May 19, 2021 - 1133 views|
The answer lies in what you’re trying to achieve with intelligent automation. Automated processes enable efficiency, while data enhances decision-making.
Over the past year, many clients I’ve spoken with have been looking for ways to make processes smarter, more adaptable and more resilient. According to our recent research, many companies see the combination of AI and automation — or intelligent automation — as key to achieving these goals.
Despite the promise of better operational performance with intelligent automation, a common question is where to begin: with the process itself or with the data that will power the process? The answer lies in identifying which outcome you’re trying to achieve. Getting the sequence wrong could counteract the very goal you’re pursuing.
The right starting point
Here are two examples that distinguish when a process-led vs. data-led approach makes the most sense with intelligent automation:
Adding data intelligence can significantly reduce errors, remove process hurdles and reveal where corrections are needed. But doing so requires a strong process automation backbone in order to shape when and how the data is applied. So in this case, a process-led approach is best.
For example, we’re working with a major insurance provider to improve customer lifecycle management. Typically, insurance customers who file a claim experience long decision times, a lack of visibility into decision making and repeated or disconnected requests for information submission.
Insurers can distinguish themselves by being fast, frictionless and responsive in how they handle claims. However, operating in a highly regulated industry and with overt risks around claims fraud, speed can never be a trade-off for accuracy and compliance.
A contributing factor to the insurer’s process challenges was the dependence on third-party systems and disparate data sources to make decisions. We helped the company implement an automated and fully integrated process for claims handling, which was then supported with AI and data modeling to segment customer profiles and personalize services.
The system has helped reduce the turnaround on claims capture by as much as 80% and shorten overall claims procedure times from 14 days to just two, all while maintaining the necessary high levels of accuracy and regulatory compliance. The insurer has also received positive customer feedback on the effectiveness and quality of services.
With so many factors and variables at play in dynamic online customer environments, companies need an agile approach that allows them to test the market, gather feedback and continuously improve in order to meet customer needs.
We’re working with an online fashion retailer to deliver this level of personalization. The company is well aware of the speed at which consumers’ tastes and styles change, and realized it needed to move swiftly to gain and keep customers’ attention.
Because it was vital to gain insights into consumer preferences, we took a data-led approach. We helped the retailer use existing data to gain a deeper consumer understanding. Using this insight, we then designed a process that segmented the brand’s customer base and enabled all interactions and product recommendations across channels like chatbots, email and social media to have the highest degree of relevance, timeliness and usefulness.
The combination of process improvements and data insights allowed for an integrated digital thread to run through all phases of the customer lifecycle, including product design and development, sales and after-sales. As a result, the retailer can now drive more relevant customer interactions and next-best offers, which in turn has improved customer mindshare, loyalty and revenue.
Accelerating the path to intelligent automation
To get the most out of intelligent automation, process and data need to work in harmony. Automated processes enable greater efficiency, while data enables better decision-making.
By coordinating these attributes — and having a clear outcome in mind — businesses can add intelligence to how and where they automate processes in a way that accelerates business outcomes while ensuring the quality of service is enhanced.