Pillar 1 · Strategy

AI Consulting for growing companies in the DACH region

Q: What sets AI consulting apart from classic strategy consulting?

AI consulting answers concrete implementation questions: which processes are suitable, which models come into question, how the solution integrates with your existing ERP/CRM, and what it costs over its lifecycle. We only advise on topics we can implement ourselves — and in the end we don't hand over slides, but an actionable roadmap with an ROI estimate for each first project.

Q: How long does a consulting engagement take?

A typical consulting engagement covers five to ten working days spread over four to six weeks. During this time, we interview key people, review relevant processes, assess five to ten automation candidates, and produce the prioritized roadmap. The scope is fixed in advance — no open-ended billing.

Q: We haven't used any AI at all yet — are we still the right audience?

Yes — that's actually the most common starting point. You don't need any technical preparation. We start with a review of your current processes, identify the biggest manual pain points, and assess whether AI is genuinely the right lever there. If it isn't, we'll tell you plainly.

Q: Is our data good enough for AI?

That's the right question at the right time. During the initial assessment, we evaluate your data quality for each automation candidate individually — not as a blanket judgment, but process by process. Some initiatives need clean master data, others work well with what's already there. If we find that a data foundation still needs to be built or cleaned up first, we say so — and prioritize the projects that already work with the current state.

Q: When is it too early for AI in our company?

If the underlying processes themselves aren't yet clearly documented or consistently executed, AI mostly ends up automating chaos. A typical warning sign: every employee handles the same task differently. In that case, the solution isn't AI — it's understanding and standardizing the process first. We say so openly when we find this pattern, and recommend the right sequencing.

Q: How big should the first AI project be?

Small enough to go live in four to eight weeks. Large enough that the result is visible in day-to-day operations. In practice, that usually means: one self-contained use case with a clear input and output format, one or two systems involved, and one primary metric. Larger initiatives get broken down into several such building blocks — each one useful on its own, each one measurable in production.

Q: Who actually delivers the consulting?

Andreas Huemer himself. FUTUREAISPHERE is deliberately a one-person company — no hand-off to rotating junior consultants. You speak with the same person throughout, from the initial analysis to implementation.

Most AI strategies don't fail because of the technology — they fail at the first concrete step. We help managing directors and decision-makers put AI to work where it actually relieves operational load — not where it just sounds good.

AI consulting at FUTUREAISPHERE is not an innovation workshop that ends without a result. It delivers an assessed list of automation candidates, an ROI estimate per candidate, and an actionable roadmap with clearly prioritized first projects. We only recommend what we would implement ourselves — and often do.

For SMEs in the DACH region, that means: no generic frameworks copied from elsewhere, no default reliance on US cloud providers, no buzzwords. Instead, GDPR-compliant consulting in the language and under the conditions that actually apply to your business.

Common questions clients bring to us

"We know we should be doing something with AI — but where do we start?"

The most common starting point. You feel the pressure in the market, see competitors experimenting, and don't want to fall behind. But you also don't want to fund the next expensive pilot project that ends up as a hype presentation.

"We already ran an AI pilot — it didn't deliver anything."

Often the issue isn't the technology, but the choice of process or the lack of integration into day-to-day operations. We look at what went wrong and where a second attempt would actually have an effect.

"Our team is overloaded — can AI free up capacity without layoffs?"

Yes — that's the most common real business value. We identify the recurring routine tasks that tie up hours without producing useful output — and show which of them are technically automatable and which aren't.

"We want to use AI, but our data can't go to the US."

A legitimate concern. We show you which models and platforms can be operated in the EU in a GDPR-compliant way, where the grey areas are, and how to build a solid record of processing activities.

"Which investment pays off, and which doesn't?"

The most important question — and, at the same time, the one generic AI consultancies tend to avoid answering. We provide an ROI estimate for each candidate based on realistic assumptions — and tell you which ideas you should shelve for economic reasons.

Our consulting approach in three phases

01

Assessment

Structured interviews with management and key personnel. We capture the operational pain points, the existing system landscape (ERP, CRM, DMS, specialist tools), and the organizational constraints. Result: a sober picture of where you stand.

02

Evaluation and prioritization

We assess five to ten automation candidates against clear criteria: technical feasibility, data availability, ROI estimate, integration complexity, risk. The result is a prioritized list with a clear recommendation on where to start.

03

Roadmap and first project

You receive an actionable 12-month roadmap with a concrete first project, estimated effort, required resources, and success metrics. On request, we take on the implementation directly — or hand over cleanly to your internal team.

What we look at in the initial analysis

No questionnaire, no scoring tool. Instead, four dimensions we assess systematically in every consulting engagement — because they determine where AI actually has an effect and where it burns money.

Dimension 1

Operational pain points

Where does your team lose the most time on routine tasks? Which processes regularly generate errors, follow-up questions, or rework? Where do cases back up because one manual step slows down the entire flow? These are the points where automation has the biggest leverage — and we always start there.

Dimension 2

System landscape and integration status

Which systems are in use — ERP, CRM, DMS, web shop, specialist tools? Do these systems talk to each other, or are there media breaks? Are there open APIs, or is manual data transfer the norm? The answer determines whether a solution goes live in weeks or in months.

Dimension 3

Data maturity and availability

Is the relevant data structured, accessible, and maintained — or scattered across emails, spreadsheets, and people's heads? Is there a single source of truth per entity, or do the systems contradict each other? We assess this process by process, not as a blanket judgment: some initiatives work fine with what's already there today.

