ScienceLogic’s study reveals AI/ML challenges in IT operations, highlighting the need for better monitoring, data management, and a clear path to GenAI.
ScienceLogic, a leader in automated IT operations and observability, today unveiled the results of comprehensive enterprise IT research with the publication of its whitepaper, “The Future of AI in IT Operations: Benefits and Challenges.” Commissioned by ScienceLogic and conducted by Vanson Bourne, the study uncovers the driving factors behind the challenges to effective AI/ML deployments that create the data and observability infrastructure necessary to support generative AI (GenAI) capabilities.
The increasing complexity of IT environments and data proliferation is outpacing human capacity, necessitating a shift towards automated, intelligent capabilities that enhance visibility, streamline issue identification, and accelerate resolution times. This automation allows IT teams to focus on delivering cutting-edge business services in a competitive landscape while paving the way for GenAI implementation. These advanced AI systems provide context-aware insights and actionable recommendations, enabling proactive issue prevention and resource optimization. However, effective GenAI deployment relies on leveraging traditional AI/ML for IT operations (AIOps), forming a foundation for more advanced AI-driven innovations.
Also Read: Explained: Hybrid AI
Key findings of “The Future of AI in IT Operations: Benefits and Challenges” include:
Effective IT monitoring, a foundational component of AIOps, remains a challenge across organizations.
- 50% of organizations use multiple, disparate tools to monitor resources, resulting in data silos, longer incident response times, and a fragmented user experience.
- Despite monitoring a large range of IT systems and services, 47% of surveyed organizations cannot map all their on-premises, cloud, and edge devices into a single business view.
- 39% of organizations prioritize consolidating IT monitoring tools as creating a consolidated monitoring environment becomes a key strategic focus.
Organizations need comprehensive observability and clear data management to automate using AI/ML.
- 38% cite the inability to monitor all IT resources as a barrier to AIOps adoption, highlighting the importance of a holistic IT estate view for effective AI implementation.
- 39% struggle to automate complex repair workflows due to a lack of critical context, exacerbating visibility challenges across the IT estate.
- 50% acknowledge security concerns as a barrier to AIOps adoption, which can be addressed through proper data management and governance policies.
Organizations recognize the benefits of GenAI yet need help implementing the infrastructure necessary to deploy it.
- 99.7% of organizations recognize generative AI/ML’s potential to address IT monitoring, alerting, and response challenges, yet only 45% actively explore its implementation.
- 45% struggle to maintain up-to-date GenAI knowledge bases, while 40% face challenges ensuring database quality, likely due to incomplete IT estate monitoring.
Also Read: Explained: Semi-Supervised Learning
“This research reveals key barriers for our customer and partner ecosystem in their AI adoption and implementation journey,” said Tina McNulty, CMO of ScienceLogic. “Understanding these obstacles allows us to guide them through AI implementation stages as we progress towards Autonomic IT.”
“Implementing and integrating AI solutions is a challenge for most businesses, regardless of industry,” said Sarah Thorp, Head of Research of Vanson Bourne. “We’re thrilled to have supported ScienceLogic in exploring this territory – through the recent research program – identifying the power and potential of Autonomic IT to shape future autonomous business practices.”
