Algorithms have changed our lives in the past decade, and they will continue to drastically alter the way we live and work in the future. In our personal lives, we’re becoming used to having our questions answered before we even ask them, and having our needs and wants addressed by recommendations that we don’t even ask for. The first thing I do when I land in a new city is open Yelp in order to see where I should eat. I can make a reservation, then take an Uber or a Lyft to the restaurant—and I can do all of this on my phone.
But when I work, the experience is often much less seamless.
I have to log in to multiple systems from my laptop, and then I have to open multiple screens to look at lists of potential work that needs to be done. Some of it is routine, some of it tedious, and some of it requires some real thought. Often, the most time-consuming work requires me to dig across multiple systems to get at the data I need to determine what to do next, and all of this takes a significant amount of time and effort.
The intelligent enterprise is the concept that the way we work should be as seamless, if not more seamless, than the way we live. We should be able to get access to our systems from any device at any time, and the systems should actually be able to:
- Prioritize the work for us
- Make recommendations on how to tackle problems
- Handle simple and repetitive tasks in an automated fashion
- Uncover patterns and insights across my work system and present them to me so I can make the best decision at the right time
Machine learning and the intelligent enterprise
Machine learning technologies are critical to empowering this vision of the intelligent enterprise. By embedding algorithms directly into multiple solutions, we can continuously learn and adapt to new data as it comes in, without a user having to be involved.
The information is presented to knowledge workers in the transaction screens they are familiar with, enhanced with new information to make them more effective at their job. For example, with enough historical data about previous sales, my system can tell me which opportunities I’m working on that will likely close this quarter, and recommend what steps I should take to make this more likely.
Watch the video replay of the recent #askSAP Live Chat: SAP Predictive Analytics, application edition I did with Karthik Palanisamy of intelligence AG. We covered the opportunities and challenges that companies face with predictive analytics, and how SAP is:
- Embedding machine learning in SAP applications like SAP S/4HANA
- Creating intelligent ERP, allowing SAP S/4HANA to utilize machine learning in any business process
- Empowering citizen data scientists with SAP Analytics Cloud
This article originally appeared on the SAP Analytics blog is and is republished by permission.