-- Resolution of i ncidents or problem tickets is a common theme in service industries in any sector, including billing and charging systems in telecom domain. Machine learning can help to identify patterns and suggest resolutions for the problem tickets, based on patterns in the historical data of the tickets . However, this process may be complicated due to a variety of phenomena such as data drift and issues such as missing data, lack of data pertaining to resolutions of past incidents, too many similar sound ing resolutions due to free text and similar sounding text . This paper proposes a robust ML - driven solution employing clustering, supervised learning, and advanced NLP models to tackle these challenges effectively. Building on previous work, w e demonstrate clustering - based resolution identification, supervised classification with LDA, Siamese networks, and One - shot learning, Index embedding . Additionally, we present a real - time dashboard and a highly available Kubernetes - based production deployment. Our experiments with both the open - source Bitext customer - support dataset and proprietary telecom datasets demonstrate high prediction accuracy. The problem of recommend ing resolutions for problem tickets or incidents on the basis of historical data is an important problem for service users, including telecom operators. Typically, service desks have dedicated manual teams that perform triaging of the issues and root cause analysis, and recommending a solution can take several hours end to end. Using machine learning models to recommend resolutions can save significant time and manpower of the operators by recommending solutions based on historical i ncident data. However, real - world application involves addressing several practical challenges: Diverse ticketing formats across service desks.

ChatGPT made a splash with its user-friendly interface and believable AI-generated responses. With a single prompt, ChatGPT provided detailed answers that other AI assistants had not achieved. Powered by a massive dataset that ChatGPT had been trained on, the breadth and variety of topics it could address quickly amazed the tech industry and the public. However the technology sophistication raises inevitable question: what are the drawbacks of ChatGPT and similar technologies? With capabilities to generate a multitude of realistic responses, ChatGPT could be used to create a host of responses capable of tricking an unassuming reader into thinking a real human is behind the content.

Artificial intelligence (AI) refers to the use of advanced analysis and logic-based techniques like machine learning (ML) for interpreting events, supporting and automating decisions, and taking action. AI techniques can enable IT leaders and data analysts to solve an array of business problems and generate a considerable return on investment (ROI). AI will reshape how work is done in the future as the technology will be able to replace some of the tasks typically performed by humans and change how everyday decisions are made. Implementing an enterprise-wide AI that identifies use cases, aligns business and technology teams, quantifies benefits and risks, and changes organizational competencies to support AI adoption will help the organization to capture the maximum benefits of AI. AI vision: Identify the focus areas that promote and enable organization-wide fluency and adoption of AI.

Chatbots have a checkered past of often not delivering the performance their providers have promised. This is especially true in the IT service management (ITSM) and multilingual NLP spaces, where service desks found support teams deluged with complaints -- yes, about the support chatbots. Just getting English language nuance right and how enterprises communicate often require chatbots to be custom programmed with constraint and logic workflows supported with natural language processing (NLP) and machine learning. If that sounds like a science project, it is, and IT users are the test subjects. Because of their complexity, chatbots were contributing to already overflowing trouble-ticket queues.

AI has become a game-changer tool in the IT sector. Artificial intelligence and automation have significantly transformed how organizations run their production lines. As AI tools can garner real-time insights, it has facilitated the companies' design and product innovation techniques. When applied correctly, AI and automation can help develop better, faster, and cheaper business techniques. Automation tools can be deployed to automate repetitive tasks, allowing the IT staff to focus on strategic tasks instead of administrative work.

In the movie "Outsourced," a call center manager named Todd is asked to train his replacement as his company moves its call center from Seattle to Mumbai. Throughout the movie, his singular focus shifts from reducing the team's "minutes per incident" to building a strong team culture that makes employees happier and more productive (not to mention funnier!). It's a great movie but one that hasn't quite stood the test of time. Today, the best way to save money is not by outsourcing, but by moving customer service operations and infrastructure to the cloud. This reduces the time and cost of setting up and running a service desk while providing built-in artificial intelligence (AI) and a great agent workspace experience.

Artificial intelligence (AI) technologies are changing the way business is done. From improving predictive analytics across marketing, cybersecurity and risk management to powering a wide range of business decisions and actions to reduce costs and grow revenue, AI is a major engine for transformation within enterprises. IDC predicts that spending on AI systems and solutions will experience a booming cumulative annual growth rate (CAGR) of 20.1% over a four-year period from 2019-2024. AI is expected to drive tremendous growth in global productivity. Given these high expectations, companies are asking themselves how they should bring AI into their enterprise.

Think about your last online order. If you're a frequent online shopper and have created profiles for sites you visit often (*raises hand*), then you're probably familiar with customized recommendations. Based on your purchase history, location, and other factors, the website may suggest other items you might be interested in buying. And if you're on the site long enough, chatbots may appear asking if you have questions or need assistance locating something. These are just two examples of how artificial intelligence (AI) and automation have made the consumer experience easier and created ways to help businesses understand their buying patterns and what they need.

New York has always been known as the city that never sleeps. In this crisis time, this definition has never been clearer. Citizens of NYC have been subjected to the stress of Coronavirus pressures, which have hit Americans with fatigue and feelings of hopelessness as a result of our grim economic situation. NYC continues to fight back and be resilient, combating the crisis situation with innovative companies like IPsoft that provide unique AI solutions for clients. By leveraging AI solutions, we can bring America back to its former glory, and instead transform the cause of sleepless nights from recession anxiety to the exhaustion of a memorable night out in the city that never sleeps.

IT service operations teams and their leaders are in the middle of the busiest weeks of their careers right now. They're scrambling to help many first-time work-from-home employees get securely connected as ITSM systems bog down under the weight of workloads they weren't designed for. Incident queues are thousands of requests long in many companies, waiting for assignment. A quick Pareto Analysis of an Incident Management queue with trouble tickets shows that approximately 75% to 80% of the requests for service are from the top 20% of connectivity and security login issues all IT users face. Adopting an AI-based approach to Incident Deflection that seeks the best IT service resource starting with help files and videos and then progressing to an IT service agent would reduce the queue quickly.