What Will a Day in the Life of an IT Professional Look Like in 2030?

It’s 2024, and despite all the talk about artificial intelligence (AI) and automation, most IT pros are still bogged down with manual troubleshooting, reacting to trouble tickets, and enduring endless meetings. There’s hardly any time left for innovation or proactive work. But the AI landscape is changing fast. Let’s imagine what a typical day could look like in just a few years.

Meet Samantha, our future colleague, and see how AI might transform her day-to-day routine.

8:00 AM

Samantha had just returned from a relaxing vacation, dreading the deluge of problems and messages that usually piled up in her absence. Bracing for the worst, Samantha reached for her phone. Instead of a chaotic inbox, she was greeted with a concise, AI-prepared digest.

“Good morning, Samantha,” the AI assistant chimed. “I hope you had a pleasant vacation. Here’s your daily digest and summary of the past two weeks while you were out. Most recently, overnight maintenance was completed successfully, with no issues detected. Would you like to catch up on the news?”

“Sure,” Samantha said. A lot can change in IT in two weeks.

“Great, here are the latest updates from your key vendors and topics. Also, a new security standard is being rolled out that might apply to our operations; do you want to learn more?”

“Add that topic to my learning sessions, please. It’s time for coffee.”

“Certainly, Samantha.”

Samantha smiled in relief. This was the best part of her morning—no frantic checking of multiple systems, no surprises, just a straightforward, curated summary of what had happened while she was away. Such a difference from when Samantha was new in the IT world, when each new day brought an inbox full of fires to put out and problems to solve.

8:30 AM

After a quick shower and breakfast, Samantha decided she wanted a few hours to get back into the swing of things before heading into the office for collaborative work, so she hopped on her bike and headed to her favorite cafe.

9:00 AM

While the morning digest said everything was all clear, Samantha wanted to dig in and see what her team had been up to while she was on vacation.

“AI assistant, show me the infrastructure status and what’s changed in the past two weeks,” Samantha asked.

On Samantha’s laptop, the AI-generated a detailed status report. “While you were out, in addition to completing the usual predictive maintenance, the compute team also deployed a new container farm, and storage systems were upgraded to the latest firmware. Networking is running within expected parameters; no anomalies or unresolved issues have been detected. Self-healing processes are running smoothly. There is also a major version upgrade for our core switching platform. I’ve simulated it in a digital twin, and it seems to be compliant with our architecture.” the AI reported.

“Shall I schedule the switches for an upgrade?” it asked.

“Yes, please follow the usual process. When do you suggest?” Samantha replied.

“Based on typical load patterns, as well as office holidays, I believe Sunday evening is ideal,” it said.

“Perfect, let’s do that,” Samantha confirmed.

The AI filed the change management request, notified the appropriate operations teams, prepared the upgrade window, and messaged Samantha a summary of what would be done.

10:00 AM

Samantha took a sip of her coffee, satisfied with the smooth operations. She marveled at how far predictive maintenance and modeling had come. Gone were the days of unexpected server failures in the middle of the night. “Great. Let’s tackle security next,” Samantha said.

The AI assistant quickly alerted Samantha to a recent potential breach. “Last night, an anomaly was detected. Automated countermeasures were deployed, and the system returned to baseline. Would you like a detailed report?” it asked.

“Yes, please,” Samantha responded.

The AI provided a comprehensive report on the anomaly, including the steps it had taken to mitigate the threat. “The anomaly was a phishing attempt. Countermeasures included blocking the sender’s IP, flagging the email for security review, and updating our phishing detection algorithms. Do you need further actions?” the AI inquired.

Samantha reviewed the details, noting how efficiently the AI had handled the situation. “No further actions needed for now, thank you,” Samantha replied.

11:00 AM

With her reviews and coffee finished, it was time for Samantha to head into the office. She had been taking a lot of professional development training, but after a few weeks away, was feeling a little rusty. Samantha donned a VR headset and immersed herself in a virtual learning environment. “AI, start my personalized learning plan where I left off,” Samantha instructed.

“Sure, Samantha. Before we start, would you like a quick quiz to refresh your knowledge?” the AI suggested.

“Good idea. I’m not sure what I remember after a few weeks away. Let’s do that,” Samantha agreed.

11:30 AM

After completing the quiz, the AI tailored the learning module based on Samantha’s performance. “Module started. Here are your progress insights and recommended areas of focus,” the AI said, presenting a customized learning path, including the new compliance regime they had discussed over the morning digest.

Samantha appreciated how the AI tailored her learning experience based on her existing knowledge and recent activities. It felt less like a chore and more like a guided journey through the latest advancements, and if she already knew something, she could easily skip ahead. The immersive VR environment made learning engaging, with interactive modules and real-time feedback.

12:30 PM

With lunchtime approaching and no urgent issues to solve, Samantha was able to enjoy a mid-day break while catching up with her colleagues over lunch.

1:30 PM

Returning to her project work in the early afternoon, Samantha joined an interactive design session with her team, focusing on the next version of their main app. “Let’s review customer behavior data on the current version. I know it’s a lot of data points, but using AI insights, I think we can eliminate some painful user journeys and streamline the interface,” Samantha suggested.

