Increasingly more companies and entrepreneurs are investing in software product development to solve their specific business needs or satisfy customers’ demand for innovation. As a result, the global software product market is expected to grow from $1449 billion in 2023 to $2529 billion by 2028, according to Research and Markets.

Whether a software product is intended for internal or external use, developing it is never easy. Project teams must not only gather product requirements and then design and develop a solution but also test and deploy it while keeping to the agreed budget and timeline. Any inefficiencies and issues emerging during these activities can increase the software’s time to market and compromise its quality.

Luckily, developer teams can use AI technology to streamline all stages of software product engineering and thus build high-quality software products faster. This article highlights seven improvements AI can bring to your software product development project.

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More efficient business analysis

Business analysis is the first step in a software development project where the team clarifies a company’s goals and requirements for the future product and assesses market demand. However, the business analysis stage can be time-consuming, especially when the project is large or there are many stakeholders, which can increase time to market and thus cause potential revenue loss for businesses.

AI can significantly accelerate business analysts’ work by automating some of their most labor-intensive activities. For example, AI can be used to transcribe stakeholder interviews and turn free-flowing natural speech into properly documented lists of software requirements.

In addition, AI can assist business analysts in market and competitor research. AI algorithms can parse consumer reviews about a competitor’s product across the web so analysts wouldn’t have to do it manually. These tools can also run sentiment analysis of the reviews to help analysts understand what people think about a competitor’s solution and assess its weaknesses and strengths. By studying the results of the analysis, a developer team can determine how to differentiate their product.

Improved product design

Due to the emergence of advanced AI-powered tools, developer teams can design software much faster and more efficiently. There can be numerous applications for AI in software design, with architecture design and mockup generation among the most popular use cases.

For instance, AI algorithms can help solution architects create product architecture blueprints based on predefined requirements and rules. AI-powered predictive analytics, in turn, allows solution architects to forecast the effect of various architectural solutions in terms of scalability, performance, and software security, which can help design more reliable products.

Additionally, AI can help software designers generate initial drafts of future products faster. Based on design specifications, AI algorithms can also generate high-resolution product mockups in just a few minutes or even perform automated A/B tests for different design solutions and analyze test data in real time to help professionals choose the best version.

Enhanced project planning

A project plan is a detailed guide for the project team, and AI technology can come in handy when creating it. Developer teams, for instance, can provide AI analytics tools with historical data related to a company’s past software projects and they can forecast the time, financial resources, and workload required to build a new product, as well foresee potential project risks. This way, a team can create a more realistic project plan and increase the chances for the final product to be delivered on time and within budget.

Optimized product development

The process of writing code manually can be too inefficient and time-consuming, especially if a product is complex and feature-rich. Luckily, the use of AI technology allows development teams to streamline and automate a range of coding-related activities. 

In particular, developers can use AI coding assistants with an autocomplete feature, providing recommendations on how best to continue or complete a specific line of code. As a specialist works on the code, an AI ​​assistant can also detect errors and inaccuracies that might otherwise go unnoticed, simplifying further testing and reducing the risk of future software reworks.

Streamlined product testing

AI technology can also be used to streamline testing preparation and execution, two labor-intensive product development aspects. Specifically, AI tools can identify the most likely application usage scenarios based on user behavior analysis to help a testing team build test cases faster. AI tools can also execute tests automatically, analyze their results, and suggest actions needed to eliminate bugs.

More efficient and fast deployment

Manual software deployment processes are typically slow and error-prone, which is why they should also be optimized. AI-powered tools can be used to automate the rollout of new software releases, thus ensuring smoother and faster deployment. AI algorithms can also analyze historical deployment data and help a team identify bottlenecks in a deployment pipeline.

Better product support

AI technology can help a development team provide more efficient support to the product’s end-users, even after the active development phase is over. For example, AI algorithms can generate training materials based on rules and keywords specified by developers. AI algorithms can also monitor product performance after the launch to help the support team detect bugs or security vulnerabilities and fix any discovered issues timely.

Final thoughts

Building a top-quality product and delivering it on time and within budget is a highly challenging goal for any developer team, and keeping development processes efficient is essential to achieving it. AI technology can be used to streamline all aspects of product engineering, from business analysis, product design, and project planning to development, testing, deployment, and support.

Whether your product is intended for external or internal users, you should consider implementing AI tools in your SDLC. If you do not know which solutions you should adopt, an experienced technology partner can help you define the most suitable and cost-effective ones. If your team lacks the expertise to use them properly and build a product with the help of AI, a partner also can join in your project to guide them and provide technical assistance at any product development stage.