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The future of Enterprise AI isn’t about more data – it’s about the right data

Paul O'Sullivan Profile picture for user Paul O'Sullivan June 5, 2024
Summary:
Paul O'Sullivan, CTO at Salesforce UKI, looks beyond the hype around generative AI to how organizations can build a foundation of trust for integrated data.

Trust silhouette © Warchi - Canva.com

Artificial Intelligence promises to transform every aspect of business operations, yet a lot of companies lack clarity on how to get from pilot to production and fully realize value. In today’s digital landscape they struggle with islands of data spread across various systems, leading many workers to not trust the data used to train AI systems and experience difficulty to get what they want out of them.

According to Salesforce research, only 28% of applications are connected, and over 80% of business leaders struggle with data fragmentation and data silos.

While three-quarters of workers surveyed in the recent “Your Data, Your AI” survey of nearly 6,000 global knowledge workers 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.

The future of enterprise AI isn’t about more data – it’s about the right data. When AI is grounded in a company’s own data, it delivers more useful results and ultimately drives greater trust and adoption.

Only by consolidating their data will companies be able to fully understand the complete customer journey. A trusted data foundation and integrating AI into workflows across the enterprise are key ingredients needed for AI success.

Implementing these together, companies can unlock enterprise deployments at scale and drive measurable outcomes from AI automation, personalization, and performance optimization, including higher sales productivity, faster customer service resolutions and higher-conversion marketing campaigns.

Building a trusted data foundation

For AI to live up to the hype, Large Language Models (LLMs) must be grounded in trusted enterprise data. However, with data trapped in disconnected applications and silos, wholesale digital transformation and value realization remains elusive. Prospects are worse when the data being used to ground AI models is incomplete, incorrect, or irrelevant — leading to inconsistent results and hallucinations.

This is why Salesforce developed Data Cloud. The heart of the Einstein 1 Platform, it 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.

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

Take, for example, real-time data that a prospective customer has just visited a company’s website. Previously, sales reps would have had no way of knowing this without manually pulling data into a custom report. Real-time data brings actionable insights, allowing for immediate customer engagement, resulting in higher conversion rates, revenue growth, and customer satisfaction.

All this explains why Data Cloud is Salesforce’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 (putting the data to use).

Trust is a key component of successful enterprise AI deployments. By unifying and cleansing their data, companies can ensure that AI models operate on the most accurate and relevant information.

At Salesforce, 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 – it's a zero-retention architecture to ensure data is never learned by AI models, stored outside Salesforce, or leaves an LLM audit trail. Most importantly, it keeps humans at the helm of every AI interaction, ensuring that we minimize the exposure to risks like toxicity and hallucinations. 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.

Integrating AI into the flow of work

As AI integrates across a variety of business functions, including sales, service, marketing, commerce, developer and beyond, it's prompting the use of conversational assistants. Employees are leaning into this tool to interact with any data or workflow across their enterprise.

With specific customer data, employees can generate useful responses which are automatically grounded in all of their organizations trusted data and metadata. From generating customer campaigns, to answering service questions, everything is personalized, based on consolidated data – all securely within the confines of their company’s data and business processes.

The powerful combination of data and CRM makes these personalized customer experiences possible. For today’s consumer, milliseconds matter. The cost of not keeping up with them could be lost sales opportunities, poor social media reviews, or a disconnect in a healthcare delivery.

While generative AI is still in its early stages for most companies, the potential for true enterprise transformation is immense. Those that can put in a foundation of data and trust, and offer consistent, contextual 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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