Artificial intelligence has become indispensable in discussions around eCommerce, sales, and customer service. But amid ChatGPT demos, automated product recommendations, and generative service bots, one key question often goes unanswered: What’s the point of it all?
Recent developments show: companies have entered a new phase of AI integration. It’s no longer about being driven by technology, but by business value. A shift from tech obsession to strategic impact. Instead of asking “What can AI do?”, companies are now focusing on questions like “What problem are we solving?” and “How do we measure success?”
The Harsh Reality: Many AI Projects Fall Short
Numerous studies confirm what many practitioners already know: around 60% of AI projects fail to deliver the expected ROI. The reasons are often surprisingly simple – lack of clear goals, vague metrics, and missing business relevance. As impressive as the technology may be, it’s useless without a specific application.
What Companies Need Now: Methodology Over Feasibility Fetish
In our upcoming SYBIT Expert Talk, featuring experts from consulting and industry, we’ll show how to ask the right questions – and what methods help integrate AI in a truly effective way. Key insights from the preparation phase highlight what really matters:
1. Which Use Case is Economically Viable?
Only when a problem is economically relevant – such as high costs, declining conversion rates, or staff shortages – is a valid ROI assessment even possible.
2. Standard Solution or Custom Application?
Companies must choose: rely on established platforms or develop tailored solutions. The challenge is to strike the right balance between speed, scalability, and integration based on maturity level and process complexity.
3. How to Get Started – Fast and with Validation?
Instead of spending years planning, successful companies use pilot projects with clearly measurable goals. Quick, pragmatic, and iterative. This relieves resources, reduces complexity – and creates a solid foundation for scaling.
Proven AI Use Cases in Practice
One example from customer service: a chatbot that automatically processes, categorizes, and creates support tickets. In a pilot, thousands of inquiries were handled within just a few weeks – achieving a first-contact resolution rate of over 60%.
Another example: a global service use case where generative AI is combined with live translation. The system not only translates conversations in real time, but also analyzes customer sentiment. Based on this, it provides staff with response suggestions – such as offering a discount or escalating the issue.
Visual analytics also shows great potential: in one case, AI identified recurring defects in a sealing ring – a pattern that had not previously been recorded systematically. This enabled quality management to act proactively before failure rates increased.
Controlled Growth: Why Governance Is the Game Changer
A frequently overlooked success factor is controlling AI processes. Successful companies rely on clear human-in-the-loop mechanisms: employees review results, refine models, and define intervention boundaries. Regulatory compliance is equally important – from data privacy to the upcoming EU AI Act.
It’s not just a question of what AI can do, but how it is governed:
- How is quality continuously monitored?
- When is human intervention required?
- How are users informed transparently?
Conclusion: AI Needs Direction – and That Starts with the Right Question
Not every innovation begins with a product. Some begin with a good question. If you want to invest in AI, don’t start by looking for tools – start by identifying meaningful business challenges. That’s how AI becomes a business case, not just a prestige project.
At SYBIT, that’s exactly what our AI approach is all about. We always approach AI from a business value perspective – focusing on efficiency, customer experience, and scalable, cost-effective solutions.
