AI has multiple applications in IT, most of which, nonetheless, pertaining to giving machines or computers the ability to imitate the human brain. The future of Artificial Intelligence is already next door and has already left significant footprints in areas as diverse as healthcare, food tech, retail, banking, financial services, healthcare, and many more. If we look at the bigger picture, because of AI, businesses benefited from automation at multiple levels, enhanced customer experience, and started scaling up faster than ever before. Narrowing this down to IT, it is safe to say that information technology has an unending potential and application that balloons at a speed of light. Let’s further explore the blend of the two.
The Connection between AI and IT
Artificial Intelligence helps technology-powered sectors discover various patterns and data clusters through automation. The IT industry heavily relies on real-time data reporting, accuracy, and refining huge volumes of quantitative data to make informed decisions. Here, AI can become a major bedrock tying the industry with what would otherwise be a mere blueprint. Here are several ways AI continues to transform information technology.
- Computer Vision
- Data Labeling
- Predictive Analytics
- AI Operations (AIOps)
- Chatbot integration
Computer Vision
As an umbrella term, AI has its influence in different domains of technology, including machine, image processing, NLP, OCR, etc. Let’s focus on computer vision for now. From business processing to employee management, computer vision accelerates processes through its ability to replicate human vision. Computer vision uses input data to perceive and recognize objects and environments. That even with sophisticated data formats, which explains its pervasive use across industries. Computer vision tasks in the IT industry include object detection, localization, tracking, image segmentation, classification, and much more. Most of the tasks are achieved utilizing Machine Learning and Deep Learning algorithms.
Some Computer vision use cases include, but are not limited to:
- Object recognition in a technical support
- Facial recognition to authenticate employees
- Automatic data filling
- Security in IT premises
- Operational insights to grow efficiency, productivity, and employee work satisfaction
Data Labeling
Collecting massive amounts of data is useless unless the data is cleaned up and labeled to train models. The annotated data assists computers in understanding the information provided by associating objects of interest to what they have been fed. The data can be collected and annotated either in-house or through annotation service providers. Alternatively, you can use open-source datasets as ready-made materials for your models. These are also referred to as training data, which in turn can be divided into training and testing sets. At the same time, the data can be of multiple formats, including image, video, text, audio, and LiDAR.
A few tips to keep in mind when labeling data:
- Set up an annotation guideline
- Makes sure your datasets are balanced and bias-free
- Have QA process established
- Keep communication open
- Make sure you have a solid feedback mechanism
- Run a pilot project
Predictive Analytics
Forecasting future trends provides the IT industry with a competitive advantage. Predictive analytics uses archived data to predict future patterns. Collecting historical customer data has always been a challenging task to understand metrics better for IT companies. Traditional models only interpret past month’s customer behaviors reports. With advanced predictive analytics, industry representatives can estimate data with more accuracy than any other traditional analytics tool — this, in turn, will lead to increases in revenues. The advanced predictive analytics algorithm must examine customer behaviors, user demographics, product usage, purchase products, navigation of past transaction history, company size, the industry, and changes in ROI.
More use cases on predictive analytics include an automated email guiding the user through how to get more value from a particular product. Else, it can be used by customer success teams to draw insights on the buyer journey and preferences.
AI Operations (AIOps)
Delays in recognizing and fixing IT issues can cause damage to a business and even a short-term crisis. It is essential to manage, examine, and monitor data and identify application issues in real-time. AI Operations (AIOps) helps detect gaps between operations process framework, conventional operations, and AI operations. This enables constraint satisfaction problems (CSPs) to re-engineer their operations to securely and safely execute and organize AI. The capability to detect, predict and fix issues in real-time make AIOps a must-have asset. Thus, the IT industry can leverage AIOps to amplify operations and refine the digital experience provided to customers.
Some AI for IT Operations (AIOps) use cases include, but are not limited to:
- Real-time anomaly detection
- Organized alerts and notifications
- Risk management and root cause analysis
- Capacity planning and management
- Automated incident management
Chatbot integration
The main objective of using artificial intelligence in customer service is to save time and reduce any kind of human-prone error. With AI chatbots, the IT industry has plenty of possibilities to optimize and automate processes, providing trouble-free services to end-users.
Utilized across numerous information technology companies, AI-powered chatbots provide excellent customer service, answering questions and inquiries 24/7. With the help of chatbots, it becomes easy to manage any size of IT companies’ audience or user base.
Some AI-driven chatbot functions include, but are not limited to the following:
- Automated lead generation
- Internal helpdesk support
- Faster acquisition of customer feedback
- Effectively conducting refund & exchange requests
- Instant response and answers to FAQs
Wrapping up
AI in the IT industry has undoubtedly refreshed the current environment we work and live in. With a myriad of opportunities to streamline and provide insights on the various elements of technology, the rise of AI in the IT industry finds its advantages in almost every remarkable field.
