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 (i.e., intelligence automation). This article breaks down how data powers intelligence automation.

The Data: The Gas That Makes the AI 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 for 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 may include:

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.
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.
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 mutli-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 such as Open.ai’s ChatGPT or Google’s Bard (commonly referred to as large language models) to incorporate automation, messaging, and document creation methods.

The data from the business’s Salesforce org is then harmonized in Data Cloud, alongside any number of data sources such as mobile apps,  or additional website platforms. This 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 while outside tools help with response framework generation.

AI 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 can get AI-ready by looking at their data-readiness factors and begin to work with groups like Perficient to ensure data-readiness. Get ready for a future where data-driven AI changes industries and makes our daily lives unbelievably interesting. When used in the correct way, intelligence automation will enable professional groups to have more time for innovation and increase work efficiencies. It all starts with the data.

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