Three Emerging Trends In AI for Software Development

During the COVID-19 pandemic, many companies changed how they work by using artificial intelligence (AI) and machine learning (ML) to automate important parts of their business. A study from MIT Sloan Management Review showed that 58% of businesses expected AI to make big changes to how they operate by 2023.

AI and ML tools are being developed and refined to tackle specific problems and automate countless manual tasks. The investment in these technologies is expected to keep increasing. According to Facts & Factors, spending on AI and ML is projected to hit $299.64 billion by 2026.

These quickly growing technologies are changing the way software developers do their jobs, enabling them to create higher-quality software faster. Teams working on software development are using AI and ML to create a lot of advanced digital products, and the number of new projects they’re working on is increasing fast. For more information on AI and ML in software development, visit Linkup Studio.

There are countless ways to use and try out these technologies. As their adoption speeds up, here are three examples of how AI and ML are starting to trend in software development.

AI Links with Customer Experience

During the COVID-19 crisis, changes in work setups and consumer behaviors made AI and analytics essential for businesses. This led to a quick shift towards using AI to create customer experiences (CX) focused on people. These are based on data that’s interactive and engaging, designed to encourage user actions.

Incorporating analytics and AI can speed up innovation within organizations. This was the main reason why a top benefits card company decided to introduce a new chatbot, aiming to manage an increasing number of routine inquiries. Learn more about how businesses can benefit from AI here: https://linkupst.com/services/ai-development/.

After looking at user data, the team found out that 20% of users regularly called the call center to check balances, change PINs, and do other basic tasks. To address this, they created a smart AI chatbot to take care of these repetitive customer questions. This led to a big drop in call center expenses and made customer responses faster and more efficient.

By using AI and ML, companies are able to set up systems that automatically analyze huge amounts of data and deliver precise results quickly. This allows designers to improve customer experiences by drawing insights from various types of user and transaction data.

Automated Machine Learning Gains Popularity

AI and ML are getting to a point where they can handle some of their own tasks, which speeds up how quickly AI-based software can be developed. This is great, especially for people who aren’t really tech experts. It’s making the technology more approachable and simpler for all sorts of businesses to give it a go and start using it.

New methods like automated ML (AutoML) are gaining popularity, especially for companies that don’t have expert data scientists or the right computing power. AutoML lets these businesses create and use ML models that have advanced features, all without needing to write any code. This helps them achieve better business outcomes.

AutoML tools take over the more repetitive parts of ML projects. This means that even developers who aren’t experts in data science can train models that are really good and tailored to what their business needs. For example, AutoML is being used to get better at spotting fraud in financial services and assessing risks in the insurance sector.

Advancements in Natural Language Processing Keep Progressing

NLP, which is part of AI and ML, lets computer programs understand and react to human language, whether it’s written or spoken. Thanks to NLP, we’ve seen big improvements in chatbot software, translation tools, and voice assistants.

NLP keeps getting better thanks to the use of pre-trained models that have been steadily increasing in intelligence over the years.

NLP works by taking unstructured data and identifying patterns to understand user behavior. For instance, in call centers, it’s used to convert audio signals into text, which can then be analyzed to gain insights into what customers are saying and needing.

Sky, a major broadcast cable TV company in Europe, employs NLP to understand voice calls in their contact center, helping them gather insights about their customers. Rather than having people listen to hours of call recordings, they use AI to transcribe these recordings. Then, NLP is applied to analyze the transcriptions and present the findings in an easy-to-read dashboard.
By implementing AI and NLP, Sky managed to cut down the costs of monitoring their contact center calls for customer insights and satisfaction levels by 80%.