Automation is one of the hottest topics in business operations. Many enterprise leaders are thinking how they can leverage automation to gain a competitive advantage and others are considering how will automation change the workforce.
In this guide we dig deep into the topic of intelligent automation, going through key topics, examples and best practices to help executives understand and take use of the opportunity.
Grab a good warm drink, sit down and enjoy! ☕
What is intelligent automation?
Intelligent automation is the use of technology such as artificial intelligence (AI) and robotic process automation (RPA) to automate mundane, repetitive tasks. Intelligent automation streamlines processes, cuts costs, and improves the efficiency of business operations.
While the interpretations of intelligent automation differ greatly between experts and organizations, you can consider IA to include different business operations that are enabled by artificial intelligence or machine learning. Examples can include:
- AI-powered process and task intelligence.
- Robotic process automation (RPA).
- Intelligent document processing (IDP) and natural language processing (NLP).
- Conversational AI and chat bots.
- API-based intelligent data integrations (iPaaS).
Roots in intelligent process automation
To a large degree intelligent automation emerged from the field of business process management - a structured and efficiency-focused way to manage business operations. The term itself was coined in 2017 by the analyst firm Forrester as part of their research into enterprise automation frameworks.
You can consider IA to be an advanced form of intelligent process automation - where the core goal is to develop processes and workflows that improve the way work is done with advanced technology and software.
Intelligent automation vs hyperautomation
Intelligent automation is sometimes referred to as hyperautomation - a term coined by Gartner. In Gartner’s definition, hyperautomation is “a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible.”
According to Gartner, hyperautomation involves the orchestrated use of multiple technologies, tools or platforms, including: artificial intelligence (AI), machine learning, event-driven software architecture, robotic process automation (RPA), business process management (BPM) and intelligent business process management suites (iBPMS), integration platform as a service (iPaaS), low-code/no-code tools, packaged software, and other types of decision, process and task automation tools.”
Is intelligent automation part of digitalization?
Intelligent automation is a major driver of digital transformation as it highly impacts all three components of digital work: people, processes and technology.
More deeply you can see intelligent automation enabling the six key success factors of digital transformation:
- Strategy - achieving a competitive advantage by leveraging a digital-first strategy.
- Organization - enabling the future of work by augmenting the modern workforce.
- Culture - democratizing data and insights - enabling value across all levels of the enterprise organization.
- Technology - connecting the best of human and artificial intelligence where technology augments experience.
- People - giving people more meaningful work without copy-paste talent waste.
- Customer experience - streamlining and improving the fully digitalized customer experience.
Why automate business processes?
Processes are the lifeblood of business operations. Each organization is built up on thousands or millions of tasks, workflows and processes where different teams and resources interact to create products or services and bring value to customers.
As business operations have advanced and matured, process excellence has become a source of competitive advantage. Many organizations have turned to digital process management and technology to keep ahead of competition.
Digitalization has not only been a blessing, it has come with many challenges to the workforce. Employees working with repetitive tasks across different digital systems struggle with swivel chair processes, while the large number of new apps and new tools used has lead to an increase in shadow processes. Ultimately, digitalization is a new resource that needs to be systematically documented, analyzed and utilized - and that's where automation comes in.
Intelligent automation can be seen as the adoption of an assembly line concept to business processes, breaking tasks into repetitive steps and digital processes. Instead of having skilled employees manage each step, intelligent automation involves creating a digitally enabled workforce where some tasks are done through automation technology.
Benefits of intelligent automation
Each organization or business will find unique benefits to automating their business processes. Some of the most common goals of intelligent automation include:
- Increase productivity. Intelligent automation helps to automate mundane, repetitive tasks, freeing up enterprise employees to focus on more important and strategic tasks.
- Improve accuracy. Intelligent automation can help reduce errors by eliminating manual processes and inputting information accurately.
- Reduce costs. Automating manual processes can help reduce the costs associated with labor and other overheads.
- Faster workflows. Automated processes can help speed up and streamline operations, resulting in faster processing of tasks and data.
- Improve decision making. Automated processes can help provide insights and analytics to help inform better business decisions.
