Last month at SAPPHIRENOW Bill McDermott said that Machine Learning (ML) and Artificial Intelligence (AI) will be the next game changers. According to SAP the next technology adaptation phase for businesses will be around how they can use intelligent applications to assist them with their operations. Tractica forecasts that the market for AI systems for enterprise applications will increase from $202.5 million in 2015 to $11.1 billion by 2024, expanding at a compound annual growth rate of 56.1%. Soon, Machine Learning will be an integral part of enterprise solutions, making machines our digital co-workers. Although ideas about AI emerged since the beginning of computers, they have become more and more mainstream in the last couple of years. The most common examples are voice recognition in your car’s navigation system or Siri in your iPhone. But why is AI becoming a main topic right now? What is AI exactly? And how can it help your business?
Let us start with the first question: Why is AI becoming a topic right now?
There are 4 main reasons for this phenomenon:
1) The first reason is the advances in processing power. Multi-core architecture and in-memory databases like SAP HANA have increased computing power tremendously.
2) The second reason is Big Data. The exponential growth of structured and unstructured data that is being generated by OLTP systems, IoT sensors, digitized documents, images and so on gives us the possibility to apply analytics on large datasets. Furthermore, storage of data is only getting cheaper.
3) The third reason is the analytics itself. Over the last couple of years there have been extensive development in so-called deep learning algorithms. These are algorithms that mimic the human brain and develop an artificial neural network. After years of slow progression in AI, deep learning research caused a big leap forward in this technology.
4) The last reason is the vast number of free, high-quality, open-source software packages and online resources on AI making all knowledge available to a large audience of data scientists and developers.
So what is AI exactly and how does it work?
Machine Learning is first defined in 1959 by Arthur Samuel as a “field of study that gives computers the ability to learn (from large amounts of data) without being explicitly programmed”. It is used to reproduce known patterns on other data in order to apply the results on decision making and actions. The goal of spotting patterns and applying it to other datasets is also the difference between ML and data mining. Although they use many of the same algorithms and techniques, the latter focuses on discovering previously unknown patterns while ML is focusing on reproducing them.
The most common ML technique is called supervised learning. In this method the computer is fed with example inputs and desired outputs and let the computer generate a general rule to connect these inputs to outputs. The computer will compare its actual output with correct outputs to find errors. It then modifies the model accordingly.
Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.
How can it help your business?
With 76% of all business transactions going through SAP systems, they are a rich source of information to apply ML on. SAP recognizes this and incorporated it in their strategy to make all enterprise applications intelligent and widely available through their new HANA Cloud Platform. Currently the SAP Innovation Center Network is working on various intelligent enterprise applications like:
- Automated SalesForecast: Only 44% of executives feel that their company is managing sales effectively and only 22% of companies are able to make accurate sales predictions. SAP is using Machine Learning to predict which opportunities will close and recommend sales representatives the best possible actions to move the deal forward based on data in SAP Hybris Cloud for Customer and unstructured text from emails and the Web.
- CV Matching: Recruiters spend substantial amount of time going through CVs trying to find the best match for open positions. With the use of Machine Learning, SAP has been able to significantly reduce the time to shortlist the best candidates for a particular job – or the best job for a particular candidate.
- Invoice Matching: Manually matching payments to invoices is one of the most labor-intensive processes in accounting, and is often handled by shared services centers. Machine Learning can significantly increase automatic matching rates providing a real world example of an intelligent digital co-worker.
- Social Media Customer Service: Social media community managers and support agents can be overwhelmed by posting volumes, for example from Twitter and Facebook. This app automatically tags and clusters inbound messages and suggests appropriate responses.
- yaaS Recommender: Most e-commerce shops suffer from low conversion rates as they have no, or only, basic recommenders. yaaS Recommender analyzes full customer clickstreams to provide tailored, contextual recommendations in real time.