“Going digital” has long been touted as a silver bullet for delivering better customer experiences and streamlining processes. Automation has become the go-to approach for solving immediate pain points, mainly in the form of tactically deploying one-off robotic process automation (RPA) initiatives to make our jobs a little easier and/or more efficient.
While achieving short-term gains, this piecemeal approach to process reinvention creates complexity due to a variety of disconnected strategies, including siloed pilot projects and an incohesive technology strategy. More importantly, it complicates businesses’ ability to adapt and inject fluidity into operations — characteristics crucial for delivering the future of work right now.
Old ways of thinking about automation just won’t cut it anymore, and the decentralized business world emerging from the pandemic has increased the pressure to deliver.
However, we’re finding that businesses are overwhelmingly ill-prepared for this journey. According to our recent Work Ahead research, 60% of companies have implemented or piloted automation technology, but only a tiny minority (8%) have said they’ve achieved automation at scale.
Moving toward intelligent automation
To unlock new value, opportunities and growth, organizations need to focus on the “why” of automation to achieve business results. They need to design processes around people — customers, employees, partners, suppliers — fused together from the front-office to the back-office, across all functions. Here’s how:
1. Anchor end-to-end process redesign to business outcomes and ensure scalability.
Organizations typically approach automation by looking for opportunities to increase speed or take complex manual tasks off their hands. It seems logical — but if they’re automating processes that just don’t work, they’re merely automating inefficiency. As customer journeys become more complex and competitors accelerate innovation, it becomes even more important to exert strategic oversight into automation initiatives.
End-to-end process change doesn’t work when organizations focus arbitrarily on finding opportunities for automation. They should first determine their overall business goals, identify inefficiencies in existing processes and then create automated systems that can scale. To succeed, they need to weave together people, processes, experiences, data insights, intelligence and technology via an automation fabric that masks complexity from users, simplifies orchestration, brings together disparate emerging technologies such as machine learning, natural language processing and intelligent document processing, and drives adoption and collaboration.
We recently worked with a healthcare provider to reduce its claims denial rate and improve net collections. We used process mining tools to identify bottlenecks and process issues, then ran possible intervention simulations to build a business case for business change. This allowed us to create a strategic blueprint for implementing process changes, automation, monitoring and people enablement. Using RPA, optical character recognition (OCR) and artificial intelligence/machine learning (AI/ML) technologies, we were able to reduce the claims denial rate from 17% to 12% and improve net collections from 23% to 30%.
Because automation was deployed strategically instead of tactically, the processes behind the technology are efficient and will remain stable through growth periods.
2. Take a people-first approach.
As businesses adjust to a digital culture, they need to prioritize the human beings working alongside software and bots (i.e., digital workers). A one-size-fits-all approach to education and upskilling doesn’t work with a multigenerational and distributed workforce. Creating a people-first automation plan requires accommodations for skill level, comfort with technology and the state of innovation.
We worked with a claims processing organization to help it navigate this type of culture change. By analyzing the day-to-day challenges and dependencies of users, we created a customized training program that showcases how technology can reduce effort and improve decision-making. We prioritized initiatives based on ease of implementation and scaled them as technology understanding improved.
By prioritizing the needs of the workforce as new technology is deployed, the business will not only enhance time-to-adoption but also create a better customer experience through skilled employees, more efficient claims handling, greater cost savings from reduced penalties and more resilient operations.
3. Use modern technology to create modern experiences.
Digital enables companies to break traditional industry boundaries, introducing supportive and complementary offerings that create seamless purchasing environments for customers. But in doing so, they’re no longer just delivering products — they’re delivering experiences.
This means that back-office metric optimization can no longer be disassociated from front-office customer interaction and overall process change. The customer experience must be at the core of how processes are managed.
One leading medical device company struggled to educate customers on the features of its new devices. Because users’ health was involved, the company needed access to accurate information as quickly as possible. After reviewing patient, caregiver, payer and supplier personas and journeys, we helped create a blueprint for simplifying the interaction across ecosystem touchpoints. We introduced chatbots, remote monitoring and AI-based patient safety services. By centering decisions around customer needs and expectations, the company was able to create a seamless user experience that reduces friction.
4. Guide widespread digitization with high-level strategy.
Automation is becoming more pervasive in enterprises. Low-code automation tools are rapidly entering the market, making it easier than ever to create digitally connected ways of working.
The key is to empower those closest to the process challenges with design and execution guide rails to holistically integrate and optimize disparate technologies as they learn, build and scale experiences and process transformation rapidly.
While the growing accessibility of automation offers a panoply of process optimization opportunities, the ease of use of low-code automation should not override the need for high-level strategic planning. To truly power customer-driven business decisions, organizations need data — and lots of it. If departments within your organization are approaching automation independently, data can quickly become trapped in siloes — making it impossible to efficiently gather the insights required to eliminate friction points.
In automation, never lose sight of the ‘why’
As process digitization evolves, it will become even more important to understand the “why” — not just the “what” — behind automation initiatives. Efficient process digitization requires a balancing act between effective technology adoption and enterprise-wide oversight.
By taking a fused, end-to-end automation approach, businesses can cut across siloes and enable data to flow freely between departments, creating an opportunity to thrive through better decision-making, reduced costs and greater business innovation.
Learn more about Cognizant Neuro, our automation fabric.