Editor’s Note: AI Cloud, Einstein GPT, and other cloud GPT products are now Einstein. For the latest on Salesforce Einstein, go here. Einstein 1 Platform is now the Salesforce Platform. For the latest, go here.
Generative artificial intelligence (AI) has the power to transform the way we live and work in profound ways and will challenge even the most innovative companies for years to come.
But generative AI is not without risks. It gets a lot of things right but many things wrong. As businesses race to bring this technology to market, it’s critical that we do so inclusively and intentionally. It’s not enough to deliver the technological capabilities of generative AI, we prioritize responsible innovation to help guide how this transformative technology can and should be used — and help our employees, partners, and customers develop and use these technologies safely, accurately, and ethically.
Generative AI at Salesforce
The potential for generative AI at Salesforce — and enterprise technology more broadly — is vast.
AI is already an integral part of Einstein 1, and our Einstein AI technologies deliver nearly 200 billion predictions every day across Salesforce’s business applications, including:
- Sales, which uses AI insights to identify the best next steps and close deals faster.
- Service, which uses AI to have human-like conversations and provide answers to repetitive questions and tasks, freeing up agents to handle more complicated requests.
- Marketing, which leverages AI to understand customer behavior and personalize the timing, targeting, and content of marketing activities.
- Commerce, which uses AI to power highly personalized shopping experiences and smarter ecommerce.
Now, generative AI has the potential to help our customers connect with their audiences in new, more personalized ways across many sales, customer service, marketing, commerce, and IT interactions. We’re even exploring the use of AI-generated code to help our customers – even those without certified Salesforce developers on staff – write high-quality code faster, using fewer lines of code and therefore, requiring less CPU.
Guidelines for Trusted Generative AI
Like all of our innovations, we are embedding ethical guardrails and guidance across our products to help customers innovate responsibly — and catch potential problems before they happen.
Given the tremendous opportunities and challenges emerging in this space, we’re building on our Trusted AI Principles with a new set of guidelines focused on the responsible development and implementation of generative AI.
We are still in the early days of this transformative technology, and these guidelines are very much a work in progress — but we’re committed to learning and iterating in partnership with others to find solutions.
Below are five guidelines we’re using to guide the development of trusted generative AI, here at Salesforce and beyond.
- Accuracy: We deliver verifiable results that balance accuracy, precision, and recall in the models by enabling customers to train models on their own data. We communicate when there is uncertainty about the veracity of the AI’s response and enable users to validate these responses. This can be done by citing sources, explainability of why the AI gave the responses it did (e.g., chain-of-thought prompts), highlighting areas to double-check (e.g., statistics, recommendations, dates), and creating guardrails that prevent some tasks from being fully automated (e.g., launch code into a production environment without a human review).
- Safety: As with all of our AI models, we make every effort to mitigate bias, toxicity, and harmful output by conducting bias, explainability, and robustness assessments, and red teaming. We also protect the privacy of any personally identifying information (PII) present in the data used for training and create guardrails to prevent additional harm (e.g., force publishing code to a sandbox rather than automatically pushing to production).
- Honesty: When collecting data to train and evaluate our models, we respect data provenance and obtain consent to use data (e.g., open-source, user-provided), while also being transparent that an AI has created content when it is autonomously delivered (e.g., chatbot response to a consumer, use of watermarks).
- Empowerment: There are some cases where it is best to fully automate processes but there are other cases where AI should play a supporting role to the human — or where human judgment is required. It’s important to identify the appropriate balance to “supercharge” human capabilities and make these solutions accessible to all (e.g., generate ALT text to accompany images).
- Sustainability: As we strive to create more accurate models, it’s paramount to develop right-sized models where possible to reduce our carbon footprint. When it comes to AI models, larger doesn’t always mean better: In some instances, smaller, better-trained models outperform larger, more sparsely trained models.
Learn more about Trusted AI at Salesforce, including the tools we deliver to our employees, customers, communities, and partners for developing and using AI responsibly, accurately, and ethically.
