Salesforce’s platform is the definition of robust. Its implementations touch all verticals within an organization, including sales, support, and customer success. As its capabilities have expanded over the years, developers have had to shift their own processes, leading to a fair share of challenges to overcome. Over 27% of developers report years of technical debt as a significant challenge. At the same time, 20% of agents feel the learning curve and skill gap are making it difficult to retain top talent. In a survey report, Salesforce Ben found that 53% of Salesforce developers perceive the platform as too demanding.
So, what can developers do to combat these issues? Fortunately, several native and third-party artificial intelligence tools have risen to the challenge to help ease Salesforce development.
In this blog post, we’ll discuss ways to use AI to help minimize Salesforce development challenges.
Understanding Salesforce Development Challenges In Detail
Before we discuss how AI assists in Salesforce development, it’s essential to cover the challenges developers or administrators frequently encounter.
Complex Customization and Configuration
Although Salesforce offers companies flexibility in allowing them to build custom solutions, this flexibility can translate into complexity. Developers create workflows according to Salesforce’s features, but dependencies in the code can create conflicts if not tracked effectively.
When the customizations become larger and more complex, the code can become messy. Moreover, Salesforce periodically upgrades its platforms, and developers must ensure their customizations are compatible with the new updates. If they do this manually, it creates a bottleneck that can have detrimental impact on the development workflow.
Integration with External Systems
Salesforce implementations usually exist alongside other business systems. These integrations can be a challenge for developers as they ensure their data remains consistent across all systems.
Testing Manually
Manual testing leads to inefficiencies. Although it was once a key part of DevOps, manual testing now drains resources while reducing development quality because Salesforce has evolved. If teams continue to rely on manual testing, deployments can become infrequent and prone to errors, decreasing their quality and reliability.
Deployment Challenges
There are a few deployment challenges developers often run into. First, they must track code changes while working with multiple environments, such as sandboxes or production, so choosing when to deploy is paramount to avoid disrupting live services. Secondly, there can be a split between testing and development in DevOps teams, which can create disparate issues and slow release time. And thirdly, traditional developer tools and CI/CD pipelines aren’t always compatible with Salesforce, and developers might need a specialized tech stack to run development seamlessly.
Data Management and Migration
Migrating to a new Salesforce environment involves several challenges, especially when it’s being done manually. Developers must prepare data in a way that ensures there are no inconsistencies during the migration process.
Skills, Knowledge, and Documentation
Traditional development skills don’t necessarily transfer to Salesforce projects, which require a combination of admin-level and programmatic skills. Salesforce developers need to have updated knowledge of Salesforce best practices to ensure smooth deployment and testing, and this can often be a challenge, especially as Saslesforce continues to change and evolve.
How AI Is Assisting In Solving Salesforce Development Challenges
Artificial intelligence (AI) can help to alleviate many of these challenges, reducing development time and helping developers test faster so they can deploy Salesforce improvements seamlessly. Here’s an overview of areas where AI is showing a significant impact.
Code Generation and Optimization
AI has made it possible to create Apex code by giving generative AI natural language input. Apex is a strong, object-oriented programming language used within Salesforce.
Salesforce's Einstein for Developers (formerly Apex GPT) is an AI coding assistant for Salesforce that helps in coding and optimization. It turns English prompts into Apex classes, triggers, or Lightning Web Components. This tool makes use of your org’s metadata to suggest code that complements naming conventions and custom objects. This tool also offers inline code completion as you type in VS Code.
Beyond Salesforce's own tool, some developers make use of AI coding assistants like GitHub Copilot for APEX development. Such tools help cover 80% of the use cases; a skilled developer can handle the rest to refine the code and meet the quality standards.
There are many other AI tools on the market that assist in code generation and optimizations. Some examples include:
- CodeGen and CodeT5. Fine-tuned for APEX and other languages.
- ApexGuru: A generative AI in Sales that produces APEX with fewer post-response changes; it detects anti-patterns and performance hotspots and prescribes post-performance.
- ChatGPT or Claude: Helps in Q&A and can generate conversational code.
