Reimagining field service operations: How AI is powering smarter operations in asset-intensive industries

Over the past decade, asset-centric industries have steadily modernised their operations by adopting Salesforce Field Service. This powerful solution combines the capabilities of the Salesforce CRM platform with a state-of-the-art scheduling engine and an intuitive mobile app. Crucially, this shift is not merely a technology upgrade. It represents a broader transformation in business processes and value delivery.

As adoption continues to grow, organisations are increasingly embracing low or no-touch automation, enabled by the advanced features of the Salesforce platform. In my view, we are now entering the next phase of field service transformation driven, unsurprisingly, by the rise of AI.

Looking ahead to the next five to ten years, AI is poised to play a pivotal role in shaping field service operations across asset-intensive sectors such as Utilities, Energy, Manufacturing, Life Sciences, Telecoms, Transport, Aviation, Consumer Products, and the Public Sector.

In this blog we look at how Salesforce Field Service can harness native AI capabilities, to unlock new levels of efficiency and value.

Work pack abstract

A typical field service work order contains many artefacts comprising both structured and unstructured data. Planning and field teams have to look for information that is scattered across many places, buried in long text fields or drawings. This is where a multimodal based agent, built in Agentforce, can create work summary information from the various data points and make it meaningful to the users to do their job. This would save time and help bring key information to the forefront to help users move the work to the next stage.

Planning and scheduling

Data-driven prediction is going to play a major role in shaping up the planning and scheduling aspect of field service. For example:

• Based on the historical data, predicting the special parts that are required to enable the successful delivery of incoming work and automatically reserving or ordering the part in advance.
• Predicting the actual duration of a work type to feed into the planning process.

All of this is made possible by using Salesforce’s Data Cloud Einstein Prediction algorithm. This unlocks the ability to positively impact several KPIs such as first-time fix rate, cost to serve, and time to value.

Image and document analysis

This area is one of the most impactful applications of AI. Field service operations generate a lot of documents, and the content of these documents requires manual intervention to validate the context of the document in line with the context where it is linked to. Especially, if the document is tied to a regulatory outcome, or health and safety, or commercial outcome. Engineers can upload images, such as before-and-after shots of repairs, which are automatically assessed by AI agents for quality, clarity, and relevance. These agents validate content against job descriptions, ensuring that only high-quality, verifiable records are accepted. When a document cannot meet the required standard, the AI agent can create an action and therefore providing huge savings in time and cost across the value chain.

AI-Powered prioritisation and scheduling: smarter, faster, more accurate

Prioritising a job is always a challenge in field service as there are multiple factors at play and often this varies within and across the sectors. The priority is key for the scheduling engine to make sure the right jobs are given consideration first over low priority jobs.

AI driven agents can be used to assess the jobs to give a priority score, based on key information on the job and data-driven prediction via historical information.

This leads to improved first-time fix rates, reduced travel time, and enhanced technician productivity. In sectors where service windows are tightly regulated and asset downtime can be costly, AI-driven scheduling delivers operational and customer experience benefits.

Data-Driven decision making. From insights to action

In the past, machine learning models have been used to analyse historical service data and IOT sensor data, predict asset failures, and recommend proactive maintenance strategies, using niche modelling tools. This is now possible using Data Cloud that can bring all the relevant data into one place to apply predictive algorithms that can drive actionable insights.

Conclusion: the future is intelligent and value-driven

The future is intelligent, and it’s already underway. It is only going to grow significantly and help companies unlock value. Companies will need to adopt a crawl-walk-run approach to embrace the power of AI into their field service operations.

The following areas are yet to evolve, and these will help even more in bringing value to field service operations

• AI driven What-if analysis
• AI driven demand forecasting
• Mobile first AI Image analysis
• Multi agent and RAG models to help take the AI beyond Salesforce platform

But for now, I’m excited to see how organisations look to adopt AI into their field service operations today and witness the benefits it can bring.

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We are already helping our clients use Salesforce Field Force technology to help them deliver high-quality, data-driven, and sustainable field services. Find out more here to discover how we are helping Scottish Water reimagine their field services with SWIFT.