ITOps always need to have end-to-end visibility across their services and networks. But, with data analysis being loosely coordinated and siloed, a reactive approach makes ITOps more awful than powerful. AIOps or artificial intelligence IT operations is thus more mainstream rather than just a choice today.
AIOps translates ITOps into a more agile framework to optimize observability by leveraging data-driven decisions. This would help adopt a proactive approach in IT operations and steadily minimize downtime.
That probably drives ITOps investments largely in AI technologies, including machine learning and deep learning 一 and now in Generative AI capabilities.
Generative AI is estimated to reach $42.6 billion in 2023 due to its rapidly expanding capabilities for enterprise use cases in sales automation and productivity gains. It is further confirmed that the Generative AI market will grow at a CAGR of 32% to reach 98 billion by 2026.
Generative AI presents significant enterprise opportunities to reshape ITOps with workflow automation, productivity gains, removal of mundane tasks, and cost reduction. From DevOps to cybersecurity solutions, Generative AI unleashes the power of large language models to complement AIOps and reduce MTTR in real-time delivering a differentiated experience for enterprise leaders to transform ITOps or ITSM.
AIOps, an acronym for artificial intelligence for IT operations, refers to the application of artificial intelligence and machine learning to IT operations.
Gartner first coined this term, stating that “AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.”
As the world moves towards digital transformation, the IT environment is becoming increasingly complex. AIOps capabilities help IT teams navigate these complexities, detect issues faster, reduce MTTR, and improve user experience.
By integrating principles from the ITIL (Information Technology Infrastructure Library) framework, AIOps supports effective IT service delivery, application delivery, and tool and asset management.
With organizations moving to a hybrid environment, traditional IT operations are less productive in facilitating tasks. On the other hand, with the rise of serverless architectures and microservice-based applications, traditional IT infrastructure falls short of adequate capabilities to support the complex needs of today's IT service delivery. The key reason is that ITOps limits the delivery of required data analytics and insights to optimize services and drive real-time business outcomes.
AI or more sophisticated machine learning data models become necessary to anticipate events and prevent their impact before they become a massive worst-case scenario for enterprise leaders.
Thus, ITOps slowly assimilates the power of artificial intelligence to evolve into powerful ITOps, known as AIOps. By combining artificial intelligence with ITOps, AIOps allows real-time visibility into anomalies and prevents IT failure.
In addition to all the advances, Generative AI can make AIOps more advanced and powerful, transforming IT operations. As a result, enterprise leaders can improve observability and automate the most mundane workflows, including alerting, correlating events, and detecting anomalies. Generative AI brings cost-effective solutions for enterprise leaders and other stakeholders while improving the accuracy rate of incident predictions.
Generative AI in ITOps is the application of Generative AI to ITOps to gain the powerful capability of advanced data processing of unstructured and structured data, which helps improve crisis communications, create new data for faster event analysis, mitigate threats in real-time, and improve future services.
Simply put, Generative AI is a sophisticated version of AI. It uses NLG, NLU, and NLP to create new data or content using large language models or massive datasets on which it trains.
Unlike AI, Generative AI leverages large language models or massive corpora of datasets such as Wikipedia, the World of Knowledge, the Internet, ebooks, company websites, etc., to understand patterns in data and use these patterns to create new content or data.
Generative AI models possess human-like brains called neural networks, which unleash human-like characteristics that help enhance deep understanding of NLP queries. They continuously self-learn from past and real-time scenarios and evolve to generate responses with similar properties in query inputs.
Traditional AIOps solutions have made significant strides in improving IT operations for long, but they have shown their limitations in handling the modern-day IT environment. Generative AI, the subset of AI, has shown immense potential in enhancing AIOps:
Since the pandemic, most enterprises worldwide have started working in a hybrid setup, with hundreds of applications running on the cloud and in on-premise data centers.
The analytics dashboard provided by traditional ITSM tools to monitor ITOps requires constant oversight to take preventive measures in advance. This can be tough for IT teams when they lack expertise, resources, or time to analyze all this data.
This is why many enterprises are adopting Generative AI for ITOps to address this challenge. Generative AI applies self-learning techniques or unsupervised learning abilities to adapt to unexpected scenarios, capture data from interactions with service desks, build predictive analytics to send predictive notifications, and urge for necessary actions.
This approach makes room for better crisis preparedness and helps IT teams save organizational costs by addressing problems before they escalate into severe outages.
Identifying the root cause of an outage is one of the most challenging tasks for IT teams, especially for recurring incidents such as data center breakdowns, printer failures, and blank computer screens.
Traditional AIOps tools sure help in automating some tasks, but they miss critical insights that will help narrow down the cause because they pull data separately from each platform for each incident. This gives IT teams over-the-surface insights.
