Assembling a Digital Operations Toolbox for Hyperautomation
In this article, we’ll explore the components of a digital opperations toolbox and how it can be leveraged in your hyperautomation journey.
Breaking Down The Digital Operations Toolbox
In a previous blog post, we introduced the popular concept of hyperautomation – a term used to describe next level automation achieved through combining Robotic Process Automation, Artificial Intelligence and Machine Learning capabilities. Now that we’ve established a hyperautomation base, it’s time to go further by breaking down the components of a digital operations toolbox, what technologies are encompassed it in, and how they impact your hyperautomation journey.
In the realm of hyperautomation, a digital operations toolbox serves as an evolutionary shift – moving task-based technologies, tools and functions like process mining, RPA and document ingestion into digital operation drivers.
What Encompasses a Digital Operations Toolbox?
The diagram below details the use-cases and tools used to match long-term business objectives. We can identify the optimal combinations of these tools according to our needs.
BPM Platforms: Intelligent BPM suites (iBPMS) have a solid foundation of tools for orchestrating processes and automating tasks within those processes. iBPMS consolidate integration services, decision management, process orchestration, ad-hoc processes and advanced analytics into a single platform.
Robotic Process Automation (RPA): RPA is a non-invasive integration technology used to automate routine, repetitive and predictable tasks through orchestrated UI interactions that emulate human actions within the workforce.
Low-Code Application Platforms (LCAP): The graphical nature of LCAP development environments can be used for modeling rapid automation of a business process. Most LCAP vendors offer business process orchestration and workflow services to rapidly automate tasks and orchestrate them into simpler processes.
Process Mining and Discovery/Analytics: Process mining is designed to discover, monitor and improve real processes by extracting knowledge from the event logs readily available in application systems. Process mining includes automated process discovery, conformance checking and various other advanced analytics features.
Decision Management Suites (DMS) & Business Rules Management Systems (BRMS): DMSs are used to supplement conventional application development and runtime tools when a business application includes decisions that entail complicated or frequently changing logic. Modern DMS products have evolved beyond business rule management systems by providing better support for analytics and decision modeling.
Image Source – Gartner
Augment Business Processes With AI: To expedite hyplerautomation, an integrated system of intelligence adequately blends digital operations tools with AI, ML, Natural Language Processing (NLP), Optimal Character Recognition, and conversational chatbots.
Image Source – Gartner
Digital Operations Tools and Use Cases
AI and ML are used specifically for process automation scenarios to enable RPA task automation, BPM/LCAP/DMS dynamic orchestratons, and to provide an added layer of intelligence. Common use cases of AI, ML and NLP within business process automation include many industry specific instances, such as claims handling, anti-money laundering efforts and product data matching in retail. Case management, contract management, legal processes and clinical trials are more examples.
Recommendations When Implementing AI, ML or NLP
In conclusion, here are some recommendations and best practices while implementing AI, ML or NLP in your digital operations toolbox, and during your hyperautomation journey:
Find use cases for optimal application of each AI area — including ML, NLP, OCR and chatbots.
Secure availability of good quality historical data to train the ML models.
Plan for narrow, quick, iterative AI wins in business operations.
Look for Auto-ML features to enable RPA processes to capitalize on ML and NLP accelerators.
Estimate the required resource skill sets, time, costs and complexity involved in building AI models to justify the business case.
Check all factors, including actors, trigger points, subsystem boundaries, interfacing APIs, exception handling and edge cases where human interventions are required.
Train the models – Auto-ML engines use input and output of data from completed manual tasks to pick algorithms, train the models and insert models into the automation in a nondisruptive fashion.
Exploit AI accelerators from the major cloud service providers (CSPs) that might be included within your LCAP, DMS, BPM, RPA and iPaaS platforms.
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