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Preparation is key for meaningful adoption of AI

This article was based on the interview with Aron Clymer of Data Clymer by Greg Kihlström for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:

Preparation is key for meaningful adoption of artificial intelligence (AI) in the enterprise. This is a key takeaway from the transcript of a podcast that discusses the importance of data preparation and quality in the context of artificial intelligence (AI) and machine learning (ML). The podcast highlights the need for organizations to invest time and effort in preparing their data in order to get accurate and valuable insights from AI models.

One of the main points made in the podcast is that simply throwing AI technology on top of data is not enough. AI models require high-quality, well-structured data in order to provide accurate predictions and insights. This means that organizations need to invest in data preparation, which involves cleaning, organizing, and structuring data in a way that is easy for AI models to understand and analyze.

Data preparation is particularly important in the context of predictive modeling. Predictive modeling involves using historical data to make predictions about future outcomes. In order to build accurate predictive models, organizations need to have a clear understanding of the data they have and how it relates to the outcome they are trying to predict. This requires defining key metrics and variables, as well as ensuring that the data is complete and accurate.

The podcast also emphasizes the importance of data volume in AI. Larger companies, especially those in the business-to-consumer (B2C) space, often have access to large volumes of customer data, which can be used to build more accurate predictive models. However, smaller companies may face challenges in this regard, as they may have less data to work with. This highlights the need for organizations to collect and store data in a systematic and organized manner, so that it can be used effectively for AI and ML purposes.

Another key point raised in the podcast is the need for user-friendly AI tools. The podcast suggests that AI tools should be intuitive and easy to use, similar to popular business intelligence tools. This would enable non-technical users to leverage AI technology and make data-driven decisions without the need for extensive technical knowledge. User-friendly AI tools can also help organizations overcome the perception that AI is complex and difficult to implement.

In conclusion, the podcast highlights the importance of preparation in AI. Data preparation is crucial for building accurate and valuable AI models, and organizations need to invest time and effort in cleaning, organizing, and structuring their data. Additionally, organizations should prioritize user-friendly AI tools that enable non-technical users to leverage AI technology and make data-driven decisions. By prioritizing data preparation and accessibility, organizations can unlock the full potential of AI and gain a competitive advantage in today’s data-driven business landscape.

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