Is AI consulting actually worth it?

Honest answer: a lot of it isn't. Most 'AI strategy' engagements end in a slide deck that nobody executes, and the budget is gone before a single process improves. If a consultancy can't tell you which process it will change and how that change will be measured, you're buying reassurance, not results.

It is worth it when the engagement is tied to one measurable process. Example with numbers: for netcos GmbH, AI Phase built an LLM-based ticket sorting and IT support assistant – 70% of tickets automated, over 90% classification accuracy, and running costs of roughly 1.4% of the original manual processing costs. That is the kind of outcome that justifies a consulting fee.

See for yourself: reference projects with numbers · how AI-native consulting compares to traditional firms

For a large consultancy, a six-figure roadmap is business as usual, and for board-level transformation across dozens of departments it can even be justified – that is what those firms are built for. Concede them that.

But if your actual goal is a working AI system for one or two processes, you don't need it. For mid-market companies, a scoped proof of concept in 2026 typically lands at 15,000–40,000 euros and takes 2–4 weeks – an indicative market range from public reports, not a price list. Pay for something that runs first, then decide about the roadmap.

The detailed context: AI consulting vs. traditional firms, with 2026 market rates · what AI projects cost by project type

A workshop alone changes nothing – that concession first. If the output is enthusiasm and a wall of sticky notes, it was theater, whatever it cost.

A good workshop ends with a ranked use-case list, effort estimates and a decision about what gets built first. That is how AI Phase runs its formats: half a day to two days, 4–10 participants, no technical background needed – and the output feeds straight into a Fast-Track project plan within 24 hours, so the momentum doesn't die in the follow-up meeting.

Related: AI workshops · Fast-Track: from idea to project plan in 24 hours

Yes – with a caveat. RAG on a messy, stale document base disappoints reliably: the model answers confidently from outdated files, and after the second wrong answer nobody trusts the system anymore. Data hygiene decides the outcome before the first prompt is written.

When the document base and the users are real, the numbers are real too. For netcos GmbH, an NLP/LLM assistant by AI Phase automates 70% of support tickets at over 90% accuracy; for a German machinery manufacturer, a RAG system over multi-level engineering documentation now delivers reliable, source-cited answers for engineering-critical decisions.

Start here: reference projects · a 4–8-week prototype on your own data

Real. For the B.B.W. Group, a partner of the BMW Group, AI Phase built a 3D computer vision quality assurance system: 80% of inspections automated and inspection time down from 10 minutes to around 20 seconds – roughly 97% faster, with inspection costs that can potentially be halved.

The honest limits: poor lighting, rare defect classes and anything that needs context still go to humans – that is why the remaining 20% stays manual. If your defect cases are visual and repeatable, this is one of the most reliably measurable AI applications in manufacturing.

The full case: the B.B.W. project and other references

Because the pilot was never designed to survive. The usual causes, in order: nobody secured access to production data before the demo; nobody in the business owns the outcome; and there is no plan for who monitors, retrains and pays for the system after go-live. IDC puts it bluntly: 88% of AI pilots never reach production.

The fix is boring: pick one process with an owner, clarify data access in week one, and budget for operations from day one. AI doesn't stay good on its own – a system without maintenance degrades quietly until someone notices in a board meeting.

Deeper dives: how long an AI project really takes, phase by phase · maintenance & optimization for live systems

Both answers are right – for different situations. Hire in-house when AI is core to your product and there is ML work every week of the year: a good engineer embedded in your domain beats any external team long-term. Be realistic, though – a solo hire without senior peers, infrastructure or data pipelines often spends a year building foundations.

An agency wins when you need the first production system fast and want senior delivery without a six-month hiring cycle. And there is a third path many overlook: structured 3–6-month AI projects with top students from TUM and LMU – a low-commitment bridge between exploring AI and implementing it, with scoping and supervision included.

Related: production-grade AI implementation · AI training to upskill your own team · student and reference projects

Three quick tells. One: ask who actually writes the code – if the answer is 'our delivery team' and you never meet them, walk. Two: demand a working artifact within weeks, not a phase plan; anyone who needs three months before you see software is selling process. Three: ask for references with numbers, and call them.

Bonus tell: how they answer 'when should we NOT do this project?'. A serious partner names situations where the honest answer is 'don't build this'. At AI Phase the founders deliver themselves – with experience from Roland Berger, Capgemini, Siemens and Sopra Steria – which makes the first question easy to verify.

The full checklist: how to choose an AI agency – the 10-point checklist · the people behind AI Phase