According to Gartner, 85% of AI projects ultimately fail. 46% fail once in production. The question "how to succeed with AI" is primarily driven by two key factors. The first spoor data quality—data entry errors, siloed information across different systems, and insufficient data intake often result in a lack of robust, clean data required for successful AI deployments. The first step in succeeding with AI is to address the underlying data. How data is ingested and how it disorganized when stored are two critical strategies to build out.
The second factor is inadequate customization. Machine learning and Gen-AI, such as recursive and auto-regressive architectures, need to be tailored to business specific data to provide accurate insights and meaningful outcomes. Without addressing these issues, AI projects struggle to succeed. Tossing Data at proprietary models like GPT can be helpful for assistant type tasks but will never provide value when results need to be business specific.
CRM optimization is an example of the need for customized deep learning and data quality. To derive insights out of the data you are tracking, you need to ensure coverage and organization. How am I capturing my customer data? Do I have a direct or indirect method of communication? Where does the data live and in what format should it be stored? Once the underlying data is cleaned, enriched and organized it can be used to create valuable insights. You can use time-series models to predict revenue forecasts, regression models for churn prediction, strategically recommend products to customers using matrix factorization, and set the foundation for your marketing strategies.
Other good examples of areas that require AI customization are inventory management and supply chain optimization, fraud detection, predicting loan defaults and late shipments, scoring leads and predicting equipment failures.
