AI is everywhere. It’s growing at record breaking pace and spreading to many industries. Statista is projecting the annual growth rate of artificial intelligence to be 28.46% between 2024 to 2030, with the biggest investments staying in machine learning and natural language processing. That’s to say, AI for software engineering is going to become more important than ever.

There’s no denying that artificial intelligence and software engineering are going to become closely linked to each other, so artificial intelligence adoption is going to become key for companies looking to stay competitive. To that end, we’ll be looking at some of the opportunities and challenges that AI for software engineering will present for companies now and towards the future.

Is Software Engineering Pointless with AI?

Not at all. AI driven development is not going to replace software engineering any time soon. There are still too many limitations to properly use AI as a completely autonomous tool. Still, that does not mean it’s not having any sort of impact. AI for software engineering is still serving as a sort of complement to their standard work practices to complete tasks faster and improve productivity.

How Will AI Impact Development Cycles?

AI driven development will bring change to the way developers interact with software development projects across all stages of the development cycle. Not only can it serve as tools for increased productivity as we’ve mentioned before, but can also facilitate collaboration between stakeholders and developers through the improved implementation of features and bug fixes.

Some of these changes can look like:

• Developers will no longer code based on desired outcomes, but instead through an understanding of the platforms they’re trying to develop through clearly established goals.
• AI can accelerate the drafting of user stories, acceptance criteria for the project and other requirements. These will still need to be read by business analysts to make sure everything aligns with the overarching strategy.
• AI can build the groundwork for basic UI necessities, like wireframes and flows, leaving more complex requirements to human teams. 
• AI can be leveraged by software development teams to continuously deliver code and PR drafts faster, making them feel continuous delivery. 
• With the sheer amount of volume of codes that AI can produce, testing will become more important than ever. Test architects need to build architectures that allows for testing end-to-end.

The Opportunities and Risks of AI Driven Development

What Generative AI Can Do?

Most of the use that generative AI can provide will be experienced by prompt engineers, though not every process will be affected. Prompt engineers use AI in software development to design inputs meant to achieve certain objectives. Some tasks that prompt engineers can handle are: 

• Routine Tasks: By establishing clearly defined steps AI can complete most routine tasks. This way, developers are freed to focus on more complex problems AI is still unequipped to handle.
• Drafting Code: Sometimes, software developers have no clue as to where to begin when developing code. AI-generated code gives these developers a base to start and play around with to finish developing a code better fitted for the project they’re handling. 
• Updating Existing Code: Finding bugs, improving prewritten programs, and making adjustments based on specifically defined criteria. These are other tasks AI is perfectly equipped to handle.
• Reducing Development Cycle Time: AI can offer quick feedback loops that improve and optimize decision making, reducing the time it takes for projects to reach a final delivery. 

What You Need Developers For?

Human developers, as we’ve said previously, are still valuable to the development process, due to the limitations AI struggles with. Human developers outpace AI in these categories:

• Complex Coding: Some projects ask developers to handle multiple requirements. AI is perfect at responding to prompt engineering, though it still struggles with juggling multiple requirements and keeping the output aligned with business expectations. 
• Context and Organizational Know-how: AI can’t consider organizational idiosyncrasies and preferences unless prepared beforehand. Prompting needs to follow specific criteria in order to align the output with strategic expectations.  
• Broader Strategies: AI functions best under a very narrow scope of work.  It’s perfect for tackling determined tasks, but aligning output with an overarching strategy requires input from human developers to better accommodate those results. 
• Autonomous Action: AI requires inputs to get outputs. AI is mostly generative, as such it requires specific prompts and inputs for it to predict and construct anything. 

How to Use AI in Software Development

Summarize Code and Documentation

Quality Assurance and control usually uses both manual and automated checks. AI is the main component for automated QA tests. The automation process refines the code and then condenses it into shorter passages making it easier to adjust. 

AI isn’t perfect. We’ve spoken about it before. This is why combining it with manual tests ensures the most code coverage. AI can also conduct A/B testing on two versions of the same program to find which version best achieves the business’ needs. AI can also generate documentation for programmers once testing is finished, ensuring developers work under the same parameters.

