Using Artificial Intelligence and Machine Learning in Application Development: What does it mean?

Let’s start with something obvious. At the risk of repeating what you already know, here is some latest reminder of how applications and their business-potential are proliferating. As per App Annie’s recent data, consumer and mobile ad spend were touching $380 billion in 2020. Its latest State of Mobile 2021 report says that over 16,000 iOS developers saw more than $100,000 in consumer spend in 2020. Not just that – almost 2,304 saw $2 million or more, up 25% in YoY terms. People also spent 82 billion hours in shopping apps, and the time spent in 2020 on shopping apps surged 45% worldwide (outside of China). As to the total downloads – they reached 4.6 billion and the global time spent on business apps grew a whopping 275% in YoY levels in Q4 2020 alone.

Translation – applications are the new wheels that are running everything in this world. They mean more customer eyeballs, more marketing purses, more time spent on devices, and more revenue numbers – in every direction. In fact, in 2020, nine mobile-first companies raised $1 billion or more in new funding.

So almost every company today is, or has to become, an application company. But creating and maintaining awesome applications are tasks which are not that easy.

What are the challenges in app development?

From the back-end challenges of speed, testing, delivery timelines and latency to the front-end imperatives of clutter-breaking user experience – an application has to handle a lot of expectations.

Ironically, in the Web Dev State 2020 Survey, it has been observed that application development players are facing several challenges like not enough time for design (72%), lack of in-house user experience (UX) resources (65%), and lack of in-house user interface (UI) resources (43%). Also, almost 90% of the respondents cannot eke out time to figure out how their software will actually help users, and how much they are ready to pay for it. Plus- they struggle with high development costs (58%), development resource availability (72%), solution scalability (79%), complexity in deploying changes (85%), and lack of time for idea validation (86%).

Then there is the high bar of being immersive and multi-experiential that today’s applications have to live up to. As a Gartner study reasoned, with a rise in user application touch-points’ frequency, coupled with a massive change in modalities and expansion in device type- the future of app development is going to be all about multi-experience. Browsers have ceased to be only touch-points of applications now. There is a flurry of devices and platforms to cater to – from IoT and hybrid screens to smartwatches, smartphones, and voice-driven devices. They are highly immersive with special needs in type, touch, gestures, and natural language in digital user journey.

Gartner’s survey found mobile apps at the top of this pecking order (91%), followed by conversational apps (73%) and chat-bots (60%). It surmised that this reflects the natural evolution of application functions to support the digital user journey across natural language-driven modes and devices. It added how cloud (AI) services are the most widely used technology to support multi-experience application development (61% of respondents), followed by native iOS and Android development (48%) and mobile back-end services (45%). It also pointed out that the top barrier to building compelling multi-experience applications is the need for business and IT alignment – as seen by 40% of survey respondents. Shortcomings in developer skills and user experience expertise (over a quarter of respondents) came up as the other set of challenges here.

Basically, applications cannot be whipped up the way they were cobbled together in the past. There is a special emphasis on speed and user-intimacy in the current context. That’s exactly where AI and ML come to the rescue.

How is AI used in application development

AI is when machines or computers or algorithms manifest and leverage a level of intelligence, smartness, and cognitive understanding that humans are capable of. Machine Learning (ML) is a subset in AI’s buffet. It is where the programs (that are designed for using intelligence and solving problems) are trained. This is done by using the ability of machines to learn from data and decisions, because they are designed for that special capability. It can be done through supervised learning, through unsupervised learning, or through reinforcement learning.

AI and ML have the power and scale to help analyze massive piles of data that is scattered everywhere. That is where developers can get better, faster, and harness real-time insights to up the ante on UI.

