The revolutionary technology has already brought lots of changes to the industry and there is no doubt that further transformation is on the horizon. This article explores these upcoming shifts, based on our experience working with leaders from multiple sectors.
The influence of AI on almost every existing industry has become an undeniable reality.
Innovative AI-powered tools assist in surgery rooms and pharmaceutical labs, pre-trained helpers write articles and compose emails, while things like Adobe Firefly reimagine design work.
Technology, Marketing and Advertising, Consulting, and Healthcare are some of those industries standing at the forefront of the digital revolution, leveraging AI to enhance customer experiences, reduce operational costs, and reach new heights.
Here’s some evidence: The seventh annual Top of Mind for Top Health Systems survey from the Center for Connected Medicine revealed that 80% of health system leaders consider AI to be the most exciting emerging technology in healthcare.
The technology unlocks a new level of insight generation, enabling health delivery organizations to make real-time data-driven decisions and improve patient care quality.
Similarly, according to McKinsey, more and more marketing companies utilize AI-based technology to better manage customer relationship services, get insight into customers’ preferences at the most granular level, create effective marketing strategies and more.
But let’s get back to the subject. Let’s take a closer look at the role of AI in Computing Technology and Software Development. What are the results of the integration of AI into the software engineering process? Is AI really a game-changer? And how will things change in the next three to five years? Read on to know.
Transformative Power Of AI In Software Engineering: Top 5 Use Cases
Here are several examples of how different members of a software development team can use AI technologies to enhance aspects of their work, both now and in the future.
1. Automated Code Generation
The most obvious use of AI is automated code generation. Advanced AI tools can automate lots of the repetitive, low-value coding tasks that bog programmers down.
For instance, rather than just providing autocomplete suggestions, sophisticated AI systems can create functions, classes, and database queries taking into account the context of the codebase.
Furthermore, AI-powered code generation accelerates prototyping, enabling software engineers to quickly generate boilerplate code in a wide range of programming languages and frameworks.
Automated code generation tools like DeepCoder or TabNine could generate customized starter code, create configuration files, prepare build scripts, and give specific instructions based on the chosen tech stack.
Recent estimates suggest that AI technology has the potential to boost programmers’ productivity by 35 to 45 percent. Additionally, AI assistants can accelerate the documentation of code functionality for maintainability by 50 percent and speed up code refactoring by 20 to 30 percent.
According to a StackOverflow survey, ChatGPT has become an indispensable tool for 84% of software developers.
Overall, software engineering is headed toward a new era, where many businesses will use AI agents to build the majority of their custom software development solutions.
Gartner predicts that over the next three years, generative AI will create new roles in software engineering, requiring 80% of developers to reskill.
2. Automation Testing and QA
AI-driven software also can do a lot in the realm of software testing.
Applitools, Testim, and similar services can analyze extensive codebases in a matter of minutes and with greater accuracy than human Quality Assurance professionals.
The capabilities of AI-augmented software testing tools cover the gamut of the testing life cycle including:
- test scenario and test case generation,
- test automation generation,
- test case prioritization,
- defect prediction and risk analysis, etc.
3. Security And Compliance
Unlike old-fashioned security systems that depend on signature-based detection schemes and can only recognize known threats, AI-driven threat detection tools employ machine learning algorithms to analyze vast datasets and identify anomalies that could indicate unauthorized access.
This capability is essential for identifying zero-day exploits and advanced persistent threats (APTs) that often bypass traditional detection methods.
The technology improves threat response, allowing security systems to execute predefined actions without human intervention. Say, if an AI-powered security software identifies a potential ransomware attack, it isolates the affected system from the network right away and tells your security team about alerts and vulnerabilities.
Regarding compliance, AI ensures deployments align with industry standards and regulations, whether it’s GDPR or HIPAA.
AI systems would examine code for any noncompliance issues, such as inadequate logging practices, poor/missing encryption, or improper data handling, ensuring strict adherence to the rules.
The projected value of the global AI cybersecurity market, from 2023 to 2030.
Source: Statista
4. AI-Assisted Design Process
A responsive and intuitive UX design is pivotal for customer retention and new user attraction.
AI tools examine massive datasets to identify patterns, interpret qualitative trends, and forecast future user behavior.
All these things empower designers to improve the functionality and boost the visual appeal of digital products, ensuring that every interaction is meaningful and user-centric.
A great example of AI design tools in action is the creation of accessible applications.
Ensuring all people, regardless of ability, can comfortably navigate and interact with a product is a commonly overlooked component in UX design.
AI tools can automatically detect accessibility issues in apps — lack of voice recognition, text resizing, color adjustment, alternative image text — and suggest fixes.
Such an approach not only demonstrates brand empathy and ethics but also makes those with disabilities feel valued and understood.
5. Customer Service
There is almost no doubt that by 2027, more IT companies will turn to leveraging AI and an end-to-end technology stack to deliver personalized service their customers want. With cost pressures rising as quickly as service expectations, this route seems like the most viable option.
Whether that be an evaluation of the systems’ problems, chatting with customers on social media platforms, or distribution of complex tasks among technical support team members, an AI-enabled customer service transformation (done well) can open the door to more proactive customer service that supports self-service and decision-making both for clients and employees.
Generative AI is set to disrupt the majority of the software market, but the effects will vary considerably across a wide range of industry categories.
Source: McKinsey
With nearly 80% of businesses already integrating AI into their processes, are you going to be left behind?
Work With Us
Elinext is a premier full-service custom software development company with a team of more than 700 vetted software developers specializing in architecting scalable custom software solutions in artificial intelligence, machine learning, blockchain, cybersecurity, IoT, CRM, ERP, data analytics, HR management, infrastructure management, augmented & virtual reality, and social media community domains.
As a tech partner with 27+ years of industry expertise, we have a good store of knowledge, making us fully prepared to help companies looking to embrace the power of Artificial Intelligence with their projects, regardless of the complexity, tech limitations, or regulatory hurdles.
An application for sentiment voice analysis is just one of the many successful projects that allow us to confirm our rich experience in developing tailored groundbreaking AI-powered solutions.
The Elinext team was hired by a large call center to develop an emotion detection software that could detect emotions in speech.
The Client expected to have a software system that could do the following things:
- Capture callers’ voices in real-time and automatically detect the emotions of a caller. Once a call center operator sees that he/she has to handle an aggressive phone call, they may direct such a call to more experienced colleagues;
- Detect happiness, sadness, and satisfaction in the voice of a caller;
- Identify the state of an operator by evaluating their emotions to substitute a tired one when necessary.
Our efforts resulted in a great outcome — Elinext’s engineers developed an Android application that detects emotions in speech, providing call center workers with valuable insights into the true feelings of their clients.
Here’s how the application works
Have a look at our case studies page for some more examples of how we’ve helped companies with the implementation of their business idea and get in touch here to see if we could be a match.
