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Artificial Intelligence (AI) is no longer an experiment. It automates processes, personalizes customer interactions, and makes data-driven decisions faster than any human team. Companies like Klarna, HubSpot, and Aldi are already saving millions, yet many others still struggle with outdated structures, internal resistance, and a lack of expertise.
Yet, many companies only use AI in isolated cases, without a strategic integration into their business processes. A key insight: Employees are already using AI productively, while companies lag behind. According to McKinsey, 91% of employees are using AI tools, but only 13% of companies have implemented strategic AI use cases. The question is no longer "Should we use AI?" but rather "How long can we afford not to?"
Many companies lack a clear AI strategy. Instead of systematically analyzing potential applications, AI projects often end up as isolated solutions or marketing gimmicks that fail to deliver real ROI.
Example: A bank implemented AI-powered ad automation without a data strategy. The result? Poor personalization, high inefficiencies, and €2 million wasted.
Solution: Companies must redesign their operating models and systematically analyze AI potential. A structured AI potential analysis identifies the best use cases and prioritizes the ones with the highest business impact.
AI does not replace people—it frees them from routine tasks and makes teams more efficient. But without proper communication, skepticism remains high. McKinsey emphasizes that companies investing in early change management accelerate their AI transformation significantly. Common fears:
Example: An insurance company introduced AI-powered claims assessment. Employees resisted the system due to fear of job losses, delaying implementation by 8 months and postponing €5 million in cost savings.
Solution: Companies must position AI as a tool that enhances human capabilities rather than replacing them. Early communication, training, and a clear roadmap create acceptance and prevent delays.
Many companies run small AI pilot projects but fail to scale them for sustainable efficiency gains. McKinsey recommends: A step-by-step but structured AI scaling approach rather than randomized testing.
Example: An e-commerce company used AI for product descriptions but ignored AI-powered customer service automation. While sales increased, support costs exploded by 40% because thousands of customer inquiries were still handled manually.
Solution: Instead of scattered mini-projects, companies need a scalable AI strategy that delivers quick wins and long-term business impact.
How to Implement an Effective AI Potential Analysis?
Before companies can successfully deploy AI, they must first analyze where it has the biggest impact. A structured AI potential analysis provides clarity and helps develop a sustainable long-term strategy.
Not every department benefits equally from AI. Companies should answer:
AI adoption requires a solid technological foundation. Companies must evaluate:
AI success depends on employee support. Key factors include:
A strong AI strategy combines short-term wins with long-term scalability:
Our AI potential analysis reveals, in just 4 weeks, how AI can make your company more profitable:
McKinsey research shows that companies adopting AI now will secure a long-term competitive advantage. AI is not a topic of the future—it is changing the game right now in 2025. Waiting means falling behind. Let’s explore the untapped AI potential in your company together.