1. Building a unified view
The roots of RPA, and now AI automation, were in getting data from paper into digital form, and it’s amazing how many businesses still need to convert or capture data from different sources.
Call it digital business, or digital transformation, getting data off paper or from an old database into a cloud service that can interact with your CRM, customer support tool and other processes is a key step to going down the intelligent automation route. Traditional RPA can handle many of these tasks, but where complexity or choices need to be made, AI automation can help with those decisions. The two can be used in concert or separately across varying processes, depending on the business need.
Within complex organizations, such as banks, AI automation can also be used to create new AI-designed robots to improve processes further, or to recommend new automated workflows. They may need management approval and validation, but are likely to become the norm in large-scale RPA-based customer experience services.
2. Creating an end-to-end digital process
With complex business processes, say legal or financial, there can also be that regulatory requirement for a paper trail. Digitizing these files and making them part of an automated process can help businesses speed up their service and offer tools like smart apps that show every step of the way to the customer, while alerting the relevant agents in the business when the next step can be taken, or if there is something holding up the process.
Another standout role for RPA is when businesses merge or takeover another, with rapid consolidation key to building brands and retaining customer loyalty. The need to create synergies and move one service (or a set of them) over to the primary system is vital. Intelligent automation can transform this process from a year-long project into one that takes a matter of weeks, again bringing new customers on-board, exposing them to new offers or products and helping eliminate concerns that businesses or consumers have when buyouts occur.
3. Build AI-driven customer journey analytics
With data aligned and a clear line-of-sight customer journey, the business can benefit from improved analytics to measure key performance indicators and successful outcomes along the journey.
AI comes into play alongside traditional dashboards and other analytic tools, with live monitoring of the RPA processes. Able to provide alerts to changes in customer preferences, from trends among the onboarding of new customers to spikes in customer service queries around a particular product or subject.
AI can also be used to monitor customer intent across app or store usage. When several items of data need to be synchronized for further analysis, bots can be created quickly (or automatically) to build a complete picture, adding value to the business and improving customer service, or highlighting the need for a new product or service.
4. Adding detail to the journey
Having created an efficient and seamless approach to the customer journey across various touchpoints, AI can be used to build a greater picture of customers through their reviews, relevant social media posts or other comments. These can help the business build a greater sense of strategic awareness when it comes to customer acquisition or loyalty, help it scale up popular products or services while driving cost savings, and all performed in a non-intrusive manner for the customer. And, as businesses grow their digital workforce, remaining staff are more focused and productive.
AI automation and RPA will keep evolving in both predictable and new ways, helping companies smooth over obstacles and roadblocks to better business efficiency. The convergence of business and automation tools with deeper AI powers will create a common destination for most companies, but those that lag behind will find themselves losing out to those that leverage the value it delivers and the happier customers they win and maintain thanks to current automation efforts.
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