If you haven’t read Part 1 of this series, be sure to go back and check it out!
Combining the task execution of Robotic Process Automation (RPA) with AI’s text capture, analysis, and auto-adapting capabilities is what enables veritable automation of business processes. Esker’s Order Management and Accounts Payable solutions both embed multiple AI-based recognition layers: after the characters on inbound orders and invoices have been gathered using text extraction or OCR, data capture technologies are applied to give meaning to those characters — for example identifying invoice date, PO number, amount totals or line-item information. The multi-layer AI engine that combines Esker Synergy, a Deep Learning neural network possessing auto-learning and teaching capabilities and trained on historical data, converts the extracted information into data that can be input into the ERP.
The real value of this hyper-automation solution lies in combining multiple technologies. For example, an order or an invoice is retrieved from a portal via RPA, its data recognised by AI and then pushed into the ERP using an API or web services integration tool, with the final step of an RPA function confirming the process on the portal. By building on RPA, AI makes the solution adaptable and flexible. While leveraging these two complementary technologies enables successful end-to-end automation, AI does not depend on RPA to create value. To analyse and automate complex business processes, AI and its various subcategories are able to provide a comprehensive and robust structure whose learning, reasoning and self-correction capabilities can provide greater process efficiency.
With great power comes great responsibility
Whereas RPA is a low risk yet short-term investment, combining it with AI adds a whole new dimension of process automation adapted to the long term. This complementary relationship alone should be of special interest to those in charge of technology investments in an organisation. Yet businesses would be amiss if cost reduction and labor replacement were the only goals that decision-makers had their eyes on.
Of course, even before industrialisation, there were fears of the replacement of workers, but humans have not been replaced (yet!). Rather than eliminating jobs, the introduction of AI capabilities into the workplace can enable staff resources to be directed to higher-value tasks such as enhanced customer service activities, performing analyses, identifying process improvements, and negotiating payment terms. This requires, however, that businesses invest in the skill development of their employees as well as focus on innovation – both technological and social.
Jacques Bughin and Eric Hazan of McKinsey Quarterly refer to this as Technological Social Responsibility (TSR). TSR amounts to a “conscious alignment between short- and medium-term business goals and longer-term societal ones.” By aligning business goals with societal interests in a sustainable manner we would be taking the wind out of the sails of those that see calamity in every new technology. According to Jaron Lanier and Glen Weyl, “regardless of how one sees it, an understanding of AI focused on independence from—rather than interdependence with—humans miss most of the potential for software technology.”
-Written by, Jean-Jacques Bérard
