Transforming Workforce And Asset Management With Cloud-Powered Generative AI

Today’s field service and asset managers understand the challenges of running a large and complex workforce and asset portfolio. The ability to drive continuous improvements is essential to success. Traditional field service management (FSM) and enterprise asset management (EAM) systems have become outdated and inefficient as stand-alone products. They have created virtual boundaries across isolated assets and workgroups, resulting in siloed data, tools and processes.

At the core of both asset management and field service management systems is asset data. For FSM, this data informs workforce planning and scheduling. It provides critical site and asset data to field workers to improve their efficacy, efficiency and safety. For EAM, improved maintenance strategies and tactics reduce downtime, eliminate excess costs and increase overall asset performance.

Challenges With Field Service Management

Traditionally, field service work management has been a manual and time-consuming process involving managing teams, scheduling and dispatching field workers, and capturing data about work and assets. This is where everything comes together for different types of work that a utility crew may perform. A crew may perform a simple service connect/disconnect, a complex repair or some type of construction. Providing relevant asset information to field crews on an as-needed basis in near real time has always been a challenge. This includes structured and unstructured data such as historical maintenance records, repair records, operating manuals, safety procedures, etc.

Even when you can deliver relevant information to field crews in near real time, the sheer volume of information related to assets, maintenance records, safety protocols and asset geographic location is overwhelming. Providing the right in-depth information about assets and their history is critical to improving first-time fix rates and the customer experience.

Within a combined cloud-hosted environment, AI allows field workers to access a more complete data set relating to assets and increase the efficiency and efficacy of their work with more complete information, better insights and more robust decision support.

What Are Generative AI And Foundation Models?

According to AWS, “Generative AI (GenAI) is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music.” Like all AI, GenAI is powered by ML models—very large models that are pre-trained on vast corpora of data and commonly referred to as foundation models (FMs).

FMs are different than traditional ML models based on their size and overall purpose. With foundation models, instead of gathering labeled data for each model and training multiple models, customers can use the same pretrained FM to adapt various tasks.

Large language models (LLMs) are a type of foundation model that are trained on large amounts of text data and can generate text such as blog posts, engage in dialogue and answer questions based on their own knowledge.

However, pretrained LLMs are not trained on your enterprise-specific asset management data, which may sometimes result in hallucinations or inadequate responses.

GenAI Services And Techniques

Pretrained LLMs face challenges in providing accurate responses to content beyond their training data. To empower enterprise applications with GenAI capabilities, it becomes crucial to enrich LLMs with enterprise-specific datasets they haven’t seen before.

To solve this problem, we can use a technique called retrieval augmented generation (RAG). Using RAG, you can bundle additional external data as context along with the original question in a prompt before sending it to the LLM to get a response. The external data can come from one or more data sources (e.g., relational databases, NoSQL databases, searchable document stores such as OpenSearch and Amazon Kendra, etc.).

A commonly used prompt engineering technique is called few-shot learning, where a few examples of prompts and desirable responses are provided to the LLM before a real prompt is given. LLM agents, an emerging technique, use the few-shot learning ability of LLMs to teach the model how to solve problems. LLM agents interact with external systems using tools that are suggested by the LLM based on the input provided.

Amazon Bedrock is a fully managed service that offers a choice of FMs from leading AI companies along with a broad set of capabilities such as Knowledge Bases and Agents for Bedrock to help you enable your existing applications with GenAI capability.

Enabling Real-Time Guidance For Field Crews

This technology can aid field workers by allowing them to start a chat dialogue from within an application by asking questions in a natural manner. Combining the language-based query with application entities like asset ID, work order ID, crew information and a worker’s current location provides the ability to execute queries against enterprise-structured databases.

The capability gives users the ability to combine structured asset management data with unstructured data, such as safety protocols or operating manuals, to provide a customized, context-sensitive input to an LLM and use it for its summarization and reasoning capability to generate a response.

It also reduces data transmission between mobile devices and the cloud by summarizing the information, resulting in lower capacity consumption over wireless networks.

The ability to deliver asset information to crews, in an easy-to-comprehend manner, in near real time, will improve crew safety and productivity. Another potential benefit is that field crews get expert-level answers without having to get a human expert online.

Looking Ahead

Cloud-based GenAI techniques are evolving, and the capabilities will continue to grow. Applying this technology to business applications will help resolve existing challenges and improve productivity. It’s easy to see how this technology can transform field service and asset management programs.

Read the Blog Here.