Automation trends transforming the Salesforce DevOps landscape

Share with

Description

Stay up to date with the state of automation in Salesforce DevOps — how it’s evolved and why it’s critical for Salesforce teams looking to scale in 2025. Join DevOps Advocate Rob Cowell as he discusses the latest trends in automated testing, continuous integration and continuous delivery (CI/CD), and security automation. Learn how top teams are using automation to enhance their development processes and safeguard their deployments.

Learn more:

Discover how Gearset can help you automate your DevOps process here.

Transcript

So without further ado, I'll introduce you all to today's speaker, Rob Cowell, DevOps advocate at Gearset. So I think if everyone's ready to get started, Rob, I shall hand straight over to you.

Thanks, Amy. Let me just press the necessary buttons to get things going.

So hello, everyone, and thanks for joining today's webinar.

Going to be running through the evolving role of automation in Salesforce DevOps.

And apologies if I keep leaning forward. I just need to move the slides forward for everybody.

So as Amy said, I am indeed Rob Cowell, DevOps advocate here at Gearset.

And I'm excited to share with you a bit about how automation has become essential for Salesforce teams that are striving to scale up those DevOps processes.

So let's have a quick look at where we've come in that process and help us understand where automation fits into Salesforce DevOps today.

In the early days of Salesforce, deployments in Salesforce were largely manual. Many teams have relied on change sets, which involve moving components between those environments by hand, often leading to inconsistent deployments and lengthy cycles.

This approach was limiting because it didn't really scale that well. Changes were prone to error, and there was no built in way to kind of validate those deployments easily or ensure that everything worked as intended before it got to production.

Now moving on from change sets, we move to the AMP migration tool for some of the older folks in the ecosystem that may remember that one. It allowed for a more programmatic approach, but it still has limitations around flexibility and speed, and that often meant, custom scripting to handle the unique complexities of each deployment.

Now as more teams began embracing agile and DevOps thinking, there was a push to bring that true CICD, that's continuous integration and continuous delivery, into the Salesforce world. And that's where tools like Jenkins or CircleCI started to become more popular, enabling teams to automate parts of their CICD pipeline.

But because these were generalized tools, they required a lot more manual configuration and maintenance, especially when working with things like Salesforce metadata, dependencies, and just some of those nuances we have on the platform.

But then came a major shift as Salesforce specific tools started to emerge, purpose built for the platform.

These tools, such as Gearset, recognize the unique metadata driven structure of Salesforce and offered ways to simplify and automate those deployment processes, particularly for those specific Salesforce orgs.

For example, Gearset brought the ability to compare environments, handle complex dependency management, and track those changes across the Salesforce orgs in a way that was automated and scalable.

With these tools, Salesforce teams can now implement CICD in a way that's much more aligned with their environment and their business needs, reducing the need for custom scripts and improving that deployment reliability.

Now a huge part of this evolution has been a shift from manual work to automated testing, security checks, and even code quality analysis.

Where earlier setups might have relied on developers manually testing deployments or running scripts to validate them, today we see automated unit tests, automated static code analysis, and tools that proactively scan for those potential issues before the code even hits production.

And this evolution has brought us closer to a fully automated, continuous delivery model, where changes are integrated and validated continuously, which minimizes the risk of those unexpected issues hitting your production orgs.

So let's move on to the next slide.

So let's look at why rolling your own DevOps stack can be a false economy.

When teams first consider setting up DevOps, the idea of building their own custom stack is quite tempting. It seems straightforward at the outset. There's open source tools available, and it feels like it could be a low cost option.

But as many teams learn, creating and maintaining a homegrown DevOps stack can quickly turn into a costly and resource intensive commitment, one that often fails to deliver the full promise of DevOps in the long run.

So I want to unpack why relying on a home brand stack can be that false economy and, ultimately, why it diverts resources away from the very value that DevOps is supposed to be creating.

