How can artificial swarm learning improve service quality in service operations?

Artificial swarm learning is picking up of late due to advancements in computing power and data science. Its a powerful technique that is based on the idea that collective intelligence is much smarter than limited intelligence of individual participants, in this case, it could mean a collection of systems and individuals. I believe its a sound idea that can result in enhanced accuracy, better decision-making, and resilience to errors. It can be applied to a number of use cases such as medical diagnosis, financial forecasting and product design. But like any other AI implementation, we need to consider data privacy, security, scalability, and bias and fairness.

One example of how we can apply ASL in real life is how airlines deal with passengers. The airline can maintain an ASL system that captures data from real-time customer satisfaction metrics, tracking issues on social media, and irregular flight disruption patterns. This sort of system can come in handy during situations where an unexpected storm causes flight cancellations and unhappy customers call the call centers. An ASL system can proactively detect rising anger trends and trigger a smart response by routing calls globally to assist the hardest-hit locations. The system can provide compensation to their most loyal customers and send updates across multiple channels to ease confusion.

Three key things come to mind (1) Most DSM today focuses on what happened, what is happening and then what may happen but don't address WHY things happen - root causes and what can be done to change future outcomes and the consequences of possible interventions. (2) Digital services endpoints are usually highly diverse across a digital services ecosystem. ASL will allow more unique actions to improve diverse endpoints while also being more effective in aggregate. Networking localized agents can help help avoid the limits of over centralized, homogeneous models. (3) Capture of not only operational data, but also tacit, expressed and experiential data. People stuff. The notion of wisdom of crowds (knowledge and experience).

Delivering ASL in digital service mgmnt or any other ASL use case is a super complex endeavor. It requires highly sophisticated AI techniques beyond what today's AI platforms can help enable. Until leading AI tools and platforms evolve to help automate the architectural design and implementation of Swarm Intelligence, it will be out of reach of most. As mentioned in other comments, key ingredients are also not easily created today either - such as causal AI, real-time data fabrics, conversational explainability, self-regulating data regeneration (rid yourself of bad data), etc. But agree with Rabi Jay that is a future game changer. I agree with Rabi Jay that the "concept" can be a game changer, but until the platforms

The concept of ASL can be a game changer for businesses when implemented effectively. It first starts with identifying the problem to be solved. For example to improve customer satisfaction, we need to figure out what customers want quickly and accurately. This means we need to identify the sources, such as the managers, the shopfloor staff, and AI systems that track inventory, customer feedback and sales data. We should identify the systems that can provide such data and continuously track them for insights. If our analysis reveals that they are looking for sustainable products, then we can tailor our offerings accordingly. This is an iterative process of continuous improvement.