HyperAutomation (HA) has been identified as one of the key strategic evolving trends in the IT industry over the next few years by the leading technology research and consultancy company Gartner.
HA is described as an integrated and interdisciplinary approach for enterprises to rapidly identify and automate the largest possible number of business processes.
With organizations facing the pressures of increased global competition and economic downturn, the need to automate more and more business processes to improve cost efficiency, agility and productivity is increasing exponentially.
Gartner estimates that by 2024, diffuse HA spending will drive up the total cost of ownership 40-fold, making adaptive governance a differentiating factor in corporate performance.
Investing in new solutions based on HyperAutomation approaches is rapidly becoming a critical business need for organizations across any industry.
Another important driver for HA is the increasing effort of enterprises to accelerate their “digital transition” in order to offer their customers faster transactions through digital touchpoints and services.
In this perspective, any standardized quality process within any organization can become eligible for automation.
In Gartner’s latest trends guidance, they describe HA as “a business-driven approach to identify, vet and automate as many business and IT processes as possible. It requires the orchestrated use of multiple technologies, tools and platforms, including Robotic Process Automation, low-code platforms and process mining tools”.
HA solutions can generate the most disruptive and notable results when applied to highly complex business processes, that normally require human cognitive capabilities to extract data, understand and interpret information and concepts contained within large sets of unstructured sources (like Web pages, News feeds, Emails) or enterprise applications like ERP, CRM and ECM systems.
Highly Valuable Assets
These sources contain highly valuable assets in the form of unstructured information of difficult fruition, since datapoints are buried within texts and documents of variable layout and format (PDF, HTML, DOC, SLX, ecc.), images, audio and video files.
In this context it is necessary to utilize unstructured data processing tools and sophisticated AI capabilities to read the contents from these sources, interpret them, extract target data, and automate tasks or even entire business processes.
This requires the integration and orchestration, in a unified platform or software ecosystem, of several different automation tools and technologies, such as:
- Artificial Intelligence (AI) with a composite/hybrid approach;
- Machine Learning (ML);
- Knowledge Representation and Reasoning (KRR);
- Event Driven Architecture (EDA) / Microservices-based architecture (MA);
- Robotic Process Automation (RPA);
- Business Process Management (BPM) and workflow-based Intelligent Business Process Management Suites (iBPMS);
- Integrated Platform as a Service (iPaaS) for managing DevOps, AI/MLOps;
- Low-code/no-code development methods.
In the past, companies have approached these problems by developing multiple automation projects, based on existing software solutions or algorithms, specifically designed to solve each problem in the organization individually.
This approach ultimately proved to be unviable in most cases, due to high design and implementation costs, low scalability and, ultimately, low ROI.
On the contrary, companies are increasingly experimenting and implementing solutions based on Intelligent Document Processing (IDP) and Intelligent Process Automation (IPA).
These kinds of solutions provide integrated platforms and workflows to simplify the transfer of domain-specific knowledge owned by business users into automation algorithms.
Additionally, the best IDP solutions can provide the key features needed to overcome the costs and scalability limits of individual automation projects, such as:
- No-code user interfaces that provide an intuitive way to train algorithms.
- Composite AI approaches, combining several different technologies and methods like computer vision, natural language processing, rule-based classifiers, symbolic knowledge representation and semantic indexing.
- Pre-built templates to simplify the configuration of data ingestion and processing workflows.
- Human-In-The-Loop AI approaches supported by intuitive interfaces to let users review and validate results and support continuous learning.
- Dynamic resource allocation to scale the infrastructure depending on actual computational needs.
- Cloud native infrastructures to allow customers to utilize GUI, APIs and services in SaaS mode, while also maintaining the option to deploy on premise when needed.
The Altilia Intelligent Automation™ platform is built to offer all these kinds of strategic features, positioning itself as the top-end solution for enabling the automation of document intensive processes, even when data comes from complex and unstructured sources.
Its adoption allows customers to reap substantial benefits in terms of manual work reduction (by up 80%), cut down costs substantially and speed-up business processes by up to 10 times, while also improving their accuracy and effectiveness.
For more information on how Altilia can help you transform your business, contact us here .