What Service Leaders Can Learn From Failed AI Deployments: Common Pitfalls & How to Avoid Them

The list of benefits for artificial intelligence in the enterprise is long and includes improved agility, more accurate decision making and reduced customer churn. Unfortunately, those benefits aren’t automatic. Transformation requires sweeping organizational change, better data policies, user buy-in and an end product that’s easy enough for the entire workforce to use. As more analysts detail the state of AI deployments across the enterprise, we are taking a closer look at how these lessons apply to the service industry.

Why are AI Deployments Failing?

Before we dive into best practices, let’s look at problem areas. Last year, IDC released a sobering report about enterprise-wide AI failures. “Most organizations reported some failures among their AI projects with a quarter (1 in 4) reporting up to 50% failure rate; lack of skilled staff and unrealistic expectations were identified as the top reasons for failure.” In addition, the report noted the cost of AI solutions and bias in the data were also major factors holding back successful implementations.