So let's start with the true cost of maintaining that custom stack. Initially, it seems like the only investment is a bit of time from your team to choose the tools and set them up. But even in those early stages, the costs begin to grow. Selecting the right tools for DevOps tasks like deployment, testing, and monitoring, it takes a little bit of research and setup and experimentation.

And then once you have that basic setup, maintaining it is a constant demand. Each tool has its updates, its patches, potential compatibility issues, all of which need to be managed.

Open source tools like Jenkins or Selenium can be great individually, but integrating them into a seamless pipeline that covers all the necessary DevOps function is another matter entirely.

And on top of that, DevOps tools each require specialized expertise to run effectively.

With a homegrown stack, you often end up needing a dedicated DevOps engineer to manage the tools, keep them updated, write custom scripts, and ensure that all the integrations are working properly.

So without specialized knowledge, the stack becomes a bit of a fragile system prone to breaking down.

If a version update or a tool configuration fails, someone's gotta troubleshoot and fix it, and that's time and energy diverted away from your product development and feature delivery.

A big part of the overhead in a custom stack, of course, is that integration.

DevOps relies on these complex processes to connect tools like source control, CICD, testing, and code quality analysis, and these tools don't always speak the same language.

Each link in that chain requires custom scripts, plugins, and a bit of careful coordination to maintain the functionality and visibility you want across your pipeline.

When one tool changes, whether it's an unnecessary update or a change to an integration point, that entire pipeline might require some adjustments.

And this continuous upkeep drains resources that could be best spent on building features, impacting your time to market and, of course, your team's morale.

And, of course, a custom DevOps stack creates a significant opportunity cost because every hour that developers or DevOps engineers are spent maintaining tools, it's time that they're not spending on the value work, like creating the new features, improving user experience, or solving those customer pain points.

The whole promise of DevOps is to speed up that development process.

But if custom stacks are slowing things down, engineers can get bogged down with maintaining infrastructure instead of focusing on innovation.

On the other side of the fence with a dedicated DevOps platform built specifically for Salesforce, all that complexity is managed for you. Prebuilt integrations, automated updates, vendor managed maintenance, all mean less time spent on the operational side and more time delivering that value to the customers.

You avoid all that fragmented tooling and custom scripts and focus on the rapid reliable delivery that we want.

Leveraging an established platform saves resources, reduces maintenance overhead, and let your team really get the benefits of DevOps, getting new features into customers' hands faster with less friction and higher reliability.

So now let's move on to look at automated testing specifically because automated testing is at the heart of any mature DevOps process. And for Salesforce teams, it's essential that we maintain that quality, speed, and consistency as we scale.

So let's have a look at why it's so critical in DevOps and how to approach it effectively.

First and foremost, automated testing allows us to implement a shift left approach.

This means catching issues as early as possible in the development process, which is far less costly and disruptive than identifying them later in production.

In Salesforce, where releases can affect complex metadata structures and integrations, identifying those bugs early can really prevent those major issues down the line.

Automating the testing with things like unit tests, integration tests, regression tests, they can all be run frequently, either with each commit or on a regular time schedule, providing fast feedback and enabling you as developers to fix those issues immediately.

Now another huge benefit of this automated testing is it can scale alongside your development process.

As your teams grow, so do the number and complexity of the deployment you might wanna make. So manually testing each feature is not really feasible.

Automation is gonna ensure that your tests run consistently across all your environments, and it removes that variability that comes with manual testing.

This consistency is absolutely key because in Salesforce, even the smallest changes to metadata can have quite wide reaching impacts.

With automated testing, you're not relying on those individual testers to catch every detail. The system handles it for you, and that creates that reliability and allows teams to release things with a bit more confidence.

Tools like Gearset make this process even smoother by allowing you to automate those unit tests with just a few clicks. You can set up daily test runs, get detailed reports on the status of all tests, and get full debugging information if a failure occurs.

Here, set's also gonna track your code coverage over time, giving you a clear view of how thoroughly your code is testing and where potential gaps might exist.

