Jane.ai Founder on The Facts and Myths of AI (Part 2)

Today, we see artificial intelligence making an impact on nearly everything we use on a daily basis. However, as I noted earlier – AI does not have all the answers, nor is it perfect. There is still plenty of room to improve products that use artificial intelligence to drive better results. Specifically, the biggest area for improvement lies in the data that’s feeding AI products.

Said differently, it’s important to realize that limitations with artificial intelligence do not correlate with a product’s design. Instead, the factor driving AI’s current limitations is the data driving its outputs. For example, artificial intelligence utilizes data input from three areas – apps, documents & tribal knowledge from your team. Commonly, these data sources aren’t easily accessible. As a result, the data feeding an artificial intelligence platform lacks organization and won’t produce optimal results.

Simply put, assembling your data input is critical to driving AI’s performance.

Furthermore, it’s critical to develop sound strategies for collecting data. Once you have easy access to data and assemble it logically, artificial intelligence will perform at maximum capacity. However, it’s a step that gets lost in the shuffle and is ultimately taken for granted. In other words, data sources are commonly scattered and illogically organized. Ultimately, artificial intelligence architecture doesn’t require a ton of improvement. Instead, we need to do a better job of assembling data for these products.

Along these lines, it’s important to consider where to best utilize artificial intelligence to provide value in specific use cases. For me, I think it’s helpful to think about opportunities in a four-box setup with the following axes:

  • Internal Use Cases vs. External Use Cases.

  • Experience-Based Use Cases vs. Revenue-Based Use Cases.

First, external revenue use cases involve lead generation or opportunities to increase your sales pipeline. Second, external experience use cases would focus on improving your customer success engagement.

Next, internal revenue use cases would drive sales enablement to give your team the necessary tools to perform their daily tasks more effectively. Finally, internal experience use cases focus on employee engagement to ensure that employees have easy access to information.

In the end, AI makes an impact on a number of business functions. The key consideration is understanding your target audience and the tools or processes that require enhancement.

 

Click here for Part 1

Click here for Part 3

About the speaker
David Karandish Capacity, CEO Member

David Karandish is the Founder and CEO of Jane.ai - bringing AI solutions to the workplace by optimizing day-to-day processes with innovative approaches to managing tasks. Prior to starting Jane.ai, David was the CEO of Answers - one of the largest platforms serving Q&A content to more than 100 million users. In addition, David was the Co-Founder and CEO of AFCV Holdings - a portfolio company focused on acquiring new technologies and businesses focused on providing Q&A content to users. David is a graduate of Washington University in St. Louis and continues to live in St. Louis where he is an active supporter of the city's startup community.

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