The Biggest Pitfalls in Your AI Project

Reality often looks different than marketing promises suggest. AI is not a magic formula that solves all your company's problems overnight. It is a tool with clear strengths — but also weaknesses and often underestimated costs.

Those who introduce AI thoughtlessly quickly fall into expensive traps. We show you what the most common mistakes are and how you can avoid them.

The five most common dangers (and our tips)

1. Hallucinations cost money and trust

Generative AI can sound convincing while being completely wrong. This is not just embarrassing — it becomes expensive. A precedent case from Canada shows the consequences: Air Canada was ordered to pay because the website chatbot gave a customer false information. The court made it clear: the company is liable for its bot's statements.

The lesson from this: for all AI touchpoints with customers, 'human-in-the-loop' plus clear approval processes apply — especially for legal, financial, or safety-relevant information.

Our tips: establish multi-level quality control. First: define critical areas where AI only serves as a draft — never as a final answer. Second: train your employees in 'prompt engineering' to get more precise AI responses. Third: use retrieval-augmented generation (RAG), which links AI with your own verified data sources. Companies that follow these approaches can significantly reduce hallucinations.

2. The pilot trap: testing without end

Many companies start enthusiastically with pilot projects but never make the leap to productive operation. A current survey of 600 data leaders confirms the dilemma: two-thirds are stuck in generative AI pilots. And a full 97 percent have difficulties proving business value at all.

Analysts also warn of inflated expectations: according to Gartner, more than 40 percent of so-called 'agentic AI' projects are likely to be discontinued by 2027 — due to rising costs, unclear business benefits, and inadequate risk controls.

Our tips: think from productive operation backwards. Define clear success criteria before the pilot: measurable KPIs, maximum implementation costs, and realistic timeframes. US SMEs are already using AI broadly: 60 percent use AI platform tools — successful companies start with simple but clearly definable applications like automated email categorization or document search. For common pitfalls in pilots and how to get to production, see our piece on AI pilot failures.

3. Legacy systems as reality check

The demo works flawlessly — in corporate reality, it then fails due to interfaces from the 2000s, authorization concepts that no one understands anymore, or data quality that doesn't deserve the name.

This is where many projects fail: not at the AI model, but at integration into the grown IT and process landscape.

Our tips: make an honest inventory of your IT infrastructure before implementing AI. Focus initially on cloud-based AI tools that can be integrated via APIs, rather than complex on-premise solutions. The Boston Consulting Group shows with the 10–20–70 rule: successful AI transformations invest only 10% in algorithms, 20% in data and technology, but 70% in people, processes, and cultural changes. Successful SMEs often start with SaaS-based AI solutions for clearly defined areas — such as intelligent document analysis or CRM integration — and then gradually expand their infrastructure.

4. Hidden costs add up

In addition to model licenses, there are expenses for data preparation, prompt engineering, monitoring, governance, training, and change management. McKinsey shows in a current study: companies that have completely rethought workflows and clearly assigned roles see real benefits.

Our tips: calculate realistically and plan 2–4× the initial license costs for integration and change management, depending on complexity. First identify your biggest pain points: do employees spend hours searching through documents? Then semantic search might be more valuable than a general-purpose chatbot. Do you work internationally? Automated translations can bring more ROI than marketing AI. Do you have many repetitive data analyses? Specialized AI solutions for your industry often outperform standard tools. The key is to identify the greatest benefit and start there, rather than immediately tackling expensive all-purpose solutions.

5. Features instead of strategy

Many SMEs only use AI 'along with' existing tools because it's embedded in them — without a clear vision. This isn't fundamentally bad, but rarely sufficient for structural improvements. The strategic component is missing.

Our tips: develop an AI roadmap with concrete business goals. Many SMEs expect a strong impact of AI on their industry in the next three to five years — use this advantage strategically. Identify three to five business processes with the greatest improvement potential and prioritize them according to expected ROI and implementation effort. Companies that proceed systematically achieve significantly better productivity gains than those with ad-hoc approaches. Also explore our perspective on the benchmark problem when evaluating AI performance in your context.

Three proven entry points for your SME

So where to start? The key lies in a pragmatic approach: start small, stay measurable, improve iteratively. Those who start with moderation today build the competence that will make the difference tomorrow. Here are three sensible entry points into the world of AI:

Assisted knowledge work

• Automation of recurring document work
• AI-supported email processing and categorization
• Intelligent appointment coordination and meeting preparation

ROI expectation: studies show 12% more completed tasks and ~25% faster processing in controlled experiments with consulting companies (Harvard Business School/BCG).

Semantic search

• Searching through document inventories, manuals, and knowledge databases
• Intelligently linked search results instead of pure keyword matches
• Multilingual search for internationally active SMEs

ROI expectation: significant time savings in information search and higher hit quality through context-based search.

AI-supported customer service

• Initial categorization and routing of customer inquiries
• Suggestions for standard responses with approval requirement
• Sentiment analysis for prioritizing urgent cases

ROI expectation: reduction in processing times and improved customer satisfaction with proper implementation.