Debunking the AI Silver Bullet: How AI is Really Transforming Work and Asset Management

December 11, 2023

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Today’s Field Service and Asset Managers are familiar with the challenges of running a large and complex workforce and asset portfolio. The ability to drive continuous improvements across businesses is essential to success. Traditional Field Service Management (FSM) and Enterprise Asset Management (EAM) systems have quickly become outdated and inefficient as stand-alone products.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 the field workers to improve their efficacy, efficiency, and safety  For Asset Management, improved maintenance strategies and tactics reduce downtime, eliminate excess costs, and increase overall asset performance.  With a unified dataset on a single platform, there is a huge opportunity to improve both the intelligence and the collaboration across work groups and drive broader process optimization throughout the organization.  

This value can be amplified with the opportunities available with artificial intelligence (AI).  The convergence of cloud-native FSM and EAM platform with embedded AI offers a new way to rapidly modernize processes and empower teams with intelligent and cost-effective end-to-end tools that collect, analyze, and optimally act on data in real-time to unlock immediate value across your operations.

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 construction type of work. 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 are able to deliver all the 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?

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 a vast corpora of data and commonly referred to as Foundation Models (FMs). 

The size and general-purpose nature of FMs make them different from traditional ML models. 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 its own knowledge. 

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

GenAI Services and Techniques

Pre-trained 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 the above 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, for example 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, which are suggested by 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

With the KloudGin Platform, field workers start a chat dialogue from within the application asking questions in natural language. Combining the language-based query with application entities like Asset ID, Work Order ID, Crew information along with the worker’s current location allows us to execute queries against enterprise structured databases. We can perform a similar document search for natural language questions against data stores such as OpenSearch and Amazon Kendra. 

The capability allows us to combine structured asset management data with unstructured data, such as safety protocols or operating manuals, to provide a customized, context-sensitive input to 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 such as KloudGin will help resolve existing challenges and improve productivity. It’s easy to see how this technology can transform field service and asset management programs. 

 To learn more about how KloudGin is evolving Field Service and Asset Management with embedded Generative AI, please visit KloudGin. To learn more about how AWS is helping customers with Generative AI, please visit here. To learn more about how AWS is helping its customers and partners transform the energy transition, please visit AWS for Energy

About KloudGin

With over 80% of today’s utility workforce conducting fieldwork, utilities require purpose-built systems and tools purpose-built for highly distributed, mobile environments.  A cloud-based platform that provides fully integrated Enterprise Asset Management (EAM) and Field Service Management (FSM) solutions enables a holistic view of maintenance and service operations to improve asset utilization, workforce productivity, and customer experience.

Powered by AWS, the KloudGin Platform is purpose-built to connect assets, workers, and work streams across all work functions on a single codebase, built on a mobile native architecture, and tied together with a common scheduling engine to improve performance and efficiency, reduce costs and increase customer satisfaction. To learn more about KloudGin, visit

About Amazon Web Services

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster. To learn more about AWS, visit

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About the Author

Arun Anand, AWS & Vikas Bansal, KloudGin

Arun Anand is a Senior Solutions Architect at Amazon Web Services Arun Anand is a Senior Solutions Architect at Amazon Web Services based in the Houston area. He has 25+ years of experience in designing and developing enterprise applications. He works with partners in the Energy & Utilities segment, providing architectural and best practice recommendations for new and existing solutions. Vikas Bansal is the Co-founder & Chief Technology Officer, KloudGin. Vikas is an accomplished entrepreneur with deep expertise in technology, product development, operations, consulting and sales. He grew his previous venture, Frontline Consulting Services, to over 500 employees and $40M+ in revenue in less than 4 years. His domain expertise in cloud, AI, mobile, IoT, field service, enterprise asset management and supply chain have formed the foundation of KloudGin’s next generation platform that solves the challenges of legacy cloud and on-premise solutions. Prior to Frontline Consulting Services, Vikas was Senior Director, Applications at Oracle.

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