Robotic Process Automation
Sep 19, 2018

The six C's of robotic process automation

Increasing the chances of RPA success

One of the most disruptive technologies today is robotic process automation (RPA). With RPA, companies can configure software to replicate how humans process transactions and perform work, albeit with far greater speed and accuracy. Since RPA works at the user-interface level, it does not require complex integrations, making it non-intrusive and fast to roll out.

Many industries are already implementing proof-of-concepts and pilots. But how can companies scale their current initiatives to realize their full potential? At Genpact, we have seen clients achieve robust results by adopting a practical approach that takes into account the six C's of automation.

1. Complexity

As a new technology, RPA may introduce new complexities, such as selecting the right processes for automation. After all, processes involve many variances, exceptions, and handshakes, both upstream and down.

But we have found that processes involving structured data, rather than unstructured data, are ripest for automation. Also, process patterns that follow solid business rules – such as registrations, transactions, and so on – are more suitable than those that require design and analysis.

Another complexity involves the impact of RPA on the workforce, since automation invariably replaces people on certain tasks. However, RPA will also augment current roles and create new ones. Therefore, readers must determine which roles still require human oversight, and which new roles around managing bots and mitigating errors will need to be created.

2. Collaboration

Collaboration between businesses, processes, and technologies is essential to any project. Such collaboration is only possible with the right mindset, as well as an operating model that accounts for all stakeholders, in place. And culture can also be an issue. Most businesses have a first-time-right mentality: They will not roll out a project without confirmation that the design works. Traditionally, that was fine, but it does not allow experimentation with RPA and AI. So companies need to adopt two-speed model – one that rewards first-time-right projects as well as one that learns from fail-fast projects.

Furthermore, projects that are solely owned by technology teams may leave out important business sponsorship and knowledge. At the same time, if a business takes the lead without much technology involvement, it can impact IT readiness. An ideal model is one where business and IT teams co-own and work on shared objectives.

Governance over the new human-plus-bot workforce is another factor. For instance, if 10 people do not show up to work, it will immediately be noticeable. However, if 50 robots cannot log on to a system, it may be several days before you notice. The future of work depends on robust governance over this hybrid workforce.

3. Compliance

Companies need to pay attention to process quality – and ensure that manual steps work properly – before letting bots take over. Then, companies have to make sure nothing goes awry afterwards. Setting compliance requirements can help, with controls designed for data integrity, data loss prevention, privacy, reconciliations, and error reporting.

4. Customer experience

Too often, organization focus solely on the front office, because it is more customer facing, while overlooking the fact that most customer interactions involve the middle and back office, too. Creating a new digital front end – and plugging it into an old, fragmented back office – only results in sub-optimal digital efforts. Instead, companies should concentrate on the end-to-end customer journey. That means deconstructing the customer journey, reimagining workflows, identifying opportunities to enhance customer experiences, and prioritizing processes where RPA can have the greatest impact.

5. Context

Only humans understand critical contextual factors – about industries, businesses, and processes – that shape day-to-day work responsibilities. This is the domain knowledge that humans acquire through experience. And in order to apply automation contextually, intelligent automation needs to consider not only the type of work, but also the type of data. Enhancing RPA efforts with predictive analytics and semantic technologies can help provide context to process automation, and enhance its footprint, to deliver more value.

6. Crossing the chasm

If you are an early RPA adopter, you are already far ahead of the competition – and you will soon cross the chasm to become members of the early majority. Such a journey requires a quantum jump from good to great in the following ways:

  • Training IT teams: Evangelizing across business and IT
  • Performing a proof-of-concept: Assessing a proof-of-value
  • Targeting FTE offset: Demonstrating quick wins and building momentum
  • Tolerating RPA failures: Being willing to experiment
  • Ensuring IT support for bots: Managing a hybrid workforce
  • Knowledge management: Planning for reusable bot development

Companies implementing RPA now stand well ahead of the curve in moving towards the hybrid workforce of the future, where intelligent machines will work side by side with people to achieve the best business outcomes. To sustain these efforts and realize the promise of RPA, keep in mind the six C's of automation – a thoughtful approach with structured coordination and governance.

About the author

Lalitha Kompella

Lalitha Kompella

VP & CTO, Global Head of Intelligent Automation

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