Ten ways AI can improve IT productivity
Every IT leader wants to build a high-performing organization, and AI is ready to help.
When it comes to maximizing productivity, IT leaders can turn to a variety of incentives, including regular breaks, free snacks and drinks, office space upgrades, mini-contests, and more. However, there is now another cutting-edge tool that can significantly boost teams. Productivity and innovation: AI.
Jeff Orr, research director of digital technologies at ISG’s Ventana Research, says any task or activity that is repetitive and can be standardized on a checklist is a good candidate for automation using AI. “When IT team members are engaged in meaningful activities, they tend to have a better experience,” he notes, “and better employee engagement leads to improved employee retention.”
So how can AI help your IT team members become more creative and productive? Check out these 10 ideas.
1. Provide more alert context
Receiving an error message that simply says “something went wrong” usually requires IT staff to look through logs and identify the problem, which is highly inefficient, Orr said. With observability technology powered by GenAI, the software can visually trace error messages back to their source and provide recommended steps to resolve the cause.
“This better access to information can deliver benefits to IT teams’ key performance indicators in most areas, from e-commerce store errors to security risks to connectivity outages,” he said.
2. Create self-service options
Using AI to automate existing processes provides a powerful new self-service tool for enterprise departments. For example, onboarding new employees follows a set of known processes such as location, role, work hours, etc., Orr said.
“Creating employee credentials and access privileges, pre-configuring security settings, and preparing individuals for a productive first day are steps that require virtually no human intervention,” he added.
3. Scale more efficiently
AI can automate a range of routine tasks, ensuring consistent operations across your IT infrastructure, said Alok Shankar, AI engineering manager at Oracle Health. “This scalability allows you to scale your business without having to scale your IT team accordingly.”
Shankar noted that AI can also provide IT teams with data-driven insights to optimize resource allocation, prioritize upgrades and plan for the future. Easy access to continuous improvement is another growth advantage of AI. “Many AI systems use machine learning to continuously learn and adapt to become more efficient,” he said.
4. Identify potential problems
By analyzing large amounts of data, AI can identify potential technical and security issues before they escalate into system outages.
“This proactive approach minimizes downtime and keeps systems running smoothly,” Shankar says. “With the lightning-fast processing power of AI, you can quickly locate and resolve issues, reducing the impact on your business.”
5. Improve the work order system
将AI引入服务管理流程，特别是自动化工单系统，可以显著提高员工的生产力，工程服务公司Halff的机器学习科学家Justin Roberts说。
Roberts noted that AI can automatically categorize, prioritize, and assign work tickets. “It can analyze incoming issues and use historical data to suggest and even implement solutions without human intervention,” he explained. “For complex issues that require human intervention, AI can prepare detailed background reports, significantly reducing resolution time.”
6. Accelerate business processes
By infusing AI into business processes, enterprises can achieve productivity, efficiency, consistency, and scale that were unimaginable a decade ago, said Jim Liddle, CIO of hybrid cloud storage provider Nasuni. He observed that monotonous, repetitive tasks like data entry and collection can be easily handled by intelligent AI algorithms 24/7.
“Complex business decisions, such as fraud detection and price optimization, can now be made in real time based on massive amounts of data,” Liddle said. “Workflows that previously took days or weeks can now be completed in hours or minutes.”
Essentially, AI automates tasks, workflows, and decisions that previously required human effort. “Enterprises have long been driving efficiency and scale through automation, initially through simple procedural rule systems and later through more advanced algorithmic software,” Liddle said. “Now, innovations in machine learning and AI are driving the next generation of intelligent automation.”
7. Reduce repetitive tasks
AI通过掌控日常任务和优化流程，可以显著提高IT团队的生产力，数据科学和软件开发公司Loka的数据负责人Henrique Ribeiro Delgado da Silva说。
“By reducing templated operations, teams can save time on repetitive tasks, while automated and enhanced documentation can keep up with code changes and project progress.” He noted that AI can also automatically create pull requests and integrate with project management software. In addition, AI can generate suggestions for fixing bugs, propose new features, and improve code reviews.
Teams looking to automate routine tasks should use tools like ChatGPT for coding exercises with simple examples and GitHub Copilot for coding assistance. “This approach works because it’s fast, requires low effort but produces good results, and is scalable enough to handle projects of all sizes and complexity,” da Silva said.
8. Enhance the observability of ITOps
As enterprises seek zero downtime and lower IT operating costs, IT operations teams find themselves having to quickly improve and adapt to meet evolving demands. To help achieve performance goals, AI operations is now moving toward unified observability, moving IT operations from traditional reactive monitoring to proactive IT management, said Efrain Ruh, field CTO at Digitate, an AI and automation software provider.
Ruh believes that AI will improve ITOps observability by providing the ability to analyze large data sets, identify patterns, detect anomalies, correlate, predict and foresee problems. These advantages promise to provide IT teams with more time to focus on more complex problems.
AI can also identify hidden dependencies, capture normal behavior and perform impact analysis. "In the event of system failures or anomalies, AI can help IT teams automate responses, which can have a significant impact on system availability and performance," Ruh pointed out.
When planning an AI-based ITOps observability project, Ruh recommends a collaborative effort that includes IT management, platform management, tools, and security teams. “It’s important to start with the right expectations and do it in phases across different teams.”
9. Automated monitoring and maintenance
通过自动化常规监控和维护任务，AI可以显著提升IT团队的生产力，Instacart的高级技术软件产品经理Aravindh Manickavasagam说。“利用AI驱动的预测性维护可以帮助团队预见潜在的系统故障，并在它们导致任何重大停机时间之前加以缓解，”他解释道，“AI可以自动生成报告、系统更新，甚至通过聊天机器人处理一线客户支持查询。”
Manickavasagam said AI can reduce the operational overhead of IT teams, allowing members to focus on strategic and complex tasks that require human intervention. "Automating routine tasks with AI not only improves efficiency, but also reduces the possibility of human error, while improving system uptime and overall service quality," he said.
As with any AI project, the planning team should include IT managers, system architects, data scientists (to assist with AI model training and integration), and end users (to provide feedback). “Senior leadership involvement,” Manickavasagam notes, “ensures that the project is aligned with broader business goals and has the necessary support and resources.”
10. Speed up encoding
AI Co-Pilot tools provide intelligent completions that can significantly speed up coding tasks, observed Pavel Torbin, COO and co-founder of Arc53, a provider of data management and machine learning solutions. "Unlike earlier systems that only suggested single words, today's AI Co-Pilot can suggest complete functions, which greatly reduces coding time and error rates."
Looking ahead, Torbin expects AI tools to make significant advances in dependency management and code translation. "As IT infrastructure evolves, AI can automate and secure the update process, reducing the risk of dependency confusion attacks." He also believes that AI will play an important role in translating legacy software to modern frameworks, facilitating a smooth transition while maintaining business continuity.
Torbin advises IT leaders to keep a close eye on AI accuracy and be aware of the phenomenon of “hallucinations,” where the model suddenly outputs confident but wrong or irrelevant answers. “Also, if all queries rely on AI without regular verification by human experts, it could lead to misinformation becoming the norm in IT operations,” he warns.