AI in software development: Key opportunities + challenges

Whether you’ve tried ChatGPT once or use automated tools daily, it’s hard to miss AI’s monumental growth. According to Grand View research, the global artificial intelligence market is expected to grow 37.3% from 2023 to 2030. And of all fields, AI in software development will see some of the most investment.

There’s no denying that AI has a place in the future of software engineering, so getting ahead of artificial intelligence adoption is crucial for tech leaders to stay competitive. To help refine your AI strategy, we’ll highlight opportunities and considerations for implementing AI in software development.

Software development AI will change the ways teams design, develop, document, deliver, and debug software. Developers could also use AI as a mediator when collaborating with teammates, stakeholders, and customers. Specifically, AI can speed up feature additions, bug fixes, and support requests. 

These changes stem from a few key sources: 

• Developers will switch from design to platform thinking. In the past, developers built code for outcome-oriented design. Now, AI developers will focus on how platforms function in goal-oriented design. 

• AI will help draft user stories, acceptance criteria, and requirements. developers will pass this information to business analysts to ensure it aligns with their overall strategy. 

• AI will assist with basic UI design and leave more complex interactive design elements to human teams. After AI lays the groundwork for pages and flows, designers create a UI that helps users navigate through them. 

• AI will deliver true continuous delivery. Agile teams can use AI to write high volumes of code and draft PRs for teams to review. With AI assistance, developers can increase their overall rate of delivery to make it feel more continuous. 

• Testing will become a higher priority. As AI produces more code, teams need to build the architecture that tests it from every angle. Test architects will assess end-to-end functionality and create new regression tests if issues emerge.

While using AI in software development won’t improve every process, prompt engineers can play to its strengths. Prompt engineers design inputs to get the desired output from AI. Prompt engineering can help teams handle work like: 

• Rote, repeated tasks: AI can complete routine tasks with well-defined steps. While this work is important, leaving it to AI frees up developers to focus on more complex problems AI can’t handle.

• First drafts of code: Software engineer AI gets the first draft off the ground quickly. When developers aren’t sure where to start or have trouble entering a flow state, AI-generated code gives a great place to start. 

• Small updates to existing code: AI perfectly suits small edits and code refreshes. You can use AI tools to find bugs, improve prewritten programs, and make adjustments based on specific criteria.

• Reduce cycle times: AI offers tight feedback loops and the ability to analyze business roadmaps. By tracking performance across projects, AI can improve predictions and find the optimal path to completing tasks. 

Despite AI’s strengths, human developers outpace it in many processes. So, you still need a team of human developers for:

• Complex coding requirements: Some projects ask developers to juggle multiple requirements. While AI can respond to prompt engineering, it struggles to manage complex criteria while keeping the big picture aligned with expectations. 

• Contextual outputs and organizational knowledge: AI can’t predict your organizational preferences, so AI-generated programs may not align with your security and performance requirements. Software developers need specific prompting or edits to align the code with strategic initiatives. 

• Broader strategic approaches: AI works best within a narrowly defined scope. It can create programs that fulfill specific tasks but can’t align its output with wider strategic approaches. You need a human dev to stylistically and functionally align all your software.

• Autonomous action: AI requires inputs to get outputs. The current crop of AI is generative, so it creates text from prompts and predicts what should come next based on the vector weights.

As AI and its benefits continue to grow, the onus is on engineering leaders to keep up with emerging trends to make the most out of the technology. For example, LangChains is an open-source framework that allows developers to chain together multiple large language models and perform more advanced actions. 

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Quality assurance and control usually take rounds of code reviews and manual checks. However, AI can improve software reviews by automating your testing process. After refining the code, it can condense long pieces of text into shorter passages. This concise rewrite is easier to parse and adjust down the line. 

While AI isn’t perfect, combining it with manual tests ensures the most code coverage. AI can also conduct A/B testing on two versions of a program to find the best solution. After testing wraps up, AI can generate documentation for programmers. This helps teams find the root cause of issues or inconsistencies in their work.

Automated tools can streamline coding processes and help deliver projects faster. Unlike total code generation, these tools can:

• Suggest individual lines of code

• Make small corrections in real time

• Insert statements to adhere to coding language requirements

These tools allow developers to focus on the creative side of coding. As they make strategic decisions about how a program works, AI helps execute those choices. This process can also help developers learn new strategies in real time. 