Generative AI leverages its machine learning and LLM capabilities to analyze and correlate incidents from different sources, such as activity logs, event data, and incident traces, to pinpoint the incident's inception instead of just showing symptoms. With this, IT teams can get a complete picture of the incidents.
Additionally, generative AI can produce intent—and context-based recommendations for every incident by working with activity logs, events, and incident traces. This helps IT teams respond faster to alerts and make smart decisions about resolution.
Traditional AIOps tools require constant manual intervention to detect, analyze, and resolve incidents. This delays resolution times and disrupts an organization’s productivity.
Generative AI tackles this challenge by automating the whole incident management process. Once the cause of an incident is identified, the tool can autonomously resolve recurring incident tickets, such as printer failure and password reset, through automated workflows.
For more complex incidents, the Gen AIOps tool can route issues to the support agent with the expertise to handle them. This helps businesses take the right actions with minimal manual intervention.
Today’s IT infrastructure is mostly IoT-based. The Internet of Things connectivity makes it easier for Generative AI to pull up enormous amounts of data and make accurate predictions by performing non-linear, NLP, or deep learning analysis.
Adding to its data analysis capabilities, Generative AI can also improve post-mortem analysis using unstructured data derived from chat files or ITSM platforms. Using predictive intelligence, Generative AI can ensure enterprise-wide performance uptime for IT infrastructure and keep ITOps health in check and resilient.
Generative AI offers predictive intelligence with recommended actions by IT event administrators. Unlike a traditional ITOps system, AIOps built with Generative AI properties can alert people much before the incident occurs. With enough remediation time in hand, it is easier for your IT people to look at incidents and prevent their impact.
Instead of depending on the break-and-fix approach, Generative AI allows the IT operations teams to analyze event logs and achieve high accuracy for future predictions into incidents. As a result, IT operations teams are better prepared to allocate resources to handle the situation in the near future. Also, with insights into more repeated issues, your IT operations team can offer an elevated experience to employees and facilitate work progress.
Generative AI processes unstructured and structured data across its large language model, making anomaly detection accurate. As a result, there are fewer chances of false positives that would otherwise result in information fatigue and panic for IT operations managers.
However, there could be a different case scenario with a traditional system. The next time an incident happens, IT people will treat it as a false alarm and take it lightly, which may incur huge damage to your IT assets.
Generative AI reduces false positives and offers a high accuracy rate with anomaly detection.
Implementing Gen AI for ITOps can improve IT efficiency by as much as 28% to 50%, a study revealed. A generative AIOps solution automates tasks, predicts issues, provides actionable insights, reduces downtime, saves costs, and improves overall performance.
Let’s discuss the benefits in detail:
Integrating conversational AI chatbots with the ITSM platforms can speed up the incident response and help remediate issues in less time. Generative AI reduces the time to develop a conversational AI chatbot by generating appropriate dialog flows and providing a large language model, which further reduces the time for model training and deployment. With fast time-to-market, chatbots get ready to provide automated enterprise support in the ITOps space.
Anomaly detection requires a granular level of visualization. With a traditional system, it is a huge manual work to comb through information from disparate sources from the web, social channels, or millions of log data in the ITSM platforms. Working too much to derive data is labor-intensive, which delays response time.
On the other side, things like IT incident correlation and contextual analysis of event behavior and patterns need extensive expertise to diagnose a problem across applications, systems, or services.
Instead of spending much time finding and correlating information manually, Generative AI can reduce the steps in event correlation and speed up the root cause analysis.
IT event correlation improves uptime and IT infrastructure performance. In an enterprise where events are rampant across operating systems, applications, databases, networks, and servers, event correlation ensures continuous monitoring of IT assets and threat detection. But the process is not easy when done manually, increasing the chance of costly mistakes.
Say a certain application crashes frequently. As manual steps of event correlation, you must combine data from different sources, such as collaboration channels, self-service portals, and event logs in the ITSM platforms, to uncover the root cause analysis. You may seek questions such as
It takes a lot of time to combine huge data points and find root cause analysis. With the power of large language models, Generative AI reduces these manual steps, increases the system’s intelligence, and offers real-time remedial suggestions for IT operations managers. As a result, ITOps reduces MTTR from hours to minutes.
Also, employees can easily handle the most mundane or common phenomenon of IT issues easily without human intervention.
For example, IT incidents such as printer issues or Wi-Fi connectivity issues can be auto-resolved at scale. As Generative AI improves Enterprise Knowledge Search for FAQ-related responses or knowledge articles, users can instantly find information and auto-resolve issues through conversational AI.
ITOps and AIOps can trigger unexpected disruption to your IT assets due to the lack of enhanced predictive threat alerts. This can damage your revenue streams. The longer the downtime, the more revenue will be lost.
