Strategies for Conquering Daily Setbacks in Predictive Artificial Intelligence
In the world of business, decision-makers are often faced with the choice of whether or not to operationalize a predictive AI model. However, without a clear understanding of its potential business value, these models may not move beyond the planning stages.
According to Unilever's Chief Data Officer, Morgan Vawter, the practical deployment of predictive AI represents the forefront of human progress. Yet, industry research and anecdotal wisdom have revealed that predictive AI projects are likely to fail if the business value isn't properly understood and communicated.
Decision-makers are more likely to deploy a predictive AI model if they can see the potential business value it offers. Standard technical metrics like precision, recall, AUC, or F-score, while useful for assessing model soundness, provide little to no insight into the potential business value of a predictive AI model.
To sell the potential business value of a predictive AI model, one must establish how much business value the deployment stands to deliver. A calculation of the business improvement a predictive AI model stands to gain, in terms of profit or other key performance indicators (KPIs), is necessary before a business can move forward with its deployment.
Recommended business metrics to demonstrate the potential value of predictive AI projects and motivate deployment decisions include cost reduction (e.g., decreased maintenance or inventory costs), revenue increases (e.g., higher sales through demand forecasting), risk reduction (e.g., fewer equipment failures), process efficiency improvements (e.g., reduced planning time by up to 90%), service availability (e.g., 99%+ inventory availability), return on investment (ROI), and customer-related KPIs such as churn prediction and ARPU improvements. Quantifying these with concrete figures like percentage cost savings, improved uptime, or inventory reductions (up to 98% stock reduction) makes the business case compelling.
Metrics showing alignment with strategic goals and overall market impact (e.g., market growth rates or adoption in large enterprises) also help secure executive buy-in. Presenting only standard technical performance metrics is a cardinal business sin as it proposes a systematic operational change with no concrete estimate of its upside.
Executives consider predictive AI crucial for improving operations with science. Closing the sale is essential for a machine learning model's deployment, even after agreement, signing, and payment. Without visibility into the business value of a predictive AI model, it is likely to be among the first projects cut during financial crunches.
It's worth noting that many models developed for deployment wind up unused on the shelf, and customers often do not use, act on, or integrate the machine learning models delivered to them. Making it a no-brainer for customers to deploy a predictive AI model by selling its potential business value gives decision-makers essentially no choice but to deploy, and gives the model a chance to realize a business impact.
In conclusion, for predictive AI models to be successful, it's essential to focus on the business value they offer. By quantifying the potential improvements in key areas like cost reduction, revenue increases, and process efficiency, businesses can make informed decisions about whether to deploy these models and reap the benefits they offer.
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