How is this different from an AI Readiness Audit?

The audit is the fixed-scope version of this work, and for most companies it is the better starting point. A broader consulting engagement makes sense when the question spans several business units, when a decision is contested internally, or when the output has to survive a board.

Do you recommend your own implementation?

The deliverable is written so any competent team can execute it, and that is deliberate. If the recommendation only makes sense when its author implements it, the assessment was compromised. We will quote for the build if you want it, after the recommendation exists.

What if we already know what we want to build?

Then the engagement tests whether it survives your data, your economics and your regulatory position, which is cheaper than discovering it will not halfway through. Confirming a decision quickly is a good outcome; finding the wrong one is a better one.

How long before we have something usable?

A ranked map and a first project definition typically take two to four weeks, driven more by data access approvals than by analysis. Identifying who grants that access in week one changes the timeline more than anything technical.

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KI-Strategie & Beratung

Unsicher, wo Sie mit KI anfangen sollen? Wir helfen Ihnen, echte Chancen zu identifizieren und eine Strategie zu entwickeln.

Was wir tun

KI-Bereitschaftsbewertung

Wir bewerten Ihre Dateninfrastruktur und Geschäftsprozesse, um zu ermitteln, wo KI den größten Einfluss haben kann.

Technologieauswahl

Wir empfehlen die richtigen Tools und Plattformen - nicht die trendigsten. Unsere Beratung ist herstellerunabhängig und auf Ihre Bedürfnisse ausgerichtet.

Implementierungs-Fahrplan

Ein klarer, phasenweiser Plan mit Meilensteinen und erwarteten Ergebnissen.

ROI-Analyse

Wir erstellen den Business Case für KI-Investitionen.

Für wen es ist

Unsere Beratungsleistungen sind ideal für:

→ Unternehmen, die KI zum ersten Mal erkunden
→ Teams, die KI ausprobiert haben, aber keine Ergebnisse erzielten
→ Führungskräfte, die einen klaren KI-Business-Case benötigen
→ Organisationen, die eine groß angelegte KI-Einführung planen

Typisches Engagement

2-4 Wochen für Bewertung und Fahrplan. Laufende Beratung verfügbar.

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What does an AI consulting engagement actually deliver?

An AI consulting engagement delivers a decision, not a technology recommendation. It ends with a ranked list of use cases scored on impact, feasibility, risk and effort, a build-versus-buy call on each, a defined first project, and the evidence behind that ranking. If it ends with a document describing what AI could theoretically do for your sector, it was not consulting, it was research you could have read.

The work is deliberately front-loaded onto the questions that decide everything downstream: which decision would actually change, whether the data behind it can be reached repeatedly, what the return looks like once lifetime running costs are counted, and where the system sits under the EU AI Act and GDPR.

A legitimate and common outcome is that nothing should be built yet, because the binding constraint turns out to be an undefined process or data that does not exist.

Für wen das ist

Leadership teams with a mandate to do something with AI and no roadmap.
Companies whose first pilot produced encouraging results and no path to production.
Teams weighing vendor proposals they cannot evaluate internally.
Regulated businesses that need the compliance question answered before committing budget.

What we need from you

Access to whoever owns the process that would change.
Whatever you already measure about it, however untidy.
A list of candidate use cases, even a rough one.
One person who can grant access to the relevant data.

What you get back

A ranked use case map with the scoring made explicit.
Build, buy or do-nothing recommendation per use case.
EU AI Act tier and GDPR exposure for each candidate.
A defined first project, scoped small enough to learn from.
A written record you can hand to any competent team.

What a typical engagement looks like

A manufacturer with five ideas and no sequence

Two weeks establishing which lines carry reachable machine data, what each idea would move on the P&L, and which one survives contact with the plant. Usually ends with three ideas dropped and one specified.

A healthcare provider evaluating a vendor

Assessment of the proposal against your data reality and regulatory position: whether the claimed performance transfers to your patient mix, whether the system is a medical device, and what you inherit as deployer.

A company with a stalled pilot

Diagnosis of why it stalled, which is usually a missing owner, an unreachable data path, or a workflow nobody changed, followed by a decision on whether to fix, restart or stop.

Regulatory and data constraints

Regulatory position is established during the assessment rather than reviewed afterwards. Every candidate use case is classified against the EU AI Act tiers and checked for personal data exposure under the GDPR, because a use case that turns out to be high-risk has a different cost profile and belongs lower in the ranking for that reason alone.

Where a system is genuinely ambiguous, that is reported as ambiguous and flagged for legal review rather than resolved with a guess. This is practical readiness work, not legal advice, and it runs alongside your counsel.

Frequently asked questions

Being found by AI search

Buyers increasingly reach a shortlist through an AI assistant rather than a search results page, which changes what your content has to do to be quoted at all.

What is Answer Engine Optimization?

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