Bug Detection
Although traditional static analysis tools help professionals catch common errors, AI goes a step further to identify subtle problems. It helps predict issues or configuration errors before they manifest.
ApexGuru optimizes code and acts as an AI bug finder. It recognizes a recursive trigger or Salesforce Object Query Language (SOQL) in a loop that might cause runtime errors or hit Salesforce governor limits.
Salesforce’s internal AI tool CodeGenie acts as a virtual code reviewer and flags potential issues or missing elements, while generating unit tests for uncovered code paths. This tool leverages LLM and code analysis to find issues like missing null checks or a missed error handler. Though it might not be a syntax error, it can lead to bugs.
For debugging, developers are increasingly leveraging tools like ChatGPT to troubleshoot Salesforce issues. You can easily give an Apex input with the problematic code snippet and the AI can guide you on how to fix it. This speeds up problem-solving.
AI helps increase the efficiency and effectiveness of development testing, but it isn’t infallible. If suggestions are taken blindly, AI can misidentify issues or introduce errors. The best results come from developers treating AI as intelligent assistants to augment manual testing.
Test Automation
AI elevates both backend (Apex) and frontend tests. It saves developers significant time, as they focus more on the product rather than building tests for it. These tests require some tweaking to manage complex logic. AI can generate the basic test, and humans can ensure comprehensive coverage and edge-case handling.
On the frontend side, tools like Functionize deliver AI-driven no-code test automation for dynamic Salesforce UI. If a lightning page or a component ID changes, this tool can easily find the right element by context and reduce test failure. For example, if a button label changes from “Submit” to “Send,” a conventional script would break, and this tool can pinpoint that.
AI also helps increase release cycles and higher-confidence deployments. Functionize claims that AI-assisted testing can increase Salesforce release velocity by 10x.
Flow and Process Automation Design
AI also benefits Salesforce administrators and consultants who design flow and process builder automation. Creating a flow with screens, decisions, and loops can be time-consuming for a professional new to the process, and AI can help fill the gaps.
Salesforce’s Einstein for Flow allows agents to build flows by simply typing the requirements in a prompt. For example, an admin can say, “Create a flow that sends an email to the opportunity owner when the state changes to closed won. It can be set to follow up after 30 days.” Based on how the admin describes the flow in the chat, the AI agent automatically configures it in the code.
Einstein for Flow is an intelligent co-pilot in Flow Builder, translating requirements into configured flow elements. It supports screen, record-triggered, or scheduled flows and basic logic like decisions, loops, and formula calculations. AI won’t know nuanced business rules, so developers will have to make a few adjustments. Still, having 80% of a flow built in a few seconds is worth it!
Documentation and User Support
AI can also help developers find answers to their questions in a timely manner. Einstein GPT for Service makes getting knowledge from help articles in real time easier. This real-time support is critical for development projects and effective collaboration between stakeholders.
The answers that AI suggests are referenced from Salesforce’s Knowledge base. This allows support reps with less experience to get information quickly. With this, the service cloud implementation can be smarter with AI rather than hardcoded with recommendation logic.
Apart from Einstein GPT, there’s Slack GPT, which allows employees to interact with Salesforce conversationally via Slack. Through generative AI, they’ll get an answer or a visualization.
Overall, AI is improving the support and knowledge management side of the Salesforce ecosystem, helping developers and other users get help faster.
How to Create a Salesforce AI Strategy
All of the excitement surrounding AI may encourage businesses to implement more AI tools into its workflow. However, a strategy should be established first so all players have a full understanding of use cases and how AI will help augment them.
Generative AI is worth considering, especially when seeing efficiency and output improvements. However, if you are looking for better potential gains, seek enterprise AI solutions. They have been around longer than generative AI and may provide better solutions for your company.
Your AI strategy should be fueled by a vision. This will ensure its effective setup so it delivers its impact across the enterprise. Involve the senior leadership team for the start. Help them understand the clear business case, with an attributable data source and a definite plan for change management. Create a plan with a timeline. This will help you foresee risks associated with the project and prepare yourself to mitigate them.
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