Getting the most value out of current resources is just one driver behind the push for digital transformation. However, the promise of AIOps is more than just diverting simple, repetitive tasks to automation. It’s taking advantage of machine learning to anticipate – and hopefully prevent – issues before they happen.
Smart companies can leverage these gains to accelerate innovation and gain a competitive edge. AIOps has the potential to:
- Increase operational efficiency by automating common processes
- Expedite issue resolution by empowering robust self-service tools
- Forecast and predict challenges using pattern recognition and machine learning
The Knowledge 2021 virtual conference includes several sessions outlining ways different companies – including ServiceNow – are applying the strategic approach of AIOps to accelerate the pace of innovation across the enterprise. These sessions can help business and IT leaders build a roadmap to integrating the principles of AIOps into their organizations.
In the session Delivering on the promise of predictive AIOps using ITOM, ServiceNow is acting as their own case study. In fact, Thangavel Viswam and Abhishek Gupta share a few different internal case studies to show their efforts to integrate AI, machine learning, and Loom Systems technology with the Now Platform. Their lessons learned can help you implement these practices to start anticipating and preventing issues, improving productivity and reducing MTTR.
In “IT Workflows Keynote: Transforming delivery from traditional to digital native” Pablo Stern of ServiceNow introduces the idea of the Digital Enterprise Fabric. Business is moving from traditional on-premise to the cloud. They’re adopting DevOps and moving from waterfall to continuous releases. Each generation has different expectations for software delivery, and companies are overwhelmed.
The Digital Enterprise Fabric weaves together the threads of data, process and tools into one centralized digital command center. This enables better collaboration and empowers faster, better decision-making when things don’t go as planned. Which means organizations can iterate faster, predict problems before they happen, and recover rapidly when issues are unavoidable.
The session “Predictive AIOPs and the game changing benefits of AI-Powered Service Operations” addresses the avalanche of incidents IT teams are dealing with as reliance on digital services continue to grow. And it’s only going to get worse. Some predict the volume of tickets could increase by 10 or 20 times in coming years. People don’t scale at that rate, and that’s where AI-powered service operations can be a true game-changer. ServiceNow’s ITOM Predictive AIOps capabilities support detecting issues early, so you can fix them before they impact users.
Want more detail on where AI-powered service operations is heading? Then we suggest you check out “The emergence of AI-powered service operations.” Maxim Aronin, who leads the global ITx Emerging Solutions team at ServiceNow Companies talks about how AI can help improve customer experiences through Virtual Agents, Agent Assist and AI search. On the ITOM side, AI and machine learning driven workflows can help with service mapping, event and alert correlation, and health log analytics to suppress noise and get ahead of issues.
Drop into this session to get tips on creating strategic priorities with an “AI/ML first” mindset, and see how to pick the right use cases to automate.
Are You Ready to Embrace AI and Machine Learning?
As the speed of technology continues to accelerate, positioning your organization to take advantage of machine learning, automation, and predictive analytics may be the critical decision that separates category leaders from also-rans.