- Less talent waste. Intelligent automation frees skilled employees to focus on more knowledge-intensive tasks.
Is intelligent automation replacing employees?
Will intelligent automation replace the human workforce? In a short answer: no.
We can't fully predict the future of work but one thing is clear. While automation won't replace all jobs, it will impact 100% of jobs within the next decade.
Generally IA is seen as an opportunity to augment human intelligence in the workforce, where it can efficiently take on mundane, repetitive tasks, freeing up workers to take on more creative elements of their work.
Another way to see it is that intelligent automation doesn’t replace the human, it takes the robot out of the human.
On the other hand, there are many job roles that are directly impacted by automation. In the Future of Jobs Report the World Economic Forum estimates that artificial intelligence alone will replace 85 million jobs worldwide by 2025. In the table they have identified examples of jobs that have already been replaced by automation in the United States.
6 examples of intelligent automation
Intelligent automation can be leveraged by all kinds of operations and organizations - from the public sector to enterprise businesses. Here are examples of applications in different industries:
1. Healthcare: Using AI-powered chatbots to respond to patient queries and robotic process automation (RPA) to automate administrative tasks.
2. Retail: Using automated inventory systems for better inventory management and customer experience.
3. Banking and finance: Using AI-powered fraud detection and automated loan approval workflows.
4. Manufacturing: Using robots to automate production lines or automate order fulfillment.
5. Logistics and transportation: Using AI-powered systems for route optimization.
6. Insurance: Using process intelligence to streamline or automate claims processes.
Intelligent automation & RPA
Robotic process automation (RPA) is the use of software robots to automate clearly defined and repetitive tasks. RPA can help to reduce manual labor, increase productivity, and streamline operations. Intelligent automation, on the other hand, is a broader term across technology enablers for business processes or workflows.
RPA also aligns well with different aspects of IA. For example, a common use of process intelligence software is to discover and kickstart automation projects. In this way two separate components of intelligent automation compliment each other.
Intelligent automation & AI
Another common source of confusion is between IA and AI. Artificial intelligence algorithms and platforms can be used within intelligent automation, but AI is also adapted across many other use-cases outside of business process management. Generally the two terms should not used interchangeably.
Many intelligent automation solutions have embedded artificial intelligence in the form of machine learning (ML) algoritms. You can think of machine learning as either supervised or un-supervised work where machines adapt and learn for themselves to make improvements based on available data. You may not always see machine learning in action, but it's helpful to ask vendors where it is being used. Just like with intelligent automation overall, ML is typically applied in mundane, routine tasks that humans don't want to do.
Intelligent document processing (IDP)
Intelligent document processing is the use of Artificial Intelligence (AI) and Machine Learning (ML) technologies to automatically extract data and information from unstructured documents such as invoices, contracts, tax forms, and other business documents.
IDP can include both object character recognition (OCR) technologies to convert images into machine-readable formats and natural language processing (NLP) algorithms to summarize the contents of text-based documents to turn unstructured data into digitalized documents.
Digital twins in automation
A digital twin is a digital copy of a business process that can be analyzed and improved through robotic process automation or process simulation. Digital twins are most common in manufacturing processes, but have increasingly spread into other functions such as business operations, insurance and healthcare. The key benefit of a digital twin is to design and implement process optimizations, such as process re-engineering in a digital form before scaling out into large-scale operations.
A digital twin of an organization (DTO) extends the concept of a process digital twin to a digital copy of a business organization, including products, structures, interconnected departments. A DTO can be utilized to simulate and evaluate the business impact of major operational changes, for example, moving to a cloud-based ERP system.
Conversational AI and chatbots
One more common area for intelligent automation is conversational AI - a technology recently popularized by technologies such as ChatGBT. In their core, they are technologies that leverage artificial intelligence, such as natural language understanding (NLU) to understand and respond to inputs and interact with humans in a conversational way.
In the enterprise, chat bots utilizing conversational AI can be seen both in internal functions (for example, purchasing and requisition bots) and in customer-facing activities, (eg. customer service chat bots on websites.)
Use-cases for intelligent automation
Advanced automation can be utilized across any business units of functions within enterprise organizations. Some of the most common use-cases involve repetitive, manual tasks.
