About the Client
The client is a leading global provider of industrial steam turbine solutions, delivering high-performance and energy-efficient systems. With decades of engineering excellence and deep domain expertise, the company designs and manufactures customized turbine solutions for several industries, like cement, steel, sugar, chemicals, fertilizers, Petrochemicals, etc.
Their products are known for reliability and adaptability to support diverse applications like cogeneration, process heating, and waste-to-energy. In addition to manufacturing, the company also provides an aftermarket service division, offering solutions for turbine repair, maintenance, and performance optimization. This ensures excellent operations and customer satisfaction across the globe.
Business Challenges
Before connecting with us, the client faced multiple operational challenges that impacted the efficiency of its complaint management and knowledge processes. These challenges and the lack of AI in manufacturing operations created delays, reduced productivity, and affected service quality.
Manual and Time-Consuming Complaint Creation
The complaint registration process was entirely manual, requiring agents to fill out 27 mandatory fields per record. Each complaint took around 10–15 minutes to log, leading to delays, high data entry errors, and poor categorization. All this affected resolution accuracy and customer satisfaction.
Delayed Case Resolution and Scattered Resources
Even a simple issue took a resolution time due to manual processes. The high case volumes and disconnected troubleshooting resources were spread across multiple systems. The manual analysis of historical Root Cause Analysis (RCAs) in complaint management and technical documents also slowed down decision-making and reduced operational efficiency.
Knowledge Creation and Gaps in Documentation
Manual creation of knowledge articles was time-consuming. As a result, only 15–25% of resolved cases were documented, leaving around 30% of recurring topics unaddressed in the knowledge base. This decreased resources for future reference and learning opportunities.
Prolonged Case Handling
Agents spent nearly 30% of their time manually searching for the right knowledge of articles, with only 10–20% of available articles being effectively utilized. The absence of intelligent search capabilities and contextual recommendations led to longer case resolution times, higher operational costs, and poor access to critical information.
Kasmo’s Solution
Our team of experts helped the client overcome the above challenges using Salesforce’s Einstein and Agentforce solutions. Using AI in manufacturing operations, the client’s complaint management and knowledge processes were completely transformed. Our solution included:
Easy Complaint Creation Using Einstein AI
Our team implemented Salesforce’s Einstein AI capabilities to automate the complaint registration process by auto-filling key fields. This saves time by reducing the number of mandatory inputs from 27 to 8. The system used contextual data analysis to suggest and validate complaint categories, minimizing human errors, and freeing engineers to focus on core responsibilities.
Implemented Agentforce Data Library
The Agentforce Data Library provided a unified repository for unstructured documents, including PDFs, historical RCAs, and troubleshooting materials. This centralized knowledge base enables engineers and support agents to access useful information in real time, streamlining workflows, and improving resolution rates.
Automated Knowledge Article Generation
Einstein AI was deployed to auto-generate knowledge articles directly from closed cases. This transformation into article drafts eliminated manual writing, helping agents to quickly review and publish. Reduced research efforts by content coverage across all service categories.
Contextual Article Recommendations
The Einstein Recommendation Engine used AI in manufacturing service, to provide article suggestions based on case details, eliminating the need for manual searches. Agents could instantly access relevant insights and solutions that reduced case handling time and improved customer satisfaction.
Key Results Achieved
Through Kasmo’s implementation of Salesforce Agentforce and Einstein AI, the client achieved measurable improvements.
- The client reduced the complaint creation time from 20-30 minutes to nearly 5 minutes, which is 66.7% reduction rate. This enables support agents to process cases faster and with greater efficiency.
- There was a ~45% improvement in data accuracy, ensuring consistent and high-quality information across all generated cases.
- Agent productivity increased through a simplified complaint registration using Einstein AI to reduce manual dependencies.
- The organization witnessed a 25–40% increase in the coverage of high-impact knowledge topics, enhancing documentation consistency.
- Case resolution speed improved by 35% using AI-powered article suggestions that provided immediate solutions from centralized knowledge sources.
- The integration of historical insights and AI-generated summaries into the workflow improved decision-making efficiency and response accuracy.
- Automated drafting of knowledge articles reduced agent workload, ensuring faster creation and delivery of critical information.
- There was a 15–25% reduction in average case handling costs and a 10–20% improvement in First Contact Resolution (FCR), increasing customer service quality.
- Salesforce implementation improved the client support team’s efficiency, reduced manual efforts, and better customer engagement.
