AI, IoT, and CRM, three acronyms. However, these three belong together and should not be treated or looked at separately. One important reason for this is that companies and organizations can provide significantly better service experiences and, more importantly, results, by combining the capabilities behind these acronyms. Good field service not only gets dispatched smartly but also equipped with the right parts and, ideally, in a proactive manner. This can get delivered by the combination of Field Service, AI, and IoT data. That’s why I found Salesforce’s early December announcement of having added a component “IoT insights” to its Field Service Lightning product quite interesting. As the press release said, this capability enables service agents and representatives to see IoT signals together with other CRM data, so that the triple p of personalized, proactive, even predictive service is possible. After all, Einstein is embedded into Field Service Lightning for quite some time now. Doing so, Salesforce wisely did not implement yet another IoT platform but enabled its system to ingest data from existing IoT platforms, thus sticking to the core competencies of the company. The solution helps in three areas:
- Enabling of early issue anticipation (rather than detection, which is responsive) and remote diagnosis
- Providing agents with more relevant information, to speed up issue resolution
- And automation via rules and workflows.
- Service agents can have a better picture of the problem with paired CRM/IoT data and previous case resolution (e.g. they can see what sort of warranty the customer has, alongside the device diagnostics, and immediately understand the type of service they should be delivering)
- Dispatchers can be more efficient in dispatching (they know who to send, what equipment to send the technician with)
- Automatic dispatching can be applied based on contracts and urgency of the fault, helping prioritization and alleviating rote work so the dispatcher can focus on higher level tasks

