4 Artificial Intelligence Trends In Software Development

Companies have automated many of their key business processes with artificial intelligence (AI) and machine learning (ML) technologies during the COVID-19 epidemic, demonstrating how these technologies can transform businesses.

According to a study by MIT Sloan Management Review, 58 percent of companies think AI will bring significant changes to their business models by 2023.

AI and ML technologies are being developed and optimized to tackle particular sets of problems and automate numerous manual operations. These expenditures will only rise. According to Facts & Factors, spending on AI and ML technologies is anticipated to reach $299.64 billion in 2026.

AI and ML-powered development teams are revolutionizing how software developers operate, allowing them to produce better code more quickly. AI and ML-based development teams have developed several sophisticated digital goods with a growing pipeline of new projects.

Build or scale up your development team in weeks, not months, with TSH. The possibilities for deploying and experimenting with these technologies are infinite.

Here are four current examples of AI/ML in software development trends that you should be aware of.

1. AI is Built on Customers’ Experiences

AI and analytics have become essential for businesses as they respond to changes in working arrangements and consumer habits generated by the COVID-19 crisis.

As a result, there is considerable interest in implementing AI to create human-centric customer experience (CX) designs based on interactive, engaging, and tailored data to elicit user action.

Analytics and AI might assist advanced businesses by allowing them to innovate more quickly. When a major benefits card firm deployed a new Chabot to handle an expanding number of standard inquiries, this was the driving force behind its adoption.

The study found that 20% of users utilized the call center regularly to check balances, update PINs, and execute other routine tasks. The development team created an AI-based chatbot that saved money by lowering contact center expenses while increasing response rates to deal with frequent client inquiries.

Companies may use AI and ML to automate models that analyze huge quantities of data and provide timely answers. Designers can then generate superior customer experiences by studying various sources of user and transactional data.

2. ML is Gaining Traction as an Automated Technique

AT&T believes that AI and ML are progressing to the point where they can self-automate, enabling developers to speed up the creation of AI-based software even for non-experts. This allows organizations in a wide range of sectors to experiment and adopt it more readily.

Automated ML (AutoML), for example, is becoming increasingly popular, allowing firms without data scientists or the required computing resources to deploy ML and drive improved business outcomes. With AutoML, companies may create and deploy an ML model with advanced capabilities without writing any code.

AutoML tools automate some of the most time-consuming activities in machine learning projects, allowing non-data scientists to create high-quality models suited to their needs without expertise.

AutoML is used for various purposes, including enhancing fraud detection model accuracy in financial services firms and performing risk assessment within the insurance sector.

3. The Field of Natural Language Processing is Growing

NLP, a part of AI and ML, is the study of how computers can interpret and react to human language as it is written or spoken. NLP has aided the creation of Chabot’s, translators, and voice assistants.

NLP continues to develop because of the availability of pre-trained models that get more sophisticated with time.

It is concerned with finding patterns in data that might be used to predict user behavior. It’s possible to use NLP on recorded speech, for example, to discover patterns that can help businesses interpret audio signals, convert them into text, and analyze the text.

NLP is used by Sky, a large European cable television company, to interpret voice calls with operators in Sky’s contact center and obtain consumer insights.

Instead of individuals monitoring the contact center conversations and listening to hours of audio recordings, AI transcribed the audio records and performed NLP analysis on the results in a dashboard instead.

Sky reduced contact center operational costs by 80% by applying AI and NLP to monitor calls for customer insights and consumer satisfaction perceptions.

4. Prepare for the Assistance of AI/ML Development

Traditional software development isn’t going away, but AI and ML will affect how developers create applications and how consumers use them. As the popularity of AI and ML grows, these technologies will impact future software development.

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