Bridging the Gap: From AI Pilot to Enterprise Success

Artificial intelligence (AI) is revolutionizing the way businesses operate, promising to transform everything from sales and marketing to finance and human resources. However, many companies struggle to bridge the gap between successful pilot programs and full-scale production deployments, hindering their ability to unlock the true value of AI.

This blog explores three key ingredients for achieving AI success at scale:

Building a Solid Data Foundation:

Great AI requires great data. Large language models (LLMs) need to be grounded in high-quality, trusted enterprise data, encompassing customer information, Internet of Things (IoT) sensor data, and more. Unfortunately, much of this data resides in isolated silos across cloud and on-premise applications, databases, and data lakes. This fragmentation hinders access and utilization, impeding digital transformation and value realization. Additionally, data used to train AI models can often be incomplete, inaccurate, or irrelevant, leading to unreliable results.

Enter Salesforce Data Cloud, the cornerstone of the Einstein Platform powering Einstein's suite of predictive and generative AI solutions. Data Cloud eliminates data silos by creating a centralized platform for accessing and utilizing an organization's entire data ecosystem. It seamlessly integrates both structured and unstructured data (documents, emails, call recordings, etc.) into Salesforce using pre-built connectors. This facilitates secure zero-copy connections to data lakes like Snowflake, Redshift, BigQuery, and Databricks. Data Cloud then cleanses, harmonizes, and prepares the data for use by employees, analytics tools, and AI systems.

By unlocking the power of previously trapped data, Data Cloud empowers better analysis, decision-making, and AI automation. It integrates customer and business data with metadata – the unifying language across Salesforce applications – to deliver trustworthy, outcome-oriented results without extensive model training.

Imagine a scenario where real-time data reveals a potential customer has just visited your website or downloaded your app. Traditionally, this information would require manual data extraction and custom reporting, likely not happening in real-time. Data Cloud seamlessly integrates this real-time data, triggering a notification for the salesperson to make a timely outreach. This exemplifies the power of the Customer 360, fueled by data, AI, and Flow – all within the Einstein Platform.

The actionable insights unlocked by Data Cloud have cemented its position as Salesforce's fastest-growing organic product. In a single quarter last year, Data Cloud processed over 7 trillion records, driving customer engagement through more than 1 trillion activations. This resulted in higher conversion rates, revenue growth, and improved customer satisfaction.

Ensuring Trust in Enterprise AI

Trust is paramount for successful AI deployments. Salesforce incorporates trust into every application through the Einstein Trust Layer, a core component of the Einstein Platform. This layer encompasses data masking for privacy protection, a zero-retention architecture to ensure data remains private and is never learned by AI models, a detailed LLM audit trail, and human oversight over every AI interaction. Additionally, a built-in feedback loop continuously refines model accuracy and relevance, with this feedback data automatically logged in Data Cloud.

Integrating AI into Workflows

For widespread adoption, AI needs to seamlessly integrate into existing workflows, eliminating the need for employees to switch between disparate systems. Introducing Einstein Copilot, the conversational assistant empowering employees to interact with any data or workflow across the organization directly within their CRM or Slack interface. Unlike generic chatbots, Einstein Copilot leverages your organization's trusted data and metadata to generate meaningful responses. It allows employees to use natural language for tasks like generating customer campaigns, crafting service responses, executing actions and workflows, and more. It can answer questions, create and summarize content, interpret complex conversations, and automate tasks – all within a unified interface spanning Salesforce applications and Slack. Crucially, Einstein Copilot operates securely within your company's data environment and adheres to the principles of the Einstein Trust Layer at all times.

A prime example of utilizing trusted data in the travel and hospitality industry is Turtle Bay Resort, a luxury vacation destination. Previously, their excursion booking system functioned as a data silo, lacking the sophistication to segment guests based on preferences and past interactions.

By implementing Salesforce Data Cloud, Turtle Bay seamlessly harmonized this data to enrich guest profiles. Data Cloud consolidated guest preferences, booking history, and resort interactions into a centralized location. This empowered them to create targeted guest segments based on comprehensive data instantaneously. Turtle Bay now offers guests relevant excursion recommendations, enhancing their vacation experience. Personalized web content for known users resulted in a 40% increase in engagement, with adventurous couples receiving distinct offers compared to curious families. Every interaction is personalized based on consolidated data.

While generative AI remains in its early stages for most companies, the potential for transformative enterprise-wide deployments is significant. By establishing a foundation of trusted data, offering AI within existing workflows, and prioritizing trust, organizations can bridge the gap between pilot programs and AI.