Look at what the Deloitte’s Tech Trends 2021 Report echoes. The world is now about scaling model development and operations with a dose of engineering and operational discipline. It underlines how MIOps (a market pegged to be worth US$4 billion by 2025) is already getting stronger and stronger to automate model development, maintenance, and delivery—and to shorten development life cycles and industrialize AI. This is AI’s version of DevOps for revolutionizing and automating software development. Enterprises can now marry and automate ML model development and operations, and accelerate the entire model life cycle process. This will help to enhance business value by fast-tracking the experimentation process and development pipeline.

AI and ML can empower developers and application players with automated pipelines, processes, and tools. They can strengthen and accelerate areas like continuous development, testing, deployment, monitoring, and retraining, etc. These technologies are also the best bets to embrace when we want speed and scale to discover patterns, reveal anomalies, make predictions and decisions, and generate insights. It allows more room for experimentation, rapid delivery, and production efficiency that amplify business value.

AI and ML help in an immense and radical way in identifying bugs. The predictive accuracy and data-advantage of reinforced learning can give a new meaning to the idea of real-time and adaptive testing. Continuous training and the ability to see patterns give a new fillip to the field of adding core security to today’s applications. Deloitte calls it a scalable, efficient, and faster approach for improving development resilience, reduce production bottlenecks, and increase the reach of ML projects. It also explains how MLOps practices encourage communication between expanded development and production teams. And how Automated machine learning, or AutoML, can inject speed in model development which helps to quickly test different models and variants.

Even when we think of the app stores, platforms and device environments; we find many advantages emanating from AI-ML. SDKs (Software Development Kits), neural engines and other intelligent ingredients of today’s Android or iOS have started helping developers to leverage machine learning capabilities of the device radically.

This helps developers to tap deep into the explosion of big data all around us. Machines can hear this constant supply of data coming from different directions and at different decibels. They can crunch and make sense of all this unstructured data, converting it all into UI insights.

And that’s not all. There are numerous ways to integrate AI and ML into application development. The direction is usually defined by use cases and the type and complexity of tasks, features, security and flexibility requirements, etc.

Building an app with AI and ML – An evolving market

A lot of AI and ML are already being used in development pipelines. For example, look at how developers may use frameworks like Ionic for accelerating the conversion of web app into a cross-platform mobile app. There are special languages and tools dotting the market now, like PyTorch and Tensor Flow. A lot of ML-level UI kits have also emerged. They help to save time and customize applications in a never-before way. There is increased attention to presence of ready-made templates, no-code, and low-code app development platforms today. They help in cutting short the time-to-market, and in arresting wastage of development resources. They can help to scale apps on the fly, while reducing application-maintenance burden to a considerable level.

In fact, as per Web Dev State 2020 Survey, 25% of the respondents expressed plans to drastically reorganize their web development approach. The low-code market can reach around $14 billion by 2024. The availability of language and speech-based Application Programmer Interfaces (APIs) has also facilitated developers in a major way.

Challenges for developing apps with AI and Machine Learning capabilities

However profound AI and ML may be, the potential of these enablers can only be fully realized when they are used in the most optimum, and contextual, way. A lot of developers struggle with model development and operational complexity of ML. In a study by Evans Data on AI and ML development (survey done with 500 AI and ML developers globally), it emerged that 55.9% of AI and ML developers rely on language APIs, and about 51% on speech. There was a clear gap on the presence of quality tools which is slowing down AI and Machine Learning app development today. Other barriers were – cost of materials and lack of necessary skills or training. Almost ten% were found dealing with the challenges of working with and integrating into legacy systems. About 38% shared the challenge of the complexity of managing operations. As the Deloitte report also reminded, just 8% considered their companies’ ML programs to be sophisticated, while 22% said it takes between1-3 months to deploy a newly developed ML model into production—where it can deliver business value.

However, most of these challenges can be addressed with the help of an expert or a partner. Irrespective of the size, more and more companies are baking AI and ML algorithms into their applications to offer a plethora of innovative and advanced services to end-users. The right mix of tools and partners can help you tap the true power of AI and ML in application development.