Whether in discussions with CEOs, in international board meetings, or at investor roundtables, one thing is becoming increasingly clear: data-driven decisions are no longer an operational advantage, but a strategic necessity.
Companies that succeed in harmonizing artificial intelligence and value creation not only transform processes but also redefine the basis for decision-making.
The shift in decision-making logic
Traditional decision-making is based on experience, instinct, and past patterns. But in a world of volatile markets, complex supply chains, and interconnected customer interactions, this is no longer sufficient.
Companies that rely on structured, captured, and interpreted data—and analyze it with the help of AI—make not only faster but also more robust decisions.
Business case
In the collaboration between ProcessMiner and a leading international manufacturer of plastic injection-molded parts, the goal was to reduce the scrap rate in the production of vials for medical devices by over 20%.
By analyzing 300 sensor data sets, the company identified pressure and temperature settings as the leading causes and optimized machine parameters through data-driven process automation. Scrap fell by 25%, resulting in six-figure savings—without the need for new hardware.1
Data literacy is a leadership responsibility, not an IT issue
A common misconception:
Responsibility for data-driven decisions is often delegated to technology or analytics teams. However, the strategic dimension lies with top management. Executives don’t need to understand AI algorithms, but they do need to know how they impact the business model—and what questions they should ask to make informed decisions.
According to McKinsey, 78% of companies use AI in at least one function. However, only a small group of them describe their AI strategy as mature. The majority of companies lack the necessary scale to achieve comprehensive value creation.2
The difference lies not in the technology, but in the attitude:
Executives who lead with data drive create structures in which AI functions not as a tool, but as a decision-making platform—from strategy to pricing, from logistics to customer service.
Key takeaways
- Data without context remains noise. AI gives information strategic meaning and enables decisions based on patterns—not opinions.
- Leadership means drawing the proper conclusions from data. CEOs who want to lead driven by data must change their mindset—not just implement systems.
- Scalable success arises where technology and leadership interact. Strategic decisions based on AI are not a thing of the future, but a competitive advantage—here and now.
Conclusion
Executives who want to drive digital transformation must rethink decision-making processes holistically. Not only to make them more efficient, but also more intelligent.
“In God we trust. All others must bring data.”
— W. Edwards Deming
The good news: It’s not about more data in general, but about better decisions in particular.
Sources
- ProcessMiner, “How a Plastic Injection Molding Manufacturer Reduced Scrap Rates by 25%”
- McKinsey, “The state of AI – How organizations are rewiring to capture value,” 2025
Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice—it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.