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Most people’s first glimpse of Artificial Intelligence has been through science fiction films and books, and so it has always held an aura of mystery, and sometimes a little distrust

Today, as more and more use cases are demonstrated in the real world, the general public are becoming more familiar with the concepts behind the technology, even as computer scientists are pushing the AI envelop further.

Historically new technologies have been developed because of a real or perceived need, and now global corporations like SAP and many others are using AI to address the needs of businesses.

AI is fundamental to the future development of technology and ERP solutions in particular, so this week IgniteSAP will explore the current extent to which SAP are using AI to optimise cloud deployments of IT landscapes.

AI vs ML vs DL

According to computational scientists working in the field, Artificial Intelligence is a category that includes Machine Learning and Deep Learning.

The phrase “Artificial Intelligence” was coined as early as 1956. Martin Minsky, one of the early pioneers of AI described it as “the science of making machines do things that would require intelligence if done by men.”

When machines and programs are given instructions to complete tasks based on defined rules to solve problems, the use of these algorithms is referred to as “intelligent”.

The discipline is working towards “Artificial General Intelligence” which could theoretically mimic any intellectual task carried out by a human, but many of the current applications of AI should be referred to as “Artificial Narrow Intelligence” because they deal with a limited number of rules and are limited in scope, unlike human intelligence.

Though current programmed AI behaviour does not approach human intelligence in complexity, its application to interpreting and classifying data: whether it is text, image, audio or spatial data, is conventionally referred to as AI.

For these tasks to be possible, the algorithm must be “trained” as part of the process of specifying the rules by which problems are solved.

Machine Learning trains algorithms by exposure to a large set of data, so distinguishing characteristics of each piece of data (a number, a word, a face or a voice) can be recognised in the course of the later operations of the algorithm. Whether this constitutes “learning” is as debatable as the term “intelligence” because there is no agency and no decision made by the algorithm.

One of the methods being explored to bring about “learning” in machines is Deep Learning, which models more closely the ability of the brain to identify patterns.

ANNs (“Artificial Neural Networks”) are a type of Deep Learning that takes the concept of modelling human brain information processing behaviours even further. Machine Learning requires more human interaction than Deep Learning which is a more automated and higher level process.

Do Not Fear The Bots

All of these methods for training algorithms are being explored as means to further the aim of AI technology: to create solutions for automating highly complex data processing tasks which would otherwise require a human.

Ethical considerations are necessary to control the use of customer data and to counteract any bias that may develop in the algorithm due to cultural bias in the way that it is trained. AI also needs to demonstrate transparency in the way interpretations and conclusions are reached, but AI and automation is a potential source for good in the world.

One of the advantages in the use of AI in a business context is that repetitive and manual processes can be carried out by algorithms: leaving employees to work on more complex, creative and value-added tasks. So the advent of AI is not a source of concern for workers, but rather a means by which their working lives can be improved.

The power of processing large data sets with AI is already seeing applications in countless areas like medicine: scanning images of the body to look for potentially malignant growths far more effectively than humans, or similarly to look for signs of disease or sub-optimal conditions in crops in the agriculture sector.

For businesses, AI can bring increased resilience, better customer service with technologies like Natural Language Processing, proactive as opposed to reactive strategic decision-making, and a more cohesive workforce: all leading to better business outcomes.

The potential benefits of AI technology are almost infinite in the world of commerce and industry. SAP is at the leading edge of research and development, combining AI with ERP, big data and related technologies, but has already rolled out a huge variety of business oriented solutions leveraging AI.

The Future Of SAP Is Now

The potential of AI-related technology to contribute to the development of the global economy is vast: potentially as much as $15.7 trillion by 2030 according to a PwC report.

There is a shortage of skills in the IT sector and particularly in AI technology skills, but SAP is democratising access to the power of AI for businesses.

Rather than just developing individual AI-powered products, SAP has embedded AI functionality across its SaaS solutions, including S/4HANA Cloud, SAP Fieldglass, SAP Concur, SuccessFactors and the SAP CX stack of solutions.

By embedding AI in its solutions, and creating AI-powered modules to extend existing SAP landscapes in the Business Technology Platform like Conversational AI and Intelligent Robotic Process Automation, SAP is helping smaller and medium-sized businesses to compete.

The SaaS model and cloud deployment of SAP systems means that the pace of AI service development is increasing with new features for AI services like SAP AI Core, Launchpad and SAP AI Business Services now being released on a quarterly basis. These services now have SAP BTP free tier access for businesses to explore use cases.

This opportunity to test and evaluate SAP AI services is complimented by the easy scalability of the cloud so businesses can explore them and scale them according to their needs so they pay for what they require and no more.

More information about the use cases for businesses considering SAP AI modules and services can be found in the repository of SAP Top Enterprise AI Scenarios. These are organised according to business area: Finance, Sales and Commerce, HR, Procurement and Customer Service.

Our discussion of SAP training for IT consultants looking to augment their skills with AI competencies can be found here.

Cloud + AI + Big Data = The New SAP

Over the past two decades emerging technologies and contexts like AI, Big Data and the Cloud have changed SAP beyond what anyone could have predicted.

These three elements have developed concurrently so that they are now co-dependent with each other. The context of Big Data has meant that businesses and organisations have access to many more data points: making the customer-provider relationship exponentially more complex.

The cloud has changed the data infrastructure, and the relationship between SAP and its customers as well, so that instead of a periodic sale of a on-premise product we now have a service relationship where the service itself is constantly in a state of innovation.

This level of data complexity requires the innovations of AI technology to help interpret the data streams in the modern business IT landscape, but automation of business processes is not enough.

The analysis of business data with AI-powered process mining products like SAP Signavio Process Intelligence are providing meaningful insights into the way the components of business interact.

Together these elements are helping businesses to understand themselves as dynamic and multi-dimensional systems which are part of a larger economic ecosystem rather than static and siloed aggregations of information.

By addressing the explosion of information AI and other emerging technologies are not just extensions of the traditional business, but fundamental to the operation of these new and highly complex informational systems.

Modern enterprises must be digital enterprises and must be connected to the real world in real time. SAP and its competitors understand this and are making sure businesses can function in the new economic environment.

Many SAP customers are afflicted with inertia: weighed down by their own past success and wary of change, so they are delaying their transition to S/4HANA and the cloud. It is only a matter of time and they will be forced to change their assumptions as their competitors experience growth through the implementation of cloud-based services powered by AI, and AI-powered technologies like process mining.

The growth of AI goes hand-in-hand with the global transition to the cloud, and it is changing SAP services for the better. Those who delay implementing cloud deployments of their business processes will also be missing out on the power of Artificial Intelligence.

If you are looking for a new role in the SAP ecosystem then our team of SAP recruitment consultants can source that employer for you and help you negotiate a competitive salary on your behalf so join us at IgniteSAP.