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Has Implementing AI Been Challenging?

Top Challenges in Implementing AI

The #1 challenge to implementing AI is data quality. The AI output will only be as good as the initial dataset used to train the AI. To be effective, start with accurate, complete, consistent, and unduplicated data. Otherwise, to say it plainly, “garbage in, garbage out”.

This leads to the #2 challenge, how can you analyze your data to determine if it's good enough to use with AI, and if it isn't, how can you clean it? This is a challenge because in today’s data-centric business world, the volume of data is beyond what your staff can hope to evaluate or manually clean unless you can invest hundreds of thousands or millions of dollars in direct costs and lost revenue.

Before implementing AI, be sure to get a data quality assessment from ActivePrime to ensure your data is AI ready!
Implementing AI can be challenging if data quality is poor.

What are the different types of AI?

If you want to use AI, you first need to understand that there are different types of AI, using data in different ways. This may affect the level of data quality that is needed.

So let’s dive in to understand the different types of AI. They are not all the same and will give you different types of data.

  • Descriptive AI: Data is used to explain what happened, summarize a dataset, or identify anomalies in a data set.

  • Generative AI: Data is used to predict next steps.

  • Prescriptive AI: Data is used to suggest what actions to take.

Descriptive AI does not need high quality data, but the applications are fewer. This doesn’t mean it isn't important. In fact, it is one of the most important applications when it comes to data quality. ActivePrime uses descriptive AI to assess your data quality to determine if your CRM is AI ready.

What is your data quality?

ActivePrime has found that CRM data that is 80% clean performs well with generative and prescriptive AI applications. Unfortunately, our Data Quality Assessment has never found a customer that reached this threshold after an assessment. The findings range from less than 1% to 72% clean data!

So, if you are considering AI-based applications, a Data Quality Assessment is your first step. Based on the data we have analyzed so far, no one is meeting the mark to deliver a successful implementation.

How do you keep data clean?

After accessing the data quality ActivePrime uses another AI-enabled platform CleanData to correct the issues identified and maintain that level of quality with ‘always on’ and ‘search before create’ protection to correct data issues in real time. This is an application of generative AI.

Don't risk the success of your AI implementation by guessing about your data quality.

Find out with ActivePrime’s Data Quality Assessment!

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