Artificial intelligence (AI) is reinventing the industry that created it: tech.
AI — computer software that simulates human intelligence — has received a mixed reception in the tech world. While some worry they are building their AI replacements, many IT professionals are cautiously optimistic about the ever-evolving technology.
55% of US tech workers feel "somewhat positive" about AI integration, agreeing that it has "great potential," according to an industry-wide survey we conducted.
While AI's potential implications can be overwhelming, we at Howdy.com see the technology as a powerful tool for streamlining operations, driving innovation, and transforming the industry for the better. We've already witnessed early results of AI adoption among our tech staffers, over 1 in 3 of whom say they use AI regularly to generate basic code snippets, automate documentation and more.
Below, we'll explore the impact of AI in IT, including applications, benefits, challenges, and predictions for the future:
Categories of AI
Artificial intelligence is an umbrella term referring to anything that allows a machine to perform tasks that typically require human intelligence. AI can be divided into different categories, each focusing on mimicking particular parts of human intelligence or solving a specific type of problem. Ahead, we've broken down AI into four major categories:
Machine learning
Machine learning (ML) is one of the most common categories of AI used in the tech world. This type of AI lets a machine learn by analyzing training data, evolving and adapting as new data becomes available. ML can distill massive quantities of data into a digestible context for humans. This technology helps systems make decisions or predictions based on patterns in data, like recommending movies or detecting spam emails.
Deep learning
Deep learning, a subset of ML, uses complex neural networks inspired by the human brain to process massive amounts of data. DL is great at tasks like image recognition, fraud detection, and understanding spoken language, but requires much more data than traditional ML.
Natural language processing
Natural language processing (NLP) lets machines understand and interact with human language. NLP is behind tech like virtual assistants (like Siri and Alexa), language translation tools, and chatbots.
Computer vision
Computer vision (CV) lets machines interpret and understand visual data — such as images and videos — similarly to humans. CV uses ML and DL models to process visual data and perform tasks like recognizing objects in photos, analyzing videos, and enabling self-driving vehicles.
AI applications in IT: What does AI help with?
Artificial intelligence in information technology has many applications. In our nationwide survey, US tech workers report using the tools for tasks like admin (47%), learning and development (44%), content creation (39%), coding (36%), automated testing and bug detection (22%), behavior and sentiment analysis (14%) and UI/UX (7%).
Ahead, we'll take a closer look at how AI is being integrated into different tech workflows:
Network management
Tech workers can use AI-powered tools to monitor network traffic, predict issues, and optimize performance without human intervention. AI tools use ML to adjust baselines and reduce false alerts.
Cybersecurity
IT professionals can use AI to detect and stop cyber threats like malware or unauthorized access. Using AI systems, tech workers can identify unusual activity in real-time, protecting systems and sensitive data more efficiently.
Automated IT operations (AIOps)
With AI-powered tools, tech professionals can automate routine tasks like software updates, backups, and system monitoring. Automated IT operations (AIOps) reduces repetitive tasks so teams can dedicate more time and energy to development and optimization.
Help desk support
AI-powered chatbots and virtual assistants provide 24/7 support by handling routine inquiries, troubleshooting issues, and resetting passwords. By using these tools, IT workers can improve response times for users while freeing up time for higher-level tasks.
Data management & analytics
AI processes and analyzes huge datasets much faster than humans. With the help of AI, tech workers can uncover insights, trends, and predictions that assist companies in making better, data-driven decisions.
AI applications in software development
One of the most common applications of AI in information technology is in software development. 32% of US tech workers agree AI is helpful in software development, assisting them with code generation, bug testing, and more.
Ahead, we explore the different ways tech workers are using AI in software development:
Automated code generation
Software developers can integrate AI tools — like GitHub Copilot and OpenAI Codex — to generate code based on natural language instructions, speeding up development. While AI handles repetitive tasks like writing boilerplate code or implementing basic functionality, devs are free to focus on higher-level logic.