Code coverage, of course, is an important metric, not just only for release readiness, but also for maintaining a healthy code base over time, especially in those environments where you've got a lot of complex customizations and frequent changes happening.

In Salesforce, UI testing is also a valuable component of your automated testing strategy.

For example, testing lightning web components and other user facing elements can make sure that any updates don't inadvertently impact that user experience.

Kia set, of course, integrates with various Salesforce testing tools of your choice, enabling teams to expand that automation into UI testing, which helps ensure that you've got both functional and interface level consistency.

Now this is essential for those environments that have quite a high degree of customization where even the smallest update can really impact that user experience.

Another powerful feature of the automated testing is the ability to set up stage gates.

Stage gates are checkpoints in your CICD pipeline that halt the process if tests don't pass, which prevents potentially flawed code from progressing into your upward environments like staging or production.

And that gives you an added layer of production, particularly important for production critical orgs where stability and reliability are nonnegotiable with the business.

With those stage gates, you're embedding that quality control right into your pipeline, making it impossible to bypass testing and make sure that everyone in the team has confidence in every single release.

By incorporating all these types of automated testing from day one, you've built a culture of quality and accountability in your teams. As the developers commit code, automated tests will immediately check it, creating a real time feedback loop.

This feedback helps you maintain high standards without requiring constant manual oversight and review.

Over time, that doesn't just reduce the rate of errors, but also builds up that confidence and consistency that I've mentioned a few times now.

So we've mentioned CICD pipelines a few times too, so let's dig into that one a little bit.

CICD, that's continuous integration and continuous delivery, is definitely the technical backbone of an efficient DevOps process.

In Salesforce DevOps, setting up an automated CICD pipeline helps teams release faster, reliably, and I'm gonna say it again with greater confidence.

So let's have a quick look why it's essential for those environments and look at some of the best practices for implementing it effectively.

The main advantage, of course, is that it automates that entire path from committing your code all the way through to deployment, meaning that any new changes are continuously integrated, tested, and prepared for release.

By automating all of this process, teams can deploy those updates more frequently and with less risk, which is absolutely crucial with Salesforce environments that are constantly evolving.

In traditional setups, teams might hold off on a release until the end of a sprint or even an end of a quarter.

But with CICD, releases happen incrementally, reducing the risks associated with big bang deployments, and that allows your teams to deliver incremental value to end users more consistently.

Automation in that pipeline starts with the continuous integration step, which means that every time a developer commits a code change, it's automatically merged and tested with a shared code base.

This practice hopefully prevents conflicts from building up and allows team to detect those issues early before they become blockers.

In Salesforce, where some of those dependencies can cause issues you didn't expect, running automated tests on every commit helps to quickly identify those potential conflicts.

CI tools such as Gearset will make it easy to set up an automated test, run them in sequence, and track the results.

You can configure the pipeline to run unit tests, integration tests, and code quality checks, ensuring that any potential problems are caught and addressed before they hit production.

Now continuous delivery, the second part of CICD, is where automated deployments come into play.

With continuous delivery, successful builds from that integration phase are automatically deployed to higher environments such as staging or UAT, where they undergo further testing, particularly user acceptance testing.

This setup ensures that every change that passes that initial testing is always deployment ready and, again, reducing the manual work required to push changes across environments.

You can choose whether you want to deploy immediately, or if you have a a setup where careful validation is needed, you can set up those approvals and stage gates we talked about. And this flexibility lets you have a a steady flow of updates but without compromising that control over what goes into production.

One of the unique challenges of CICD and Salesforce is managing those dependencies, and that can make deployments a little trickier.

Here, platforms such as Gearset will help handle those metadata dependencies for you.

Our intelligent dependency analysis will ensure that all the required component components are included in your deployment, eliminating that frustration of those failed deployments due to missing dependencies.

We've all seen them.

And that streamlines the pipeline and reduces the need for those manual fixes so your team can focus on development rather than troubleshooting deployment issues.

