As IT operations teams face increasing pressure to enable digital transformation and more, generative AI is a key enabling technology that can help and improve outcomes.
At every organization, the digital landscape is evolving rapidly, presenting IT operations teams with unique challenges.
Teams require innovative approaches to manage vast amounts of data and complex infrastructure as well as the need for real-time decisions. Artificial intelligence, including more recent advances in generative AI, is becoming increasingly important as organizations look to modernize how IT operates.
As a result, organizations are turning to AI to automate tasks—from code development to incident response—to reduce manual effort and human error, and to boost workforce efficiency.
At the same time, challenges remain as organizations aim to become more automated. Some of these challenges involve basic tasks—such as data collection. Others involve introducing new threats as AI becomes more integrated into IT systems as a whole.
In this article, we explore recent survey data from Enterprise Strategy Group (ESG), sponsored by Dynatrace, on how organizations approach IT automation, as well as the benefits and challenges they encounter as they adopt it.
Unleashing automation and AI
According to recent ESG research, 85% of organizations are using, planning to use, or considering artificial intelligence, such as generative, causal, and predictive AI, in many of their functional areas, including IT operations. One could say that AI has moved beyond the “hype cycle” phase and entered a new phase of implementation.
A survey of 360 IT professionals at organizations in the U.S. and Canada involved with observability, IT service management, and IT automation technologies offers insight into the current status and future of AI in IT operations.
Three kinds of AI
The ESG report “Generative AI in IT Operations: Fueling the Next Wave of Modernization,” defines causal, generative, and predictive AI as follows:
Causal AI: A type of AI that analyzes real-time, context-rich data and causal dependencies to provide precise answers for issue prevention, deterministic root-cause analysis, and automated risk remediation.
Generative AI: A type of AI that uses an algorithm trained on large amounts of data collected from diverse sources to generate various types of content, including text, images, audio, and synthetic data. While ChatGPT and Google Bard are well-known examples of generative AI tools, several organizations are now utilizing proprietary, open source, or self-made generative AI large language models to help improve productivity, efficiency, and customer experiences.
Predictive AI: A type of AI that analyzes patterns, trends, and data using statistical algorithms and other advanced machine learning techniques to anticipate future behavior in systems.
AI in production
Sixty percent of respondents indicate generative AI is in production, 54% indicate causal AI is in production, and 53% indicate predictive AI is in production.
Generative AI awareness is most widespread and has an early adoption lead given the popularity of ChatGPT, Gemini, and similar tools on the consumer side, as well as the proliferation of generative AI-enabled natural language querying interfaces. As a result, many organizations are adopting it into production environments.
The heavy burden of collecting and correlating logs
Forty-five percent of respondents find collecting and correlating logs as burdensome or complex.
But organizations still wrestle with even the basics of log management. While respondents have made progress in terms of instrumentation,
This suggests there is ample opportunity for organizations to use a log management and analytics platform such as Dynatrace to ingest and analyze log data. Dynatrace Grail enables organizations to ingest data without predefining schema. Grail, alongside Dynatrace Davis AI, enables organizations to move beyond simple event correlation and to identify the root cause of problems in their applications and infrastructure.
The most likely beneficiaries of generative AI
The top three areas most likely to benefit from generative AI are IT operations (72%), cybersecurity (47%), and application development or DevOps (30%).
Organizations are turning to AI to automate manual tasks and see immediate benefits in IT operations, cybersecurity, and application development or DevOps. For IT operations, this means streamlining resource allocation, automating tasks, and enhancing incident response. For cybersecurity, it means detecting anomalies, strengthening defenses, and evolving alongside emerging threats. And for DevOps, it means accelerating DevOps processes, improving agility, and speeding time to market.
Security remains top of mind
Twenty-seven percent of respondents indicated security vulnerability is a top concern with integrating AI into IT operations.
Traditional and new challenges are emerging when integrating AI into IT operations. Therefore, it’s no surprise that 27% of those surveyed mention security vulnerability as a top concern when it comes to integrating AI into IT operations.
How generative AI improves IT operations metrics
Thirty-four percent of respondents whose organizations use or plan to use generative AI and subsequently measure or plan to measure its value indicate a 31% to 50% improvement in IT operations metrics from generative AI integration in 24 months.
The value of AI in operational acceleration carries tangible value above and beyond incremental features. This acceleration translates to a better return on assets, but it can also increase greenhouse gas emissions, complicating organizations’ ability to sustainably meet acceleration objectives.
To dive deeper into this research, download the free ebook, “Generative AI in IT Operations: Fueling the Next Wave of Modernization.”
Source: Enterprise Strategy Group, a division of TechTarget, Inc. Research Report, Generative AI in IT Operations: Fueling the Next Wave of Modernization, February 2024.
Looking for answers?
Start a new discussion or ask for help in our Q&A forum.
Go to forum