Enterprise artificial intelligence versus intelligent automation:,What's the difference?

Intelligent automation is not a form of enterprise AI, strictly speaking.

As artificial intelligence (AI) has increased in efficiency and spread across the country and across the world, more and more managers have turned towards AI solutions to improve their processes and become more agile. The technology has drastically transformed the workplace, making it a much more intelligent, accurate, and productive space than it was even ten years ago.

Two of the most prominent systems used by companies today are Robotic Process Automation (RPA) and Cross-Enterprise AI. Organizations seeking to employ these systems often know that they can greatly improve their internal operations but not the extent, and in many cases they aren’t even sure how, without the help of extensive implementation consulting, to properly use these systems and install them in a value-maximizing way. RPA and Cross-Enterprise AI are extremely powerful, but their actual purpose must be understood, and proper expectations must be set in order to fully harness them.

Strictly speaking, intelligent automation is not a form of AI, as it represents more automation and performance of prescribed tasks than the use of data to create new information. RPA is designed to perform the same actions a human would through user interface and descriptor technologies. RPA operates in place of a human via a “bot” that uses various pathways and triggers. It does not create new data or enhance existing business models, but rather accelerates the timeline upon which those models operate.

The benefits of RPA are centered around cutting costs through decreasing activity cycle times, removing the need to re-enter data and the errors associated with human re-entry, thus decreasing profit loss associated with errors and complaints around them, and most importantly the possibility of improved processes thanks to automation beyond the simple increase in efficiency any company could find.

Secondary benefits involve the ability to decrease headcount, although many companies use the manpower freed up by RPA in more productive ways. Continuity in both quality of work and twenty-four-hour availability ensures that transactions are logged quickly and correctly. And the security provided by the audit trail of who did what and why provides an additional level of comfort with the automation of these processes.

RPA can be augmented through more advanced cognitive technologies, allowing the automation to operate at a higher level and go a step beyond just simple calculation or movement of data. RPA is compatible with language processing, speech recognition, and machine learning, allowing it to complete the tasks that in the past would have required human perception or some capability beyond that of a computer.

IT teams should be integrated early on in order to coordinate security, consider on-site and third-party solutions, and create optimal coding designs for the systems at large. RPA can greatly improve the existing IT systems, particularly enterprise systems, as well as allow businesses and IT to legitimize activities that had previously been ignored or undermanned. As well, RPA uses its own metalanguage featuring a graphic user interface (GUI) into which the script is directly designed, and which is easily learned by IT or even by typical businesspeople.

Cross-Enterprise AI, as opposed to the RPA, is not used to facilitate existing processes, but rather, it’s used to find new potential hidden opportunities and predict how best to go forward as a company. By harnessing internal and external data, organizations can detect better predictors and use them to drive more educated business decisions. The largest benefit of Cross-Enterprise AI is the use of multi-dimensional data as opposed to singular sources. Companies can replace their mostly static models with constantly updating and dynamic ones that are constantly encouraging interaction and contact with customers as well as stronger decision making.

By bringing data together from many different formats and locations and securing them into a private cloud, cross-enterprise AI creates a new market model that has been unified through multiple different types of data and sections of markets. As well, Cross-Enterprise AI does not require expensive projects or the replacement of existing business systems. It allows you keep existing systems and infrastructure, in turn allowing you to keep the stability provided by those systems, but simultaneously provides a much stronger boost to your business operations.

Essentially, Cross-Enterprise AI makes your business smarter and allows you to take advantage of both active data and siloed data to create completely modern and thorough models of business markets and activity. Opportunity is often hiding between the silos. Rigid processes were created for a reason: they’re the pillars that support the enterprise. The challenge is to make them work better to together to enhance enterprise agility. Cross-Enterprise AI provides a mechanism for freeing the explosion of internal and external data (structured and unstructured) from silos.

This approach is novel, as cross-enterprise AI can create a living model of a market from the data. It is truly living. The market model changes as the data changes. Using machine learning, the market model detects the relevant signals from the noise in closed-loop process. New data sources can typically be added without data modeling in the traditional sense, as Natural Language Processing promoted automated ingestion and entity resolution.

Organizations that are looking to incorporate RPA into their business model should have a plan in place as to the implementation and expectations regarding robotic automation. RPA should appeal very broadly, across a variety of sectors and can very quickly allow corporations to make large gains in efficiency. Companies still with large sections of workers performing repeatable tasks such as data entry and data management can easily benefit from the installation of RPA through the re-allotment of worker hours to more productive tasks than basic journaling of transactions, and most likely should begin employee re-training programs in order to build a more skilled worker base.

On the other hand, Cross-Enterprise AI poses little to no crossover towards humans and the repeatable tasks in their jobs. Nor does it act as a replacement of your existing business systems and operations. It acts in conjunction with your current business practices, giving you a better idea of where to deploy your company’s assets. Rather than automating current tasks, it gives you better predictive powers as to the best future deals and dealings, whether we’re talking about with the customer directly or with assets deployed in service of commerce.