Now let's look at some of the key best practices.

The biggest one for me is making sure that testing is an integral part of that life cycle from day one.

Incorporating automated testing within the pipeline maintains high standards across all deployments.

Stage gates, as we discussed, can be set up automatically, halting that pipeline if the tests don't pass and ensuring that only well tested code progresses.

That gives you that continuous feedback loop where testing and integrating the code are seamless parts of the deployment process.

And it also means that you've got that culture of accountability because developers get that instant feedback on the quality and the stability of their code with each commit.

So with a CICD pipeline in place, you get the visibility and the traceability across that whole process. Every change is logged. Every deployment is tracked, and that makes it easy for teams to understand the history and the impact of every release.

Tools like Gearset will provide full deployment histories and reporting so you always know what's in each environment, when it was deployed, and by whom.

That traceability allows for auditability and governance as well, especially in those large organizations that require strict compliance.

Now although we're discussing CICD automation in the context of Gearset, the overall approach is tool agnostic.

For your other platforms like GitHub actions or Jenkins, the principles element still applies. The key takeaway I want you to have is that the CICD pipeline automation is about setting up that seamless, repeatable process that delivers the changes reliable at any scale.

So now let's look at environment monitoring.

Effective environment monitoring and change management are other key elements of maintaining that stability and quality in complex Salesforce DevOps setups.

In Salesforce, where teams often make direct changes across sandboxes and production, tracking these adjustments helps prevent what I call environment drift where configurations will diverge and cause deployment issues later down the line.

Environment monitoring provides visibility into every change capturing the what, who, when, and where across all those environments. And that stops unexpected issues by allowing teams to document or roll back any unapproved changes.

And alongside metadata, you can track metrics like data usage and security settings, making sure you've got that compliance and performance elements sewn up.

So change management will build on that monitoring by enforcing workflows for controlled updates where every change is tracked, reviewed, and approved.

Now Gearset will streamline that deployment tracking and rollback aspect, ensuring that there's no change that will go unaccounted for. If you integrate monitoring data with CICD processes, you can further minimize that risk by only allowing validated changes to proceed. And, again, those stage gates that will halt deployments if there's any issues that have been flagged.

So overall, environment monitoring combined with a well structured change management process will help you have proactive control, and it will help you maintain consistency, prevent that environment drift, and, overall, improve your deployment reliability as those orgs scale up.

Now we mentioned data there. Let's talk a little about data.

So effective data management is kind of a vital aspect of DevOps that's often overlooked, but it's useful especially for creating those realistic test environments that reflect production.

Realistic test data is gonna allow developers to catch issues early and improve test accuracy.

But handling those complex data structures and protecting sensitive information can present some challenges.

Data seeding will help you populate those test environments while data masking anonymize there anonymizes, a difficult word there, sensitive fields, protecting that privacy and compliance needs that you have with regulations like GDPR and CTPA and others.

So data masking is essential for that compliance, and it prevents the exposure of personal information and reduces the risk of any breaches and ensuing fines and reputational damage.

Gearstick can help simplify this process by masking those fields across environments consistently. And that means that your data is usable, but you haven't compromised any privacy.

Automated data seeding and masking stream and and sorry. Data seeding and data masking, that streamlines your environment setup, reducing that manual work and ensuring that you have consistent data across testing and production across all those environments.

Standardizing those data templates will further enhance efficiency because you've then got stable reusable configurations for those larger teams.

Now like so many of these aspects, if you integrate data management and masking into the CICD pipeline, you've then got that continuous data compliance.

With every environment update, the sensitive data is automatically protected, and it also reduces that manual effort and safeguards the compliance for you.

So now we get to the fun part, predictions and trends for twenty twenty five.

And as we look towards twenty twenty five, the DevOps landscape is gonna evolve pretty quickly. There's new technologies, rising customer expectations, there always is, and increasing regulatory requirements, all of which are shaping the future of DevOps in the Salesforce ecosystem.

