In the world of Salesforce development, one principle has stood the test of time: “Configure where you can, code only when you must.” It’s a mantra that aligns with Salesforce’s declarative-first philosophy — empowering administrators and consultants to build robust solutions using tools like Flow, Process Builder, and Dynamic Forms without ever touching Apex or Lightning Web Components.

But in today’s rapidly evolving ecosystem, where AI-assisted development environments are reducing the cost of writing and maintaining code, we find ourselves facing a new conundrum:

If AI makes coding faster, cheaper, and smarter — does the old rule still apply?

 

The Traditional Argument: Configuration > Code

Salesforce best practices have long emphasized configuration over customization, and for good reason:

• Maintainability: Declarative tools are easier for non-developers to understand and update.
   

• Upgradability: Out-of-the-box features are less likely to break during Salesforce releases.
   

• Cost Efficiency: Admins can make changes without needing a full-fledged development cycle.
   

This approach is ideal for most standard use cases — managing approval processes, automating email alerts, or building guided UI flows. For many orgs, this approach has powered incredible growth with minimal technical debt.

But here’s the rub: not every problem fits in a Flow box.

 

The Reality: Business Complexity is Growing

Today’s Salesforce implementations are more ambitious than ever. Public sector agencies want end-to-end permit systems. Nonprofits want custom CRM + case management hybrids. Enterprises demand quote-to-cash processes with complex logic, integrations, and UI needs.

And even as the platform offers increasingly powerful tools like OmniStudio and Dynamic Interactions, there’s a ceiling to what declarative configuration can achieve before it turns into a brittle maze of Flows, record-triggered updates, and unscalable admin overhead.

This is where code has traditionally stepped in — and until now, that came with trade-offs.

 

Enter AI: A New Era for Developers

The rise of AI-assisted development environments is fundamentally shifting the balance between configuration and code. Tools like GitHub Copilot, Salesforce CodeGen Studio, and even ChatGPT-based copilots can now:

• Autogenerate Apex classes, triggers, and test methods from natural language
   

• Suggest LWC scaffolds and reusable UI components in seconds
   

• Refactor legacy code for better performance or security
   

• Write SOQL queries based on high-level prompts
   

• Explain existing code to new developers for faster onboarding
   

What used to take hours of developer time — understanding requirements, writing boilerplate, debugging — can now be accelerated dramatically with AI assistance.

Code is no longer the bottleneck. In many cases, it’s the enabler.

 

Redefining Best Practices: Productivity > Purism

As AI continues to reduce the friction of writing, maintaining, and explaining code, we need to evolve how we think about technical strategy on Salesforce:

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Old Thinking:

• Avoid code unless necessary

• Declarative is cheaper

• Code is hard to maintain

• Admins > Developers

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Evolving Thinking:

• Use code if it improves scalability or clarity

• AI-driven code can be just as cost-effective

• AI makes code more self-documenting & modular

• Collaboration between both drives real agility

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The new question is not “Can this be done with config?” but rather:

“What is the most scalable, maintainable, and productive way to solve this — and does AI make code the better path?”

 

Real-World Impact

Here are a few areas where this shift is already changing how Salesforce teams operate:

• Custom Integrations: AI helps quickly scaffold Apex callouts, error handling, and Named Credential wrappers.
   

• Dynamic UI with LWC: Complex components that once took days can now be prototyped in hours.
   

• Assessment and Refactoring: AI tools can analyze orgs and suggest optimized code or conversion to config.
   

• CI/CD and DevOps: Auto-generating test classes, validation scripts, and metadata diffs speeds up the pipeline.
   

 

Conclusion: The Rise of AI-Native Salesforce Teams

We’re entering an era where “AI-native” development teams will have an unfair advantage. They will ship faster, iterate cleaner, and maintain with confidence — whether the solution is built with Flow or with LWC.

To be clear, this isn’t a call to throw out configuration. It’s a call to rethink dogma.

Code is no longer the enemy of maintainability — not when AI helps you write it better, explain it faster, and refactor it smarter.

The right approach is not one or the other. It’s a balance.

To code, or not to code? The real question is: How can AI help you choose wisely?