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Data is at the core of so many decisions in the digital age. To some, data is considered the gold that is stored in the digital bank vault of any given company. 

When you want to figure out how to best market to your customer base, you consult your data. 

When you want to figure out when to order the next shipment of whatever wholesale product or raw material your company depends on, you consult the data. 

When you want to figure out your customer churn rate for this year, or predict next year’s, you consult the data. 

You might have already automated these processes, but there is another increasingly popular and important process that you may not have considered automating: Systems integration. 

Incorporating a mind-bogglingly large dataset is a valuable service to enterprise clients, and so are analytics processes, cognitive-based or otherwise, and prediction platforms. 

AI and Systems Integrators

It makes sense that AI systems are a perfect fit for systems integration, because most AI systems are integrated: They combine multiple processes, such as speech recognition and semantic search, to form a unified, goal-striving agent, such as a chatbot that analyzes customer’s messages to figure out which products they are looking for. 

A systems integrator (SI) knows the terms “legacy system,” which refers to systems that are old, more precisely outmoded, that could use an upgrade into a more advanced and unified information architecture. 

Much of the time, systems integrations involves implementing an analytics or prediction application that automates the integration of external data with internal company data, all to the end of creating new predictions or analyses that are stronger and more well-informed than the company’s previous efforts. 

Read on for some examples of integration-based AI services that companies like Findability Sciences offers for systems integrators looking to offer clients business-boosting and data-integrating AI tools. 

Real World Example of Cognitive AI in Action

Findability Sciences partnered with a leading technology and support services provider to employ cognitive AI that connects freelancers with end customers. 

How this cognitive AI works is by integrating data from a wide variety of sources, such as the self-authored posts of freelancers across the web, and the projects and assignments posted by end customers. Figuring out who is fit for whom is at the core of this project, and connecting customers to the right workers is the main goal. 

Cognitive AI has another data-integration use case in the form of persona modeling, which we will discuss next. 

Persona Modeling Learns About Your Customer Base, Then Defines It

Persona modeling is a method that involves integrating data about a given company’s customer base from a multitude of sources. For the purposes of demonstrating this method along with the necessity of employing AI for integration, we will stick to one source external to a company that is a goldmine for integration-ready data: Social media. 

There is a strong chance that your customers are on social media. Since 2017, the amount of people on social media has increased 1.5x, from 2.86 billion to 4.41 billion. It is official: More than half the world is on social media. 

Your odds are good, then, that the customers in your records also have a life on social media, liking, commenting, posting pictures, etc., sharing whatever floats their boat with however many people follow them and are willing to listen. 

A persona modeling tool, which Findability Sciences offers, will integrate this data with your own data about your customers (what they buy, how often they spend money on your products or services, etc.) You can get a few things as a result, which we will detail below. 

Fake customer profiles (the titular “persona models”) that are built from, and represent the manifestation of, the cognitive insights gained from the persona modeling tool’s analysis of the abundance of social media profiles and posts collected. 

These can go as deep as your customers’ moral sentiments, or as superficial as their favorite colors, both of which are vital for customer outreach efforts (use purple next time in your print or video ads, if your customers like it.) 


Systems integrations involves implementing something useful, but external, to a company’s existing system, dataset, process, etc., and creating a more efficient version of the preexisting thing as a result. AI comes in when a company, for example, wants to use cognitive insights to learn about their customers, or potential employees, where data the company cannot analyze on its own, such as thousands of social media posts, is integrated with the company’s own precious data, such as company records about who spends the most money on their products.