3 AI trends in software development – TechBeacon

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During the COVID-19 pandemic, companies have transformed their operations by automating many of their key business processes with artificial intelligence (AI) and machine learning (ML) technologies. A study by MIT Sloan Management Review found that 58% of organizations predicted that AI would bring significant changes to their business models by 2023.
AI and ML tools are being built and optimized to solve specific sets of challenges and to automate innumerous manual tasks. These investments will only continue to grow. Spending on AI and ML technologies is expected to reach $299.64 billion in 2026, according to Facts & Factors.
These rapidly scaling technologies are transforming how software developers work, helping them more rapidly build better-quality software. Software development teams are bringing into existence many advanced digital products using AI and ML, and the pipeline of new projects in the works is rapidly expanding.
The opportunities to deploy and experiment with these technologies are endless. With adoption accelerating, here are three examples of emerging AI/ML trends in software development.
AI and analytics became critical to enterprises as they reacted to shifts in working arrangements and consumer habits brought about by the COVID-19 crisis. As a result, there is rapid movement toward using AI to build human-centric customer experience (CX) designs based on data that is interactive, engaging, and crafted to drive actions by users.
Using analytics and AI can help increase the pace of innovation for organizations. That was the prime motivation for a leading benefits card company when it implemented a new chatbot to handle a growing percentage of common inquiries.
Based on user data, the team was able to identify that 20% of users were using the call center consistently to check balances, change PINs, and accomplish other mundane tasks needed by cardholders. The development team designed an intelligent AI-based chatbot to handle repetitive customer queries, driving a significant reduction in call center costs and improving customer response rates.
With the help of AI and ML, companies can automate models to analyze massive amounts of data and quickly return accurate results. Designers can then craft better customer experiences by learning from different sources of user and transactional data.
AI and ML are evolving to the point where they are automating themselves, creating ways to speed up the development of AI-based software even for users who are not really experts in the field. This is making the technology more accessible and easier for companies in many different industries to experiment and adopt.
New techniques, such as automated ML (AutoML), are becoming increasingly popular, helping companies that might not have skilled data scientists on staff or the necessary computing resources to deploy ML and drive better business results. With AutoML, businesses can build and deploy an ML model with sophisticated features and no coding.
AutoML tools automate some of the most repetitive tasks across ML projects, allowing developers without data science expertise to train high-quality models specific to the needs of their businesses. The use cases of AutoML include improving the accuracy of fraud detection models for financial services companies and doing risk assessment in the insurance industry.
NLP, a component of AI and ML, allows a computer program to understand and respond to human language as it is written or spoken. NLP has advanced the development of chatbot software, translators, and voice assistants.
NLP continues to improve because of the availability of pre-trained models that get more intelligent consistently over the years.
NLP takes unstructured data and finds patterns to determine user behavior. For example, it can be used in call centers to allow companies to interpret audio signals, translate those signals into text, and then analyze the text.
Sky, a leading European broadcast cable TV company, uses NLP to interpret voice calls with operators in Sky’s contact center and glean customer insights. Instead of humans monitoring the contact center calls and listening to hours of recordings, they used AI to transcribe the audio recordings and performed NLP to compile results in a dashboard.
Using AI and NLP, Sky reduced by 80% the operational costs of monitoring contact center calls to get customer insights and customers’ satisfaction perceptions.
Although traditional software development isn’t going away, AI and ML will affect how developers build applications as well as how users interact with those applications. As the interest in AI and ML increases, those technologies will certainly affect the future of software development.
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