Bug testing & QA
With the help of AI-enhanced testing tools like Selenium, developers can automate bug detection, identify vulnerabilities, and perform regression testing with greater accuracy. These AI systems can also predict areas vulnerable to errors and offer real-time fixes.
Natural language processing for documentation
NLP tools — such as Document AIor Jupyter Notebooks — let devs automate documentation generation. By analyzing the codebase and extracting relevant information, these systems ensure documentation stays accurate, up-to-date, and easy to understand.
Benefits of AI in IT
Many tech workers are excited about AI, with 71% saying they'd like more resources and training on using AI. IT professionals report finding AI most helpful for data engineering and analytics (42%) and software development (32%).
AI offers profound advantages to the tech world. Below, we've outlined the key benefits of using artificial intelligence in tech.
Efficiency
AI-powered systems can boost efficiency by automating repetitive tasks, from monitoring networks to code review. This lets tech workers dedicate time to strategic work while minimizing human error, improving productivity, and cutting down time-to-market for new technologies.
Security (Response to threats and risk assessment)
While AI is associated with cybersecurity risks, the technology has also proven to be a powerful tool in countering security issues. IT teams that harness AI can analyze vast amounts of data to enhance their threat detection, predict potential risks, and respond to attacks more effectively.
Cost savings
For the tech industry, AI automates tasks and reduces the need for manual intervention, lowering operating costs. AI’s ability to detect and prevent cyberattacks also minimizes financial losses from breaches and reduces the need for human monitoring and error correction — all of which result in cost savings.
Decision making & planning
AI-powered systems can fuel faster and better decisions. AI tools provide tech teams with valuable insights by analyzing vast amounts of data quickly, supercharging decision-making processes. AI systems can model scenarios, predict outcomes, and help with resource allocation, ultimately leading to more strategic planning.
Challenges of AI in IT
Every leap forward in technology is a leap forward in both benefits and risks. Ahead, we break down some of the major challenges AI brings to the tech industry:
Job displacement
Evidence suggests AI will create more jobs than it eliminates. Nevertheless, AI-driven automation has the potential to lead to job losses across the tech world, particularly for entry-level developers. 38% of US tech workers believe their company will replace jobs with AI, and 26% believe these jobs will be lost within the next five years, DTC data finds.
Tech professionals believe some sectors are more at risk than others. 31% of IT workers say that data engineering and analytics roles are most likely to be eliminated, followed by software development (28%), UI/UX (13%), finance and operations (12%), product management (11%) and DevOps and infrastructure (5%).
Data privacy
AI systems require troves of data, raising concerns over how personal information is collected, stored, and processed. AI systems can leak sensitive information or become targets for data breaches, exposing users to identity theft and other security threats.
Skill gaps and IT training
As AI becomes more integrated into tech work, the demand for AI skills — such as AI and machine learning fundamentals, data management, AI ethics, and risk management — continues to grow. 47% of US tech workers feel like AI is developing too fast for them to keep up.
IT professionals need continuous upskilling to understand and work with AI technologies. Closing this gap requires investment in specialized training programs that can keep pace with AI's whirlwind evolution.
Complexity of implementation
Implementing AI solutions can be highly complex, involving intricate hardware and software integration. 29% of tech workers say they have struggled with AI integration.
The future of AI in IT
As with all new technologies, no one can predict the future of AI in tech— though industry workers have some ideas. 44% of IT professionals believe the tech industry will fully adopt AI within the next 2 to 5 years. The rest of the world is expected to trail closely behind: 76% of tech workers predict the general public will completely embrace AI within the next decade.
Conclusion
AI has come a long way in just the last few years, but the tech is only just getting off the ground. As AI technologies continue to evolve, tech workers must adapt and acquire new skills to use AI in innovative, effective ways while mitigating risks. Tech teams that apply AI responsibly have much gain in terms of efficiency, cost savings, and growth.
At Howdy.com, we're energized by AI’s potential to redefine the software development landscape, streamlining processes and empowering teams worldwide to reach new levels of creativity and productivity. We’re dedicated to connecting forward-thinking companies with skilled professionals who will lead in this new era.
