Large capital market organizations are facing challenges due to stiff competition from fintech companies and depressed commodity prices, which have forced many to realize their current operating model is not sustainable.
With automation now well-suited for business-critical functions, robotic process automation (RPA) is gaining keen interest as it gears up to revolutionize business processes, automate tasks and eliminate monotonous human activity.
RPA is quickly making its way to the forefront of commodity and financial organizations’ digital roadmaps. In fact, the RPA market is predicted to exceed $8 billion by 2024, according to U.S. market research and consulting firm Grand View Research. That is up from just $125 million in 2015.
Automation itself is not revolutionary. The difference today is the combination of advancements in emerging technology (i.e. cognitive computing) and automation that has enabled companies to implement and deploy end-to-end, intelligent automation for business-critical functions. This includes moving from simple-action automation to more advanced decision automation.
RPA can change business functions in three key ways: cost efficiencies, time compression and adding value. The chart below highlights real world examples.
To achieve success, it is crucial to focus on optimizing the operation of RPA. Organizations should not use it on a case-by-case basis or without the consideration of different infrastructures, applications, and functionalities, as well as architecture, security and different RPA components. What’s more, implementation should not occur in silos. Multiple bots running on different machines without standards can rapidly drive high and unsustainable maintenance costs.
A typical RPA journey can be observed in three steps: initialization, industrialization and institutionalization. The initialization phase mainly consists of continuous improvement and involves understanding all components of an RPA implementation. It may involve the deployment of 10 to 15 bots across different functions, data-intensive processes and those involving simple validations.
The industrialization step comprises large-scale deployment of RPA and can include the automation of several hundred thousand transactions per month. Shared services, back-office operations and front-office support functions are good candidates for RPA in this phase. It is critical to understand that setting up the initialization phase is quite different from the industrialization phase.
Organizations thinking of scaling with the same set-up generally do not succeed or add bots appropriately.
Institutionalization extends automation across all units of the organization and automates the handoff between different business units (e.g. RPA to corporate functions). Successfully scaling RPA relies on fully understanding and implementing all six elements of the robotic operating model.
These six elements include:
Identifying the expected business benefits and aligning those benefits to strategy, such as:
- The potential percentage automation and efficiency gains across different business functions.
- The different modes of interaction and touchpoints with other business functions and/or other business units.
- The human and bot interactions across different business functions and use cases.
- The different user journeys and user profiles in the current state and target state across business areas and functions.
Aligning post-implementation strategy and culture, and effectively retaining capabilities and journeys that matter most to the organizations, such as:
- Human and bot interactions.
- Reporting and communication.
- User journeys.
- Roles and responsibilities.
- Upskilling people.
3. RPA Service Model
Creating the engagement model that best supports operational processes, such as:
- Human assist: Performing the processes handled and controlled by humans.
- RPA support: Handling unexpected scenarios not performed by a bot as designed.
- System support: Managing and resolving all application issues.
- Process support: Resolving any issues and making changes in the process.
- Develop and deploy: Productionizing any fixes, enhancements and new automation.
- Bot scheduling: Managing the demand for bots based on workload.
- Business continuity: Providing resilience and disaster recovery.
Ensuring scalable infrastructure and architecture design.
- Execution intelligence: Designed to work intelligently and autonomously; continuously verifying outcomes and providing insights into processing activities.
- Case recoverability: Ability to reboot itself from where it left off.
- Resilience: Ability to handle unforeseen application responses; harvesting and reporting the maximum amount of data in case of exceptions bringing efficiencies downstream.
- Data integrity and management: Data security, work queue keys and the elimination of data duplicity and any orphan data.
- License optimization: Batch or continual processing so that licenses are used optimally.
- Hosting: Host the bots on resilient, secured, virtualized technology and follow standard architecture principles.
- Access management: Data governance and logical access management for the bots.
- Cloud: Use of a private cloud to enable the creation of workers on demand to meet business agility needs and align with the growth strategy.
5. Data Readiness Assessment
Assessing the required data such as availability, accessibility and the current state (completeness, timeliness, validity, accuracy and consistency).
Identifying the roles and responsibilities of people, including training people to perform higher-value work.
Optimizing the Organization
As the capital and commodity markets transition to a digital world, RPA will become an inherent part of any digital roadmap. At the same time, implementing RPA will have implications for the organizational structure.
This means companies will not just add bots; they will also redesign how the organization operates. Employees will perform different tasks with robots taking over manual processes, while certain areas of the business, such as the back office, could be completely automated. This will leave companies to think carefully about how they will redeploy staff or change their current workflow and infrastructure.
Many organizations will have their sights set on quickly realizing the benefits of RPA—whether it’s reducing costs, creating more time for high-value work or delivering value beyond what is currently achieved with human output. However, it is crucial to recognize that the benefits of RPA are only truly possible with a logical implementation plan backed by a thorough governance framework and strategy.
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