For example, robotic process automation (RPA) projects are designed to automate simple activities involving data processing, reducing the cost and time of human hours dedicated to these tasks and often improving accuracy. Think of RPA as a form of “non-invasive” system integration that can scrape data off screens and manipulate it without having to change the systems in which the data is stored. For example, a company may have multiple product websites where customers can place orders. If these websites do not share a backend system to aggregate customer orders, the time, cost and risk of error of manually collecting order information can be significant.
RPA software can be programmed to ”scrape” the order information from various sources and integrate it into fulfillment, inventory control or customer systems. It could even send order status e-mails to the customer. This kind of automation can deliver cost savings of between 5% and 25% depending on the business function.
For all its advantages in driving cost efficiencies, RPA does have limitations. RPA tools must be configured to read data from specific places and require defined sets of rules within which they will operate. If the data is unstructured (e.g. free text) or an organization is dealing with judgement-based activities, another type of technology called “cognitive computing” may be a better option. Cognitive computing software uses machine learning techniques to turn unstructured data into structured data — for example, by labelling text to give it structure — and to replicate the decisions of experts.
One step further, “Intelligent Automation” combines RPA and cognitive technologies to automate business processes from end to end. Some companies have already experienced meaningful cost takeouts using this powerful combination; for example, Japan’s Fukoku Mutual Life Insurance has used Intelligent Automation to replace 34 employees, representing an annual cost saving of US $1.65 million.
2. Improved decisions
AI can help to mitigate the impact of information overload and capacity constraints on the quality of decisions. For example, a mining company deployed machine learning algorithms to automatically flag policy compliance issues on thousands of free-text purchase orders. In another case, an energy company’s senior engineers trained a cognitive computing system to give advice to junior engineers, based on a knowledge base consisting of thousands of technical specifications.
3. Better customer experiences
One of the most popular uses of AI is to improve customer and employee experiences. This is especially important in a time when customers are demanding better, faster and more customized service and response options. Chatbots allow people to contact an organization or internal department without downloading another application, calling a service center, visiting a website or writing an email. They enable people to interact with services on their own terms and can be configured to aggregate multiple services into one user interface with consistent brand language and tone.