Is AI in software development right for your project? Let’s investigate

Accelerated time to market

Incorporating AI into the software development cycle has the potential to accelerate your time to market. From code generation to documentation to testing, AI can accelerate the rate at which it takes to complete all kinds of tasks. The result? Your software application gets into your users’ hands faster than the competition. This gives you the upper hand when it comes to deploying updates, resolving bugs, and responding to user feedback.

Whether you plan on using AI to streamline existing tasks or generate entire applications from scratch, the potential productivity gains could be significant. Research shows that AI productivity gains are most evident in tasks like active coding (with time allocation down by 13% compared to coding without AI), planning and design (-3%), and documentation (-2%).

Greater focus on innovative thinking

By using gen AI tools to automate a wide range of development tasks, software development teams have more time and resources to focus on more innovative solutions.

For example, a software development team may choose to invest more resources in improving the user experience. They may be able to conduct more user testing, uncovering insights that were previously inaccessible to them due to time and budget constraints. This is just one of the many possibilities of reducing the need for software developers to perform repetitive tasks, instead delegating such tasks to properly trained agentic AI tools.

Higher-quality products

When incorporated into the development cycle properly, the right gen AI tools can help raise the quality of software applications and systems. This is achieved by using AI to help align software development tasks with client goals and user requirements. AI can help create plans, case profiles, and user data, giving software developers deep insight – insight into what problems they’re trying to solve, and how best to solve those problems. This way, software development teams can deliver software applications and systems that more closely align with a client’s goals.

Teams can also use AI to enforce strict software development standards, such as coding practices and industry-specific guidelines. For example, developers can train gen AI agents on the company’s best coding practices, ensuring they follow the right informal rules and guidelines. Doing so can help improve consistency and increase code quality, enhance scalability, and reduce technical debt.

At Orient Software, our 400+ dedicated team is highly skilled in using AI to build high-quality software applications and systems that meet user expectations. Our use of the latest tools helps augment our developers’ existing skillset – not replace those essential skills. We conduct human oversight on every gen AI tool that contributes to your project. This way, you reap the AI productivity gains and get the assurance that your software application is vetted by a human expert.

Enhanced collaboration and communication

Software development teams can deploy AI to foster greater collaboration and communication. Since AI can handle routine tasks like code generation, teams can more effectively work together. They have the freedom to brainstorm, experiment, facilitate feedback, challenge preconceived notions, and form unified goals. This shifts the development process from a lonely, isolated experience to a more dynamic, collaborative space; a space where acceleration and innovation are championed equally.

Gen AI-powered tools can also help streamline other project management tasks. Such examples include task allocating and monitoring, deadline estimating, and allocating resources for specific cases. With the potential for more accurate timelines, clients can be better prepared for the deployment phase.

Inception

The inception phase is where planning and design happens. Software developers use AI to define client and technical requirements, generate project plans, create custom case studies and user profiles, and clarify questions asked by the team. Essentially, the inception phase is where AI helps turn intent into a unified plan, ensuring teams are on the same page.

Humans thoroughly validate the outputs generated by the AI, addressing ambiguities and mistakes before any coding begins. The successful completion of this phase ensures that the AI has the right guardrails in place to follow through on its plan during the Construction phase.

Construction

With a plan in place, the AI starts building the software application. It takes care of coding, building and executing test cases, and building CI/CD configurations. Humans then thoroughly review, modify, and approve all outputs at predefined checkpoints.

All context decided upon in the inception phase is carried through to the construction phase, especially security and technical considerations. Architectural decisions, security considerations, and business limitations are retained to ensure consistency between all stages.

Operations

The operations phase is where the AI deploys and monitors the final project. It handles tasks like deployment orchestration, the observation of runtime behavior, and anomaly detection. Developers not only observe the outputs but also help maintain governance and security compliance. Human intervention is also a core aspect of responding to incident escalations.

Promote continuous human oversight

It’s important to remember that AI is not a ‘set it and forget it’ solution. Human review is an important component of the AI-DLC framework. Without the right people overseeing an AI’s output, unpredictable results can occur.

Choose a software development team that takes human oversight seriously. They should be involved in each stage of the cycle, from designing and coding to testing and deploying. Your team should have a contextual understanding of what each AI solution is doing, why, and how it’s doing it better than the manual way.

How important is human oversight? Very important. According to the Stack Overflow Survey 2025, the number one frustration among respondents was AI output quality: solutions that were almost right, but not quite there. Having people to fine-tune those slight errors is vital to improving product quality and the user experience.

Enforce rigorous AI code testing

It’s no secret: AI-generated code is not perfect. It has the potential to be unpredictable, to suggest less-than-ideal code snippets. That’s why it’s important for software development teams embracing AI to enforce rigorous AI code testing standards.

That means taking the time to review the quality of code generated by these outputs. Has it produced the best possible suggestion? Or is there a lighter, more optimal alternative out there? Your software development team should be able to justify the code they use in your products. This way, they can more easily maintain your software, as they know exactly what’s going on under the hood.

Partnering with a team that “vibe codes” their way through a project is a recipe for disaster. When something goes wrong, they won’t know what caused the issue – at least not straight away. It could take hours, possibly days, to diagnose a problem and deploy a fix.

Employ version control for seamless rollbacks

Version control is an effective way to keep track of progress. It enables a team to safely rollback the software to an older version, one that is stable and accessible. In the event of a major error, being able to “turn back the clock” is a valuable resource. It enables them to compare the two versions, to determine where the source of the problem may lie.

Gen AI can help streamline version control. It can run routine checks, explain complex changes, and provide context reviews. This helps speed up the process of comparing versions and identifying problems.

Break down complex tasks into manageable steps

When it comes to shipping AI-generated code, it’s important to break down the review process into batches. This gives the team ample time and resources to carefully review each line, ensuring no stone is left unturned.

Gen AI can help out in other ways, too. Gen AI can help break down complex tasks based on skills, capacity, and available resources. Prompts like, “How long would it take for an engineer to create an API integration plan?” can help project managers allocate resources and deadlines accurately.