Published
Author
The rapid pace of technological development towards cloud-based enterprise resource planning (ERP) systems undoubtedly presents new opportunities, but for the public and financial sectors it also poses significant challenges. These sectors want to stay updated and relevant by adopting the latest functionalities of ERP systems, while simultaneously ensuring they comply with strict regulations regarding security and data protection and provide robust solutions for both customers and employees. This requires strategic planning and a deep understanding of both technology and legislation.
Smart systems are based on the principle "if this happens, then do this". They are controlled by fixed algorithms and rules where the outcome is predetermined. A classic example is inventory management: when the stock level falls below a certain threshold, the system automatically generates a purchase suggestion. This is valuable and saves time, but it is fundamentally a reactive process. The system does not question whether the purchase is the best decision right now from a broader perspective – it just follows its instruction.
A transition to a new business system can therefore be taxing for the organisation and frustrating for the employees.
A thinking system instead operates based on defined business goals. This is not about following a straight line, but about navigating a complex landscape where different courses of action are balanced against each other. Imagine the same scenario with an emerging stock shortage. A thinking system does not just detect the shortage but analyses the consequences. It weighs various options against each other:
The thinking system strives to balance goals such as a steady production pace, high delivery reliability, and optimal stock coverage. It makes decisions based on what best benefits the overall business goals rather than being based on individual and isolated parameters.
The difference between these two models becomes especially clear when analysing how AI is developed and implemented.
1. AI as an add-on in smart systems
In smart systems, AI often functions as an add-on. External AI services can be used that call the existing business logic of the business system to perform specific transactions. This is effective for targeted interventions, but is limited by the fact that the system lacks an overarching process engine with relationships between all sub-processes.
2. AI at the core of thinking systems
In thinking systems, a different model is applied. Here, AI services need to be embedded in the fundamental logic. To simulate different scenarios and balance complex goals, intelligence must reside in the system's core and defined process flows, where it has full access to all data and all dependencies between processes in real time.
Understanding the difference between smart and thinking is important in the face of future increasing competition. Smart systems with external AI services go a long way, but thinking systems with built-in AI services create greater potential for automation – provided we dare to relinquish control.
We are still at an early stage of the shift towards thinking systems. But AI development is happening at a high speed, and with a cloud-based infrastructure, new opportunities for increased automation and efficiency arise daily.
When you look ahead, the critical question is no longer just what functionality you need in your future systems, but what kind of intelligence you want to govern your business. Are you ready to move from a system that just executes orders to one that can actually think for itself and assist in daily planning?
Updated
