10 ways AI can make IT more productive

When it comes to maximizing productivity, IT leaders can turn to an array of motivators, including regular breaks, free snacks and beverages, workspace upgrades, mini contests, and so on. Yet there’s now another, cutting-edge tool that can significantly spur both team productivity and innovation: artificial intelligence.

Any task or activity that’s repetitive and can be standardized on a checklist is ripe for automation using AI, says Jeff Orr, director of research for digital technology at ISG’s Ventana Research. “IT team members tend to have better experiences when they’re working on meaningful activities,” he notes. “Better employee engagement leads to employee retention.”

How can AI help your IT team members become more creative and productive? Check out the following 10 ideas.

1. Provide more context to alerts

Receiving an error text message that states nothing more than, “something went wrong,” typically requires IT staff members to review logs and identify the issue. This is highly unproductive, Orr says. Software incorporating observability technology, enabled by generative AI, allows an error message to be visually traced back to its source along with recommended steps to address the cause.

“This approach to better information can benefit IT team KPIs in most areas, ranging from e-commerce store errors to security risks to connectivity outages,” he says.

2. Create self-service options

Automating existing processes with AI gives enterprise departments a powerful new self-service tool. Onboarding a new hire, for example, follows a set of known processes, such as location, role, hours, and so on, Orr says.

“The steps to create employee credentials and access permissions, pre-configure security settings, and prepare the individual for a productive first day on the job really doesn’t require human intervention,” he adds.

3. Scale more efficiently

AI can automate an array of routine tasks, ensuring consistent operations across the entire IT infrastructure, says Alok Shankar, AI engineering manager at Oracle Health. “This scalability allows you to expand your business without needing a proportionally larger IT team.”

Shankar notes that AI can also equip IT teams with the data-driven insights needed to optimize resource allocation, prioritize upgrades, and plan for the future. Easy access to constant improvement is another AI growth benefit. “Many AI systems use machine learning, constantly learning and adapting to become even more effective over time,” he says.

4. Identify potential issues

By analyzing vast amounts of data, AI can identify potential technical and security issues long before they can escalate into system outages.

“This proactive approach minimizes downtime and keeps your systems running smoothly,” Shankar says. “With AI’s lightning-fast processing, you can pinpoint and address problems quickly, reducing the impact on your business.”

5. Improve ticketing systems

Adding AI into service management processes, particularly automated ticketing systems, can dramatically improve staff productivity, says Justin Roberts, a machine learning scientist at engineering services firm Halff.

Roberts notes that AI can automatically categorize, prioritize, and route tickets. “It can analyze incoming issues and use historical data to suggest or even implement solutions without human intervention,” he explains. “For complex issues requiring human attention, AI can prepare a detailed context [report], reducing resolution time significantly.”

6. Accelerate business processes

By infusing AI into business processes, enterprises can achieve levels of productivity, efficiency, consistency, and scale that were unimaginable a decade ago, says Jim Liddle, CIO at hybrid cloud storage provider Nasuni. He observes that mundane repetitive tasks, such as data entry and collection, can be easily handled 24/7 by intelligent AI algorithms.

“Complex business decisions, such as fraud detection and price optimization, can now be made in real-time based on huge amounts of data,” Liddle states. “Workflows that spanned days or weeks can now be completed in hours or minutes.”

At its core, AI enables automation, including tasks, workflows, and decisions that previously required human effort. “Enterprises have long sought to drive efficiency and scale through automation, first with simple programmatic rules-based systems and later with more advanced algorithmic software,” Liddle says. “Now, innovations in machine learning and AI are powering the next generation of intelligent automation.”

7. Slash repetitive tasks

AI can significantly enhance IT team productivity by gaining control over routine tasks and optimizing processes, says Henrique Ribeiro Delgado da Silva, data head at data science and software development firm Loka.

“By reducing boilerplating, teams can save time on repetitive tasks while automated and enhanced documentation keeps pace with code changes and project developments.” He notes that AI can also automatically create pull requests and integrate with project management software. Additionally, AI can generate suggestions to resolve bugs, propose new features, and improve code reviews.

Teams looking to automate routine tasks should practice by using tools such as ChatGPT to code simple examples and GitHub Copilot for coding assistance. “This approach is effective because it’s fast, requires low effort for satisfactory results, and is scalable enough to handle projects of varying sizes and complexity,” da Silva says.

8. Enhance ITOps observability

As enterprises seek zero downtime and lower IT run costs, IT operations teams are finding themselves pressed to improve and adapt quickly to meet evolving demands. To help reach performance goals, AI operations are now moving toward unified observability, shifting IT operations from traditional reactive monitoring to proactive IT management, says Efrain Ruh, field CTO at AI and automation software provider Digitate.

Ruh believes that AI is set to take ITOps observability to the next level by providing the ability to analyze vast datasets, discern patterns, detect anomalies, correlate, forecast, and even predict issues. All of these benefits promise to give IT teams additional time to focus on more complex issues.

AI can also identify hidden dependencies, capture normal behavior, and perform impact analyses. “In case of a system failure or anomaly, AI helps IT teams automate the response, providing a major impact on system availability and performance,” Ruh notes.

When planning an AI-based ITOps observability initiative, Ruh recommends a collaborative effort incorporating teams responsible for IT management, platform management, tooling, and security. “It’s important to start with the right set of expectations and in phases with different teams.”

9. Automate monitoring and maintenance

By automating routine monitoring and maintenance tasks, AI can significantly boost IT team productivity, says Aravindh Manickavasagam, senior technical software product manager with Instacart. “Utilizing AI-driven predictive maintenance can help teams foresee potential system failures and mitigate them before they cause any significant downtime,” he explains. “AI can automate the generation of reports, system updates, and even handle first-level customer support queries through chatbots.”

Manickavasagam says that AI can reduce an IT team’s operational overhead, allowing members to focus on strategic and complex tasks that require human intervention. “Automating routine tasks with AI not only increases efficiency, but also reduces the likelihood of human error while enhancing system uptime and improving overall service quality,” he says.

As with any AI initiative, planning teams should include IT managers, system architects, data scientists (to assist with AI model training and integration), and end-users (for feedback). “Involvement from executive leadership,” Manickavasagam notes, “can ensure that the project aligns with broader business objectives and receives the necessary support and resources.”

10. Accelerate coding

AI copilot tools offer smart completions that can drastically accelerate coding tasks, observes Pavel Torbin COO and co-founder of data management and machine learning solutions provider Arc53. “Unlike earlier systems that suggested single words, today’s AI copilots can suggest entire functions, greatly reducing coding time and error rates.”

Looking ahead, Torbin anticipates significant advancements in AI tools, addressing both dependency management and code translation. “As IT infrastructures evolve, AI can automate and safeguard the update process, reducing the risks associated with dependency confusion attacks.” He also believes that AI is poised to play an important role in translating legacy software into modern frameworks, facilitating smoother transitions while maintaining business continuity.

Torbin advises IT leaders to closely monitor AI accuracy and to watch out for “hallucinations,” when a model suddenly begins spewing confident but incorrect or irrelevant answers. “Additionally, reliance on AI for all queries without periodic verification by human experts can lead to misinformation becoming a norm within IT operations,” he warns.