How does AI impact software development?

Radhika Madhavan

Director of Marketing

Table of Contents

Artificial Intelligence (AI) hasn’t left many industries untouched—software development is no different. Given the rising popularity of this technology, many companies are rushing to adopt it for the many benefits it offers. With respect to app and software development, the primary benefit is that it helps developers produce higher quality products—at a lower cost.

Today’s business climate is fast-paced, meaning it requires developers to create, test, deploy/ discard applications as quickly as possible. It’s only possible when developers have a second mind—or in this case, an artificial one to help them out. It frees their mind to make decisions critical to a business’s innovation cycle.

A recent report by Deloitte indicated that when developers use AI-powered technology, their productivity increases 10 times as opposed to when they don’t. It shows how companies can benefit from incorporating AI in application development workflows.

This article will delve into how AI affects software development, its benefits, and what the future holds in this regard.

How are businesses incorporating AI in their software?

Data has always been a critical business asset—but only now do we recognize its value. In a data-driven world, companies are crippled without it. In addition to that, with the giant flood of data streaming through their systems, they’re unable to play catch up—this is where AI can help.

An O’Reilly report indicated that 26% of surveyed enterprises already have AI-led projects in production, with another 43% still evaluating its potential. It only shows the rising popularity of AI in the business context—with a potential increase in adoption over time. But, the question remains as to how you can incorporate it into current workflows.

The image depicts a pie chart on how AI adoption is increading in the market
The adoption and maturity of the AI market

Image Source

There are several ways businesses incorporate AI usage within their existing software. Some of them include:

  • Reasoning: To start, many companies explore options like chatbots that need reasoning capabilities. The reason is that customer service is their priority—which means anything that could mimic those functions is of extreme value. Apart from enhancing customer experience, they can optimize existing business services too. Any capability that bears the burden of human reasoning can be explored here.
  • Recommendations: Relevancy is key in today’s day and age. Most software tools can create comprehensive reports, but creating relevant ones is hard. Using AI, businesses can leverage automation and trained models to receive relevant insights for their operations.
  • Behavioral insights: This is particularly useful for securing your business. AI can analyze current behavioral patterns within the organization and chalk up recommendations that best serve you and your operations.

A few industries have been using AI in various ways. A few use cases include:

  • Healthcare: to increase the speed of diagnosis, identify potential vulnerabilities in electronic health records, etc.
  • Insurance: Using visual intelligence for claims processing
  • Energy: To develop intelligent power plants, create predictive maintenance models, etc.
  • Legal: Using robotic processing automation for document uploads

How does AI impact software development?

Now that we’ve seen how AI works in the business context—let’s look at how it impacts the software development process.

Helps you consolidate requirements

Previously, requirements were sought manually. Developers had to meet with the clients multiple times, hone in on their needs, and draft a document. Once a basic idea is in place, using tools like Infosys Nia and Google ML Kit, they can automate certain parts of the process.

 Plus, they can use Natural Language Processing (NLP) to train these models & create high-level software models to do the work for them.

Hastens the design process

The software and app development or, instead, designing process is a time-consuming one. First, the developers draw up an initial plan based on the clients’ requirements. Based on a review process, they need to keep iterating until they achieve a satisfactory result. 

Using AI, developers can build comprehensive software models and cut the development time significantly. An example of an AI-led assistant is Artificial Intelligence Design Assistant (AIDA).

Generates code on-demand

The biggest misconception is that coding is the only skill developers get hired for—which is far from the truth. Instead, they’re being hired for their ability to understand the product concept and build it. It is where AI can help them turn their idea into functioning code. 

Developers can input the product requirements and use several AI tools and NLP frameworks to generate functioning code. What’s even better is they can identify potential bugs in the code, and the developers can fix them as needed.

Improves deployment process

Here, developers can’t automate the entire testing process. Instead, they can use it to identify errors and fix them as they come. Machine Learning (ML) algorithms are beneficial as they improve the deployment process by checking for these errors. 

Developers can upgrade their applications as and when they’re being deployed—without having to wait, correct, and then deploy. Also, you can use AI to create test cases and perform regression testing to create better functioning applications.

Benefits of AI in software & application development

While the impact on the software development process is evident, here’s what it looks like in action:

Increases scale & time to market

The DevOps approach has pushed stakeholders to measure potential by the amount of time it takes. Everything is time-bound—application creation, deployment, frequency of deployment, and even overall time to market. 

The differentiating factor artificial intelligence offers the app and software development process is speed. The time to market is drastically reduced as developers can automate designing, deployment, and iteration resulting in a fast-paced software development life cycle (SDLC). 

Improves developers’ productivity

Developers can spend more time doing things that require their attention. It includes tasks such as:

  • Identifying areas for improvement
  • Fixing necessary bugs
  • Perfecting the product from the user’s standpoint
  • Contributing to a quicker SDLC

Informed decision-making

AI is handy for making informed decisions as the algorithms are constantly fed real-time data. It means that it’s continuously learning and providing relevant insights depending on what the data is saying at the moment. 

It can assess what worked and didn’t—and help stakeholders make informed decisions. Another advantage is that the decisions can be free of biases. However, that depends on the data being put in—a conversation for another time).

Precise error-management

In this case, error management goes beyond the standard errors found in the application code. It includes downtime, a severe issue for most applications, irrespective of where they’re hosted. 

It costs businesses a lot of time, effort, and money to resolve these issues, which is where AI can be helpful. The algorithms can flag errors, and developers can resolve them at pace. In the post-development phase, you can also use it to analyze logs and flag problems.

Real-time feedback

The reason the entire development process is faster is because of real-time feedback. A dynamic feedback cycle can be created using AI to provide a more personalized experience for users. Based on their behavior and input, it can also give more accurate and relevant recommendations.

Potential challenges for application developers

The biggest challenge developers face is the fear that AI will take over their jobs. While it seems like it, that’s not the case. Instead, the role itself is meant to change with time—from a developer to an AI programmer.

Most applications are developed with insights from a human because there’s only so much an algorithm can do. It can automate your processes and maybe even contribute in a thinking capacity, but it can’t do everything. Developers will still have to source the data and feed it to the training models to create more accurate software and applications.

The real challenge will be in adapting to the progressing role of a developer, or in this case, an AI programmer. They will have to adapt to the wave and build on other overlooked skills like communication, collaboration, etc. The intention is to go beyond coding—into a more creator role—which is more reflective of their responsibilities. They might write less code over time but will have to adapt to work in a monitoring capacity. 

Another challenge would be building the skill to use popular AI tools in the market. As most companies are looking for skilled developers who are well versed in its usage, honing these skills is necessary.


All in all, software and app development processes are only set to improve with time. While the initial concerns about AI adoption seem to be fading with time, there’s still much headway to be made. As a business, time is money—and with AI-led development cycles, you can be assured of much more precise production and faster cycles.

Our team at High Peak Software can help you build products in line with your business needs. If you’re looking for a software development partner, reach out to us today.

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