As a key enabler of digital transformation, the adoption of intelligent automation (IA) technology is increasing across geographies and industries. Companies which have derived business value from robotic process automation (RPA) are now embarking on automating business-critical processes by leveraging artificial intelligence and machine learning. By 2022, Intelligent Automation (IA) will help businesses generate over $134 billion in labor value. Intelligent Automation adds a layer of cognitive functioning on RPA and enables end-to-end business process automation.
An effective intelligent automation program requires paying attention to the following three requirements.
1. Make automation a shared goal at the enterprise level
Establish business alignment with a top-down approach to automation. Chalk out an automation program that is well supported by business, IT, and the leadership. Bring clarity to the automation objectives by defining the right vision for intelligent automation. Consider embedding intelligent automation within the digital strategy for the organization and aligning it with the overall transformation road map.
Enterprises that have already implemented some form of robotic process automation are in a better position to integrate intelligent automation. The latter adds incremental value by introducing a cognitive use for the data and infrastructure already in place. Hence, intelligent automation can be a later stage implementation objective for the same value chain that focuses on Robotic Process Automation (RPA).
2. Build an automation roadmap for core complex processes beyond back-end functions
As automation becomes pervasive, its drivers are shifting from pure cost-reduction to more strategic objectives. See beyond routine back-end functions and build an automation road map for core, complex processes. A clear understanding of how to capture value from intelligent automation leads to much higher reductions in costs.
Focus on reengineering processes before automating them and adopt an integrated approach, rather than individual point solutions. Quite often, automating a bad process yields poor results. It is essential to structure business processes for high performance.
The key motive behind RPA has been reducing costs at the back of increased productivity. RPA would take care of the back-end processes so the human capital could be deployed on more value-generating processes. Intelligent automation enables value creation across the process and is not dependent on rule-based approaches necessary to implement RPA. Hence, instead of conceptualizing Intelligent Automation as a replacement or a more efficient form of RPA, you can use it as the tool to reimagine the entire business process. This way, intelligent automation can be leveraged to take the structured data inputs, AI capabilities, and intelligent execution at scale to bring innovation beyond a touchpoint and reshaping the process itself.
COVID-19 has induced companies to investigate newer RPA use-cases. Several use cases are now allowing businesses to have end-to-end intelligent automatons processes.
3. Embrace a Partnership-based model for scalable IA implementation
A federated CoE model can help to not only achieve faster implementations and better program governance but also to mitigate talent gaps and scaling challenges. Investing in a CoE also means that the firm will take up several process-automation projects and integrate them on its unified platform.
A common challenge faced by businesses while implementing wide-scale automation projects is managing change. Organizational Change Management (OCM) can be centralized within the CoE to ensure benefits are realized.
It pays to take a strategic and integrated approach to automation. There are three tenets of creating systemic intelligent automation adoption across the enterprise – embedding it with the digital transformation strategy, creating an evolutionary chain that may begin with backend processes but should end with comprehensive coverage of the process, and the creation of Centre of Excellence for efficient and systemic IA deployment.