How Artificial Intelligence is Transforming the App Development Process

Artificial intelligence and machine learning are bringing powerful benefits to the app development process, signaling one of the next new phases in the digital transformation of the workspace.

Competing In the Age of AI, a book by two Harvard professors detailing the potential of artificial intelligence on business operation, begins with a stunning example: Created in 2014, Ant Financial Services Group had seen such explosive growth to its dizzying array of business services that by 2018 had a valuation of $150 billion dollars.

How could a company handle such tremendous development in such a short amount of time?  By building around a digital core that features artificial intelligence.

The potential power of AI on business operations is obvious: According to a study by Forrester, 71% of respondents claimed that AI could improve business efficiency, while 59% said it can improve scalability.

But it’s way more than potential these days.  It’s a reality.

A research study by Accenture found that 84% of C-suite executives believe they must leverage AI to achieve their growth objectives, while companies already strategically scaling AI reported a 3x return on investment.   What’s more, the same research shows that 3 out of 4 of those same executives believe failing to adequately scale AI in the next 5 years could put their entire business at risk.

Embracing a modern approach to app design and development is crucial for any company striving — not only to operate at peak efficiency and productivity — but to drive innovation and attract top talent with the core-business apps of tomorrow.

How are Artificial Intelligence and Machine Learning connected?  

Often used interchangeably, artificial intelligence (AI) and machine learning (ML) are interconnected technologies.

AI refers to the larger project of creating computer systems that can predict and simulate human behaviors, accomplishing tasks typically associated with human intelligence and thought.

Machine learning is one of the many branches of AI, dealing specifically with computer programs designed to gather data, identify patterns and make decisions on their own or with token human assistance.

Many artificial intelligence systems are powered by machine learning, which aims to improve at a task on its own without having to be programmed for that specific task, saving untold amounts of coding time and analysis.   ML tools are some of the most in-demand AI-powered tools for businesses, based on their capacity for translating massive data sets into pragmatic next step solutions.

These solutions are everywhere.

Virtual personal assistants — think Siri and Alexa — use machine learning AI to continuously “learn” how to respond better through the constant gathering and applying of data.

ML is what redirects your GPS to a faster route while you’re driving, recognizes a face when you post on social media, filters out your spam emails and refines your search engine results.

And it’s present in everyday life in all types of other ways: from how banks determine possible fraudulent activity, to the way retailers customize the customer experience, and even in how healthcare is delivered to patients.

The aim of AI isn’t simply to automate repetitive tasks through simple learning; it’s to respond intuitively, not with clumsy predictability but instead with hyper-specific insights based on accumulated knowledge. In finding patterns and pinpointing regularities in data, AI algorithms can develop a specific skill that predicts and reacts according to the moment.

Developers and designers already recognize these technologies as hugely influential tools that are saving time and cutting costs for the forward-thinking companies that have embraced them and are looking for ways to apply them to app development.

The challenges facing app developers in the future

As digital transformation restructures how businesses approach traditional work, new methods of app development are putting AI into the spotlight, showing off the benefits of increased integration of AI into software design and delivery.  Which is needed, because developers today are under intense pressure to produce.

Developers today face an onslaught of challenges: 

• Time pressure to produce and maintain complex web apps  
• Cost concerns of testing, fixing and updating these apps
• Matching the interoperability and interface needs of a diverse workforce and customer base who use a range of software and devices
• Constantly meeting raised expectations for performance and design which demands responsive innovation

Designers have to balance the demands of creating apps that satisfy various employee and customer needs while accounting for pragmatic pressures of the development process.  All within an enterprise context that is embracing customizable content production on dynamic platforms that are ever-evolving.

A shift toward multi-experience development platforms (MXDPs) that allow companies to more quickly scale and deliver their apps is mirrored in how the apps are being built.

Speed and flexibility are crucial, as developers must be able to adjust in hours instead of days, with a range of tools that allow for intuitive collaboration.

Why businesses are adopting AI and ML tools into app development

The need to meet these demands — and be positioned for success going forward — has companies integrating AI into their operations more and more.  This increase in adoption is driven by access to high-quality learning models, and the necessity of steering away from investment in slow and expensive infrastructure for data management.

And the pace of adoption is only increasing.  A recent Forbes article said that more than half of organizations would spend between $500,000 and $5 million on AI technology and talent — up by more than a third over last year.

Projected spending on AI technologies will be $98 billion by 2023, according to IDC — a yearly growth of almost 30% between 2018 and 2023.

Companies are gravitating towards AI-technologies because they speed up development, integrate efficiently and safely, and make scaling effortless.  AI algorithms can automate many painstakingly arduous tasks that previously would have sidetracked developers for hours upon hours, freeing up time to focus on more high-value tasks.

Increasing velocity through a streamlined process

Creating velocity in both app development and adoption depends on a fast design process. From concept to prototype to iteration to approval, app development has to move quickly while maintaining peak standards. AI-powered machine learning infuses design software with new tools that gives developers newfound freedom to create and test with speed and precision.

AI gathers and processes data on a whole new level, automates development and puts ML-tools to work.  These tools aren’t pushing talented developers aside, but rather extending their capabilities by providing better source code analysis, automated help with code production, and rapid prototyping.

New low-code design software, aided by AI, lets designers build and integrate more easily with a variety of relevant digital technologies such as intelligent chatbots, wearables and conversational apps.  Thus, app development software meets the needs of audiences that are embracing textured, interactive and customized online experiences.

So, while lots of coding still requires laborious, line-by-line work by developers, AI-powered low code software is cutting down on expensive design bottlenecks.  This not only saves time and money, it reroutes developer energy to the constant innovation of new online experiences rather than the maintenance of old ones.

This includes the development of platforms like MXDP’s, the utilization of Progressive Web Apps (PWAs), and the embrace of design ecosystems like intelligent development environments and OpenAI.  These tools are changing how businesses operate, and how customers engage with them.

ML-powered algorithms and AI-driven analytics provide not only deep insights into how apps are being used — and can be improved — but they also connect to other tools that are revolutionizing how businesses design innovative systems.  Developers are using AI to build apps that fit into a modern business ecosystem and solve modern business operation challenges.

The next step in the ongoing digital transformation of the enterprise workspace is continued integration of artificial intelligence and machine learning.

Resistant companies that disregard AI do so at their own risk.  Embracing and integrating AI into core business operations might not result in a valuation of $150 billion within five years but ignoring the clear signs of change could result in no business operations in five years.

Learn more about our industry-leading low-code app-dev platform, HCL Volt MX, which features an AI-powered built-in test recorder that generates test cases to be executed on the cloud as part of the continuous integration/continuous delivery, or CI/CD, pipeline.