So let's have a look at some of those key trends that we can expect to see and what they mean for those teams that wanna stay ahead of the curve.

So one of the biggest trends on the horizon, of course, is the integration of AI and machine learning into DevOps practices.

It's hitting every aspect of technology at the moment.

We're already seeing lots of AI driven tools in code analysis, automated testing, and even deployment strategies, and that's only gonna grow.

AI driven code generation, in particular, will become more common in Salesforce development.

Now while that's an exciting development with the potential to accelerate delivery, it does come with a few risks.

Automated generated code can introduce some quality and security vulnerabilities, which may be less predictable than those created by human developers.

And that means that any automation tools and code analysis platforms are gonna need to be more vigilant than ever, continuously performing rigorous quality and security checks on AI generated code.

So organizations are gonna need to adopt advanced monitoring and validation tools to make sure that AI enhanced productivity doesn't come at the cost of security or stability.

Again, by automating these checks, teams can better ensure that that code generated by AI meets those same high standards expected of any manually written code.

And another major trend is the continued emphasis on security and compliance automation.

With increasingly strict data privacy regulations, organizations are gonna need to ensure that data protection is built into every part of their development and deployment process.

Security and automation is likely to become even more integrated into DevOps pipelines with real time security checks embedded at each stage of the deployment.

Now for Salesforce teams, this is gonna mean not only automating your data masking, but things like vulnerability scanning, security posture monitoring, and regulatory compliance checks as part of their everyday workflow.

This approach, often called DevSecOps, Sec being security, will make it easier to maintain compliance but without slowing down development. And that's a critical advantage in industries where those regulations are constantly evolving like finance or health care.

As more organizations rely on Salesforce for business critical operations, we're also expecting to see a big rise in advanced monitoring and observability practices.

Environments are gonna need proactive monitoring tools that provide visibility across all stages of development from sandbox to production.

Real time monitoring will allow teams to detect and resolve those issues before they impact the users. That means you've got high reliability even in those big sales or sort of with lots of integrations, for example.

Monoun tools are likely to provide detailed insights into things like code performance, data usage, and overall system health, helping you optimize and streamline your processes.

And, of course, with the rapid development of low code and no code tools, DevOps is gonna need to support a much broader spectrum of contributors from those highly technical developers and architects down to the citizen developers that are doing things declaratively.

So the challenge here is gonna build to be the challenge is going to be building those DevOps, pipelines that can accommodate those different skill levels, but still maintaining that consistent quality.

Any platforms that can well support both declarative components and code components all within a single pipeline are gonna become invaluable.

And this means that DevOps processes need to be more inclusive, ensuring that some of those slightly more, slightly less technical contributors can participate in development, but still maintain the high standards of governance of quality that we expect.

As Salesforce implementations have continue to grow in scale and complexity, we're gonna see an increased need for orchestration across multi cloud environment.

Many organizations are expanding beyond Salesforce to integrate multiple platforms within their business processes.

So coordinating those deployments, monitoring, and governance across all these interconnected systems is gonna require much more sophisticated orchestration tools.

DevOps platforms that can bridge multiple clouds and on premise environments, They're gonna empower organizations to handle that complexity and keep Salesforce environments in sync within that broader ecosystem.

So let's wrap up. We're down to the last minute. I'm running a little behind, but let's bring some of those key elements of Salesforce DevOps success with automation together.

We've touched on automated testing, CICD automation, environment monitoring, data management, and staying in tune with those emerging trends, all of which are essential for building a modern and scalable DevOps strategy.

Automated testing is a foundational piece, and it's more than just finding bugs. It's about building confidence.

If you incorporate all that tests, you're more likely to find them early and set your team up to a position where they're easier to fix with that shift left testing approach.

Automating it into the CICD pipeline creates a rhythm of accountability and consistency across the team, and it helps you scale with all the confidence that you need for your org.

