It is an exciting time for the tech industry. With all the buzz around AI, INRY, a ServiceNow elite partner, is in a great position to combine AI technologies like Generative AI (GenAI) on the Now platform. We are powered by proprietary ServiceNow large language models (LLMs) tailored to understand the Now Platform, workflows, and automation use cases.
Case Summarization for IT workflows
The Large Language Model (LLM) for case summarization is based on a specialized version of the 15-billion-parameter StarCoder model. It was developed as part of ServiceNow's co-led open BigCode initiative and tuned with Nvidia accelerated computing and DGX Cloud.
Case Summarization leverages generative AI to seamlessly filter information, including the data from IT environments, HR records, and customer service cases. The aim is to streamline and enhance the efficiency of IT operations. The feature enables faster handoffs between team members and achieves more efficient resolution processes.
Case Summarization: Focus on Value by Reducing Manual Efforts
The Case Summarization feature is designed to help employees focus on more valuable work, improve employee satisfaction, and reduce burnout. It reads and summarizes relevant information, including case or incident details, previous touchpoints, and actions by all involved parties, to create detailed case summary notes within seconds.
Objectives of Case Summarization
The technology reads and distils information from IT tickets, case files, service requests, and conversations for customer or employee issues to create summary notes in seconds. It helps automate manual tasks, reduces hand-off times between teams, speeds resolutions, and increases productivity for employees and customers.
Case Summarization models are trained on real-world data sets specific to the Now Platform, workflows, and automation use cases. The features enable faster and more reliable results and are now available to select customers through the ServiceNow Assist product.
4 Key Use Cases of Generative AI Case Summarization
One of the critical use cases for generative AI is summarization — refining all the actions and touchpoints in a case into a single summary. This could make handoffs smoother between agents and allow teams to resolve problems more quickly, improve processes, and increase alignment.
Additionally, generative AI significantly simplifies navigating institutional knowledge and policies within the service platform's knowledge base. This enhancement aids users in quickly finding relevant information and understanding organizational guidelines, thereby improving overall efficiency and user experience.
A recent study by Valoir revealed that when applied effectively, AI can substantially reduce a worker's workload, possibly by up to 40%. This significant reduction in workload can lead to enhanced productivity and work-life balance for employees.
Benefits of Case Summarization
Case Summarization helps organizations realize immediate productivity gains. It simplifies processes and enables employees to focus on solving problems quickly and efficiently.
For instance, Case Summarization utilizes generative AI to meticulously read and filter key information from various domains, including IT, HR, and customer service cases. This process includes analyzing customer or incident details, previous touchpoints, actions taken, and the eventual resolution. Consequently, it efficiently automates the generation of summary notes, which are invaluable for future reference and streamlined operations.
The Take!
The new generative AI capabilities use specialized LLM optimized for the ServiceNow platform. The GenAI models are designed to learn ServiceNow workflows, automation use cases, and processes. They are currently available to a limited number of customers within the Vancouver release.