How can we make the leap forward in AI?

Why are we still wasting millions when AI could do the job long ago?

Artificial intelligence (AI) is no longer an experiment. It automates processes, personalizes customer interactions, and makes data-based decisions faster than any human team. Companies like Klarna, HubSpot and Aldi are already saving millions — yet many others are still struggling with outdated structures, internal resistance and a lack of know-how.

The figures are clear

- Klarna reduced its marketing costs by 37% — saving $10 million annually
- Aldi Nord reduced its advertising costs by 34% and increased Unique Reach by 52%.
- HubSpot doubled its conversion rates with AI-optimized content.
- Mooris (Swiss furniture retailer) achieved 16% more conversions through AI-driven personalization.

Nevertheless, many companies only use AI in a fragmented way, without strategic integration into their business processes. A key finding: Employees are often already using AI productively, while companies are lagging behind. According to McKinsey, 91% of employees use AI tools, yet only 13% of companies have implemented strategic use cases. The question is no longer: “Should we use AI?” , but “How long can we afford not to?”

Three fatal mistakes that cost millions

1. Lack of know-how: “We don't know where AI really makes sense.

Many companies do not have a clear strategy for using AI in a targeted manner. Instead of systematically analyzing potential, AI projects often end up in stand-alone solutions or marketing gimmicks that do not deliver real ROI.

Example: A bank introduced AI-powered advertising automation without first developing a data strategy. The result? Lack of personalization, high wastage — €2 million fizzled out.

Solution: Companies must redesign their operating models and systematically analyze potential. An AI potential analysis uncovers the best use cases and prioritizes measures with the highest economic benefit.

2. Internal resistance: “Our employees are afraid of AI.

AI doesn't replace people — it frees them from routine work and makes teams more efficient. But without proper communication, skepticism remains high. McKinsey emphasizes: Companies that rely on change management at an early stage are significantly accelerating their AI transformation. Typical fears:

- “AI is taking jobs away from us! ”
- “The algorithms are prone to errors! ”
- “We already have automation software...”

Example: An insurance company introduced AI-based claims assessments. Employees blocked the system — fearing job losses. Implementation was delayed by 8 months, cost savings of €5 million were postponed.

Solution: Companies must position AI as a tool to expand human capabilities, rather than as a threat. Early communication, training and a clear roadmap create acceptance.

3. Wrong priorities: “We'll test a bit first...

Many companies rely on small AI experiments, but miss out on the opportunity to achieve sustainable efficiency improvements. McKinsey recommends gradual but thoughtful scaling rather than isolated testing.

Example: An e-commerce company used AI for product descriptions but ignored AI-powered customer service automation. While revenue grew, service costs exploded by 40% as thousands of support requests continued to be handled manually.

Solution: Instead of relying on mini-projects, you need a scalable AI strategy with quick wins and long-term business impact.

How do you implement a successful AI navigation plan?

‍Before companies can use AI profitably, they must specifically analyze where they have the biggest lever. A structured AI navigation plan helps to create clarity and develop a sustainable strategy in the long term.

Step 1: Identify relevant business areas

‍Not every department benefits equally from AI. Companies should therefore answer the following questions:

- Which processes are time-consuming and resource-intensive?
- Where are the highest costs currently incurred?
- Which activities follow recurring patterns and could be automated?

Step 2: Evaluate Existing Technological Infrastructure

‍The introduction of AI requires a solid technological basis. Companies must check:

- Is the existing data clean, accessible and structured?
- Are there already AI solutions that are being used but are not optimally integrated?
- Which systems must be modernized for successful use of AI?

Step 3: Ensure internal acceptance

‍The success of AI depends on the support of employees. The decisive factor is:

- Early involvement of teams and clear communication of benefits
- Reducing anxiety through training and change management measures
- Definition of roles and responsibilities for using AI

Step 4: Identify quick wins and long-term levers

‍A successful AI approach combines short-term success with long-term scaling:

- Which AI projects produce measurable results within a few months?
- Where is the potential to reduce costs and increase sales over several years?
- How can successes be scaled to transform other areas of the company?

Our offer: An AI navigation plan that quickly creates facts

‍Our own AI navigation plan shows within just 4 weeksWhere and how AI makes your business more profitable:‍

- Identification The Top-use cases in marketing, sales and customer service
- analysis of technological infrastructure & transformational ability
- Evaluation of Quick Wins & long-term ROI
- Overcoming internal resistance & development of a sustainable AI roadmap

Act now before the competition does! Make a free appointment here

‍McKinsey shows: Whoever acts now creates sustainable competitive advantage. AI is not a topic of the future. It is now massively changing the rules of the game in 2025. Whoever waits loses. Let's find out together what potential your company has.