This video is part of a series with Jill Konrath, Bestselling Author and LinkedIn Top Sales Influencer and Rudy Agovic, Chief Analytics Officer at Clarity Data Insights. Agovic discusses the three biggest mistakes he sees people making in AI from a marketing perspective. The first mistake is a lack of proper metrics, which can lead to a disconnect between what the business wants and what the technology delivers. The second mistake is buying into the hype around AI without doing proper research or having realistic expectations. The third mistake is treating AI as something that you build and forget without considering the need for continuous reevaluation, retraining, and reassessment.

In this era of data-driven decision-making, organizations increasingly turn to AI-enabled analytics to extract valuable insights from vast amounts of data. However, the journey toward successfully implementing these advanced analytics solutions can be fraught with challenges.

One of the primary hurdles faced by companies is ensuring the quality and accessibility of data. This article delves into the intricacies of data quality and accessibility, explores their impact on AI-enabled analytics, and discusses strategies for overcoming these challenges.

I recently attended CONEXPO, the construction industry trade show held every three years. In my conversations with multiple marketing leaders, the big question was: