Those who consider themselves members of the programming cognoscenti know that software application development is a slow and methodical process of careful strategic code composition, builds, tests, debugging procedures, and user acceptance refinements.

If you recall SAP’s 2010 acquisition of Sybase, you may remember PowerBuilder being billed as a rapid application development (RAD) tool. But even RAD isn’t always that fast in the grand scheme of things.

The Age of Artificial Intelligence (AI) Automation

However advanced, automated, and accelerated previous methods of software development have been, the arrival of contemporary artificial intelligence (AI) can now give programmers the potential to do things faster, smarter, more effectively, and more intuitively.

AI and machine learning (ML) techniques allow programmers to discover patterns, repetitions, and trends in data sets in an automated way. They can then apply this knowledge to map, model, and ultimately deploy functions in software applications that will be fully cognizant of the underlying data trends generated through user behavior. And increasingly machine behavior, too.

Finding Anomalies and Predicting Outcomes

AI and ML techniques also help programmers discover associations, connections, and integration points between data sets in an automated way. This knowledge allows developers to map, model, and ultimately deploy functions in the software applications they are building that will be capable of highlighting anomalies, uncovering efficiencies, and predicting future outcomes.

How far any single programmer/developer can go in terms of building smarter AI-driven apps will depend on the depth and quality of the data pool they are able to use for their work.

Knowing What We Don’t Know

Where AI and ML really start to make a difference is when they show us not just the things we didn’t know, but the things we didn’t know we needed to know. As humans, we still must code our software applications based on what we think we need them to do for us.

But as Metamaven CTO Mariya Yao explains on Forbes, with AI and ML on board, a software engineer does not give the computer rules for how to make decisions and take actions. “Instead,” she wrote, “[the engineer] curates and prepares domain-specific data that is fed into learning algorithms, which are iteratively trained and continuously improved. A machine learning model can deduce from data what features and patterns are important, without a human explicitly encoding this knowledge.”

What Yao is saying is that AI and ML outputs can start to completely surprise us and highlight application features that we didn’t even know we needed to create, develop, maintain, or extend. SAP echoes that in its definition of machine learning, which states, “Machine learning technology teaches computers how to perform tasks by learning from data instead of being explicitly programmed.”

Schooling Developers in Artificial Intelligence

How should ASUG Members be charging their software application development teams with AI- and ML-driven advancements? SAP’s Ewan Maalerud has written on this subject to explain a few use cases for these advancements, including how AI enhances what you can do with analytics in SAP Analytics Cloud.

Maalerud points to SAP Analytics Cloud’s ability to perform as an analytics platform that uses AI in its predictive modeling. It uses rich data visualizations with an intuitive user interface. Developers can channel this AI brainpower into applications so they can convey a richer contextual understanding and situational awareness of any firm’s activities to help employees make informed decisions.

A Jump-Start for AI and ML Projects

This discussion would be incomplete if we did not mention the SAP Leonardo Machine Learning Foundation. Although the name may sound like a charitable foundation, it’s actually an offering to help advance machine learning at organizations through ready-to-use services and models available through APIs and web services. If you’re thinking of bringing AI or ML into your processes, this can act as a jump-start.

Developers can simply point their applications in the right direction to APIs connected up with services that can (for example) detect and identify objects in pictures, find similar images and text content, or extract keywords from natural language text.

Machine Learning Models to Tune Up

According to SAP, “Besides using pretrained ML services you can also deploy custom ML models or tune existing models with your own training data. This allows you to easily serve customized ML models for critical business processes in a scalable and secure manner.”

AI for programmers inside or outside of an SAP environment brings automation possibilities, contextual knowledge advantages, application efficiency, and decision support. Surely that’s knowledge worth gathering and applying today.

If you’re planning to attend SAPPHIRE NOW and ASUG Annual Conference, don’t miss the day of learning about SAP Leonardo. There will be an ASUG Pre-Conference Seminar on Business Process Innovations for the Intelligent Enterprise and all things related to SAP Leonardo. Register today and save your spot.