Einstein for Field Service

Do you want to discover the story your data has to tell? As the field service industry rapidly evolved towards a proactive and predictive model, innovative technologies like machine learning, artificial intelligence (AI) and augmented reality are helping to transform field service. These technologies provide optimal asset uptime, along with greater visibility, efficiency, and profitability.

AI-driven Field Service

Do your service appointments sometimes take more time than expected, and not all scheduled appointments can be completed? Or are your technicians showing up at the customer, only to find out that they do not have the right parts or skills to complete the job?

For most companies, the basis of a streamlined field service execution lies in consistent and accurate work preparation: estimating expected duration, required parts and the skills needed to perform the work on site.

AI, such as Einstein, in field service management, allows you to enhance service efficiency by improving the accuracy and speed of information and reducing the scope of human error. It can drastically help in predicting the different variables important to field service.

A good example is predicting the correct expected duration of the job. Before, these predictions were often based on the historical knowledge and expertise of your staff. But when using Salesforce Field Service, this historical data is available in your Salesforce environment, and with Einstein, you can use this data to make predictions and improve the quality of work preparation even further.

Salesforce’s Einstein custom AI Tools offer two standards, easy-to-use solutions that can offer valuable insights for Field Service:

• Einstein Prediction Builder: Einstein learns from data in the system and predicts the number of field values (e.g. expected duration) or the probability of a certain event happening (e.g. probability of a no-show)
• Einstein Recommendation Builder – Einstein investigates past interactions between objects and uses these to suggest combinations. A good example of this is recommending parts for a certain job.

Creating a new prediction or recommendation with Einstein is not a time-consuming process; results can be gained from these solutions quickly and efficiently. Having a good understanding of Field Service objects and data is vital for setting up a good prediction or recommendation because the quality of the prediction results depends strongly on the quality of the data.

If you want to learn more about how Einstein can provide contextual insights and recommendations to drive your Field Service business every day, we are delighted to help you out. Read more about our field service practice here.