Navigating Enterprise-Level Software Development with AI

Artificial intelligence (AI) is an increasingly common feature in enterprise IT tools. As impactful as this technology is for end users, its potential on the other side of the software supply chain deserves attention, too. A rising number of organizations are implementing AI in enterprise-level software development to build better programs faster.

How AI Improves Enterprise-Level Software Development

Fewer than 10% of enterprise software developers today use AI assistance, but experts predict up to 75% will by 2028. It’s easy to see why when considering how AI can improve the process along several fronts.

Streamlined Coding

The most popular way to use AI in the enterprise software development process is to automate coding tasks. Generative models can synthesize lines of code for an application or create test data to verify their functionality. In either case, it saves dev teams considerable time.

Writing code is an ideal use case for automation because it’s highly repetitive and detail-oriented. Such characteristics make it challenging and slow for humans but are where AI shines. Early results show code generation takes up to 45% less time when devs use generative AI.

When coding doesn’t take as long, teams can develop enterprise-level applications more quickly. Consequently, they enjoy a shorter time to market and decreased development costs.

Automated Testing

Testing and validation is another stage of enterprise-level software development historically prone to inefficiency and error. As a result, it is a great use case for AI.

Machine learning models can analyze code for flaws and even debug programs automatically. This rapid testing and optimization ensures developers produce a higher-quality product without the time and resource waste of manual alternatives. Devs can also run such analyses after hours to further streamline the enterprise software development process.

These benefits are most notable in light of the software talent gap that will impact over 90% of global organizations by 2026. When businesses don’t need as much time or as many people to verify their solution’s quality, the shortage is less impactful.

Automatic Documentation

Alternatively, AI can improve the enterprise software development life cycle by automating documentation. Generative models can create and update developer logs as teams work through new iterations of a product to boost transparency. Such functionality can also streamline compliance for software that must meet stricter regulatory standards.

Similarly, AI could generate summaries about individual teams’ progress on a project for DevOps meetings. Scrums and standup meetings would then take less time and remain just as — if not more — informative for closer collaboration.

Such processes may not seem particularly crucial, but they’re part of why enterprise-level web application development takes so long. Some developers spend less than an hour per day coding because of repetitive tasks like documentation. Consequently, companies can become far more productive by automating such workflows.

Best Practices for Using AI in Enterprise Software Development

In light of these benefits, it becomes clear why 66% of brands today believe AI is central to ongoing success. However, it’s important to recognize that enterprise AI development is not always easy, and positive results do not come automatically. It takes attention to several best practices to use this technology to its full potential.

1. Determine Optimal Use Cases

Before getting started, you must specify how you will use AI in your enterprise-level software development workflow. Do not adopt AI for AI’s sake, but approach it as a solution to a real problem your team experiences.

Analyze your current development process to identify where your biggest weaknesses lie. Then, compare the gaps to AI’s capabilities — generating simple code, checking things with greater accuracy, and streamlining documentation, to name a few. Any overlap between your inefficiencies and AI’s strengths is an ideal use case to target.

2. Compare Available Solutions

Once you know how you’ll apply AI in enterprise-level development, look for solutions serving your niche. There are likely multiple, but you can narrow down your options by considering several factors.

A lack of in-house AI expertise is the most common barrier to adoption in development, with 36.4% of software companies citing it as a challenge. Consequently, a user-friendly AI tool may be more beneficial than one with higher functionality but a steeper learning curve. Security features, integration with existing tools, costs, and supported programming languages should also play into your decision.

3. Tighten AI Access Controls

Cybersecurity and privacy can be significant concerns when dealing with AI. After all, the technology requires an extensive amount of data, making it a prime target. In light of the risk, you should keep security in mind when implementing these enterprise software development tools.

Specific security measures may vary between instances, but all should prioritize restricting access controls. Only users who need to access AI tools for their jobs should have the authorization to do so. Similarly, the AI program itself should only be able to access the data and applications it needs to function correctly.

4. Create Formal AI Use Policies

Next, develop a formal code for how devs can use AI and how they must keep it in check. While AI can help teams develop enterprise-level applications in less time with fewer errors, it’s imperfect. It takes careful, responsible use to reach its full potential, so guidelines are necessary.

These rules should outline steps for reviewing AI-generated content before using it. Similarly, they must hold the company responsible for using AI as a tool for developers, not a replacement. Copyright protections may also be necessary, depending on the industry.

Your AI policies are also a good opportunity to train employees on how to use this technology effectively. Include best practices for how to engineer prompts, which improves generative AI accuracy and further streamlines tasks.

5. Train and Monitor AI Tools Carefully

Finally, remember that AI — like other tools — may take experimentation and adjustment to optimize. Pay close attention to its training and usage to gauge its efficacy and tweak your approach if necessary.

Use plenty of relevant data when training your AI solution, and only deploy it once its results meet your desired outcomes. Benchmark your current performance before implementing the model to help you quantify its results. From there, review the same KPIs at least annually to determine if and how your enterprise AI development strategy should evolve.

Upgrade Your Enterprise-Level Software Development Today

Enterprise-level software development is a competitive and complex business. Your team needs accuracy, agility, and insight to remain competitive, and that’s precisely what AI provides. Once you understand what the technology offers and how you can use it safely, it can take your dev workflows to new heights.

Organizations ready to explore AI’s potential in enterprise software development can start with Pieces, your AI development companion. Extract code from screenshots, save useful snippets for later, streamline snippet sharing, and enjoy leading data security to keep your dev environment fast, safe, and collaborative.