In this article, we discuss some important innovations that can make the usually challenging job AI enterprise deployment rather easy and more effective.
Artificial intelligence (AI) is exceptional in its capabilities of bringing in transformational changes in the digital world. AI and assorted technologies under its umbrella continue contributing a great deal towards bringing more efficiency in products and service processes.
Why is enterprise AI deployment challenging?
AI can address process inefficiencies at different levels of a business that no other technology is capable of doing. A lot of companies are finding it difficult to adopt AI at the enterprise level solely because their own technological setup isn’t yet ready. This makes the technical innovations that we are going to discuss a little later all the more important. AI has unlimited potential. We just need to figure out how we can make the most of what it has to offer.
There remains substantive confusion withing business leaders about understanding the proper applicability of AI and what recent innovations can facilitate this move. Machine learning and several other technologies have become much advanced than what anyone could have imagined a few years ago. In order to maintain a competitive edge, you need to have a clear view of what this technology is all about and how it can usher in developments that can have a lasting impact on how you perform as a business on a global scale. Begin with understanding what the most influential companies are doing – study their investment in AI, deployment of the technology, and outcomes. Now let us shift our focus towards three technical innovations that can help drive enterprise AI adoption in the times to come.
Innovations that can drive enterprise AI adoption in the coming years:
Enterprise AI: These are intelligent digital systems that are offered to businesses as-a-service by enterprise infrastructure services. They are used as oversight to define the strategic direction of enterprise AI deployment. This includes natural language processing or NLP and other systems that can be used to perform jobs that were previously performed by humans.
Automated machine learning: The process of automating the application of machine learning for solving problems. Automated machine learning does away with the need for training the models. So, there is no need to process data or carry out feature selection, extraction, or other related tasks to make a data set ML-friendly. Algorithm selection and other steps usually performed to improve their machine learning model’s performance are also not required. Automated machine learning allows you to address complex problems without getting caught in the process requirements. You can focus more on the problems and less on the workflow.
Robotic process automation (RPA):
This innovation allows you to software to interact with other applications in the infrastructure to replicate data, communicate with digital systems, and process transactions – tasks that are usually performed by a person. RPA, when combined with AI, can be used to put specific solutions to work within the existing technological environment of a business.
In addition, you will need fast hardware to meet the demands of computing-intensive AI technology. Managing this on your own can be an expensive undertaking. So, you will need third-party hardware support to fulfill this requirement.
