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How Data, Trust, and Contextual UX Deliver AI Success

Artificial intelligence is the most promising technological disruption of our lifetimes, and will significantly affect every aspect of business operations, from sales and service to marketing, finance, legal, and human resources. Yet, even with global corporate investment in AI set to hit $200 billion by 2025 and generative AI making astonishing progress in the last year, I’m hearing from many company leaders that they lack clarity on how to get from pilot to full production and value realization. 

While three-quarters of workers surveyed in the recent “Your Data, Your AI” survey from Salesforce believe accurate, complete, and secure data is critical to building trust in AI, more than half do not trust the data used to train AI systems today. And nearly 60% of AI users worldwide find it difficult to get what they want out of AI, the report found. 

But there’s good news. Working with early adopters, we’ve learned there are three ingredients needed for AI success: a data foundation, an AI trust layer, and contextual UX workflows. By deploying these together, companies across industries and geographies, like AAA, ADP, and Turtle Bay, are unlocking enterprise deployments at scale and driving measurable outcomes from AI automation, personalization, and performance optimization, including higher sales productivity, faster customer service resolutions, higher-conversion marketing campaigns, and more.

Great AI depends on great data

First, for AI to live up to the hype, generate the highest quality results, and deliver tangible business value, large language models (LLMs) must be grounded in trusted enterprise data, including customer data, IoT and telemetry data, and more. Unfortunately, most company data today remains trapped in disconnected silos – various cloud and on-premise applications, databases, data warehouses, and data lakes – making wholesale digital transformation and value realization elusive. Even worse, the data being used to ground AI models is often incomplete, incorrect, or irrelevant — leading to inconsistent, incorrect results.

That’s why Salesforce developed Data Cloud, the heart of the Einstein 1 Platform, which powers Einstein predictive and generative AI. Data Cloud eliminates data silos, creating a single platform for accessing and using all of a business’ enterprise data. It easily integrates both structured and unstructured data (such as PDFs, emails, call transcripts, videos, and more) into Salesforce with a library of connectors, utilizing zero-copy integrations to securely connect data lakes including Snowflake, Redshift, BigQuery, and Databricks. Data Cloud then cleanses, harmonizes, and preps the data for use by your employees, analytics, and AI systems. 

Data Cloud unlocks the power of your trapped data for better analysis, decision-making, and AI automation, grounding customer and business data and metadata — the common language that integrates all Salesforce applications — in ways that deliver trusted, outcome-oriented results without expensive model training. 

For example, real-time data that a prospective customer has just visited your website or downloaded your mobile app might live in a data warehouse such as Snowflake. In the past, your sales reps would have no way of knowing this without a manual data pull and custom report, and if this happened at all, it certainly would not occur in real time. With Data Cloud, this real-time data in Snowflake is seamlessly ingested into Data Cloud, where it can trigger a flow to notify the salesperson to reach out in a timely manner. This is the power of the Customer 360, driven by data, AI, and Flow, all part of the Einstein 1 Platform. 

The actionable insights unlocked by Data Cloud have made it the company’s fastest-growing organic product ever. In a single quarter last year, more than 7 trillion records were ingested into Data Cloud and more than 1 trillion activations drove customer engagement, resulting in higher conversion rates, revenue growth, and customer satisfaction.

Trust is table stakes for enterprise AI

Trust is a key component of successful enterprise AI deployments. We have engineered trust into every Salesforce application through our Einstein Trust Layer, a core part of the Einstein 1 Platform. The Einstein Trust Layer includes data masking to ensure data privacy protection, a zero-retention architecture to ensure data is never learned by AI models or stored outside Salesforce, an LLM audit trail, and keeps humans at the helm of every AI interaction. We have also built-in a feedback loop that continuously improves model accuracy and relevance, and this feedback data is automatically logged in Data Cloud.

AI in the flow of work is key to enterprise adoption 

The final ingredient for success is delivering AI in the flow of where your sales, service, marketing, commerce, developer, and other employees work, rather than asking them to swivel to yet another disconnected system and screen. Enter Einstein Copilot, the conversational assistant for your employees to interact with any data or workflow across your enterprise, directly in the flow of work of CRM or Slack.

Unlike other copilots that lack specific customer data to generate useful responses, Einstein Copilot is automatically grounded in all of your organization’s trusted data and metadata. It enables your employees to generate customer campaigns, service responses, actions, and workflows using natural language and can answer questions, create and summarize content, interpret complex conversations, and automate tasks – all from a single conversational interface that spans all Salesforce applications and Slack. Einstein Copilot operates securely within the confines of your company’s data and business processes and is backed by the Einstein Trust Layer at every step.

A great example of putting trusted data to work in the travel and hospitality sector is the luxury vacation destination Turtle Bay Resort. Previously, its existing booking system for island excursions was an island of trapped data, and wasn’t sophisticated enough to segment guests based on detailed preferences and past interactions. 

So, they turned to Salesforce’s Data Cloud to seamlessly harmonize this data to enrich their guest profiles. With Data Cloud, the resort pulls together guest data — like preferences, booking history, and resort interactions — all into one place. Now, Turtle Bay Resort can create targeted guest segments based on all of this data — instantly. Turtle Bay knows guests are seeing relevant excursion recommendations that will make their stay even better. In fact, personalized web content for known users has led to a 40% increase in engagement. Adventurous couples get one set of excursion offers. Curious families get a different set. Everything is personalized, based on consolidated data.

While generative AI is still in nascent stages for most companies, the potential for true enterprise transformation is immense, and those that can put in a foundation of data and trust, and offer AI in the flow of where their employees work, will be able to shift from pilot to production and realize tremendous value, employee satisfaction, customer loyalty, and business growth. 

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