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Madhu Raman
Hyperautomation is the future of business.
A hyperautomation strategy allows organizations to handle simple, scalable, and repetitive business-related tasks by eliminating manual steps or optimizing multi-step workflows. Standard automated processes rise to “super” status when they reduce costs by 20% to 60% and improve operational efficiency by up to 50% for targeted tasks.
Implementing a hyperautomation strategy is not always simple and straightforward. Many business tasks and workflows can benefit from hyperautomation, but deploying these solutions often requires greater investments in human expertise and software deployment, often accompanied by annual licensing fees.
But a new breed of hyperautomation solutions, reimagined and simplified for business use, are now positioned to usher in broader business adoption, increasing profitability margins and organizational efficiencies while freeing up the workforce to put their talents to work for bigger things. .
Automation Problems
The adoption of thousands of hyperautomation solutions between the first half of 2020 and the second half of 2023 has revealed several key friction issues that could limit the outcomes of the strategy.
These challenges include the lack of talent with the skills to implement hyperautomation solutions, the need to set up extensive infrastructure to run the operations; And workflows must be continuously updated after deployment to manage changing business process rules. Adding to these challenges are the costs associated with automation associated with purchasing annual software licenses.
In one use case, an organization might introduce software to automate manual processes for performing twice-annual statutory financial audits of its financial general ledger. In this scenario, the company would need to purchase a full year's worth of solution software licenses, although it would only need to use the software twice in that period to save 240 hours of manual effort.
Liberating human creativity with AI
Standard hyperautomation scenarios also depend on talent, which can be difficult to find and hire.
An expert in configuring process automation vendor software, “Leah” serves as both a business analyst and expert automation software tools expert for the organization. She is a rare unicorn who leverages her technical and business expertise to create software-based workflows. She sets up and continually updates execution of use case-specific steps to maximize your organization's Direct Processing (STP) automation (steps that do not require human intervention).
After getting results, Leah builds and grows a hyperautomation Center of Excellence (CoE) and hires “David,” a program manager and IT cloud deployment engineer. David is another unicorn responsible for managing the overall cost of automation adoption in the CoE.
Recognizing that the annual IT cost of maintaining automated workflows can be 3-6x beyond the initial development costs, David prioritized high-ROI strategic use cases, enabling up to 50 mid- to high-end implementations across the business. Build a CoE portfolio of complexity workflows. , aims to establish hyperautomation efficiency. These workflows eliminate 15,000 hours of manual work per year and save the business $750,000 per year.
Unfortunately, rare talent like Leah and David are difficult to find and retain.
However, since early 2020, mature artificial intelligence (AI) and machine learning (ML) services continue to power the rule-based flows required for solutions to achieve hyperautomation efficiency. This expansion could include the potential support of skilled workers under Leah to set up work flows and accelerate David's ability to build his production portfolio.
A new vision for hyperautomation
The new vision of hyperautomation is very different from the standard model.
In this vision for 2024 and beyond, hyperautomation begins with “Sally,” a human employee who is business-savvy but not a software expert. Sally must describe a use case for her own software interface and provides a description of the automation in plain language. To do this, she can now use hyperautomation tools in conjunction with generative AI automation software to learn how to interpret non-technical use case descriptions and assemble relevant actions in the field into a set of actionable intents.
This Gen-AI-powered engine can securely advance Sally's intent workflow and pull relevant data from other business software. After testing the edge alongside mainstream conditions, her team can securely deploy new workflows to the company's production cloud account, which self-adjusts to changing business conditions.
While Sally's workflow runs in the cloud, the optimal level of compute and storage she needs is initiated without intervention from IT staff. This evolution from traditional license-based automation software that embeds AI and ML into rules-based flows to today's emerging cloud service hyperautomation using AI agents further supports the pay-as-you-go model.
Hyperautomation is here
This advanced hyperautomation vision is here and can help organizations reduce operating costs and increase efficiency by investing only what they need to invest and only when they need to invest. This frees your talent to focus on their unique workplace value rather than wasting time managing manual workflows or switching between multiple tasks.
Contact AWS Unleash the future of automation in your organization.
Madhu Raman He is the Director of Automation Solutions at Amazon Web Services.