AIOps (AI for IT operations) is a strategy — not a tool, not an algorithm. Gartner has predicted the use of AIOps will surge in the years ahead, growing 500% by 2023, compared to 2018. It requires deep thinking and understanding of the role machine learning can play in an organization. It also requires focused investment over time, a prioritization of initiatives that add early business value, and an emphasis on hiring talent with the right skills.
Every great strategy requires a plan, so teams know what to execute when. But when there is no plan in place, machine learning and AIOps can turn into “shiny objects syndrome.” Throughout my experience working with tech leaders and industry professionals, I have seen many fall into this trap. Artificial intelligence, machine learning and AIOps are almost buzzwords at this point.
To truly reap the benefits of these advanced innovations, organizations have to develop a strategic investment plan that involves foundational reasons for adoption to secure organizational buy-in. They also need a deployment strategy followed by an ongoing evaluation plan.
Here’s a closer look at the obstacles to a solid AIOps adoption strategy and some key factors that must be considered to ensure long-term success with a complete AIOps implementation:
Identifying the Common Challenges to AIOps
Many IT teams face a slew of challenges that can inhibit their ability to fully adopt and leverage AIOps.
First, there’s the issue of reactive vs. proactive action. Many businesses often operate in a reactive state, only responding to incidents as they arise. For IT teams, this includes data outliers, false alarms and notification noise, which spreads focus thin and makes the development of a proactive prevention strategy difficult to achieve.
Internal silos also are common across a tech organization. This creates a lack of visibility, limited data connection, tool sprawl, security gaps and numerous other pitfalls. A lack of cross-team collaboration creates struggles to identify incidents and proactively solve problems.
Finally, the siloed and reactive structure we commonly see today makes effectively dealing with data difficult, particularly the overwhelming amount produced during times of rapid digital transformation like the past year. Siloed teams struggle to make sense of complex data sets since there’s limited transparency and collaboration.
Many business leaders often think of AIOps platforms as a one-size-fits-all tool that can work miracles and solve these problems. However, AIOps platforms are only a part of an overall strategy and should be adopted as such.
Enacting a Strategy Behind the Platform
Adopting AIOps doesn’t just affect IT teams. It requires a multiyear investment strategy across an entire business. If executed properly, AIOps can be a game changer for improving customer experience, proactive response, organizational unification and overall business outcomes.
But before embarking on this journey, IT leaders must evaluate all of these pain points and identify which are affecting their organization the most. Then they must reexamine all organizational tools, processes and the entire IT infrastructure to ensure that effective automation, data collection and service management are in place.
If the proper steps are not followed, an AIOps implementation can create more problems. If this occurs, organizational buy-in is often lost as employees see more headaches and inefficiencies than solutions.
Ensuring Success Long Term
Before throwing an AIOps platform into your tool stack, outline a deployment strategy. Start by ensuring at least the baseline levels of data management and automation are in place throughout the business. Work to solve any pain points that exist. For example, if organizational silos are in place, rewire your culture to promote cross-team collaboration.
Finally, work with a trusted advisor who can help establish a long-term vision for your AIOps implementation. Your strategic advisory will partner with you to help keep the process on track and perform post-implementation audits to make adjustments as needed. These teams also can help smooth out the machine learning and automation processes, which helps the entire organization realize the full potential of AIOps.
Adopting and effectively implementing AIOps doesn’t occur overnight. Advanced technology of this nature must have a strategy behind it. It must be adopted over time, and carefully, to ensure success and prevent the creation of more problems.
Working with a trusted partner is a great place to start. Helping enterprise IT teams understand how to truly benefit from AIOps and view it as a strategy rather than a tool can serve as a critical competitive advantage.
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