Dimension 4

Team readiness and capacity for change

How open is the team to new ways of working? Are there internal concerns that need to be addressed? Who owns operational responsibility after rollout? Even the best automation fails if the team isn't brought along. We address this honestly during the consulting engagement.

Who is AI consulting a good fit for?

Growing companies with an organically grown system landscape

Our consulting is tailored to the reality of growing B2B companies: limited IT resources, organically grown system landscapes, clear budget ownership, and a direct line between management and operations.

Managing directors and decision-makers who intend to implement

We work best with people who actually intend to implement after the consulting engagement. If you're only looking for material for an internal discussion, we're not the right partner.

Companies with multiple manual routine processes

If you know your employees spend a lot of time on data entry, document checks, categorization, or status updates, there is almost always an economically viable lever.

DACH focus (AT, DE, CH)

We know the legal, linguistic, and cultural conditions in Austria, Germany, and Switzerland. GDPR is not an obstacle for us — it's part of the design.

What you can expect from an initial consultation

Concrete answers instead of platitudes

We talk about your processes, your systems, your numbers — not generic "AI trends." If a topic isn't relevant to your situation, we'll say so.

An honest assessment of your readiness

Sometimes the right recommendation is: clean up your master data first, finish the ERP migration, then AI. We won't sell you a project you can't successfully complete today because of where you currently stand in terms of readiness.

Clear cost frameworks

You'll learn what range implementation sprints typically fall into, and what ongoing costs Managed AI Operations involves. No hidden line items.

An honest read on whether we're the right partner

If your project falls outside our pillars (e.g. pure data analytics, custom LLM training, industry-specific applications outside our experience), we'll say so — and point you to alternatives.

From Practice

From initial assessment to a production system

An example from a typical project situation shows how a strategic starting point turns into measurably lighter day-to-day operations within a few months.

Manufacturing company · Austria · Automated order processing

Starting point

Over 200 orders per day were entered manually. Error rate of 8%, processing time of 12 minutes per order, rising staff workload with no relief in sight.

Consulting

Prioritized assessment of six automation candidates. Recommendation: start with order processing (highest ROI leverage), then incoming invoices. A 12-month roadmap with a clear first project.

Implementation

AI-based document recognition, connected to the existing ERP, exception routing for special cases. Went live after six weeks running in parallel with the manual process.

Result

Turnaround time per order reduced by 70 percent, error rate cut to under one percent, ROI achieved within four months of going live. The team freed up capacity for higher-value work instead of adding headcount.

70 %

Time saved per order

< 1 %

Error rate after go-live

4 mo.

to ROI

Example from a typical project situation. Actual figures are measured individually for every project.

What happens after the consulting engagement

Consulting only makes sense if it can be followed by concrete implementation. The following four pillars connect directly to the consulting phase:

AI process automation →

When the roadmap turns into a clear implementation sprint.

AI Agents →

When multi-step tasks with context need to run autonomously.

ERP and CRM integration →

When the solution needs to be embedded into your existing systems.

Managed AI operations →

When operations after go-live need to be in reliable hands.

Deep Dive

AI governance →

When governance questions need to become part of the strategic roadmap.

Deep Dive

On-premise AI →

When data sovereignty is a hard decision criterion in the consulting process.

Guide

What an AI pilot really needs →

The next step after consulting: what a pilot needs in practice to go into production instead of ending up in the showroom.

Frequently asked questions about AI consulting

What sets AI consulting apart from classic strategy consulting?

AI consulting answers concrete implementation questions: which processes are suitable, which models come into question, how the solution integrates with your existing ERP/CRM, and what it costs over its lifecycle. We only advise on topics we can implement ourselves — and in the end we don't hand over slides, but an actionable roadmap with an ROI estimate for each first project.

How long does a consulting engagement take?

A typical consulting engagement covers five to ten working days spread over four to six weeks. During this time, we interview key people, review relevant processes, assess five to ten automation candidates, and produce the prioritized roadmap. The scope is fixed in advance — no open-ended billing.

We haven't used any AI at all yet — are we still the right audience?

Yes — that's actually the most common starting point. You don't need any technical preparation. We start with a review of your current processes, identify the biggest manual pain points, and assess whether AI is genuinely the right lever there. If it isn't, we'll tell you plainly.

Is our data good enough for AI?

That's the right question at the right time. During the initial assessment, we evaluate your data quality for each automation candidate individually — not as a blanket judgment, but process by process. Some initiatives need clean master data, others work well with what's already there. If we find that a data foundation still needs to be built or cleaned up first, we say so — and prioritize the projects that already work with the current state.

When is it too early for AI in our company?

If the underlying processes themselves aren't yet clearly documented or consistently executed, AI mostly ends up automating chaos. A typical warning sign: every employee handles the same task differently. In that case, the solution isn't AI — it's understanding and standardizing the process first. We say so openly when we find this pattern, and recommend the right sequencing.

How big should the first AI project be?

Small enough to go live in four to eight weeks. Large enough that the result is visible in day-to-day operations. In practice, that usually means: one self-contained use case with a clear input and output format, one or two systems involved, and one primary metric. Larger initiatives get broken down into several such building blocks — each one useful on its own, each one measurable in production.

Who actually delivers the consulting?

Andreas Huemer himself. FUTUREAISPHERE is deliberately a one-person company — no hand-off to rotating junior consultants. You speak with the same person throughout, from the initial analysis to implementation.

Ready for an initial strategy call?

30 to 60 minutes, free of charge, no obligation. You'll receive a written initial assessment within 48 hours.

Request a strategy call