Optimize Specific Coding Processes

AI in software engineering can help streamline certain coding processes, allowing for faster deliveries. Some of these functions are:

• Suggesting individual lines of code instead of generating entire passages
• Real time corrections
• Following coding language requirements by inserting statements

As developers make strategic decisions about how a program is going to work, AI helps execute those choices in real time, letting developers focus on the more creative side of coding.

Finding and Fixing Bugs and Coding Errors

Automated debugging programs are extremely common software development tools that leverage AI. While developers can already spot issues manually, combining manual processes with AI can streamline the process, since they can instantly detect mistakes in the code and fix them. Depending on the tools used, you could even predict future errors based on the content it finds in your database, saving hours of debugging.

Allocating Resources and Project Planning

Yes, helping companies allocate their resources properly as well as planning their projects is possible. By gathering data on past projects and how your resources are currently being used, AI can recommend new projects to start on, or even determine if specific projects are even worth pursuing at all. 

Some parts of a project that AI can keep up to date through this analysis we mentioned before are: 

• Project Milestones
• Project Timelines
• Budgeting
• Project Scope
• Workflows
• Assigning Responsibilities

Reducing Skills Gaps

The more experience teams require to carry out tasks, the bigger the skill gaps in teams become. Thanks to AI code generation, less experienced developers can acquire a more active role in the software development process. This extra resource allows these less experienced developers to pursue more complex tasks and grow their skills alongside their more experienced team members. 

How to Mitigate the Risks of AI Driven Development

Using AI still comes at a risk, despite the obvious benefits it provides. Even the best automated tools still generate inaccurate work, so anything AI-generated will still require human review until further developments are explored. Other common risks include: 

• Excessive Dependance: Making everything revolve around AI can lead to your team overly relying on it. This means that the moment it becomes unavailable, it can deeply cripple workflows and productivity. 
• SEO Challenges: Search engines scan webpages for signs of AI-written content. Search engines reward unique content, so if anything on the frontend of the page indicates the use of AI, that could affect your ranking.
• Weak Data Security and Privacy: AI-powered security systems base their risk prevention by using historical data. This means that it doesn’t necessarily take into consideration new methods, leaving your products unprotected.
• Unreliable Software Output: We’ve mentioned before that AI does not work well with complex requirements. While AI can deliver code, it might not integrate seamlessly with the general project. Errors or suboptimal processes can lead to flawed software.

If you want to implement AI into your products or business processes, these are risks you need to keep in mind to better prepare your team for success. Here we list a few recommendations to help you implement AI in software engineering. 

• Work Based on Use Cases: AI is not a one size fits all type of solution, so don’t treat it as one. Instead, understand how you’re planning on using AI and find similar use cases for you to base your work off of.
• Risk Management is Key: Develop safeguards to ensure your project is protected. AI in software engineering is not an exact science, so ensuring the proper safeguards are in place allows to attend to inaccuracies faster.
• Restructure Teams: Equipping your team with the right competencies to address concerns with AI is another great solution. From SEO to code reviews, having human elements to evaluate AI generated work will bring a more balanced approach to its implementation.
• Provide AI Training to Your Staff: AI is becoming more democratized every year. Take advantage of this and provide training to both developers and none developers. This increases tech literacy and mitigates the risks associated with it. 

How Tech Leaders Should Treat AI Moving Forward

AI is an ever evolving branch of technology, and it’s here to stay. Here’s how we believe you should treat and leverage AI to ensure your organization keeps up with the competition and isn’t left behind:

• AI is No Trend: AI in software engineering is already bleeding into other industries and their products as well. Finance, health care, and manufacturing are just a few industries embracing this technology. Your business needs to embrace it as well if you desire to persevere.
• Invest in AI Models that Fit Your Needs: Whether it means developing a new AI model or customizing a pretrained one, AI models create a foundation for your work. As such, choose a course that best suits your needs.
• No-code/low-code solutions: Low-code solutions are great for helping experienced tech users and non-technical users work together. This breaks down silos and mitigates skill gaps, allowing for cross functioning teams to be established. 
• Pair AI and Human Developers: Pairing humans and AI when talking about AI in software engineering makes for a dynamic combo. The best work of AI still requires the intervention of human developers.