Challenges in Adopting AIOps and Generative AI

Adopting AIOps (Artificial Intelligence for IT Operations) and Generative AI (GenAI) represents a significant transformation for organizations, offering the potential to enhance efficiency, accelerate problem resolution, and support data-informed decision-making. However, the journey towards AIOps and GenAI adoption is often complicated by several challenges. This discussion will explore some of the primary obstacles that organizations face, including organizational barriers, the importance of change management and strategy, and the skills gaps within IT teams.

Organizational Barriers to AIOps and GenAI Adoption

Adopting AIOps and GenAI requires significant changes to processes and organizational culture. One major obstacle is the prevalence of data and team silos, which hinder the integrated approach necessary for these technologies to deliver full value. To successfully implement AIOps and GenAI, organizations need to foster cross-functional collaboration and knowledge sharing across IT, development, and business units.

Here’s how to overcome these barriers:

• Create cross-functional teams: Bring together IT, development, and business stakeholders to break down silos and improve collaboration.
• Unify and govern data: Implement tools and processes to integrate data across departments. This could start with simple solutions like a shared drive for collecting knowledge base documents.
• Establish collaborative workflows: Promote regular communication and shared accountability through channels like Slack.
• Provide comprehensive training: Enhance understanding and reduce resistance to AI adoption through accessible training programs.
• Start with small-scale projects: Demonstrate measurable value with targeted initiatives, such as AI-powered sales tools or process automation.
• Share AI insights and best practices: Organize forums and workshops to disseminate knowledge and encourage adoption.
• Designate AI advocates: Empower individuals within teams to champion AI adoption and guide implementation.
• Link AI to business objectives: Align AI initiatives with clear business goals to ensure measurable results and strategic alignment.

By taking these steps, organizations can effectively break down silos, foster a collaborative environment, and pave the way for successful AIOps and GenAI adoption. For instance, the Forrester Total Economic Impact™ (TEI) study found that organizations implementing Azure OpenAI Service achieved efficiency gains, including a 50% improvement in chatbot resolution rates at contact centers, by breaking down these silos.

The Role of Change Management and Strategy

Beyond technical implementation, successful AIOps and GenAI adoption hinges on a strong change management strategy. Effective change management ensures that employees are prepared, trained, and aligned with the shift toward AI-driven processes. Resistance to change can be minimized through clear communication, targeted training, increasing trust in AI results, and active employee involvement in the transition. According to the TEI study, when companies employed a strong change management strategy, they reported productivity improvements of up to 60% per full-time employee in content generation tasks. This allowed employees to focus on higher-value work, reducing resistance and enhancing engagement.

Unclear Return on Investment (ROI)

Many enterprises need help establishing a clear ROI for GenAI and AIOps adopting initiatives. CFOs increasingly demand tangible returns and defined KPIs, making it difficult to justify investments in GenAI infrastructure. The lack of systematic A/B testing between traditional and GenAI-enabled approaches further complicates the situation, as organizations find it challenging to demonstrate measurable productivity improvements. For example, First Line Software calculates clients’ expenses and provides a clear strategy for GenAI Proof of Concept (POC) Development. This approach enables enterprises to validate the benefits of GenAI within AIOps, measure outcomes effectively, and make data-driven decisions about future investments.

Skills Gap and Training Needed for IT Professionals Adopting AIOps

Another major challenge is the skills gap. AIOps and GenAI require expertise in IT operations, data science, and AI—skills that many IT professionals currently lack. Addressing this gap demands an investment in training and fostering a culture of continuous learning to keep teams adaptable. Practical steps to bridge this gap include certifications in data science, GenAI, and IT operations, along with hands-on workshops to ensure teams can effectively work with AI-driven solutions.

To support this effort, First Line Software can provide trained personnel to strengthen customer teams, boost efficiency, and promote day-to-day knowledge sharing. The TEI study highlights that addressing the skills gap is crucial for maximizing the impact of GenAI solutions, which can lead to revenue growth and efficiency improvements. Organizations reported revenue increases of up to 8% from better engagement with existing customers, thanks to improved GenAI capabilities, such as personalized content and chatbots.

Increasing Trust in AI Results

Explainable AI (XAI) is one of the key requirements for implementing responsible AI, a methodology for large-scale deployment of AI methods in organizations that ensures model transparency and accountability. Understanding how these systems arrive at their decisions is crucial for building trust in their results. XAI helps shed light on the “why” behind an AI decision, fostering transparency and allowing stakeholders to thoroughly examine and understand the reasoning process.

