When scaling to 1,000 employees and 40 million users, maintaining project visibility across workflows and keeping up with demand might seem impossible. But not for Canva. The software leader was able to save 150+ hours/month and collaboratively served 42 million+ users using AI-enabled ITSM and ESM solutions.

That’s the power of Artificial Intelligence (AI). The technology delivers unmatched benefits for IT/ITES firms, from accelerated DevOps to effective incident management, intelligent documentation, threat detection, and more.

The AI market is expected to grow annually at 28.46%, reaching $826.70 billion by 2030. Read as we demonstrate the power of AI for IT/ITES firms.

Reinventing IT/ITES with AI – Understanding Use Cases

IT/ITES companies that excel at quickly bringing products to the market and delighting customers stand to grow their topline. AI is crucial in helping these firms achieve these objectives, offering several benefits across the development lifecycle. From service management to operations to incident management, let’s look at the top use cases of AI in IT/ITES:

Proactively Identify and Mitigate Security Threats with Advanced AI Detection: With the security threat landscape constantly expanding, tools like GitLab allow effective security threat detection. AI-enabled features can automate threat detection and response, identifying potential vulnerabilities before they can be exploited. IT/ITES teams can detect anomalies in data, receive real-time alerts when security issues arise, reduce downtime, and improve product quality. They can also use AI to perform root cause analysis on issues, identify the underlying cause of the problem, and take steps to prevent it from happening again.

Similarly, Sonar AI can help developers write clean code, improving the quality, maintainability, security, and reliability of software. By validating AI- or human-generated code through structured analysis, Sonar ensures every line of code meets necessary quality guidelines before moving to production. 

Accelerate Dev and Ops via a Single AI-powered Platform: Tools like Jira Service Management unite business, Dev, and Ops teams on a single AI-powered platform, enabling them to deliver exceptional service. This can help supercharge collaboration across changes, incidents, and requests and accelerate Dev and Ops. For example, virtual service agents in JSM can search for answers across linked knowledge bases and resolve issues by providing necessary information or instructions.

Optimize Incident Handling with Real-Time AI Analytics and Decision Support: AI-powered service management allows IT teams to quickly spin up an intelligent service desk to manage all requests in a single place. Other teams, like HR and legal, can leverage AI-enabled incident management to streamline requests, centralize knowledge, and work how they want.

Supercharge Code Quality with AI-driven Automated Code Reviews: IT/ITES teams can use AI to review existing code for quality and functionality. Using large language models, they can identify inconsistencies with coding standards and detect security issues and vulnerabilities. They can also get suggestions or enjoy automated fixes, save time, improve code quality, and manage code changes effectively.

 Streamline Development with Next-Level CI/CD Automation for Faster Delivery: When it comes to continuous integration and continuous delivery or deployment (CI/CD), AI helps automate the process of building, testing, and deploying code. This ensures that any changes that pass appropriate tests can be integrated into the existing codebase and immediately deployed to production environments. This allows IT/ITES teams to reduce the risk of errors and improve the overall quality of the software being developed.

 Accelerate Development Cycles with Smart and Scalable Automated Testing: AI can also automate testing processes, which is critical for organizations that want continuous delivery. By using AI to run tests on new code automatically, developers can quickly identify and fix any issues that arise, ensuring that the code is ready for deployment as soon as possible.

 Revolutionize User Support with a Robust, Self-Service Portal for Quick Solutions: Intelligent self-service portals can ensure anyone can get help quickly. Teams can leverage AI-powered virtual service agents to deflect requests, save time, and confidently deploy code. The built-in AI engine leverages powerful Natural Language Processing and generative AI capabilities, allowing ITSM agents to deliver fast, conversational support. Agents can use intent templates, cut down on escalated tickets via automation, and capture powerful insights on helpdesk effectiveness.

Elevate Knowledge Sharing with AI-Powered Intelligent Documentation: AI-powered tools can also help automate and enhance technical documentation creation, maintenance, and management. By analyzing code, commit histories, and developer comments, AI can generate documentation that is not only accurate but also contextually relevant.  This minimizes the time spent manually writing and updating documentation while ensuring it remains current as the software evolves.

Using Atlassian Intelligence features in Confluence, teams can draft or edit content, generate summaries or action items, and even create rules using natural language. They can also brainstorm new product ideas with AI, analyze project requirements, and assign tasks to team members with deadlines.  

Maximize Response Speed and Accuracy with Fully Automated Customer Support: IT/ITES teams can automate support interactions with AI, link requests to Jira issues, and triage unplanned work. AI also allows them to capture relevant customer information, customize the help center accordingly, and automate change requests.

AI Adoption Challenges and Considerations

Adopting AI into daily tasks and workflows comes with its own set of challenges. According to Accenture research, AI-led processes nearly doubled from 9% in 2023 to 16% in 2024. Compared to peers, these organizations achieve 2.5x higher revenue growth, 2.4 x greater productivity, and 3.3x greater success at scaling generative AI use cases compared to peers.

Yet, while some companies have moved to the highest level of operations maturity, 61% report that their data assets are not ready for generative AI and 70% find it hard to scale projects that use proprietary data. In fact, 78% of executives indicate that AI and generative AI are advancing too fast for their organization’s training efforts to keep pace.

Here are some hurdles that impact the success of AI adoption for IT/ITES companies:

• Data quality and availability: Data quality and availability are vital in successfully adopting AI. However, AI models fail to make reliable predictions without the proper data governance measures, leading to inaccurate or biased outcomes. To ensure sufficient and good-quality data, IT/ITES firms must collect and feed AI models with diverse datasets that can generalize well across different contexts and environments.
• Technical debt: Technical debt can significantly impede AI adoption by creating inefficiencies and increasing complexity. Outdated systems and poorly maintained infrastructure make it difficult for teams to integrate new AI technologies. To overcome this challenge, they must modernize legacy applications, unify data pipelines, and embrace modern AI models and frameworks that adapt to growing datasets or evolving business needs.
• Reliability and trust: To navigate the challenges of trust and reliability, IT/ITES teams must establish strong cross-functional collaboration between data scientists, engineers, and business stakeholders. This can ensure alignment of the model’s objectives with real-world requirements, ensuring necessary visibility and transparency, smooth deployment, and ongoing success.
• Complexity in model development and deployment: AI model development and deployment can be complex due to the multifaceted nature of data, algorithms, and operational environments. To ensure an effective AI model, teams must choose the right algorithms, preprocess large volumes of diverse data, and address biases as they arise. They must also fine-tune model parameters, monitor integration with existing systems, and ensure the model can handle evolving data streams.

Push the Boundaries of What’s Possible with AI

Artificial intelligence is transforming the IT/ITES sector via automated testing, enhanced ITSM, and intelligent code generation, reducing time to market and improving product quality. To ensure the best outcomes from tools like Atlassian AI, teams must begin with a clear strategic picture and a cohesive plan to personalize services, improve user experiences, and create new business models.