How Digital Service Providers (DSPs) can Implement Hyperautomation

Implement Hyperautomation to Scale Automation Programs by 3X

Most Digital Service Providers (DSPs) struggle to accelerate their path to Hyperautomation due to the complex processes with legacy systems and applications. Although Robotic Process Automation (RPA) plays a vital role in an organization’s ability to automate business processes, its scope remains limited due to siloed automation initiatives, disparate systems, and volatile cross-functional processes. With digitalization growing at a rapid pace, DSPs find it difficult to scale the automation rate and are confined to mere task automation.

In the traditional scenario, IT systems, applications, and automation are often not integrated across the entities. DSPs lack the standardized interfaces to capture data and interpret non-digitized and unstructured inputs, leading to increased manual efforts. These challenges often result in high OpEx, poor customer experience and revenue loss.

While RPA can deliver several benefits, DSPs need a more sophisticated approach to reap the full benefits of automation. To accelerate their Hyperautomation journey and digitally transform their operations, DSPs need to move beyond task automation.

Gartner has placed “Hyperautomation” in the Top 10 Strategic Technology Trends in 2020, 2021 and 2022 reports. As per the reports, a hyperautomated future can only be achieved through hyper agile working practices and tools. RPA alone is not sufficient for digital transformation. DSPs who want to scale automation truly, both at the process and business level need to embrace Hyperautomation.

DSPs need a new strategy that can effectively combine RPA with AI-powered digital technologies and other complementary tools. Also, they need to integrate functional and process silos to automate and augment business processes. In this article, we have discussed how DSPs can address the above-mentioned pain points and accelerate their Hyperautomation journey by implementing the Hyperautomation framework.

Hyperautomation framework for DSPs to scale automation rate by 3X

Fig. Hyperautomation framework and its key components

By implementing the 4 key components of the Hyperautomation framework, DSPs can improve operational efficiency and increase the automation rate by 3X. To explain the implementation of the end-to-end framework and its key components, we have an example of an order-to-activate (O2A) use case. However, this framework applies to any eTOM business process.

Key components of the Hyperautomation framework 

  1. Intelligent process orchestrator to orchestrate bots, people, IT applications, AI, and low-code apps, unifying the end-to-end order journey. Some of the major challenges in the traditional approaches are:
  • Siloed functional teams, IT systems, and RPA implementations hinder the end-to-end process automation
  • Traditional BPM systems cannot be easily integrated with RPA bots and AI components
  • High process variations and human intervention result in data integrity issues leading to a low automation success rate

Fig. Showcases how Intelligent Process Orchestrator orchestrates end-to-end Order-to-Activate (O2A) journey

DSPs can leverage pre-built connectors specific to telco applications, RPA, and next-gen components to reduce implementation effort by 60-70% and set up an auto-alert mechanism on pre-defined task SLAs to improve process efficiency. Implementing Intelligent Process Orchestrator can enhance the automation rate by 20% and reduce cycle time by 15%.

Despite automation, there is a lot of human dependency due to a lack of cognitive capabilities. The amber highlighted boxes require high manual intervention. DSPs need to further implement next-gen components to automate these manual tasks. The upcoming sections describe how an intelligent process orchestrator triggers these next-gen components to complete the end-to-end automation.

  1. DSPs can combine conversational AI with an intelligent process orchestrator to automate the O2A sub-processes that require a conversation with humans. Key O2A sub-processes that can be automated with AI integration are the address and provisioning fallouts, updating order status for customers proactively, scheduling appointments, setting up a payment plan, etc.

The Intelligent process orchestrator triggers an outbound message/call to the customer. Then the customer responds with the required information. Conversational AI understands natural language and converses with the customer, integrates with the systems to fetch and update the data and passes back the details to the intelligent process orchestrator for order progression.

It is recommended to build platform agnostic connectors to trigger the RPA bots and ready-to-use functions to integrate conversational AI with different telephony/IVR systems to accelerate the deployment time by 40-50%. This improves customer experience by 20% and OpEx savings up to 15%

Fig. A leading DSP in North America automated O2A sub-processes that require human intervention by integrating conversational AI with intelligent process orchestrator and RPA bots

  1. Low-code applications help to automate the sub-processes requiring aggregate data from humans in the O2A process by rapidly building the applications/interfaces. Low-code applications can be used to create and deploy multi-experience applications faster (e.g., order request, site survey, fieldwork, dispatch process, etc.) and interpret non-digitized and unstructured inputs to reduce manual efforts.

Leverage pre-built telco app templates which are reusable and easy to use. Some considerations to improve app user experience are scanning QR/barcode with customization, capturing signatures digitally on a handheld device, including navigation and geolocation capabilities and designing configurable layouts to reorganize the UI/UX.

  1. A unified hybrid dashboard gives a clear picture of the automation performance, ROI and provides continuous insights for process optimization. It gives a real-time integrated view of human & bot orders completion, AHT, automation success rate, and many other KPIs. It also highlights the actionable notifications/insights.

Comparing metrics such as AHT for human and digital workforce helps DSPs to monitor the trend and identify deviation.

In conclusion…

By adopting the Hyperautomation framework presented in this article, DSPs can scale the automation rate by 3X. Implementing the 4 components of this framework helps DSPs to reduce the cycle time by 40%, OpEx by 25% and improve customer experience.

I thank my colleagues, Richard Abraham – Senior Project Manager, and Abhay Goyal – Senior Analyst – Strategic Insights for their continuous efforts in shaping up this article.

By Rajesh Khanna

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