If I were to ask someone why they chose a career in information technology, I doubt they would respond with “I love data entry!”, “I could debug code all day long!”, or “Handling tickets is so much fun, I’d do it even if I didn’t get paid for it.”
3 IT tasks that can be automated with AI
Fortunately, AI can help. Here are the top three ways AI can help automate manual IT tasks, thereby freeing up precious resources and benefiting your teams, businesses, and customers.
1. Debugging software
Grace Murray Hopper was a Navy rear admiral and computer programming pioneer who worked on the Mark II computer at Harvard in the 1940s. On September 9, 1947, Hopper traced an error with the Mark II to – of all things – a dead moth in the relay. The insect’s remains were taped in the team’s logbook with the caption, “First actual case of a bug being found.”
While Hopper and her team weren’t the first to use the term “bug” to describe a system glitch, they certainly helped popularize it. Of course, software bugs are decidedly unpopular. IT departments and software engineers have all felt the pain of toiling over lines of code trying to reproduce and locate problems.
[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]
To be as good as human engineers, an AI tool would need to possess levels of reasoning and creativity it simply hasn’t yet reached. But AI can still be tremendously effective in exception and anomaly detection. You train it on normal usage and it detects when something is off.
Another advantage AI has over humans is its pattern detection. Let’s say a system is crashing at the same time every week or after memory usage hits a certain level. An AI tool could easily connect the dots. AI can learn which behaviors of your developers and which code patterns that are checked into your repo are correlated to bugs. This can be used to notify developers that they have done something that is likely to break and ask them to check again.
If you had a moth infestation in your home, you could certainly go around swatting them one by one. But wouldn’t it be a lot easier to discover where they hide and put out traps?
2. Predicting future issues
The adage “an ounce of prevention is worth a pound of cure” is as true in IT as it is in medicine. Monitoring operations and taking proactive action instead of just reacting to problems as they arise can prevent unexpected downtime and expensive failures.
CIOs and IT professionals are familiar with the value of preventative maintenance to some degree, whether it’s installing software updates or creating backups. That kind of maintenance is done after a certain amount of time has elapsed or usage has been logged. It’s like eating vegetables or getting exercise – they’re sound practices for a company.
[ Read also: 4 Robotic Process Automation (RPA) trends to watch in 2022. ]
Predictive maintenance, on the other hand, is individualized and custom-tailored. It monitors the equipment and its environment, performs tests, and receives equipment feedback to generate individualized predictions. It’s like having a blood test show that you’re pre-diabetic and in response, you design a low-sugar diet.
People may be uncomfortable with the idea of machines watching them all day. But with AI-enabled predictive maintenance, you watch the machines – with other machines.
3. Filtering lower-tier incidents
Dealing with IT tickets can feel like playing a perpetual game of Whack-A-Mole, but with all of the exhaustion and none of the fun carnival music and prizes.
As we all know, some incidents are worth your attention and others aren’t at all. And without a proper way to triage incidents, IT departments become overwhelmed. Enter intelligent filters. They’ve been around for years in search engines and email inboxes, distinguishing between good and bad, important and unimportant. For IT departments, they can distinguish between real incidents and noise.
Using AI techniques like case-based reasoning can help decide which solution to explore first or what additional information to request from a customer to make a diagnosis quickly and accurately. Case-based reasoning systems learn from success and failure, apply sophisticated probabilistic reasoning to identify promising solutions, and create a valuable knowledge base.
With intelligent filters and case-based reasoning, IT managers can better allocate resources for incidents that require human intervention.
While there are numerous existing AI applications that help IT departments – and many more yet to be discovered – debugging, predictive maintenance, and intelligent filtering are three applications of AI that are essential for any great IT department today.
As AI becomes increasingly integrated into our work, any organization that is not actively exploring automating its more manual IT tasks is wasting valuable financial and human capital – and may eventually fall behind.