A Modern Machine Learning Approach to Change
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. Purpose-built Machine Learning models and analytic capabilities can help uncover key risk factors, predict which changes are likely to fail, and help teams identify actions that reduce change failure.
Watch Numerify's Director of IT Business Analytics Services explore:
- What is a modern Machine Learning driven approach to change success?
- How historical data uncovers your key risk factors
- Why failure probability predictions must be integrated into existing processes
- Examples of interactive dashboards that help prevent change failure
- Why managers should operationalize Machine Learning Change Risk Factor analyses