Many companies are struggling to find the value (the “beef”) in Artificial Intelligence (AI) / Machine Learning (ML).
Companies are forecasted to spend $407 Billion on AI/ML technology in 2027 up front $86.9 billion in 2022 according to Forbes. As companies invest in the latest technology, they find that their expenditures have been influenced by typical hype cycle expectations.
This frustration can be avoided by following these three adoption cornerstones:
- Have a clear, intentional focus on the business problem you want to solve using AI (not a solution looking for a problem)
- Create a foundation of repeatable data integrity to fuel your AI solution (garbage in / garbage out)
- Manage the adoption of AI/ML solutions to provide rapid, repeatable and actionable results (not boiling the ocean)
The focus of this three-part series is to help companies avoid the hype cycle in AI investments. Investments that empower an organization to have game changing cost and profit performance insights for every product sold to every customer through every channel.
Part 1: Have a clear, intentional focus on the business problem you want to solve using AI
Companies have long recognized that having high-level cost and profit details, based on allocation methods, do not support making the critical types of decisions that are required in today’s competitive marketplace. The following highlights come from an article published by Clorox, Georgia Tech and Competitive Insights:
“Grouping customers by their revenue contributions or products based on their market category is commonplace. But zeroing in on discrete segments based on their exact contribution to bottom-line profits is another matter.”
“To be actionable, the profit contributions must be based on the actual realized net revenue for each product sold to each customer as well as all costs required to service that order from the sourcing of the product to the order-fulfillment activities.”
-Soumen Ghosh, Mark Hersh, and Richard Sharpe - Supply Chain Xchange
This was certainly the case for Hospira, a pharmaceutical company. The Executives realized that they needed to have complete end-to-end visibility of the costs associated with their complex product portfolio. The following provides a summary of the segmentation of their products based on accurate and specific profit performance for every product being sold.
