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Slower releases, overworked teams, and complicated processes. The challenges facing DevOps teams today are both complex and persistent.
Whether you’re a QA tester or a developer, you’ll no doubt be familiar with the issues that plague almost every enterprise and threaten the value delivered to the business. While there’s no silver bullets, we believe the answers lie in transforming your teams, processes, and tools — and that AI has a central role in achieving this.
You may already be using AI on a day-to-day basis in your own work. But the potential of AI goes much further than code gen. If used correctly, AI can act as a bridge between your team and the complex manual processes that still exist elsewhere in the organization—with potentially transformative outcomes.
Let’s take a closer look at some of the challenges persistently faced by DevOps teams today — and how AI can address them head on.
When it comes to DevOps as we know it today, the reality is that it’s very difficult to take an innovation or idea all the way forward to business impact. What ultimately stands in the way of DevOps and business value is complexity.
Complexity is everywhere. It’s in your data, overly complicated by your dealings with multiple regions, currencies, prices, and tax structures. It’s also in your teams. According to the DevOps Institute, 31% of IT leaders report that a lack of skilled resources remains their organization’s biggest challenge. What this means in practice is that those who do have the requisite knowledge and skills are often having to work harder to communicate with non-technical teams, leading to further inefficiencies and difficulties.
On top of all that, it’s in your tools. Salesforce is no longer an island; it is deeply integrated with other clouds, with multiple environments, each featuring multiple teams, pipelines, and processes. Multiple Gartner reports have highlighted the challenges of managing Salesforce governance and spend as a result, with multiple acquisitions leading to greater outgoings and more complexity.
These are not just individual challenges — they are compound challenges that are complicating the work of DevOps teams at nearly every level. That means there’s a need to think outside the box when it comes to teams, processes and tools if you want to truly deliver value and remain competitive.
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Integrating AI into DevOps addresses these challenges head-on, starting from planning and continuing through to release. By leveraging large language models and intelligent automation, AI can transform traditional DevOps workflows, empowering faster, more accurate and less labor-intensive processes.
Traditional agile wisdom would say your plan needs just enough detail just in time because your developers are going to be in the room with you. But these days, most teams are geographically distributed in different timezones, and even speak different languages. Additionally, effective use of AI requires clear, well-executed prompts.
What this means is that your plan now needs to be as unambiguous as possible with optimal clarity in place before production. If for example, your organization has multiple product owners writing user stories in different ways, or you have details missing at any stage in the pipeline, AI can fill the gaps in communication. For example, you could use it to:
In this way, AI can reduce the burdens of repetitive or inconsistent documentation by ensuring consistency in language and requirements across an organization. This can not only reduce inaccuracies but workloads too — freeing up teams to focus on refining their ideas ahead of build.
There are numerous AI-driven tools out there for software development, with many developers having already implemented these into their work processes. GitHub’s Copilot is a fantastic example of how AI can greatly increase a developer’s efficiency. However, tools like this have limitations and cannot fully account for complex architectural decisions or business requirements. We therefore believe a lot more can be done when it comes to deploying AI in build environments.
One of the key things we focus on here at Copado is ensuring that AI can work in tandem with a company’s existing guidelines and best practices when it comes to code. Generating code with an AI tool should not only be fast, but specific and precise in terms of how your company gets things done. Additionally, AI has a role to play not only in generating new code, but in identifying and correcting inconsistencies in existing repositories.
Test cases aren’t exactly the most exciting stage of the DevOps cycle, but are absolutely important to ensuring you don’t introduce bugs or bottlenecks in production. There is so much work that goes into testing and AI has a huge role to play in this stage of DevOps. It can not only accelerate your testing work but also free up developers to test new scenarios and edge cases.
The first use case may seem obvious, but AI can be used to quickly generate both unit and functional test code. Whether your tests are written in Apex or Javascript or plain English, they’re all language-based, which means you can quickly implement AI and begin using it as soon as possible. This ensures comprehensive testing coverage first and foremost.
Secondly, AI can quickly analyze new features and create automated regression tests, generating all the happy scenarios and common scenarios on behalf of the testing team. This has huge potential for testing workflows, as it means all the routine scenarios are covered—freeing up the team to explore edge cases and stress tests.
For example, you could use AI to generate a variety of test datasets and use this to explore new test scenarios, generating endless possibilities for new innovations and features.
Finally, AI can help DevOps teams meet deployment challenges with ease. Regardless of which CI/CD tool you’re using, there’s always room for improvement. Particularly when it comes to Salesforce, it’s possible to encounter many different kinds of deployment errors simply because there’s a conflict somewhere or an inconsistency in configuration between environments. Whatever the problem is, it can seriously hinder your release to production for hours or even days.
AI can help accelerate your troubleshooting process by automatically detecting and deployment conflicts, analyzing configurations across different environments, and suggesting resolutions. Additionally, you can use AI to automate your release notes for different audiences — allowing you to fine tune for either technical or executive audiences.
The impact of AI on DevOps is profound, enabling organizations to address some of their most pressing challenges. By implementing AI into the entire DevOps lifecycle, your team has the opportunity to unlock new levels of efficiency and innovation through new, optimised workflows.
At Copado, we’re building a native DevOps solution for Salesforce to overcome these challenges. By combining our expertise with AI tools, you can ensure your team is getting the most out of its DevOps pipeline. Learn more about Copado AI.
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