The Future of
Generative AI for ITOps

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 一 delivering a differentiated experience for enterprise leaders to transform ITOps or ITSM.

What is ITOps?

Just by looking at its name, “ITOps,” it is easy to understand that ITOps is about Information technology infrastructure, which relies on scalable processes and services to remain functional and profitable for organizations.

Based on the ITIL or Information Technology Integrated Library framework, ITOps is the management of application delivery, IT service delivery, and operational management of IT assets and tools.

To drive desired business outcomes, ITOps constantly seek support from an IT operations manager and their skilled tech operators in addressing IT outages and keeping IT infrastructure running smoothly with day-to-day monitoring and maintenance.

In addition to traditional IT assets such as hardware, servers, and networks, ITOps facilitates end-to-end cloud infrastructure management. So, modern-day ITOps must also combine artificial intelligence and machine learning properties to be recognized as AIOps.

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The transformation of ITOps into AIOps

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.

Gartner further describes AIOps as an Artificial Intelligence for IT Operations that streamlines and automates IT process workflows.

Artificial Intelligence for ITOps improves IT performance in several ways:

AI for IT Ops
  • Threat alerts in advance
  • Real-time MTTR using automated workflows
  • Early detection of anomalies through data analytics
  • Reduction of impacts and issues
  • Granular-level analysis for improvement of future responses

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.

What is Generative AI in ITOps?

AI for IT Ops

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.

AI for IT Ops

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.

Why Generative AI for ITOps?

AI for IT Ops

Moving forward from what ITOps lacks, it has many negative implications your service desks can experience. However, Generative AI can address these challenges.

1. Advanced analytics for incidents

Challenges: AIOps or ITOps provide an analytics dashboard that needs regular monitoring to take necessary steps in advance. The lack of resources and time for data analytics expertise can affect the work of crisis preparedness.

GenAI solution: GenAI 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.

2. Root cause analysis of incidents

Challenges: AIOps or ITOps derive data from platforms in silos. Although AIOps can automate some parts of tasks for harnessing data, it can miss critical data to narrow down to a granular picture. Incidents can be repetitive, such as data center breakdown, printer failure, a blank computer screen, etc.

GenAI solution: Your IT operations team can leverage Generative AI's extensive potential in processing vast amounts of data using its LLMs and cutting through noise. As a result, GenAI can produce intent and context-based recommendations for an upcoming outage or when you have an outage by working with activity logs, events, and incident traces.

Hence, Generative AI reduces the probability of continuous checks for data analytics, instead delivering predictive notifications for root-cause analysis of performance issues. This lets you anticipate future incidents and better prepare to prevent IT risks.

3. Generation of revenue streams

Challenges: 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.

GenAI solution: 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.

AI for IT Ops

Accelerating ITOps performance with Generative AI

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.

Predictive capabilities

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.

Proactive approach

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.

Reduction of false positives

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.

After researching 5 different chatbot solutions, we decided to go ahead with Workativ for its’ easy integration with systems that we use internally at GoTo.

Gabriel Grecco

Global Manager - IT Service Desk

Read GoTo Case Study →

Benefits of Generative AI for ITOps

Generative AI for ITOps offers several benefits and use cases,

 benefits -generative-ai

Conversational AI

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.

Automation of manual tasks

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.

  • Not only can Generative AI learn through historical events, but the AI-based technology can use script event prediction to predict the next sequence of events
  • Generative AI reduces noise and works on the correlated data to help escalate remediation workflows, such as sending out IT incident alerts, raising the incident tickets for remediation of the IT incident

Reducing MTTR

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

  • How many times does the same incident occur
  • How many logs are reported in the ITSM platform
  • What was the troubleshooting area last time
  • What are the present glitches

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, 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.

How Workativ helps you leverage Generative AI for ITOps

Workativ helps youautomate 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.

Enhanced ITOps performance with Generative AI

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.

What’s ahead for Generative AI in the ITOps domain?

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.

FAQs

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.

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Deepa Majumder

Content Writer

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.

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