Successful project deliveries take careful planning and budget setting. AI can review past projects, gather data on your allotted resources, and recommend new projects. This planning can also help determine whether an initiative is worth pursuing. 

After allocating your resources, AI can predict outcomes. Specifically, automated platforms use this data to update:

• Timeline and project milestones

• Budget and resource planning

• Project scope

• Workflows

• Assigned roles and responsibilities 

Despite the benefits, AI comes with its fair share of risks. Even the best automated tools can provide inaccurate outputs. AI-generated work will require human review for the foreseeable future. A few other common risks include: 

• Technology dependence: Restructuring teams and processes around AI can make you dependent on it. So, if you lose access to AI, that throws a wrench in your core workflows, which means lost time and resources.  

• Changes to search engine optimization: Search engines scan webpages for signs of AI-written content. If a page misses that human touch or repurposes existing content as AI tends to do, expect a ranking drop on your website. 

• Weaknesses in data security and privacy: AI-powered security systems base risk prevention on historical data. New attack methods can breach defenses or trick the system into giving away private data. 

• Untrustworthy software output: While AI can deliver code, it doesn’t give an under-the-hood look at how it was produced. Errors or suboptimal processes can lead to software with subtle flaws.

Organizations that want to use AI can’t ignore these risks if they want to grow. More to the point, you can’t make the most of AI within a team without working around these problems. To manage these risks, here are three solutions you can try:

1. Research specific use cases: Don’t treat AI like a one-size-fits-all solution. Instead, find use cases you can build specific AI tools for. 

2. Invest in risk management: You need to set security review controls to ensure your AI meets data compliance and security standards. From there, have your teams build safeguards to ensure they can quickly react to any issues. 

3. Restructure teams based on skill shifts: As more teams use AI, rethink how your employees work around it. From SEO to code reviews, ensure your human staff checks AI-produced work to get the best of both worlds.

4. Provide generative AI training: Since AI can handle code generation, teach your developers and non-developers about generative AI. This increases AI tech literacy across departments and mitigates the risks of AI-generated code.

AI isn’t a small tool or niche type of software—it will have sweeping effects across industries. As AI adoption rises, organizations must weigh their ethical and social obligations. It’s no secret that AI has plenty of teams worried about layoffs. But with tech this cutting-edge, that’s just the tip of the iceberg. 

On top of job displacement, there’s a risk of AI ingraining biases into its models. When creating models, developers risk building assumptions and prejudices into an AI’s core design. So, developers, managers, and tech leaders have to prioritize fairness, transparency, and accountability in AI systems.

As new AI models emerge, tech leaders should watch out for emerging best practices and guidelines for ethical AI development within the software industry. Here’s how you should leverage AI to ensure your organization keeps up:

1. Accept that AI isn’t a passing trend: AI has already impacted industry norms across fields. Engineering, finance, health care, and manufacturing have already embraced the technology. Your business needs to embrace the change to keep up.

2. Invest in or customize AI models that suit your business: AI models create a foundation for your work. Choose software suited to your work to see the best results or customize a pre-trained foundation model. 

3. Try no-code/low-code solutions: Low-code solutions help tech experts and non-technical users work together. This breaks down silos and allows experts with different skill sets to collaborate on solutions. 

4. Embrace the best of AI and human developers: Pair AI’s productivity with developers’ intuition, creativity, and problem-solving skills. The best work produced by AI still has human fingerprints on it. By contrast, AI can’t replace a developer's output independently.

As with all industry changes, context is key. Regarding AI, Corey Coto says:

As new artificial intelligence platforms roll out, their impact on software development is impossible to deny. While automation will change how developers work, human developers are just as important as ever. By researching how AI in software development will change processes and strategies, you can prepare to make the most of cutting-edge tools.

Pluralsight Flow can ensure your AI adoption lands with the biggest impact. Our platform tracks metrics related to software delivery and team performance to cut down on wasted effort. Flow can ensure you use AI to the fullest by listing KPIs tracking your output. Schedule a demo and see how Flow can help your dev teams.