Speaking of that CICD, it's the engine that's gonna keep your DevOps moving. And an automated CICD pipeline is gonna manage every change, test it, and prepare it for deployment, saving you time, minimizing potential errors, and just making things a lot smoother and more reliable.

We can throw in those stage gates to give additional checks and balances in our process and just make sure that nothing too nasty gets through when we don't want it to.

And then environment monitoring and change management are kind of the tools that allow us to have that additional layer of confidence that as configurations may drift or you may have an emergency change or hotfix into production, we can still accommodate that process in a controlled effective manner.

And then data management is another aspect where we need that high level of confidence and reliability.

And that's where things like data seeding to give you, reliable test data in your lower development environment, coupled with data masking to protect the privacy of that data come into play. And we can automate those processes as part of that pipeline too.

And then finally, we looked at some of the trends in twenty twenty five. The biggest one, of course, is inevitably going to be AI, but it comes with a note of caution to make sure that, you know, you have those additional checks in your DevOps pipeline to make sure that the quality is being maintained, particularly around AI generated code.

So a whistle stop tour of wrapping up things there.

But at its core, Salesforce DevOps is about maximizing that speed and quality, but managing the risk, and that's what automation is gonna bring to you. By combining all the aspects that I've talked about today and keeping a focus on future advancements, you're gonna build a strategy that scales with your organization.

Salesforce DevOps is an ongoing journey, so you're always gonna need to refine your processes and embrace best practices.

But then, ultimately, by the power of automation, that's gonna bring measurable value back to your business.

So with that, I'd like to thank you for joining today's session. I hope some of these insights have been useful to you, and I hope it drives some innovation and excellence in your own DevOps processes.

In the meantime, hopefully, you can join us for more webinars in the future, and I shall hand back to Amy to close things out for us.

Thanks so much, Rob. I know we're running a couple of minutes over folks, but there is one there have been lots of questions.

I think I'd love it if we could just end with, this question, Rob. I think it's gonna be a fantastic takeaway for folks.

So Laura has asked here, what kind of automation is the best to get started with?

I absolutely think that testing automation is the main thing. Testing automation will will give you so much, in your org. So one of the main things that it'll give you is obviously the confidence that that I've mentioned a few times. It's it's making sure that you're catching any potential issues well before they get, excuse me, before they get to production.

But the other aspect is it'll not only increase the quality, it'll give you, confidence in the the speed as well because by not having to rely on manual testing and, you know, it's kind of set up your tests to to automate once, and then you reap that benefit with every cycle. So you're gonna get the benefit of speed and quality as well.

Awesome. Thanks so much, Rob.

There is one quick question in the q and a, from Venkova that I just want to pick up. Sadly, no. We we don't have a time, for a product feature demo today, but we do have a thirty day free trial, which is not, you know, not restricted. You can try out as many of these features as you like.

If you just jump on the website, there is a thirty day free trial link on there.

You know, in the interest of time, clicking through things, isn't really practical for today's webinar, but do feel free to jump on that. As part of that, you do get access to our customer success team for any questions you might have. You get the same level of support as our paying customers do. Not many folks offer that, but, yeah, for the lifetime of your thirty day trial, explore, enjoy, and ask questions as you see fit.

Awesome. Thank you so much, Rob.

So thanks once again for everyone who joined. If if we didn't manage to answer your question, feel free, as Rob said, to reach out to us via our live chat, which at gearset dot com. Or if you are a customer, you can reach out to us in app as well. As Rob mentioned, if you're not a customer, I think, Rob, if you click one more time on the slides, maybe we might have a QR code for folks to scan.

We do.

There we go.

So there's a QR code if you're not a Gear Set customer. As Rob mentioned, you can try all of our, Gear Set features, for thirty days completely free, and that includes all of our, all the automation bits and bobs we've spoken about today.

So that is all we have time for. Once again, a huge thank you to Rob for presenting today's session, and thanks everyone for joining. We hope you found it super useful, and we hope to see you all again very, very soon. So thanks so much, everyone.

Thanks, everyone. Okay.