Success, Don't Guess
Without a predefined metric, you can't say afterward whether AI advisory was worth it. Which numbers work, which mislead, and when to collect them.
The success of AI advisory can only be measured if the baseline was recorded beforehand — two weeks of tracking is enough. Useful metrics are processing time per case, the proportion of cases completed without intervention, and external response time. Team satisfaction in the first two weeks is not a useful benchmark.
01 — Measurement
How you measure success
- Define the one number beforehand. Without a baseline value, nothing can be proven afterward. Two weeks of tracking is enough for a reliable reference point.
- Processing time per case. The most direct metric: How long does the affected process take today, how long afterward.
- Percentage of cases that complete without intervention. This ratio typically rises significantly in the first weeks when refinements are made — it's the best early indicator.
- External response time. How quickly does someone who inquires receive an initial binding answer.
- What you should not measure: Team satisfaction in the first two weeks. Every transition feels worse initially than it actually is.
02 — Calculation
How to calculate it
Four numbers are enough: How often does the process occur? How long does it take? What does an hour cost internally? And what does the solution cost — one-time plus operation over three years?
An example: A process that occurs twice a week and takes 40 minutes each time ties up around 69 hours per year. At an internal hourly rate of 45 euros, that's roughly 3,100 euros per year locked in there. If a solution reduces this effort by two-thirds, it often pays for itself — depending on the actual quote — within one to two years. We'll give you the exact figure for your case after the initial meeting.
This calculation is deliberately conservative: it only accounts for the time saved, not the errors avoided or the orders that come through due to faster response. In practice, the benefit is usually higher — but you shouldn't count on it before you've measured it.
03 — Context
What experience shows
The most common reason for failed AI projects is not the technology, but a use case that misses the actual work.
04 — Questions
Frequently asked questions
Isn't this just a disguised sales event?
The recommendation regularly includes items you can implement yourselves or with your existing service provider, as well as cases we advise against. The document is yours and works without us too.
What funding is available in Austria?
Options include KMU.DIGITAL and aws digitalization funding — current terms at kmudigital.at and aws.at. Both require the application to be approved before awarding the contract — that's why we bring it up early and not at the end.
How does this differ from consulting by the Chamber of Commerce?
The Chamber offerings provide a good overview of funding options and basics. We go a step further and look at your specific processes and systems — with effort estimates that we ourselves would have to meet.
Isn't a conversation with management enough?
No, that's the most common reason for a list that doesn't work later. The people who do the processes daily know the exceptions that an outside perspective never sees.
Do we also get recommendations for providers other than you?
Yes, where it makes sense. Some use cases are better suited to standard software or a specialized provider — that's stated in the document, not hidden.
What does it cost us if nothing comes of it in the end?
Only the agreed price for the workshop and document — there's no success fee and no hidden follow-up obligation. The preliminary conversation is free and is meant to catch exactly these kinds of mismatched inquiries beforehand.
What if nothing makes sense for us?
Then that's what the report says. It happens, especially with businesses that have very few recurring processes or no digital data foundation. An honest no is cheaper than a project that runs aground.
05 — Next
Related pages
- AI consulting — Overview
- AI consulting — Pros and cons
- AI consulting — requirements
- AI consulting — process
- AI consulting — Provider comparison
- AI consulting — How it works
- AI consulting — Data protection
- AI consulting — risks
- AI automation
- Process automation
- AI training
- All guides
AI consulting by region
Next step
Let's talk briefly about whether this makes sense for you.
A twenty-minute conversation is usually enough to clarify whether a project makes sense. If not, we'll say so — that's cheaper for both sides than an offer nobody needs.