According to O’Reilly’s AI Adoption in the Enterprise 2021 report, interest in automation has tripled in the last year and with good reason. Considering the current labor shortages, logistical challenges, and increasing competition it is no wonder businesses are adapting and discovering new ways to achieve a competitive advantage. The buzz of developers and digital transformation leaders alike, it is important to discuss to what purpose and for whom these technologies are intended. Business leaders are looking for solutions to labor shortages, data inaccuracies, and operation inefficiencies. Their management teams have intimate understanding of the day-to-day challenges but not the time or talent to run an internal software team. Data scientists and software developers are looking to the machine learning platforms with the best data management, version control, and integration capabilities available. Machine learning development platforms, such as SAP’s Data Intelligence, provide a full suite of toolkits and baked in features ready to bring these solutions to life. Such platforms allow data scientists and software developers to focus on the machine learning models’ capabilities rather than building and managing the infrastructure needed to get started. On the other end, digital transformation leaders are looking for non disruptive robotic process automation (RPA) technologies. This is where the balance of collaboration between digital transformation leaders and professional data scientists creates the ideal solution.

What further propels the Machine Learning space is the increasing capabilities of platforms and toolkits to choose from. Providing data science teams and software engineers with platforms like SAP Data Intelligence create the perfect innovation environment. Developers don’t have to write programs to read and write into data files. SAP DI data lake manager already does that. They don’t have to program the connections between databases, manage libraries, construct complex logic gates or write long scripts to trouble shoot. SAP DI already provides tools that do this. This means engineers and data scientists can better use their time for researching, testing and improving the algorithms used to build intelligent products. These platforms already work with ERP software and require little to no integration to interact with material, customer, and inventory data in the backend. Furthermore, developers and data scientists with little to no ERP experience can still operate and utilize SAP Data Intelligence allowing them to immediately add value and showcase their talent in machine learning and artificial intelligence in the ERP space.

Transformation leaders and operations specialists need access to pre-designed, ready to go, intelligent products that can be trained with business specific data in a matter of minutes. This allows the product delivery team to build customized results according to customer specific data and business challenges. This information assembly line, is built, tested and ready to install, it just needs to be set up to handle automobile parts, purchase order documents, or inventory data. Once the business challenge is identified, it can be tweaked to send information whichever which way, but the assembly line itself is pre-built and ready to go. By customizing upon a predesigned solution suite, the latest state of the art Optical Character Recognition and prediction algorithms have already been researched, tested and the solution suite designed to integrate seamlessly with pre-existing ERP software.

With customizable products on the market, it is an unfortunate circumstance that many industry experts believe a customized AI automation solution is not available or could be built in-house. Although seemingly customized, home grown, quick, and underfunded solutions rarely provide the adaptable, revolutionary automation needed to compete and advance in the digital transformation landscape. However, with better development platforms, more software engineers and data scientists can utilize their talents to research and build solutions. As machine learning development platforms enter the space, it is imperative to consider if such a solution is designed for a data science team or an operations manager. Many robotic process automation solutions are currently showcased as ready to use products that require little to no experience with data science when, in-fact, they are the toolkits ready to assist professional teams. It is important to consider the knowledge necessary to manage data effectively and further the research needed to test and develop the best ways to use it. Thankfully with the rise in awareness robotic process automation has received, more players and solutions are entering the space for data science teams and digital transformation leaders alike.