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By Madhu Raman
Hyperautomation is the future of business.
The strategy of hyperautomation lets organizations tackle simple, scalable, or repetitive business-associated tasks by removing manual steps or optimizing multi-step workflows. Standard automation processes ascend to “hyper” status when they drive cost reductions of 20% to 60% and increases in operational effectiveness of up to 50% for the tasks they target.
Implementing a hyperautomation strategy isn’t always simple and straightforward. While many business tasks and workflows could benefit from hyperautomation, building these solutions often requires greater investment in human expertise and software deployments that entail annual licensing costs.
But an emerging type of hyperautomation solution, reinvented and simplified for business use, is now positioned to usher in broader business adoption, increasing profitability margins and organizational efficiency while freeing the human workforce to use their talents for bigger things.
Automation Pain Points
Thousands of hyperautomation solution adoptions between the first half of 2020 and the second half of 2023 reveal several key frictional issues that may limit the strategy’s outcomes.
These pain points include a scarcity of talent with the skills to enable hyperautomation solutions; a demand for extensive infrastructure setup to execute the tasks; and a need to continually update workflows post-deployment to manage changing business-process rules. Adding to these pain points are the automation-specific costs of buying annual software licenses.
In one use case, an organization might introduce software to automate manual processes for performing statutory financial audits twice a year on its financial general ledger. In this scenario, the company must buy a solution software license for the full year, even though it only needs to use the software twice in that time to cut 240 hours of manual labor.
Freeing Human Ingenuity with AI
A standard hyperautomation scenario also depends on human talent that may be difficult to find and hire.
“Leah,” an expert conversant with configuring process automation vendor software, acts both as a business analyst and as a specialized automation software tool expert for her organization: a rare unicorn who channels her technical and business expertise into creating a software-driven workflow. She sets up and continually updates the execution of use-case specific steps to maximize her organization’s straight through processing (STP) automation—the steps that need no human involvement.
After delivering results, Leah establishes and grows a hyperautomation center of excellence (CoE) and hires “David”: an IT cloud deployment engineer who doubles as a program manager. David is another unicorn whose role in the CoE is to manage the end-to-end cost of automation adoption.
Recognizing a 3x to 6x annual IT cost for maintaining an automation workflow beyond the initial development cost to create, David prioritizes high ROI strategic use-cases, assembling a CoE portfolio of as many as 50 mid-to-high-complexity workflows across the business, with the goal of establishing hyperautomation efficiencies. These workflows could free up 15,000 hours of manual effort annually, saving $750,000 a year across the business.
Unfortunately, finding and retaining such rare talent as Leah and David is difficult.
However, since early 2020, maturing artificial intelligence (AI) and machine learning (ML) services continue to augment the rule-based flows that solutions need to achieve hyperautomation efficiencies. This augmentation could include the supportability of a skilled worker under Leah to set up workflows and accelerate David’s production-portfolio building capability.
A New Vision for Hyperautomation
The emerging vision for hyperautomation looks very different from that standard model.
In this vision for 2024 and beyond, hyperautomation starts with “Sally,” a human employee who is business-savvy but not a software savant. Sally must describe her use-case to her software interface, and she provides her automation description in plain language. To do so, she can now use hyperautomation tools with generative AI automation software to interpret her nontechnical use-case description and learn on the job to assemble relevant tasks as a set of actionable intents.
This gen-AI-powered engine can securely progress Sally’s intent workflow and pull in relevant data from other business software. After testing with edge alongside mainstream conditions, her team can securely deploy her new workflow on the company’s production cloud account that self-tunes to changing business conditions.
While Sally’s workflow executes in the cloud, the optimal level of compute and storage it needs kicks in, unattended by IT staff. This evolution from traditional license-based automation software that embeds AI and ML into rule-based flow to today’s emerging cloud-service hyperautomation with AI agents is more supportive of a pay-for-what-you-use model.
Hyperautomation Is Here
This advanced hyperautomation vision is now here, and it’s available to help your organization reduce your operational costs and increase your efficiency by investing only what you need to invest, and only when you need to invest it. That way, your human talent is free to focus on their unique workplace value instead of spending their time chaperoning manual workflows or toggling across tasks.
Contact AWS to unlock the future of automations in your organization.
Madhu Raman is Head of Automations Solutions at Amazon Web Services.
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