vance based on time-varying signals. ML predicts which machines could potentially go down the next few days, weeks, or months. When combining those predictions with other data (previous seasonality patterns, weather events, peak demand behaviors), AI can interpret the data to recom- mend and automate traffic routing actions to mitigate customer impact. This example of hyper automation with ML and AI evolves beyond policies, or rules, that guide the system on what best next action to take. Moreover, as the system continues to iterate and more data are collected, ML and AI are able to learn from success and failures, and adjust their models accordingly to further optimize performance. 18.4 HYPER AUTOMATION WITHIN THE ENTERPRISE In D3, organizations will transform into learning businesses by forming communities of relevant data and capabilities; in the prevailing ML/AI environment, every community action provides fur- ther training resulting in a perpetually optimizing environment. To illustrate this, today call centers open every morning and agents begin the day the same way as they did the previous day. In a hyper- automated world, the 10,000 calls (e.g.) taken the previous day advance the automation and influ- ence outcomes for the current day. Similarly, smarter sales generation tools will help sales personnel better understand their audience's needs and preferences. Field technician dispatch processes will be optimized to send the closest tech with the best track record of success for that specific type of job, and the workflow itself will be augmented with a digital assistant that guides the technician to Building the Network of the Future the most efficient and effective way to complete the job, based on AI recommended actions learned from the best of all technicians. Every business unit (community) can benefit every minute, result- ing in smarter operations, with minimal manual touches. The marriage between humans and AI will drive the need for a different prioritization of skills for knowledge workers. Skil