No one knows the importance of maintaining a healthy Salesforce org more than us. A good number of our clients approached us after their growth was stalled by a surging Salesforce technical debt. The problems were hiding beneath the surface. Years of customisations. Unused automations. Fragmented data. Accumulating workarounds. All of them turned Salesforce from an AI-ready platform into an obstacle course for innovation. If you are looking to scale AI in your organisation, you need to first address the technical debt that’s weighing down your Salesforce environment.
Key Takeaways
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It’s all the inefficiencies and complexities that build up over time when organisations prefer short-term solutions over sustainable configurations. According to McKinsey, roughly 20% of the budgets allocated for tech is diverted to just resolving tech debt.
There are 6 different types of debt that contribute to Salesforce technical debt. They are:
All these 6 types of debts are the byproducts of hurried Salesforce implementations. They quickly slow down innovation and increase your maintenance costs. End result is suboptimal ROI.
There are some classic signs that your organisation is plagued with too much Salesforce technical debt. Noticing them on time can help in reducing it before it starts impacting your business. Here they are:
When you bring AI into the whole Salesforce technical debt equation, two things are going to happen no matter what. Let us understand them with an example. Imagine you are a mid-size B2B services company. And you are implementing an AI agent that will build summaries for support cases.
On paper, building the above agent is a simple 3-step process. AI will read the incoming cases. Tag the priority. And then, draft a first response. But to make the triage agent work inside Salesforce, you need to give it somewhere to read from and write to. This will help AI features tie directly into the data model. That means you will need to create custom objects to store priority scores, new fields to store summary text, a new flow to call the endpoint, and a trigger to update case status.
As you can see, four new components have been created just to support a three-step idea. The agent might work. But your Salesforce org now has new moving parts. In future, if an admin has to find these components without any documentation, it’s not going to be easy.
In the above company's case, we must understand that the "Case" records were never standardised. Some came from web forms, and some were manually entered by support reps. The AI agent will summarise records with inconsistent fields. It will even suggest next steps based on cases created from incomplete intake. Finally, it will draft replies based on unstructured notes.
During the demo, it will all look great. After all, you are going to use cherry-picked cases. But in the prod environment, you are going to be hit with years of accumulated case data. The reps will have no choice but to ignore the AI's suggestions altogether.
Technical debt doesn't get resolved by accident. It takes deliberate habits built into how your team documents, builds, and maintains Salesforce day to day. Before layering AI on top of your org, here are five practices worth putting in place first.
Documenting before you build is just as important, if not more so, than documenting what you’ve built. The documentation of the “why” is critical. You need to work with users to clearly identify the business requirement, pain points, and purpose before designing a solution. These form the basis of your user stories and inform the solution design.
As your business grows and changes, requirements may also change, making it difficult to assess a solution without knowing why it was built. Documenting before you build captures the business requirements, context, and pain points for future reference, making maintenance and succession planning much easier.
Hardcoded values are bad design because they can go unnoticed until they cause an issue, and they can be avoided so simply. For example, if you hardcode a Record Type ID in a Flow built in Sandbox, everything may work perfectly during testing but fail after deployment to Production because the Record Type is assigned a new ID value. Your Flow is then looking for an ID that doesn’t exist in Production, so it never fires.
This is why you should never hardcode values in your automations. Instead, always consider building dynamically, such as using Get Records to find the Record Type, ensuring the solution continues to operate regardless of the environment or Id value.
A culture of good architecture means Salesforce Admins should not simply implement requests exactly as business users make them. Instead, their job is to ask questions, understand the need properly, understand the org as it currently stands, and design scalable solutions that meet the needs of the business as a whole. There may be a better way to achieve the same outcome, a better tool, or a new angle to approach the problem.
Admins should also document and share their learnings and the reasons behind architectural decisions. This encourages the business to think of Salesforce as a platform that needs to be treated with respect, focusing on platform longevity rather than rushed changes or quick fixes.
Maintenance is as important as development and new features, yet it is often overlooked and under-respected. Ignoring maintenance for too long allows quick fixes, patchwork, and “we’ll do it properly later” solutions to create long-term pain and obscene amounts of technical debt. This is why you need to make space for maintenance and communicate its importance clearly to the business.
Maintenance isn’t just something that happens; it deserves dedicated space, time, and attention. It is not a once-off activity, but an ongoing effort to keep your Salesforce org healthy, identify potential problems early, and prevent avoidable technical debt from growing throughout the business.
Salesforce uses different tools to grant granular access to features, objects, and records, but some approaches are better and more modern than others. Specifically, you should use Permission Sets and Permission Set Groups over Profiles, as Salesforce continues to decouple permissions from profiles.
Unlike profiles, where you can assign only a single profile per user, multiple permission sets and groups can be layered together to achieve the structure you need. This provides more granular access and avoids giving users more permissions than required. It is also worth reviewing existing permissions and migrating away from profiles, particularly where too many users have been given the System Administrator Profile as a quick fix.
Reducing Salesforce technical debt means building processes that prevent your debt from accumulating in the first place. At Brysa, we help you achieve that by turning debt management into an integrated part of our day-to-day Salesforce delivery. From enforcing consistent naming conventions to flagging missing documentation on Flows and objects, we dot the i’s and cross the t’s. Our proactive approach catches “clicks and code” debt before it reaches production.
We apply the same diligence to your AI implementations and AI-related changes. We analyse component dependencies and eliminate issues that can turn your small deployments into hours of detective work. By detecting missing dependencies ahead of time and visualising their impact, we help you implement AI with confidence and without worrying about piling Salesforce technical debt. Contact us to know more about how we can help.