3 AI trends in software development
During the COVID-19 pandemic, companies have transformed their operations by automating many of their key business processes through artificial intelligence (AI) and machine learning (ML) technologies. A study of MIT Sloan Management Review found that 58% of organizations expected AI to 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 countless manual tasks. These investments will only grow. Spending on AI and ML technologies is expected to reach $299.64 billion in 2026, according to Facts & Factors.
These rapidly evolving technologies are transforming the way software developers work, helping them build better software faster. Software development teams are creating many advanced digital products using AI and ML, and the pipeline of new projects underway is growing rapidly.
The possibilities for deploying and experimenting with these technologies are endless. With adoption accelerating, here are three examples of emerging AI/ML trends in software development.
Customer experience connects to AI
Artificial intelligence and analytics have become essential for businesses as they respond to changes in working arrangements and consumption habits brought about by the COVID-19 crisis. As a result, there is a rapid movement towards using AI to create human-centered, data-driven customer experience (CX) designs that are interactive, engaging, and designed to inspire users to action.
The use of analytics and AI can help accelerate the pace of innovation for organizations. This was the main motivation for a major 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 regularly use the call center to check balances, change PINs and perform other mundane tasks needed by cardholders. The development team designed an intelligent AI-based chatbot to handle repetitive customer queries, resulting in 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 huge amounts of data and return accurate results quickly. Designers can then create better customer experiences by learning from different sources of user and transactional data.
Automated ML is gaining ground
AI and ML are evolving to the point of automation, creating ways to accelerate AI-based software development, even for users who aren’t really experts in the field. This makes the technology more accessible and easier to experiment with and adopt for businesses in many different industries.
New techniques, such as automated ML (AutoML), are becoming increasingly popular, helping companies that may not have skilled data scientists or the IT resources to deploy ML and drive better business results. . With AutoML, companies can build and deploy an ML model with sophisticated functionality and no coding.
AutoML tools automate some of the most repetitive tasks in ML projects, allowing developers without data science expertise to train high-quality models specific to their business needs. Use cases for AutoML include improving the accuracy of fraud detection models for financial services companies and risk assessment in the insurance industry.
Natural language processing continues to advance
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 due to the availability of pre-trained models that are getting smarter 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 businesses to interpret audio signals, translate those signals into text, and then analyze the text.
Sky, one of Europe’s leading cable TV companies, uses NLP to interpret voice calls with Sky’s contact center operators and glean customer information. Instead of humans monitoring contact center calls and listening to hours of recordings, they used AI to transcribe the audio recordings and performed NLP to compile the results into a dashboard.
Using AI and NLP, Sky reduced the operational costs of monitoring contact center calls for customer insights and customer satisfaction perceptions by 80%.
Prepare for AI/ML development assistance
While traditional software development isn’t going away, AI and ML will affect how developers build apps as well as how users interact with those apps. As interest in AI and ML grows, these technologies are sure to affect the future of software development.