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The Journey to scale: Moving from RPA to Intelligent Automation

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The Journey to scale: Moving from RPA to Intelligent Automation

In their embrace of more digitized ways of working, many organizations have adopted robotic process automation (RPA) to automate repetitive rules-based processes. They are now seeking to scale these solutions and make them smarter by integrating AI (Artificial Intelligence) capabilities.

RPA (Robotic Process Automation) can bridge multiple different systems and interfaces. RPA uses precisely programmed ‘bots’ to automate specific routine tasks, reducing processing time dramatically, and ensuring more outstanding quality by providing consistent results with negligible errors. Besides, if implemented effectively, RPA can free up the workforce to focus on more strategic activities or customer-focused tasks.

Few analyses revealed that organizations are continuing to use RPA and are moving beyond by seeking to ramp up Intelligent Automation deployment. 58% of surveyed officials report they have started their Intelligent Automation journey. Of these, 38% are piloting (1-10 automations), 12% are implementing (11-50 automations), and 8% are automating at scale (51+ automations). The sign of organizations deploying at scale has doubled compared to our 2018 findings.

RPA can free up the workforce to focus on more strategic activities or customer-focused tasks.

Building smarter bots

RPA holds great and apparent benefits, but also limitations. Bots can only follow logical rules-based processes, and they do not recognize patterns in data or extract meaning from images, text, or speech. The RPA bot is programmed to process functions, like registration, invoicing, or data transfer, without understanding the logic behind them.

Possessing many of the available low-value opportunities through simple task-based automation, organizations are now seeking to implement next-generation solutions. These hold multiple advanced technologies and data science, such as AI, to make automations smarter and provide more value to the organization.

Installed in this way, RPA software aggregates and processes data under the direction of more advanced – or intelligent – technologies. When AI has performed its functions on the raw data, RPA then pushes the target systems’ answers.

Expected benefits of Intelligent Automation

With Intelligent Automation technologies, organizations can transform business processes – not only achieving higher speed and precision but also automating predictions and decisions based on structured and unstructured inputs. An analysis unveils that three primary benefits are driving uptake of the technology. Organizations expect to achieve increased productivity and cost reduction, greater accuracy, and improved customer experience.

Officials estimate Intelligent Automation will contribute 22% cost reduction on average and an increase in revenue of 11% over the next three years. The organizations currently scaling Intelligent Automation claims have already achieved a 27% reduction in average costs from their implementations to date.

47% of organizations have already combined RPA and AI as part of their Intelligent Automation strategy. They report higher increases in revenue to date due to their automations, averaging a rise of 9%. Those are only using RPA reports, just a 3% increase in revenue.

Additional evidence suggests Intelligent Automation implementations are exceeding expectations. Furthermore, organizations piloting Intelligent Automation expect an average payback period of 15 months; those in the scaling phase report an average payback after 9 months.

Based on research, most organizations are making steady progress in Intelligent Automation, though significant barriers thwart many.

Building a winning Intelligent Automation strategy

Given the returns that Intelligent Automation offers, organizations seem slow in their implementation and scaling. Significant barriers must be tackled before implementation can proceed, but Skcript notes a distinct difference between organizations in the piloting phase and those in the scaling phase.

Mainly, officials in organizations scaling Intelligent Automation are more inclined to have a clear understanding of how they will capture value from their projects – 78% of them do so. Only 50% of companies piloting solutions claim the same.

Besides, organizations in the implementing and scaling phases also have a clear and accepted vision and ambition for Intelligent Automation (71%) and an enterprise-wide Intelligent Automation strategy (49%). Organizations implementing or scaling Intelligent Automation are also optimistic that their workforce has the capacity and skills to implement the solution.

Other aspects that distinguish organizations scaling automation are a highly supportive IT function with the required technology, infrastructure, and cybersecurity in place of agile, multidisciplinary teams capable of implementing automation at pace. There is also a strong emphasis on appropriate governance, project management, and technology.

Scaling organizations have also made significant efforts to create mature process definitions, standards, and process management, with the standards controlled by a Centre of Excellence. Some 65% of organizations in the implementing and scaling phase hold this latter proposition accurate instead of only 32% in the piloting stage.

Conclusion

Organizations that have developed process definitions, standards, and process management and have the support of an effective Centre of Excellence are most likely to benefit most from Intelligent Automation. Besides, those organizations that develop the skills to redesign workflows and enhance the capabilities needed to harness Intelligent Automation will be better placed to take advantage of the opportunities.

Start your Intelligent Automation journey today with Skript. Just leave us a message with your contact details, and we will help you initiate a discovery workshop.

What is Intelligent Automation? RPA + AI

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