Decide first, buy tools later.

We work with the management team to find where AI actually moves money in your business model — and where it only burns budget.

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Companies already recommended by LLMs through getSichtbar

What we do.

AI strategy consulting here means we take your business model apart, price every AI use case against contribution margin and effort, and hand you a ranked list with a business case. McKinsey State of AI (2025) found 88% of companies use AI in at least one function, but only 39% report any EBIT impact. The difference is use-case selection, not technology.

Buyer questions

Where does AI actually pay off in our business model?

Why does our AI pilot produce no numbers?

Which AI use cases should we build first?

Evidence

Use-case list with contribution margin and effort

Business case per use case, calculated rather than guessed

Build sequence with stop criteria

Outcome

You know which three AI use cases you build first, what they cost, what they return and when you stop them.

Why it matters

The MIT study „The GenAI Divide“ (2025) found 95% of enterprise AI pilots deliver no measurable return. The cause is almost never the model — it is the use case that was picked.

7 years

management consulting

Felix Ament spent seven years in strategy consulting before founding getSichtbar.

95%

pilots without return

MIT „The GenAI Divide“ (2025), based on more than 300 enterprise deployments.

39%

report EBIT impact

McKinsey State of AI (2025): only 39% report any EBIT effect from AI.

high performers

Only 6% reach more than 5% EBIT impact from AI (McKinsey 2025).

Pilot purgatory.

McKinsey (2025) reports roughly two thirds of companies run AI experiments that never reach production. Only 7% report fully scaled AI and only 6% achieve more than 5% EBIT impact. Technology is rarely the gap. The missing piece is a decision about which use case deserves funding.

Several AI pilots are running and none has a number.

Every department wants its own tool.

The business case was presented but never calculated.

Nobody can say when a project gets stopped.

From technology topic to business decision.

AI is not an IT project. It is a question of contribution margin, process cost and capacity. We treat it that way.

Result

AI should not just find your company. AI should understand when your offer fits.

Four steps to a decision.

We work from the business model to a ranked portfolio. Every step ends with a number, not an opinion.

Understand the model

Where does margin come from, where do costs sit, where is the capacity limit?

Collect use cases

Every plausible AI case across sales, operations, service and product.

Calculate

Per case: build effort, running cost, effect on contribution margin.

Rank

Sequence, stop criteria and who decides.

What we actually do.

Analysis of business model, margin and capacity constraints

Interviews with management and department leads

Collection of AI use cases across all functions

Business case per case with effort, running cost and effect

Ranking by contribution margin rather than visibility

Build plan with owners and stop criteria

What you get.

Scored use-case portfolio

Business case per prioritised use case

Build sequence for six to twelve months

Decision paper for the management team

Stop criteria per project

Good fit if you want implementation.

Decision makers join the sessions themselves.

Numbers on margin, process cost or capacity are available.

You want to make a decision, not just be informed.

Not a fit if you only want a report.

You only want a report and no implementation.

You expect a guaranteed mention in one exact AI answer.

Your offer is not clear enough to recommend yet.

When this service is especially useful.

Several AI pilots are running but none delivers numbers.

The management team has to justify an AI budget.

You want to know which processes AI should touch at all.

What this should not be confused with.

Large consultancy

Delivers a strategy deck and leaves. We build the prioritised use cases afterwards.

AI vendor

Sells a tool and then looks for the use case. We work the other way around.

Internal IT

Knows the systems but rarely the margin per process. We bring both together.

What it needs.

Two to three weeks of analysis and interviews to start.

The output is a decision paper, not a slide marathon.

Implementation afterwards by us or by your team.

Clear answers.

How is this different from classic management consulting?

The analysis is the same: business model, margin, process cost. The difference comes after. A classic consultancy hands the paper to your team. We build the prioritised use cases ourselves if you want, which means we carry part of the delivery risk.

Why do so many AI projects fail?

The MIT study „The GenAI Divide“ (2025) found 95% of enterprise pilots produce no measurable return. Across more than 300 deployments the cause was almost always use-case selection and missing process integration, not model quality.

Which numbers do we need to start?

Revenue and contribution margin per product group or service, rough process costs in the affected areas, and a view on your capacity limit. Where numbers are missing we estimate together with ranges and mark the assumption visibly in the business case.

How long does the analysis take?

Two to three weeks to the decision paper, depending on the number of business areas and availability of interview partners. After that you decide whether we build, your team builds, or a use case does not get built at all.

What is a stop criterion and why is it in the plan?

A stop criterion is the number that ends a project when it is missed. Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027. Defining the exit up front costs you months instead of years.

Do you consult without doing the implementation?

Yes. The analysis is bookable on its own and you get the same decision paper. We will not build a strategy nobody owns, though — without a decision maker in the room we decline the engagement.

The services work together.

Generative Engine Optimization

We check buyer questions in ChatGPT, Perplexity, Claude and Google. If your company is missing, we build the missing evidence.

SEO with AI

We use AI to find pages that lose demand or get too few clicks. Then we improve those pages.

Website content with AI

We build service pages, articles, comparisons, FAQs, cases and product proof that answer real buying questions.

Let us check whether this service should come first.

One call is enough to see the rough direction: website, content, sources, product data or measurement.

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