- A multinational telecommunications company in Europe standardized on BMC’s Helix Chatbot to serve over 120,000 employees across all divisions with self-help articles and catalog items and support AI-based automated resolutions.
- Enterprises that excel at DevOps have a higher probability of succeeding with AI-based IT Service Management and Operations Management initiatives, according to Gartner and also reflected in the achievements of this telecom company, which are a good example of what BMC describes as an Autonomous Digital Enterprise (ADE).
- By combining DevOps expertise, a centralized knowledge base, the BMC Helix portfolio and Autonomous Digital Enterprise (ADE) framework to guide its AI strategy, the company reduces Mean Time To Resolution (MTTR) of service requests, leading to vastly improved response times for employees needing IT assistance.
This multinational telecommunications company’s success in adopting an AI-based approach to upgrading and consolidating its ITSM and ITOM strategy is an excellent case study of how a large-scale enterprise can succeed in transforming itself and elevate service operations management.
Lessons Learned in Telecom: Change IT’s Operating Model
AI-based insights are the catalyst IT needs to excel at serving internal customers and adapting to the fluid IT environment the pandemic continues to create. IT Service Management (ITSM) and Operations Management (ITOM) need to be part of a unified framework to support the virtual-first world of IT services today. AI and machine learning need to provide guardrails that keep operating models focused on how customers define success.
The following illustration, from the recent BMC report, ADE Enterprise 2025, compares traditional IT operating models versus an operating model based on centralized IT centers of excellence that better fit the virtual-first requirements of supporting employees so they can, in turn, excel at creating and growing digital-first sales channels. In the Enterprise 2025 model, AI-based insights serve multiple digital business units while also providing Centers of Excellence with much-needed real-time performance data.
The goal is to move away from the past traditional IT operating models to the more adaptive Enterprise 2025 IT operating model shown on the right. With great AI insights, line-of-business leaders can move more quickly and with greater agility to create new digital businesses, support all virtual employees and continue adapting to the pandemic’s quickly changing conditions. The following figure is from the recent BMC Report titled ADE Enterprise 2025.
Why Now Is The Time To Improve ITSM and ITOM With AI
The telecom company successfully transitioned from between 40 to 50 individual portals supporting IT users to just one and that single portal is now receiving more than six million views a year. One of its product managers explained how the company chose cognitive service management as the end goal of its Autonomous Digital Enterprise journey. Starting with centralizing its knowledge base, the company next explored how they could use AI to execute on insights automatically. Having ITSM and ITOM systems that could continually learn turned out to be pivotal to improving ITSM performance and metrics, including MTTR.
10 Ways AI Can Elevate Your Service and Operations Management
Based on the insights gained from this telecom case study, the following are ten ways AI can elevate your Service and Operations Management:
- AI delivers the much-needed insights to transition any ITSM and ITOM platform away from focusing exclusively on IT to include users and all IT customers. Left unchecked, ITSM platforms and the ITOM systems supporting them become disconnected from internal users and fail to deliver value to all IT customers, including partners and suppliers. AI-based insights can turn the situation around by choosing to measure customer outcomes first and use them to guide future development.
- AI-powered Virtual Chatbots are proving invaluable in responding to first-level requests for assistance. Choosing to implement Virtual Chatbots supported by a unified knowledge base reduces MTTR by providing articles, service catalog items and support. The company is also deploying its Chatbots on an omnichannel basis across mobile devices using Skype, Teams and Slack. They have also gone so far as to automate resolutions entirely through the Chatbot.
- Use AI to integrate ITSM and ITOM to deliver overall health insight across the IT landscape. By using event correlation and Discovery data to understand service impacts, the company is driving greater operational efficiencies and cost savings and successfully using AI to improve its ITSM and ITOM systems’ operational stability.
- Use AI to gain greater control over IT security, service performance and application monitoring, all in the same knowledge base. Augmenting ITSM and ITOM with AI also helps to gain a unified view of all IT activity, creating a more accurate service and application monitoring platform.
- Understanding root cause analysis of the more advanced IT tickets and see if there is a systemic problem in the configuration of systems, servers, or applications. Using AI to troubleshoot why the specific combination of servers, operating systems, applications and connectivity options create network delays or, worse, applications not working is an advanced use case IT departments are pursuing today. LogicMonitor’s approach to using AI and machine learning is proving very useful at troubleshooting performing bottlenecks across complex system configurations.
- Using AI to understand what features of a given portal are most and least used and why it is invaluable for improving the UX of an ITSM and ITOM system. Using AI to analyze clickstream data, generate heat maps and learn how users interact with a portal is invaluable data that can improve navigation and the UX of each screen. The organization’s goal is to deliver an “Amazon-like” user experience, drive new capabilities onto the platform and achieve more significant cost savings by lowering service costs.
- Improving the closure rates non-joint ITOM and ITSM trouble tickets and deescalated events by gaining a 360-degree view of what’s causing an issue. AI can provide a complete view of why shared ITOM and ITSM trouble tickets prove problematic to close out, even with causal data. AI can help decipher why specific business applications that cause joint trouble tickets fail and work to solve the issue and get users productive again. One of the most valuable insights from the telecom company is how effective this strategy is across a large-scale enterprise.
- Helping customer service agents prioritize their workloads to have more time for training to take on the more challenging IT problems and grow their careers. Front-line customer service agents use AI-based applications to delegate user inquiries that can be handled through automated workflows. It’s common to find customer service managers providing agents with more flexibility in designing their jobs.
- Using AI to expedite ticket routing by interpreting unstructured text then routing the issue to the best possible technician saves time and improves user satisfaction. Combining multiple AI technologies, including Natural Language Processing (NLP) and random forest-based analysis to identify the best possible technician, is automating assigning trouble tickets across IT service organizations.
- Updating a company’s knowledgebase using AI to define the best possible new categories, then adding new solution sets to revised taxonomy is the future of knowledge management in ITSM. An advanced use case of AI in ITSM is creating and fine-tuning the taxonomies that define a knowledgebase. Using AI to accomplish this can save hundreds of hours a year and ensures the latest knowledge created from solving problems gets captured and retained where it can be used.
