Revolutionizing Salesforce Development With Generative AI

The opinions in this article are my own and do not necessarily reflect the opinions of my employer.

Salesforce Development is Prediction

Salesforce development is an exercise in making well-informed predictions. The processes of system design, configuration, and customization — each a unique piece of the Salesforce development puzzle — hinge on the development team’s ability to interpret user stories and to predict how a given approach will address the user stories, potential future requirements, and what blowback/side effects there may be. This requires a deep understanding of the project needs and a comprehensive understanding of what the Salesforce platform, add-ins, and integrations can deliver.

Design starts with an architect reading through the user stories in order to understand the system's structural needs. They map out the design by predicting how different Salesforce components, such as sales cloud, service cloud, marketing cloud, etc., and the capabilities available through configuration and customization (Apex) should interact to address the business requirements expressed in the user stories. Essentially, they see the project's strategic vision and predict the design that aligns with the desired outcomes.

Configuration involves adjusting the Salesforce settings to meet the needs highlighted in the user stories. A configurator must predict the potential impacts of different settings and how they might affect the user experience and functionality. They need to be aware of the repercussions of each decision they make, considering both the immediate and future needs of the project. For instance, setting up sharing rules, flows, creating custom objects and fields, or downloading something from the AppExchange all require predicting how these configuration changes will impact the overall system's performance and usability.

Customization involves coding to tailor Salesforce beyond what's possible with configuration alone. A developer reads the user stories and predicts what Apex is necessary to accomplish tasks that fall outside of Salesforce's out-of-the-box and configuration functionality. They must anticipate potential system limitations and predict what Apex to write (and how to write it) so the system remains robust, maintainable, and scalable.

Throughout the process, Salesforce developers need to predict how their decisions will affect the project's overall quality, ensuring it meets the agreed-upon standards and project delivery timeline. This predictive ability doesn't only depend on technical knowledge but also on an understanding of business needs, user behavior, and the dynamic nature of the Salesforce ecosystem. By accurately predicting the implications of their decisions, they can ensure that their solution meets the project requirements and delivers the optimal outcome.

Generative AI does Prediction

Generative AI services fundamentally operate as prediction engines, leveraging vast amounts of training data to generate new, original content that aligns with patterns recognized from the input data. Whether creating a new piece of music in the style of a specific artist, composing a poem, designing a new graphic, or writing a piece of code, generative AI models 'predict' the best outputs based on patterns they've learned. The AI uses these predictions to choose the best next output, continually updating its understanding as each new piece of information is generated. This predictive nature of generative AI allows it to create impressively realistic and coherent outputs that closely mimic human creativity.

Leveraging the capabilities of generative AI, we can revolutionize how we approach the design, configuration, and customization of Salesforce. Starting with enough training data and a well-crafted model, AI can consume project delivery, development, and documentation standards, comprehend epics and user stories, and "predict" optimal solutions in the context of the Salesforce ecosystem.

For the design phase, an AI model trained on a vast dataset of successful Salesforce architectures and solutions will predict an optimal system design. It will analyze the given user stories and use its learned patterns to suggest a design that aligns with successful past solutions that had similar requirements. In essence, it can help to foresee the necessary Salesforce components and capabilities and how they should interact to predict the optimal design to meet business requirements.?

For configuration, a generative AI will predict the best Salesforce settings and configurations. Trained on a large number of successful Salesforce configurations and their associated user stories, it will provide suggestions for the optimal setup of sharing rules, flows, custom objects and fields, user roles, AppExchange offerings, and more. Understanding the correlations between specific configurations and successful project outcomes will guide the configuration process to meet the project's unique needs, ensuring robust performance and usability.

For customization, an AI model will generate Apex to extend Salesforce's capabilities beyond its standard functions. The AI model, having been trained on countless Salesforce solutions and their associated user stories, will write the Apex for the requirements in the user stories. It will, in effect, 'predict' the Apex necessary to accomplish specific tasks and adapt the Salesforce system for the project's particular requirements.

