Durée de lecture : 2 minutes
Kowledge silos cost time and money
For service providers, a single error message can trigger an immediate race against time. When machine components fail, entire production lines can come to a standstill within minutes. The consequences are well known: financial losses, dissatisfied customers and intense pressure on field service technicians.
The core challenge for manufacturers’ service centers is rarely a lack of technical expertise. Instead, the problem lies in the availability of critical knowledge at the moment it is needed. While teams request diagnostic data, search for comparable past incidents or dispatch technicians with spare parts based on educated guesses, valuable time slips away.
Demographic change and efficiency targets increase pressure on service organisations
Rising competitive pressure and an ongoing shortage of skilled workers make maintaining the status quo unrealistic. Service organizations are facing multiple structural challenges at once. The good news is that solutions already exist to make an organization’s collective knowledge accessible and usable.
One of the main reasons for long lead times and delays in service operations is a highly fragmented data landscape combined with isolated ways of working. Critical information is spread across different systems, often unstructured or only accessible within individual departments. Similar issues are described in different ways, and valuable patterns remain hidden. This creates a gap between existing knowledge and its practical application. Agent-based, learning AI solutions are designed to close exactly this gap.
How Agentic AI Solutions close knowledge gaps and connect silos
Agentic AI refers to AI systems that go beyond generating answers. Within clearly defined boundaries, they act autonomously, orchestrating data from multiple sources, identifying patterns, deriving diagnoses and generating concrete recommendations.
In a new service case, an AI agent can automatically check whether similar incidents have occurred before. It analyzes technicians’ free-text reports, material bookings, diagnostic data from the data lake and expert knowledge documented in historical tickets. Modern language models help normalize different descriptions, making it clear that a “brake fault on axle 6” represents the same underlying issue as “error 306 on the axle brake.”
Learn more about the importance of well‑structured service data, the practical use of agentic AI, and the role of solid governance in the full blog post by our IoT specialist Device Insight:
