It’s no secret that intelligent automation (IA) which is a combination of Artificial Intelligence (AI) and Robotic Process Automation (RPA) is accelerating digital transformation after the pandemic. In a recent annual global survey by Deloitte, 73 percent of executives responded that their organizations have embraced intelligent automation. This number indicates a whopping 58 percent jump from that reported in 2019.
While it’s evident that intelligent automation is the future, organizations have to overcome a few hurdles to scale their automation program effectively and reap full benefits.
Process fragmentation: the top barrier to scaling intelligent automation
Business-critical processes are spread across various software systems and teams – creating fragmented processes. Completing a process end-to-end involves the execution of a set of complex workflows and handoffs between functions and information systems. Every handoff produces an opportunity to induce errors and delays.
While automating these processes seems like the best solution, it is also seen as the biggest barrier to the adoption of intelligent automation. In fact, Deloitte notes that 36 percent of survey respondents identified fragmented processes as the most common reasons for the inability to scale their automation program.
Leveraging Process Discovery for Consolidating Fragmented Processes
Automating fragmented processes and piecemeal tasks brings limited and often short-term benefits. Also, the cost involved with automating multiple sub-optimized processes is huge: as the number of processes increases, so do the number of bots and implementation costs associated with them. Therefore, it’s imperative to consolidate end-to-end processes and automate one single optimized process.
An advanced process discovery solution like SurfaceAI allows businesses to record “AS-IS” processes as they are being executed on various systems including enterprise software like ERP, CRM, BPM, etc., and productivity applications like Microsoft Excel, Outlook, etc. Unlike manual discovery where consultants conduct several hours of interviews, automated process discovery tools work in the backend without distracting the users. All the while, it keeps recording the user’s activity across multiple applications and documents end-to-end processes.
These insights can be used to identify process fragments and the best way to consolidate them before automation. The consolidation process involves the removal of process redundancies and standardizing by removing variations. This is critical in RPA projects as automating a bad process will only result in fixing bots in the long run. If the process itself is inefficient, then automation will only amplify the inefficiencies – garbage in, garbage out.
Unlock Intelligent Automation at Scale with SurfaceAI
SurfaceAI’s intelligent and automated process discovery enables businesses to identify end-to-end automatable processes, saving up to 40% in RPA efforts. Our AI-enabled process discovery approach accelerates RPA deployment by providing X-ray-like visibility into real-time processes. The insights help to answer critical automation implementation considerations like the frequency of process execution, duration to complete a process, the common patterns and similarities between processes. Studies show that 70% of enterprises that have implemented RPA have not scaled beyond their pilot project. Don’t let process fragmentation lower the efficiency of your automation program or stall your change program. To unlock the true potential of RPA/IA for your business, try SurfaceAI. Get a personalized demo!