It's important to note that while these AI-enhanced processes hold significant potential, they are complementary to, rather than replacements for, the skills and expertise of human developers. The intuition, creativity, and strategic thinking that human experts bring to Salesforce development are irreplaceable. The goal of AI in this context is to amplify these human strengths, augment decision-making, and make the development process more efficient and effective.

Training Generative AI to do Salesforce Development?

Generative AI models learn through a process known as machine learning, where the model is trained on a vast dataset to understand patterns and relationships within the data. In the context of Salesforce, generative AI models will be trained on a corpus of data related to successful Salesforce projects, including project documents, design blueprints, configuration parameters, Apex, and more. This data will span a wide array of use cases and scenarios so the AI can learn a broad spectrum of strategies and approaches for solving different classes of business problems.

The first step in this process will be collecting and organizing this data in a structured, machine-readable format. Data will be sourced from a company's past projects, public repositories, or purchased from external sources. It's crucial that this data is accurately labeled, ensuring that the AI can understand the relationships between, for instance, a set of user stories and the corresponding Salesforce configuration or custom code.

Next, the AI model is trained on this data through a process called supervised learning. The model is exposed to the input data and the corresponding desired output, and it learns to predict the output given a similar input. For example, given a user story and the corresponding Salesforce design, the AI learns to generate a similar design when presented with a similar user story. Over time and with enough data, the AI model can effectively predict the design, configuration, or customization needed for a Salesforce project based on the project's requirements.

As the AI model is being trained, it's essential to continually evaluate its performance and optimize it to improve its predictions. This evaluation could involve adjusting the model's parameters, increasing the diversity of the training data, or refining the model's architecture. The goal is to maximize the model's ability to generate high-quality Salesforce solutions that align with the project's unique needs and the company's development and delivery standards.

Lastly, to ensure the model aligns with the specific standards of a company or project, the AI can be trained on project documentation outlining these standards. By understanding and incorporating the unique requirements and guidelines outlined in these documents, the AI can tailor its output to align with the specific expectations of the company or project. This might include adherence to particular coding standards, compliance with specific business processes, or consideration of unique constraints such as data security or privacy. This way, the generative AI becomes a powerful tool customized to the particular needs and standards of each Salesforce project.

Salesforce Development in a World of AI

In the near future, Salesforce development will undergo a profound transformation thanks to advances in AI technology. In a world where each project team member has a personal AI assistant, these tools will act as individual consultants, capable of understanding the developer's tasks and helping them navigate the complexities of Salesforce design, configuration, and customization. These AI assistants will be trained to 'predict' optimal solutions based on the developer's specific tasks, having learned from a vast corpus of data spanning successful Salesforce projects, company-specific standards, and delivery timelines.

Meanwhile, a project-level AI will act as an orchestrator, coordinating the personal AI assistants' efforts ensuring they align with the project goals, standards, and schedules. This project AI will not only monitor the project's progress but also provide proactive suggestions to keep the project on track, akin to an advanced project manager who has a perfect memory of every project detail and an ability to predict the impact of any decision on the project's timeline and quality.

The integration of AI in this manner would allow for a more streamlined and efficient workflow, where developers can focus more on strategic decision-making and creative problem-solving while the AI handles the more routine tasks and ensures that every decision aligns with the project's overall objectives. This would reduce the cognitive load on the developers and speed up the development process without compromising on quality.

Moreover, the AI's ability to learn and adapt will ensure that the system continuously improves over time, learning from each project to enhance its predictions and suggestions for future projects. This continuous learning loop will produce a robust AI ecosystem that drives efficiency, effectiveness, and innovation in Salesforce development.

Ultimately, this vision of Salesforce development powered by AI represents a future where technology and human ingenuity work harmoniously. The goal is not to replace human developers but to augment their capabilities, allowing them to do more with less effort and focus on what they do best - solving complex problems, innovating, and creating value. It's a future where AI transforms how we work and helps us work smarter, delivering better outcomes faster and more reliably.