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Data – The Lifeblood of Artificial Intelligence

Big Data Technology And Data Science Illustration. Data Flow Concept. Querying, Analysing, Visualizing Complex Information. Neural Network For Artificial Intelligence. Data Mining. Business Analytics.

With all the hype around artificial intelligence (AI), AI can feel complicated, distant, or foreign. But guess what? It’s not new news and it doesn’t need to be complicated; it’s just a revolution of how we leverage data!

Let’s take a quick dive into the data-driven universe of AI. For the purposes of this article, we will use the terms AI and intelligence automation interchangeably. This article breaks down how data powers intelligence automation. Key terms are bolded for reference.

The Data: The Gas That Makes Artificial Intelligence Go

What good is a stand-alone AI tool for businesses if it isn’t grounded in trusted, contextual data? If the tools we use for intelligence automation are not built around our customers, we will not be providing a truly personalized user experience. This is called grounding; the art of incorporating business-focused data into the foundation of intelligence automation.

The ability of intelligence automation tools to be able to quickly and accurately grab the most relevant and accurate information regarding a customer’s inquiry, sales deal, or more is based on many data-readiness factors. As organizations begin to look at opportunities for optimizing their processes and enhancing their customer experience by using these tools, they will need to create a roadmap for their business data to be at the foundation of that work.

Data-readiness factors for artificial intelligence may include:

  1. Data cleanliness – Do we have a unified view of our customers? What are going to use to identify and unify a customer profile when we interact with them on many channels? If we want to be able to speak to our customers using automated tools, we will want the tools to be able to understand what their needs are.
  2. Use-case specific data incorporation – Are we making the right type of data available for our automation use cases? For example, if we want to automate the service chatbot and case handling in Salesforce for purchase issues, we will likely want to pull past order history and shipping/tracking information into the available data set.
  3. Data completeness – Various data sources typically exist within an organization. Some data may exist in secure warehouses, others within Salesforce, or Amazon Web Services S3. Based on the use cases defined, we will want to ensure we have the data points needed for the intelligence automation to address multi-tier decisions or processes.

Salesforce Data Cloud Securely Brings It All Together

Businesses can leverage the power of tools such as Salesforce’s Data Cloud to bring together multiple data sets. Data Cloud provides processes and integrations to harmonize and unify multiple data sets to create a singular customer profile. The business can then hook up trusted, secure tools to incorporate automation, messaging, and document creation methods.

The data from the business’s Salesforce org is then harmonized in Data Cloud. Alongside CRM data brought into Data Cloud, secondary data sources including mobile apps and website platforms provide meaningful context about our customers. Business data creates the foundational context for the automation tools to leverage. Using Salesforce capabilities, the business and personal information from Salesforce remains secure within Salesforce and Data Cloud.

The idea here is that businesses do not have to build their own large language models (LLM’s) to leverage the power of AI within Salesforce. Salesforce uses an inherent trust layer to integrate with public LLM’s with a zero-day retention (ZDR) policy. ZDR means that the LLM’s do not retain the data sent. They do not use it for training content. They do not store the data. It is just used to generate a response. Through this trust layer and ready-to-use integrations, businesses can use publicly available LLM’s such as ChatGPT or Google’s Bard to provide conversational structure to automated responses within Salesforce. In addition to structure, public LLM’s can also provide additional resources relating to solving customer inquiries.

Using the generally available tools provided by Salesforce within the Data Cloud and Einstein (Salesforce’s AI) space, businesses can trust their data will remain secure. With some strategic planning, use case definition, and implementation, leaders can choose what data to share or not share with the outside LLMs. This ensures proprietary and personal customer data remains secure.

Artificial Intelligence is a Data-Driven Transformation

In the grand scheme of intelligence automation, data is the hero. Data is the gas that lets the automation tools move, adapt and infer, and execute tasks. Organizations should get AI-ready by looking at their data-readiness factors. Get ready for a future where data-driven AI changes industries and makes our daily lives unbelievably interesting and optimized. When used in the correct way, intelligence automation will enable professional groups to have more time for innovation and increase work efficiencies – and it all starts with the data.

Interested in AI but not sure where to start? Contact us to discuss a data and artificial intelligence readiness assessment.

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Samantha Ziegel, Lead Business Consultant

Samantha Ziegel has a passion for data, ai, healthcare, technology, information security, and operational effectiveness. She understands the complexities of the technology industry and seeks to provide information and materials to help others navigate the space. Outside of information sharing, as a working mom herself, Samantha is a committed advocate for working parents and enabling them to bring their best self to work and home each day.

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