Digital transformation, data-driven decisioning and automated workflows have become the holy trinity of business strategy in the pandemic-transformed economy. As we stare down a possible recession and continue to confront rising inflation, every business is being challenged to do more with less. The promise of using technology to streamline clunky, error-prone, manual processes all while delivering customers faster, more personalized experiences sounds like it should be the magic solution.
Despite the promise, however, many of these projects fail. EY estimates that 30% to 50% of process automation projects end up missing the mark. Common excuses for these failures include everything from a lack of clear project goals to bad technology design execution, but the most common reason is tunnel vision.
Silos: The Enemy Of Automation
Far too often, automation projects are approached as siloed, one-off opportunities to fine-tune a specific business process or function. Maybe it’s the introduction of a conversational AI tool to improve front-line customer support functions or the development of a new payment processing or credit decisioning solution to build out a digital payments infrastructure. Whatever the specific use case, automation projects that approach a singular vision to make one part of the business faster or more efficient regularly fail because they simply aren’t big enough.
Businesses that understand this distinction and embrace automation not as a focused cost-cutting project but as an opportunity to transform legacy business and IT processes into a fully synchronized, smart workflow should be well positioned to confront the challenges of the current marketplace. Those that do not will spend lots of money on software that won’t solve their problems. To put it in the words of Gartner Inc. Research Vice President Fabrizio Biscotti: “Hyperautomation has shifted from an option to a condition of survival.”
Putting The Pieces Together
To put this in context, consider one of the most common types of automation projects—the introduction of conversational AI as a first-line customer support solution. Many companies have implemented all manner of chatbots, more advanced AI-powered natural language processing technologies and other automation tools to act as a first line of customer engagement. Most of them fail to deliver on their promises.
There can be many reasons for these failures, but the most common is a lack of integration with the rest of the business. Simply adding a chatbot to the front-end customer engagement funnel will not transform your business. To really reap the rewards of this technology, businesses must hyperautomate all related functions, integrating customer engagement, data collection and analytics, and digital operations to transform the entire process from the initial customer inquiry straight through to sales, marketing, product development and all other aspects of the business.
As an example, consider a project my firm recently completed with a large, multinational insurance brokerage firm. What started as a fairly focused initiative designed to help it organize the petabytes of unstructured information coming into the company from the call center, email exchanges with customers and even snail mail became an opportunity to hyperautomate. Once the company started ingesting and organizing this data, it became clear that the information could be used to inform and improve other business processes like arming account managers with the information they need to offer more personalized solutions, executing faster cycles and new customer onboarding times, and surfacing critical intelligence on at-risk customers in need of further intervention.
By putting all of the pieces together—the conversational AI, comprehensive, fully integrated customer data, advanced analytics and digital workflows—we were able to hyperautomate a full spectrum of interconnected business processes. Importantly, by taking this comprehensive approach, we were able to do more than just reduce costs and customer contact deflection; we were able to improve the end customer experience.
Where To Start
Before implementing the first hyperautomation solution, enterprises must take three critical actions.
1. Enterprise-wide support. The first step is securing enterprise-wide buy-in and project governance. The various interconnected systems and processes that will ultimately be involved in a hyperautomation process require a breaking down of legacy silos and the ability to take a holistic, enterprise view of end-to-end processes and results. It’s critical that the right teams are empowered to support that effort across the organization.
2. Data-led discovery approach. Perhaps the most critical technical aspect of any hyperautomation project is to ensure that it’s following a strict, data-led approach that relies on process mining techniques to convert traditional processes into a cohesive data flow. This allows project developers to focus on quickly identifying and integrating the most critical functional data into the build as opposed to waiting years for a companywide data integration project before the build can even start.
3. Set realistic expectations. The scope and time frame of major automation initiatives is measured in years, not weeks, and the implementation process is an iterative one that learns as it grows. Projects undertaken with expectations for immediate, out-of-the-box results will invariably fail to deliver substantive change.
A Cycle Of Continuous Improvement
Far too often, enterprise automation initiatives get a bad rap for prioritizing cost-cutting over customer experience. However, that’s only the case when the automation projects in question are too narrow in scope to provide a more strategic outcome. By switching the focus away from automation and toward fully connected hyperautomation initiatives, businesses can take a more holistic approach toward improving the way they connect and communicate with customers while delivering faster, more accurate insights and reducing the number of manual processes and data handoffs along the way.
Hyperautomation is not a one-and-done journey. By using data to build connected processes enhanced at each step through the right digital solution, organizations can put themselves on a road to constant improvement, better profitability and enhanced customer experiences.
Original Article: Why Businesses Need To Think Bigger When It Comes To Automation (forbes.com)