A McKinsey Global Survey on Automation in 2020 revealed that 66% of organizations had automated one or more functions or business units, which was 57% in 2018. There is an increase in the number of organizations implementing automation.
As Alain Dehaze, CEO of Adecco, observed, “Warp speed developments in technology — automation, artificial intelligence, and the arrival of the sharing economy — are transforming how we work.
Beyond technology, traditional working patterns are also being disrupted by changes in society, organizations, and workforce management, leading to the rise of a more independent and dispersed workforce.”
Evolution of Automation
Automation is the result of applying technology to eliminate repetitive manual tasks. Here are a few examples of enterprise automation:
- Automated Early Warning Systems – monitor process variables in a factory and provide automated alerts.
- Purchase Order Automation – trigger approval workflows based on inventory levels and send the order directly to the vendor after approval.
- Automated Lead Management – input lead information into CRM systems, auto-assign leads to sales, and notify through email.
- Customer Support Automation – manage, track, and respond to support tickets from customers.
- Employee Services Automation – trigger approval workflows for various employee requests.
Why Are Companies Investing in Automation?
The “Great Resignation” shows no sign of abating. In the Annual Work Trend Index Report 2022, Microsoft found that 52% of Gen Z and Millennials are likely to consider changing employers this year, up 3% year on year. “63% of frontline workers are excited about the job opportunities tech creates, and technology ranks third on the list of factors that workers say could help reduce workplace stress.”
Over the last few years, companies that have implemented automation have achieved measurable benefits such as:
- Higher productivity
- More efficient processes
- Faster turnaround time
- Better employee engagement
Consider the example of a global specialty chemicals company, Lubrizol, which implemented a lab automation solution. Before implementation, their labs were finding it challenging to monitor exotherms. Scientists had to watch out for temperature rise, which was stressful and time-consuming. Automation reduced their stress, eliminated human errors, and improved productivity.
Understanding Process Automation
Task Automation
Task automation can be as simple as automating data entry. When you combine a Business Rules Engine with task automation, you gain the ability to create automated workflows.
For instance, an employee sends an email to the helpdesk requesting support in installing software. A support ticket is automatically created in the tracking system. The system assigns a support executive and sends a notification. When the support executive has ‘closed’ the ticket, the system triggers an email to the employee for confirmation.
Workflow Automation
While workflow automation is effective for automating repeatable steps to complete a task, process automation has a larger goal comprising of multiple tasks and workflows that, when automated together, will help companies increase the efficiency of a business process.
HR Process Automation
HR Process Automation software such as ADP Workforce Now, Ceridian Dayforce, Zoho People, and Oracle Cloud HCM leverages process automation to offer end-to-end HR process management capabilities.
These platforms can automate payroll processing, HR administration, vacation tracking, performance management, and expense reimbursements.
Similarly, tools and platforms such as Adobe Marketo, HubSpot, Salesforce Pardot, and Active Campaign help marketers create automated marketing campaigns to increase engagement throughout the customer journey/lifecycle.
Rise of Artificial Intelligence and Big Data Analytics
Before exploring how advances in Artificial Intelligence and Machine Learning help companies implement intelligent automation, it is worth remembering what Alan Turing said.
“A computer would deserve to be called intelligent if it could deceive a human into believing it was human.”
The pursuit of artificial intelligence began with the ‘Imitation Game,’ which questioned whether a machine could ‘think.’ AI has now evolved to learn from past data that will help predict future values.
Of course, big data and machine learning share a similar growth trajectory as both are dependent on each other.
Robotic Process Automation — the Confluence of Automation and AI
Robotic Process Automation (RPA) can simply be defined as creating and deploying software robots that mimic human actions on a computer.
A Deloitte Global RPA Survey has highlighted the following outcomes of RPA adoption:
- Improved Compliance (92%)
- Better Accuracy and Quality (90%)
- Enhanced Productivity (86%)
- Cost Reduction (59%)
According to a recent Forrester research report, there are 200+ companies that offer RPA and Intelligent Automation (IA) solutions. The market for RPA software solutions is expected to reach $22 billion by 2025.
On a conference call with analysts to discuss the first quarter of 2022 results, the management team at UiPath, a leading RPA software company, highlighted two interesting case studies of customers who have implemented RPA recently.
