In today’s enterprises, the potential for AIOps is massive. However, the reality is that many organizations have only scratched the surface in terms of what’s possible. In recent weeks, I’ve been writing posts that describe some key AIOps use cases, focusing on those areas that offer organizations some of the most significant near-term potential.
In recent posts, we examined how teams can leverage AIOps to perform intelligent probable cause analysis and reduce event noise and enable predictive alerts. In this post, we’ll look at how, by employing automation, teams can fully harness the power of AIOps-fueled insights, and so reap maximum rewards from their AIOps investments.
The Problem: Why is AI Proving so Difficult?
While the move to AI is definitely on, the reality is that the journey is proving to be filled with obstacles for many organizations. A recent IDC report offers some sobering stats as to how widespread these challenges can be. Through their research on more than 2,400 hundred organizations that are employing AI across their operations, they found that only 25% have established an enterprise-wide AI strategy. Further, one-quarter of organizations are seeing a 50% failure rate in their AI projects.
Looking at AI in IT Operations – AIOps – more specifically, it is clear that while AI and machine learning can yield tremendous insights, it can be a challenge for IT operations teams to act on, and fully harness, these insights. One key reason is that teams remain mired in manual tasks; too many efforts are time consuming, costly, and error prone. Exacerbating matters is the siloed nature of many IT organizations. Different teams are often employing distinct tools and workflows, which presents increasing problems as environments continue to grow more complex, dynamic, and interconnected. These disjointed, labor-intensive efforts have profound implications for teams:
- It takes too long for staff to resolve issues, putting SLA compliance at risk.
- Struggling to keep pace with existing implementations, teams are ill equipped to support innovation and strategic efforts.
Not only do these manual efforts hinder staff efficiency and organizational agility; they limit the team’s ability to act on insights from machine learning and analytics. Therefore, adopting an AIOps strategy but leaving teams to struggle with these manual efforts will diminish the benefits of AIOps investments or negate the value completely. Ultimately, to fully capitalize on the advantages of AIOps, IT teams need to address these shortcomings.
Key AIOps Capabilities
To make AIOps initiatives pay off fully for the organization, it is vital for teams to leverage AIOps’ rich, machine-learning-driven insights to power automation. Automation represents a key way to ensure that the insights delivered by AIOps ultimately are employed to maximum advantage. For example, automated remediation can be a key initiative that delivers significant near-term value. IT Ops teams can automate the triage and remediation of commonly recurring issues and tasks, such as restarting services, cleaning up temporary resources, and provisioning additional capacity. This automation can deliver significant enhancements in staff efficiency, mean-time-to-resolution metrics, and service levels.
In our earlier post, we’d outlined how AIOps can power intelligent probable cause analysis that enables the fast identification of the cause of issues. Automation represents an optimal complement to this capability, ensuring team can both find—and fix—issues fast.
In addition, automated, closed-loop processes with the service desk can be established, setting the stage to maximize automation through the entire lifecycle from event identification to ticket generation to remediation, change and incident management and ticket close. This is an excellent step towards breaking down the silos between ITOM and ITSM to drive maximum value from AI for the business.
How AIOps Helps
Through employing AIOps to establish automation, organizations are realizing a number of compelling benefits:
- Speeding remediation workflows, enabling up to 75% improvements in mean-time-to-resolution metrics.
- Reducing costs and risk by minimizing the potential for human errors.
- Offloading repetitive administrative tasks from skilled IT resources, enabling them to focus on higher value, more strategic efforts.
- Facilitating the unified, end-to-end workflows that help bridge the gaps that often exist between various groups in the IT organization, including IT operations and IT service management.
- Automating tens of thousands of event responses, and therefore saving thousands of hours of staff time.
Transamerica Life Insurance Company is an example of one leading enterprise that has done much more than scratch the surface of the potential of automation. By harnessing machine learning and automation, the financial services firm has realized significant benefits from increased productivity related to event management. In the first seven months of its implementation, the organization automatically handled 94,273 events, saving more than 9,000 hours of staff time. Further, event-driven automation has reduced the load on level-2 staff, freeing them to spend more time focusing on strategic activities. (View the Transamerica customer success page to learn more.)
AIOps and automation: better together
Within many organizations, automation is the use case that will enable breakthroughs in the realization of AIOps value. Through automation, teams will be able to harness AI-driven insights, and ensure they are leveraged and acted upon in the most comprehensive, efficient manner possible. To learn more about how our solutions are enabling customers to realize breakthrough value from AIOps, be sure to visit the TrueSight AIOps page.
These postings are my own and do not necessarily represent BMC's position, strategies, or opinion.
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