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Vinay Mummigatti, Enterprise Head of Robotic Process Automation and BPM - Technology, Solutions, and Architecture, Bank of AmericaLegacy operations landscape and the “last mile” challenge
Legacy operations landscape and the “last mile” challenge
Most enterprises are spending a big part of their IT efforts in reducing operational costs and improving customer engagement. However, their efforts sometimes yield only marginal success because of “last-mile” challenges caused by:
a. Operational silos created over the years to address business dynamics or regulations
b. Technology silos created through acquisitions and product or channel-specific investments
c. High costs of integrating these operational and technology silos
d. Lack of technologies to suitably emulate human intelligence through natural language processing and interact meaningfully with humans.
These challenges have led most enterprises to invest in a vast pool of human operators, most of them deployed in operations—handling data, documents, emails and dynamic communications through various physical and digital channels.
As enterprises address these challenges, operating costs may shoot up, hurting profitability. Business may not be agile enough to grow and adapt to emerging customer, market, and regulatory needs.
"Organizations that have succeeded in realizing a sustainable ROI for RPA, have figured out that the secret of their success lies in picking the right technology, establishing a governance model and an enterprise shared services organization"
The focus of this article is to help enterprises understand and deal with these challenges as they embrace robotic process automation (RPA) for digital operations transformation.
The emergence of robotic process automation (RPA)
RPA is a new breed of technologies focused on solving the last-mile challenges. RPA platforms may cover capabilities that can:
a. Automate repeatable data-handling work across any class of enterprise applications through screen-based interactions
b. Extract intelligence from documents and emails or other unstructured media to automate tasks done by knowledge workers
c. Initiate meaningful interactions through chat, voice or other media to engage customers or knowledge workers
d. Offer business continuity and resiliency through execution and decision making scalability
e. Provide real-time reporting, transaction logging, monitoring and change management capabilities
Most enterprises are still experimenting with the value RPA can bring and how to justify the investments in technology and operational change management. As RPA evolves, many emerging technology platforms are bringing a diverse set of capabilities. The scope of intelligent work automation spans three broad dimensions:
(a) Repeatable structured data handling
(b)Natural language processing (documents and emails)
(c) Dynamic interaction (natural language generation)
RPA helps solve a new class of problems never addressed before. Hence, enterprises face a new set of challenges in dealing with technology adoption, business change management, controls, and governance.
Five critical success factors for achieving sustainable ROI from Robotic process automation
RPA offers pervasive value across an enterprise, including customer-facing channels, back-office operations, information security and fraud, infrastructure management, risk management, supply chain and customer service. The ubiquity of RPA use cases should compel enterprises to look seriously at defining a strategic approach to a roadmap for transforming digital operations. Let’s take a look at five critical success factors for sustainable business value and return on investment (ROI) from investing in RPA:
1. Picking the right mix of technology platforms: As mentioned earlier, RPA covers a wide array of capabilities. No single technology addresses all classes of RPA capabilities, so enterprises must assess short-term and mid-term needs for intelligent automation that will drive value. Most platforms offer traditional RPA capabilities for structured data handling. However, the ability to perform natural language processing and dynamic interaction require advanced capabilities in machine learning, and few vendors offer artificial intelligence capabilities. Enterprises need to conduct a thorough analysis of their needs and pick one or more technologies to address all classes of automation needs.
2. Planning Technology and Operations change management: Most RPA solutions rely on screen-based interactions with legacy technology applications. As enterprises automate data handling activities with RPA, the robots that perform work will interface with one or more legacy IT applications. Robots are programmed or trained to perform according to legacy data structures, screen design or application logic. However, once the robots are in production, any changes to those legacy applications will bring them to a halt, potentially causing serious customer experience issues. It is important to plan for such changes in back end applications in order to avoid disruptions to business operations.
3. Creating a blueprint for target-state digital process and operations: Automation of discrete tasks is low-hanging fruit when experimenting with RPA tools. But in a more serious phase of enterprise adoption, digital automation must go beyond mere discrete task automation. Employing the holistic capabilities by RPA requires enterprises to redefine business processes and operations. They will need to complete a systematic redesign before they can draw a digital operations blueprint for the interactions of humans and robots.
4. Establishing a governance model: Introduction of robotic operators and digital workforce into operations brings with it a number of risks and challenges. Areas such as information security, identity and access management, transaction monitoring and auditability, data quality and lineage management pose the biggest risk. The ability to achieve sustainable ROI from robotic automation is largely dependent on how the governance model is defined across various risks associated with operationalizing the digital workforce.
5. Establishing a Digital Automation Center of Excellence (COE): As enterprises adopt digital automation technologies, they must align the people, processes, and technologies to maximize return on investment and avoid redundancies. The key goals should be to establish shared services, technologies and skilled resources in addition to defining and implementing a common set of delivery practices, governance models and measures of success to drive a unified enterprise.
RPA is evolving into a digital operations transformation tool that spans many industries. Robotic automation is reducing dependency on humans for most types of repetitive data handling, allowing enterprises to employ knowledge workers in more value-added work, innovation, and customer experience. Enterprises embarking on the RPA journey must take a long-term, holistic view of establishing an enterprise governance model that includes operational excellence, change management, and risk mitigation. Enterprises looking to RPA as a transformational tool must invest in appropriate people, process, and technology governance and make full use of the critical success factors listed in this article.
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