By focusing on this aspect of AI work, we achieve the following effects:

• Trust and Adoption: XAI strengthens trust in AI systems, especially in critical areas such as healthcare, finance, and the legal system. Understanding how AI arrived at a result promotes trust and responsible use.
• Knowledge Base Transparency: XAI can explain where the chatbot retrieves information for its responses, allowing users to assess the credibility of the information and potentially access the source directly.
• Debugging and Improvement: Understanding the reasons behind the obtained results allows developers to improve the model’s creativity, coherence, and controllability.
• Human-AI Collaboration: Explanations help people work effectively alongside AI systems, making informed decisions based on a shared understanding.
• User Acceptance: If people understand how an AI-based system works, they are more likely to trust it and use it effectively.

Leading AIOps Platforms

Currently, several commercial systems are shaping the future of AIOps. Among them, we would highlight systems such as PagerDuty, Dynatrace, BigPanda, Datadog, and New Relic.

Key features of such systems include:

• Big Data Processing: These systems use technologies to process large volumes of data from various sources, such as logs, metrics, traces, and events. This allows for a comprehensive, real-time view of the IT systems state. With integration into cloud platforms and distributed databases, AIOps systems are capable of scaling to handle growing data volumes.
• Intelligent Monitoring and Anomaly Detection: A key feature is proactive monitoring. By utilizing machine learning algorithms, these systems can analyze historical data and identify anomalies that could lead to failures.
• Incident Management Automation: AIOps systems not only detect issues but also offer mechanisms for automatically resolving them. For example, they can redistribute the load or initiate horizontal scaling. This reduces downtime and alleviates the load on technical support teams.
• Event Correlation and Noise Management: In large IT ecosystems, there is often the problem of “noise,” where numerous events and signals do not provide useful information. AIOps systems use correlation methods to group related events and provide a unified view of the root cause of the issue. This simplifies the diagnostic process and reduces false positives.
• Support for DevOps and ITIL Processes: Commercial AIOps systems integrate with DevOps tools such as CI/CD platforms and incident management systems. They also support ITIL-based processes, such as change management, problem management, and configuration management.
• Forecasting and Analytics: AIOps systems not only resolve current issues but also predict potential risks and take preemptive actions. For example, analyzing resource usage trends can help forecast the need for scaling or hardware upgrades. This ensures more efficient use of IT resources and improves budget planning.

Commercial AIOps systems are becoming a vital element in modern IT operations, providing powerful tools for automation, analysis, and optimization. By integrating artificial intelligence, they help companies cope with the increasing complexity of their infrastructure and achieve high reliability and resilience in IT systems.

Ready to Start AIOps and GenAI Adoption?

First Line Software is well-positioned to support organizations in their AIOps and GenAI transformation journey. With a proven record of delivering AI-driven solutions and a strong focus on these technologies, we help companies navigate challenges and fully realize the benefits of AI adoption. Our expertise spans breaking down data silos, providing comprehensive change management support, and delivering practical training to ensure a smooth and effective transformation.

Adopting AIOps and GenAI involves challenges like organizational barriers, the need for change management, and skills gaps. Addressing these challenges early can ensure a successful transformation. With the right strategy, communication, and training, AIOps and GenAI can enhance IT efficiency and resilience. The TEI study projects millions of dollars in benefits over several years from effective GenAI service implementation, underscoring the importance of strategic adoption. Contact First Line Software today to learn how we can assist with your GenAI transformation.

Roman Borovsky

Support Operations Manager

With seven years of extensive experience in the IT industry, Roman thrives in his role as a Support Operations Manager. Tasked with overseeing and advancing the company’s support services portfolio, he places a strong emphasis on achieving operational excellence in application support services. Roman plays a pivotal role in driving business expansion, utilizing his expertise anchored by ITIL v3 and v4 certifications.

Anton Samsonkin

Senior Service Engineer

For over 20 years, he has successfully been involved in building efficient support processes and teams across various technological sectors, including banking, telecommunications, and transportation. His extensive experience covers a wide range of tasks related to ensuring the uninterrupted operation of critical systems and infrastructure in high-demand and dynamic environments.
Currently, his focus is on implementing innovations, including in the field of artificial intelligence, into support and maintenance processes for applications. He shares optimism regarding the use of advanced technologies and methods to achieve outstanding results in application support and customer service.