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Hyperautomation 2.0: How Business Analysis Can Lead the AI-Powered Process Automation Revolution

by Business Analysis,

In today’s digital age, businesses are constantly seeking ways to improve efficiency and productivity. Hyperautomation has emerged as a powerful strategy, but it’s not just about automating repetitive tasks anymore. It’s about leveraging a combination of advanced technologies, including a new game-changer: Generative AI. But what exactly is hyperautomation, and how can business analysis help your organisation implement it effectively?

What is Hyperautomation?

According to Gartner, hyperautomation is “a business-driven approach that uses multiple technologies, robotic process automation (RPA), artificial intelligence (AI), machine learning, mixed reality, process mining, intelligent document processing (IDP) and other tools to automate as many business and IT processes as possible.”

In simpler terms, hyperautomation is about leveraging a combination of advanced technologies to automate a wide range of tasks, from repetitive data entry to complex decision-making processes.

The core of hyperautomation

The core of hyperautomation lies in its ability to orchestrate a powerful blend of technologies. These technologies, while not necessarily involving cutting-edge AI like generative models, lay the groundwork for a comprehensive automation strategy. Here are some examples of how these foundational technologies can transform business processes:

  • Automated Document Processing and Approval: RPA bots can automate data extraction from invoices, contracts, and other documents, triggering predefined approval workflows. This can significantly reduce manual data entry and processing time.
  • Intelligent Customer Onboarding and Account Opening: Business process automation tools can create a seamless onboarding experience by automating tasks like scheduling appointments, sending personalized welcome messages, and gathering customer information. This leads to shorter onboarding times and improved customer satisfaction.
  • Advanced Claims Processing and Insurance Claims: Algorithms can analyse past claims data to identify patterns for fraud detection and automate notifications. Furthermore, these algorithms can generate standardised reports, improving claims processing efficiency.
  • Streamlined Employee Onboarding and HR Processes: RPA bots can automate tasks like scheduling interviews, sending offer letters, and collecting new hire information. This frees up HR personnel to focus on more strategic initiatives like employee development.
  • Supply chain management: automation enables businesses to automate and streamline various workflows, including order processing, inventory management, demand forecasting, logistics tracking, and supplier management
  • Enhanced Customer Service and Support: Chatbots powered by AI can answer frequently asked questions, resolve common issues, and personalize customer support interactions. This offers a faster and more convenient customer service experience.

Hyperautomation with a Generative Boost

Generative AI allows machines to learn from existing data and create entirely new content, processes, and even code. This opens up a whole new world of possibilities for hyperautomation. Here are some ways generative AI can be used:

  • Automated Content Creation: Generate marketing copy, legal documents, reports, and even presentations based on existing data and pre-defined templates.
  • Intelligent Process Design: Use generative AI to analyse business processes and suggest the most efficient automation workflows.
  • Advanced Data Analysis: Leverage generative AI to identify patterns and trends in massive datasets, leading to more informed decision-making.
  • Automated Code Generation: Generative AI can create code snippets or even entire programs based on specific requirements, accelerating development cycles.

The Role of Business Analysis in This Evolving Landscape

Business analysts play a critical role in guiding organisations through the hyperautomation journey. Their expertise in understanding business processes, identifying automation opportunities, and facilitating change management is essential for successful implementation. With generative AI, their expertise takes on a new dimension:

  • Business Process Analysis: Business analysts meticulously examine current business processes to pinpoint areas ripe for automation. They identify repetitive, manual tasks that can be streamlined through technology.
  • Feasibility Assessment: Business analysts evaluate the feasibility of automation for each identified process. This includes considering factors like cost, complexity, and potential return on investment (ROI).
  • Technology Selection: Business analysts collaborate with IT teams to select the most appropriate automation tools for each process.
  • Identifying Generative AI Opportunities: Business analysts can work with data scientists and IT teams to pinpoint areas where generative AI can be most impactful.
  • Human-in-the-Loop Design: Business analysts ensure a human-centric approach by incorporating expert knowledge and judgement into the generative AI models.
  • Change Management with Upskilling: Business analysts develop communication plans, guide employees on new processes, and address any concerns or resistance. As automation evolves, business analysts can design training programs to equip employees with new skills to work alongside intelligent automation systems.

Business Analysis Best Practices for Hyperautomation with Generative AI

  • Enhance Business Value: Don’t automate for the sake of automation. Ensure that each automation initiative aligns with clear business goals and delivers measurable value.
  • Focus on Strategic Impact: Don’t just automate tasks; leverage generative AI to solve complex problems and unlock new business opportunities.
  • Embrace Experimentation: Implement hyperautomation in phases, starting with quick wins and gradually scaling up. This allows for continuous learning and refinement.
  • Involve Stakeholders: Actively involve key stakeholders from across the organisation throughout the hyperautomation process. This ensures alignment and buy-in from all levels
  • Data Governance is Key: Ensure robust data governance practices to maintain data quality and trust in the outcomes generated by AI models.
  • Communicate Transparency: Clearly explain how generative AI works and its role within the automation process to build trust and address ethical considerations.

By following these best practices and leveraging the power of generative AI, business analysts can guide their organisations towards a future of hyperautomation that unlocks not just efficiency, but also innovation and growth.

 

Hyperautomation: Unlocking Efficiency, Innovation and Growth in the Hyperautomation Age. By Sam T Nelson · 2024

Hyperautomation with Generative AI: Learn how Hyperautomation and Generative AI can help you transform your business and create new value (English Edition). By Navdeep Singh Gill, Jagreet Kaur · 2023

Intelligent Automation: Welcome To The World Of Hyperautomation: Learn How To Harness Artificial Intelligence To Boost Business & Make Our World More Human. By Pascal Bornet, Ian Barkin, Jochen Wirtz · 2020

https://www.gartner.com/en/information-technology/glossary/hyperautomation

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