Effective data generation capability improves visibility into future incidents. This makes allocating and escalating appropriate resources easy and mitigates incidents or IT support issues in real-time. As GenAI helps optimize resources, it ultimately saves costs and boosts efficiency.
Workativ helps you automate your ITOps requests and provides the self-service capability for real-time resolutions.
Workativ integrates Generative AI with its conversational AI chatbots to enhance IT support functionalities.
With app workflow automation for various ITSM platforms, users can get real-time responses to solve ITOps issues, which are most mundane and repetitive, such as password resets, account unlocks, device provisioning, software installs, etc.
Using Workativ Hybrid NLU, our conversational chatbot tries to deliver accurate responses based on natural language queries. Workativ ensures every ITOps query gets an accurate response via conversational AI or by indexing information across a large language model.
By combining both conversational AI and Generative AI in its chatbot platform for ITSM operations, Workativ offers great opportunities for enterprise leaders to leverage app workflow automation and transform workplace support for ITOps.
It would be unfair to say that Generative AI will not make mistakes in the ITOps space. Due to unsupervised learning, the probability of providing hallucinated responses is high, so one cannot blindly believe Generative AI and its ability to provide just any response for prompt input.
Though there is a risk, this captivating AI technology needs time to evolve and learn from datasets and continuous interactions to improve performance.
We need to ensure the data integrity on which the large language model is trained.
So, enterprise leaders need to harness data that is devoid of misinformation or bias to enable model training. Also, LLMs need continuous monitoring to ensure the veracity of responses and avoid unexpected results.
On the ITOps side, Generative AI holds great promise to transform the existing challenges by augmenting response delivery. As a result, IT operations can gain enhanced performance with ITSM conversational chatbots improving auto-resolution capability and faster remediation of incidents in real-time.
Are you interested in implementing workplace support automation for your ITOps with the power of Generative AI and conversational AI? Request a demo today.
1. What is the significance of AIOps in modern IT operations management, and how does it differ from traditional ITOps?
AIOps, or Artificial Intelligence for IT operations, brings a paradigm shift to IT operations management by leveraging artificial intelligence, including ML and deep learning. Unlike traditional ITOps that use siloed data analysis, AIOps can improve data-driven decisions to help optimize observability and help ITOps transition from a proactive ecosystem to a reactive approach. This way, AIOps help minimize downtime and improve overall efficiency.
2. How does Generative AI contribute to the evolution of AIOps and its impact on IT operations management?
Generative AI is transformative for AIOps, extending the current state of automation for artificial intelligence across ITOps. By leveraging large language models, Generative AI makes it easy for IT leaders to reduce mundane ITOps tasks by implementing automated workflows to help enhance productivity across various IT operations, such as DevOps and cybersecurity. Additionally, Generative AI augments AIOps by reducing MTTR or mean time to resolution in real-time. The overall implications of GenAI for AIOps are to deliver differentiated experiences for IT leaders who aim to transform IT service management.
3. What are the key benefits and use cases of Generative AI for ITOps, and how does it accelerate IT performance?
Generative AI unlocks use cases and amazing benefits for ITOps. By combining conversational AI into Generative AI interfaces, IT leaders can build automated workflows to help reduce false positives, speed up anomaly detection, accelerate root cause analysis, and allocate the right resources for IT operations teams to mitigate incidents in real-time.
Using Generative AI, IT leaders can increase efficiency in IT operations tasks, reduce manual work, and improve IT performance and uptime.
4. How does Workativ leverage Generative AI for ITOps automation, and what opportunities does it offer for workplace support transformation?
Workativ offers ChatGPT-like responses through Generative AI integration with its conversational AI platform. The power of large language models allows Workativ users to transform IT operations by using app workflows via integrations with ITSM platforms.
This allows everyone to use a conversational AI interface to simplify IT responses, speed up password resets, software installation, and device provisioning, improving workplace support and transforming IT operations management.
5. What challenges and considerations should enterprise leaders be aware of when implementing Generative AI for ITOps, and how can these be addressed effectively?
Generative AI nuances can impact business outcomes, encompassing bias or misinformation during response delivery.
Unsupervised learning results in hallucinations and wrongful answers.
Industry leaders must implement stringent human oversight during model training to ensure that the data is safe to use and does not contain misinformation, which can create hallucinations and bias. Besides, Generative AI should be monitored continually to flag potential risks and help improve the benefits for ITOps.
Deepa Majumder is a writer who nails the art of crafting bespoke thought leadership articles to help business leaders tap into rich insights in their journey of organization-wide digital transformation. Over the years, she has dedicatedly engaged herself in the process of continuous learning and development across business continuity management and organizational resilience.
Her pieces intricately highlight the best ways to transform employee and customer experience. When not writing, she spends time on leisure activities.