Samantha’s team members nodded in agreement. “These data-driven design choices will definitely improve user experience,” one colleague remarked. While the team talked, sketched, and reviewed mockups, the AI provided real-time feedback and suggestions, streamlining the design process.

2:30 PM

Mid-afternoon brought interdepartmental collaboration. Samantha met virtually with data scientists and department leads, discussing how to integrate AI insights for an interdisciplinary project. “Combining our expertise will drive innovation,” a data scientist commented, echoing Samantha’s thoughts.

“We can leverage AI to predict market trends and adapt our strategies accordingly,” Samantha added. The meeting was productive, with the AI assisting in real-time data analysis and generating actionable insights alongside the live conversation. By incorporating feedback from multiple data sources, the AI was able to foster innovation and uncover insights that each department couldn’t see on their own.

3:30 PM

As Samantha was wrapping up the meeting, a new notification appeared. It was a message from their boss. “Good morning, Samantha. Exciting news! We’re expanding our operations into Europe. We need you to plan the IT capacity for our new office and factory in Germany. You’ll receive the digital blueprints from the architect shortly. Thanks!”

Moments later, the digital blueprints arrived, and Samantha quickly got to work. First, she opened the blueprints in their AI-assisted design software. The software scanned the blueprints and generated a 3D model of the new office and factory. “AI, what do you think of the layout for the wireless network?” Samantha asked.

The AI analyzed the blueprints and presented options. “Based on the building materials and layout, I recommend these placements for optimal coverage. Here are three options with varying access point distributions to choose from. Would you like to see the signal coverage heatmaps for each?” the AI offered.

“Yes, show me the RF heatmaps,” Samantha replied.

The AI displayed detailed maps showing the recommended positions for each wireless access point, complete with signal coverage heatmaps. Samantha reviewed the options, noting how the AI had efficiently minimized interference and maximized coverage. “Option two looks best. Let’s go with that.

Over the next five minutes, Samantha then conversed with the AI assistant about other requirements for the wireless network, such as user density, seamless roaming, and latency requirements. “Based on capacity and the other additional requirements we have discussed, please make the needed adjustment to the wireless design,” Samantha instructed.

The AI analyzed all the additional requirements and replied, “Because of the high density of users and other requirements, I am recommending 10% more APs and an adjusted placement of the APs. Additionally, I am providing recommended radio configuration settings for the APs in various facility locations. Would you also like me to recommend SSID and security settings?”

“Thank you, let’s go with the adjusted design you just presented. Please build a bill of materials and send it to our suppliers for quotes,” Samantha continued, “I do not need new Wi-Fi security recommendations at this time; instead, let’s match the corporate SSID strategy and access policies that we currently use at other remote locations.”

4:00 PM

Next, Samantha needed to estimate the data center requirements. “AI, analyze our current US office data center usage and project the needs for the new European location, based on the headcount numbers from our hiring plan. Assume growth is similar to our current sites,” she said.

The AI quickly accessed the data from the US office and began its analysis. Since it was connected to systems beyond just IT operations, it considered the number of employees, the types of applications used, peak usage times, and other relevant factors. Within minutes, the AI presented a detailed report.

“Based on the current data, the new office and factory will require a data center with the following specifications: X number of servers, Y amount of storage, and Z network bandwidth. Suggested redundancy and backup solutions are also included, following corporate standards. Would you like to review the detailed breakdown or proceed with this plan?” the AI asked.

“Let’s review the detailed breakdown first,” Samantha said.

The AI provided a comprehensive analysis, showing how it had arrived at the recommended specifications. Samantha carefully reviewed the AI’s recommendations, confident of what it suggested but still wanting to double-check the accuracy and thoroughness of the analysis. The AI had even factored in future growth and potential increases in data traffic, ensuring that the new infrastructure would be scalable.

Feeling accomplished, Samantha prepared a presentation to share the plans with her boss and the rest of the team. The AI-assisted tools had streamlined the entire process, turning what could have been weeks of work into just a few hours.

4:30 PM

With the project work and the new assignment completed, the last task for the day was a meeting with the recruiting team to fill the open headcount on the IT team.

“Which job description should we use for this new role,” asked the recruiter. “Server Admin? Network Admin? Operations?”

“We don’t really use those terms anymore,” said Samantha. “How about something like ‘IT Analyst’?”

With the spread of AI tools and the shift in workload, job roles had changed dramatically in the past few years. Lines blurred between technical and organizational boundaries, leading to a team of generalists focused on analysis and synthesis rather than specific technical knowledge.

“That works for me. I’ll put something together and do market research on salary bands,” said the recruiter.

With her last meeting over, Samantha wrapped up her day and reflected on the efficiency and innovation AI had brought to her work compared to just a few years ago.

5:00 PM

“AI has truly transformed our work, making us more efficient and innovative and letting us focus on new challenges rather than just being reactive,” she mused. Samantha felt a sense of accomplishment, knowing she had navigated the day’s challenges with ease and foresight.

Samantha closed her laptop with a satisfied smile, ready to enjoy the evening, trusting automation and AI to handle anything that came off while she enjoyed her downtime.