Case No. 1: A global financial services and insurance company
Case No. 1: A global financial services and the insurance company has identified intelligent automation as the means to achieve a cost reduction of $100 million by 2025. Their automation strategy:
- AI-Document Processing
- Machine Learning and Deep Learning
- Automated Testing of Software
- No-Code and Low-Code Citizen Development
Case No 2: Global Healthcare Exchange (GHX)
Case No 2: Global Healthcare Exchange(GHX) is a cloud-based supply chain network that connects 4100+ hospitals with more than 600+ integrated, global suppliers. GHX automation roadmap includes:
- Large Scale Document Conversion using AI Document Processing
- Image Identification and Processing using AI-Computer Vision
- No-Code and Low-Code Citizen Development
The Robotic Process Automation Ecosystem
Since the IPO of UiPath with a pre-IPO valuation of $35 billion, there has been a tectonic shift in the RPA ecosystem where large companies such as SAP, Salesforce MuleSoft, SS&C, and ServiceNow are pursuing inorganic growth through acquisitions of smaller RPA companies.
- In 2021, SAP acquired Signavio, a process analytics company that helps customers align internal processes based on customer experience.
- Last year, Salesforce acquired Servicetrace, an intelligent software robotics company.
- This year, SS&C, an intelligent automation services company, completed the acquisition of Blue Prism, a leading RPA solutions provider.
IT companies such as Accenture and IBM are, so far, content to play the role of system integrators and resellers of RPA software to their customers globally.
The Path Ahead for RPA
The Deloitte RPA Survey also points out that 78% of companies that have piloted RPA Bots to automate few processes will increase spend on RPA in the next few years.
The survey poses an important question: why do most organizations who have successfully piloted RPA find it difficult to scale their digital workforce?
Business Process Modelling:
The prerequisite for implementing intelligent automation at an enterprise scale is to identify and model all processes within the organization. BPM leverages flowcharts to represent the workflows and processes visually.
RPA on Cloud:
According to a study commissioned by Automation Anywhere called “Now and Next: State of RPA,” one out of four companies that have implemented intelligent automation runs their RPA bots on the cloud to scale their digital workforce on demand.
Integrations:
Companies like UiPath offer integration capabilities with niche RPA solutions such as Myndshft (automated benefits and insurance management), airSlate (document workflows automation).
Citizen Development:
No-code development empowers business users to ‘drag and drop’ elements from templates and create programs that automate tasks and workflows. Citizen Development and Automated Testing capabilities will help organizations scale their digital workforce by eliminating dependencies on coders and testers.
Accessible Hyper Automation — The Future of RPA
In the past, RPA bots worked well with structured data that were organized and accessible. Intelligent Document Processing (IDP), a component of Hyper Automation, combines Optical Character Recognition (OCR) and Machine Learning (ML) to read and understand information from unstructured documents such as printed forms.
Cognitive Text And Voice Automation
Cognitive Text and Voice Automation leverages Natural Language Processing and Speech Recognition to mimic human interactions. Automated workflows and approvals can be triggered from unstructured information such as a chat message from a customer or an employee.
Robotic Data Automation (RDA):
In 2019, Gartner identified data volume and complexity as one of the major challenges affecting digital transformation goals of companies. Robotic Data Automation helps automate data operations (DataOps) and workflows pertaining to data collection, contextualization, and statistical analysis. RDA does to data pipelines what RPA does to workflows and tasks.
RPA-as-a-Service:
Companies such as UiPath and Automation Anywhere are helping customers track Time-To-Value (TTV) to determine the time taken by RPA solutions to offer measurable benefits such as improvement in productivity, faster resolution of service tickets, reduction in errors, and faster approvals.
RPA-as-a-Service reduces the time taken to purchase and implement the bot solution framework for an organization. Similar to any SaaS offering, Cloud RPA eliminates dependencies on on-premise IT infrastructure.
Conclusion
The automation industry is progressing toward building a truly no-code Robotic Process Automation platform that will allow citizen developers to build and integrate bots with enterprise apps such as SAP, Salesforce, and Microsoft 365.
Developments in Process Modelling, Automated Testing, Machine Learning, Natural Language Processing, and Document Understanding will enable RPA solution architects to provide more sophisticated bots that can automate every process in the organization.
The objective of a successful intelligent automation program is to help employees perform high-value-added activities.
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