A Modern Machine Learning Approach to Change

As companies continue to embrace digital transformation, the consequences of change failure are critical and include negative impacts on revenue, productivity, and customer satisfaction. However, even high performing teams report failure rates of 7.5% on average. With this in mind, change managers face difficult decisions about where to spend their limited time and budget to improve change success.

While Machine Learning is best known for generating predictions, certain methods can prevent expensive outages, increase change velocity, and drive faster innovation within an organization. Numerify's purpose-built Machine Learning models and analytic capabilities, uncover key risk factors, predict which changes are likely to fail, and help teams identify actions that reduce change failure.

Join us in this on demand webinar to hear Joe Foley, Director of IT Business Analytics Services, discuss how a machine-learning driven approach to change can prevent operational disruption, reduce the cost of recovery, and enable organizations to focus on growth and innovation. He will also share his experiences with how IT organizations are leveraging advanced analytics to identify issues before they happen and evolve with the business.
 
- What is a modern Machine Learning driven approach to change success?
- How to automatically uncover key risk factors from your historical change data
- How to predict which changes are most likely to fail
- How to prevent change failure across your organization
- How to monitor for new threats to change success

 

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