The “Onion Peel” Approach to Hyper Intelligent Automation
Let's see how the Onion bulb scales represent various stages in the journey of Intelligent Automation that an enterprise can look to undertake systematically.
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Hyper Intelligent Automation (HIA) was verily the flavor of 2021 and going ahead into 2022 and beyond it’s set to stamp its importance in more ways than one. The growing clout of “Intelligent Automation” is buttressed by the fact that a recent Zinnov Zones report on Hyper Intelligent Automation (HIA) shows the segment will grow at a rate of 50-55% year-on-year to cross USD 18Bn by 2026 from the existing USD 2.4 Bn. The deal ecosystem has seen a steady rise in demand for HIA and 30-35% of the overall HIA deals are over USD 500K in size.
This trend underlines a fundamental shift of large enterprises who are no longer looking at automation focusing only on efficiency and cost savings but at outcomes around enhanced stakeholder experience and business resilience.
Gartner defines Hyperautomation as a business-driven, disciplined approach that organizations use to rapidly identify, vet, and automate as many business and IT processes as possible. Hyperautomation involves the orchestrated use of multiple technologies, tools, or platforms, including AI, ML, event-driven software architecture, RPA (Robotic Process Management), BPM (Business Process Management), iBPMS (Intelligent Business Process Management Suites), iPaaS (Integration Platform as a Service), Low-code/No-code tools, packaged software, other types of decision, process, and task automation tools.
The important aspect to note here is that organizations are maturing fast to make a smooth transition from having an automation approach focused strategy (strategy to deploy RPA, AI, or ML) to having an automation outcomes-based strategy (that brings the best of all approaches available to meet a target business outcome). But while this maturity is at the mindset level, several factors such as the absence of a well written HIA strategy, inability to spot use cases, lack of talent across various automation approaches, and most importantly, the urge to land quick results is what is letting most organizations down at an execution level.
Hyper Intelligent Automation is a Marathon, Not a Sprint
The truth is Hyper Intelligent Automation is a marathon and not a sprint and hence results would take some time to materialize. Patience, therefore, is of paramount importance and when backed with the right strategy, would more often than not result in the desired outcomes. There is another thing to remember though. Marathons are won by athletes with a very strong core and hence the Hyper Intelligent Automation journey is best approached by ensuring that the process core is strong enough.
The “Process out journey” or the “Core Out journey”, is what will help organizations approach the Intelligent Automation journey in a methodical manner. This is a layered journey and is best explained with an example of the good old Onion. The Onion, apart from its antioxidant benefits, also shows the layered discipline that an enterprise can follow to inject oxygen into their Intelligent Journey pursuits.
The apex/terminal bud of the Onion constitutes the core of the process which requires automation. Enterprises should start here, look at the processes minutely, undertake a detailed process mining and task mining, evaluate the existing process maps, look at non-value adding components and then optimize those portions before moving ahead in the journey.
The three bulb scales, thereafter, each represent a higher-order automation approach, progressing logically to help enterprises achieve defined and specific goals through focused automation. While the first bulb scale represents automation at a UI level, best achieved through RPA, the second scale seeks automation of the business flow in order to provide superior customer experience to the end-users while the last scale represents deriving intelligent insights by applying Artificial Intelligence and Machine Learning.
The outer tunic of the onion, assuming the internal processes and business having been optimized looks at applying IoT, Edge Analytics, and AI to provide prescriptive analytics to the enterprises that would help in future decision making.
This is shown in the illustration below:
Figure: The Layered Onion Approach to Intelligent Automation
There is no point in applying bandages on the exterior body if the bleeding in question is internal. Likewise, it is illogical to simply adopt an RPA or a BPM or an AI/ML solution on top of a process that’s unoptimized and inefficient. Doing so wouldn’t help an enterprise reap the potential benefits of an automation approach. Likewise, there’s no point in adopting AI and ML when the business processes are unoptimized with the inadequate data flow. Data is the energy for AI and ML and when the source of such data is unoptimized when the underlying business processes and case management approach is inefficient, AI and ML wouldn’t reap any rewards.
No wonder, 25% of enterprises worldwide that use AI solutions are reporting around a 50% failure rate. Likewise, around 30%-50% of early enterprise RPA projects fail as they are ineffectively adopted in a pre-matured manner and hence yield poor results.
Having said that, the above is a very flexible approach. If an enterprise conducts an automation audit and finds that they are already at Layer 2 above (automating using RPAs), then they can verily look to proceed to layers 3, 4, and 5.
Hence, enterprises can start where they feel an opportunity exists, but the hierarchy of layers will act as a guiding plane for them.
Thus, looked closely, the Onion peel approach is very methodical. It optimizes the process, automates the UI level routine tasks on top of the process using RPA. Thereafter, automates the business flows using BPM tools, leverages the rich pool of business and process-level data, applies AI and ML on them to derive insights, and finally, once the data pool is truly robust and processes are perfectly optimized, uses IoT and Edge Analytics to undertake predictive and prescriptive decision making.
Layers have an innate nature of enveloping other stages from the stage in progress and hence promote focus and attention of the work being done. Likewise, the Onion bulb scales represent each of those stages in the journey of Intelligent Automation that an enterprise can look to undertake systematically.
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