From BPM to Hyperautomation: How Process Automation Evolved and What’s Next

Hyperautomation - future or present ?

Introduction – Some Personal Reflections on Automation

I still remember when most companies treated the word “process” as if it were magic. Offices were buried in paperwork, procedures ran only because someone remembered who needed to forward each document, and “automation” sounded like something out of a science‑fiction film. Looking back over the years, I can see how drastically our approach to management and automation has changed. It’s been a journey full of ideas, tools—and, most of all, human stories.

In this article, I’ll walk you through how workflows evolved from the earliest efficiency experiments to classic Business Process Management (BPM), and finally to hyperautomation, which analysts predict will be one of the hottest trends in the coming years. You’ll find a deep dive into each stage, illustrated with real‑world examples, practical tips, and clear, skimmable sections so you can jump straight to what interests you most.

1. Before BPM – The Roots of Process Automation

To truly grasp BPM, we need to go back to the dawn of industrialization. Adam Smith noted in 1776 that dividing labor boosts productivity. A century later, Frederick Taylor measured every worker motion to optimize tasks—a method known as scientific management. Henry Ford then demonstrated that a well‑designed assembly line could revolutionize manufacturing. Meanwhile, Walter Shewhart introduced statistical quality control in the 1920s, and Toyota refined the lean production system in the mid‑20th century.

These early milestones taught us to view work as a sequence of steps that could be analyzed and improved. By the 1980s, computers entered the picture: MRP (Material Requirements Planning) systems planned factory orders, while FileNet launched the first digital document management system, scanning and routing files automatically.

At the same time, methodologies like Six Sigma (introduced by Motorola in 1986) focused on defect reduction through statistical analysis, and lean principles spread from the factory floor into services and IT. These foundations set the stage for the next big leap.

2. The Birth of BPM – From Process Design to End‑to‑End Management

In 1990, Gartner coined the term ERP (Enterprise Resource Planning) for integrated business software. A decade later, the concept of BPMS (Business Process Management Suite) emerged, offering graphical process modeling, execution, and monitoring—all in one package. Suddenly, you could drag and drop a process diagram, define rules, launch workflows, and track performance in real time.

Key benefits of early BPMS:

• Visual modeling: Create clear, shared process maps.

• Business rules engine: Centralize decision logic and update it without code.

• Real‑time dashboards: Monitor KPIs and spot bottlenecks instantly.

I remember seeing my first BPM platform years ago: the thrill of clicking a “Deploy” button and watching a new process run was mixed with disbelief. But these systems were complex and costly, requiring specialist skills and lengthy waterfall projects.

3. The Rise of Intelligent BPM (iBPM) and Digital Process Automation

Around 2012, Gartner introduced iBPM, integrating analytics and case management into BPM platforms. Suddenly, process mining tools could analyze system logs, uncover the “true” workflow, and suggest optimizations. At the same time, Robotic Process Automation (RPA) arrived: software “robots” mimicked human clicks and data entry, automating tasks without deep system integration.

Combine RPA with iBPM and you get Digital Process Automation (DPA): low‑code platforms let business users build apps, AI and machine learning analyze data and predict outcomes, and cloud‑based services scale automatically—all without writing traditional code.

Why this mattered:

• Speed: Launch digital processes in days, not months.

• Flexibility: Business teams build and adjust workflows themselves.

• Insights: Data‑driven optimization replaces guesswork.

  
  

4. Hyperautomation – Why Everyone’s Talking About It

In 2019, Gartner defined hyperautomation as the orchestrated use of multiple tools—RPA, iBPMS, AI, process mining—to identify, analyze, and automate every possible repetitive task. The magic lies in combining technologies end‑to‑end rather than relying on any single product.

Core elements of hyperautomation:

Technology	Role
RPA	Automates routine, rule-based tasks by simulating user actions.
AI/ML	Provides intelligence, pattern detection, and continuous learning.
Process Mining	Discovers actual workflows and pinpoints inefficiencies.
Low‑Code/No‑Code	Empowers non‑technical users to build and adjust applications rapidly.
iBPMS	Models, executes, and monitors processes in a unified environment.

When these tools work together, entire value streams become automated. Imagine onboarding a new employee: the system aggregates resumes, analyzes candidates against job profiles, schedules interviews, generates contracts, and provisions IT accounts—with HR only needing to review and approve exceptions.

4.1 The Digital Twin of the Organization

A key by‑product of hyperautomation is the Digital Twin of the Organization (DTO), a dynamic model of your company’s workflows, systems, people, and data. A DTO lets you:

• Visualize real‑time interactions between processes and KPIs.

• Simulate “what‑if” scenarios before making changes.

• Incorporate sustainability metrics—CO₂ emissions, energy use—to drive ESG goals.

  
  

5. Why Hyperautomation Is More Than the Sum of Its Parts

Hyperautomation isn’t just about replacing manual work. It’s about creating an intelligent, self‑optimizing ecosystem.

• Employee empowerment: Free teams from tedious data entry and let them focus on creative, strategic tasks.

• Scalability: Deploy bots and digital processes across departments in days, not quarters.

• Adaptability: Tune workflows on the fly with low‑code platforms and machine‑learning insights.

• Citizen Development: 74% of IT leaders plan to shift app creation to business teams using no‑code tools, boosting innovation and ownership.

  
  

6. Case Study: Hyperautomation in a Mid‑Sized Logistics Company

The Challenge: Slow, Error‑Prone Order Processing

XpressLogix, a regional logistics provider, struggled with manual order entry. Staff spent 60% of their day copying details between customer emails, ERP, and shipping systems, leading to delays, data errors, and frustrated clients.

The Solution: End‑to‑End Automation Platform

1. RPA bots automated data transfer from email attachments to the ERP.

2. AI‑powered document processing extracted order details and flagged any anomalies.

3. Process mining mapped the actual workflow and identified redundant steps.

4. Low‑code portal allowed operations managers to update rules—like new shipping zones—without IT support.

The Results: 60% Faster Fulfillment, 30% Cost Reduction

• Order throughput increased by 60%, cutting average processing time from 8 to 3 minutes.

• Error rate fell by 80%, improving on‑time delivery to 98%.

• Operational costs dropped by 30%, allowing reinvestment in customer service.

  
  

7. FAQ

Q: What exactly is hyperautomation?A: It’s the coordinated use of multiple digital tools—RPA, AI, BPM, process mining—to automate as much of your business as possible.

Q: Do I need to rip out my existing systems?A: No. RPA and integration platforms let you automate on top of legacy applications without large‑scale replacement.

Q: How do I get started?A: Begin by mapping your core processes, identifying high‑volume, error‑prone tasks, and running a small pilot with RPA or process mining.

8. Kick-Start Your Hyperautomation Journey

Ready to transform your workflows? Start with a free process audit: map your top three repetitive tasks, measure their impact, and explore targeted automation solutions. The future belongs to organizations that automate intelligently—will you be one of them?