- Finance - automating or streamlining approvals or monthly reporting.
- Procurement - automating high-volume processes, such as invoice approvals or end-to-end accounts payable.
- Customer service - on-boarding new customers or supporting customer services.
- Information technology - detection of risk or security breaches within an organization.
- Human resources - automating and streamlining repetitive processes, such as payroll.
- Sales - identifying and setting ideal prices based on market dynamics and trends.
- Marketing - improving the level or quality of customer or prospect audience data.
While use-cases can be found across any business function or department, a common misconception is that intelligent automation is developed to replace the skilled workforce. The greatest advantages of intelligent automation come when it is combined with the expertise of subject matter experts. Rarely does this work without active investment and support of people - and even more broadly you can see intelligent automation as the augmentation of human intelligence in the workplace.
6 step framework for intelligent automation
When you're building out your intelligent automation strategy it's important to recognize the time and resources required for effective implementation. IA should be considered a journey and not a destination - requiring considerable strategic planning. You can leverage this six step framework to get strated.
- Clarify your IA operating model - A good starting point is a clear understanding of overall strategy. Start by describing the goal and steps needed to achieve it.
- Leverage process discovery - for many organizations, the first starting point for automation is process discovery - where you identify the biggest areas for improvement.
- Design for wide-scale implementation - many organizations take a test-and-learn approach to IA. While it’s good to prove new concepts, identify test cases with measurable impact on key business goals.
- Set realistic expectations - any changes to business processes will impact your workforce and culture. Invest in robust change management communications.
- Build a minimum viable product (MVP) - intelligent automation can require significant effort and resources. Consider developing an MVP to prove results before rolling out through a large implementation.
- Capture and communicate value - automation in itself does not make business processes better. Identify and measure concrete ways that IA improves your key business metrics. Once you've reached the finish line it's time to start again!
Business and IT collaboration in automation
Business leaders and IT should collaborate in the adoption of intelligent automation in order to ensure the successful and compliant implementation. IT professionals can provide the technical expertise to help set up and maintain the necessary infrastructure, while business leaders can provide the business knowledge and insights to ensure that the technologies are used in the most effective and efficient manner.
Collaboration can also help to identify the best opportunities for automation and ensure that the technologies are used to their full potential.
The importance of data culture
Most companies implementing intelligent automation face challenges with data quality, but an equally or even more important question is the level of data culture.
A data culture is the collective behaviors and beliefs of people who use data to improve decision making. You can consider an organization data-driven when data is embedded into decision making, operations, mindset and the identity of an organization.
Many organizations face resistance to data culture when you disrupt existing ways of working or implement new tools, processes or workflows. Having a strong data culture helps an organization adapt effectively to change in a way that leverages the skills and experiences of the workforce.
A good data culture improves the level of data literacy and empowers employees to ask the right questions, find insights, improve ways of working as well as the employee experience by eradicating inefficiency and wasted work.
Intelligent automation tools and software
There are many software solutions and tools for enterprise businesses to adapt intelligent automation. Common examples include:
- Process mining software
- Robotic process automation tools
- Hybrid process intelligence software
- Integration platforms as a service, iPaas
- Intelligent document processing
Rarely is one software used for all intelligent automation needs. In most cases, enterprise organizations develop a modern intelligence and automation stack as seen in the infographic below.
In the intelligent automation intelligence and automation stack you have four core areas: opex insights, management, automation and governance. Data itself can flow interchangably between different systems but the core division comes between the work data layer and the system data layer.
Work data layer - focuses on operational data, such as the process and task activity measured, managed and automated in business operations.
System data layer - focuses on data mining and management of enterprise data captured from enterprise IT systems, for example, ERP or CRM systems such as SAP, Oracle or Salesforce.
Notable software modern companies in each category include:
Operations management - Workfellow
Execution management - Celonis
Business process management - Bizagi, Nintex, Appian and Camunda
Data management - Snowflake, Databricks Amazon Redshift and Azure Synapse Analytics
Automation solutions - UiPath, ABBYY, Expert.ai and Nice
Intelligence solutions - Alteryx, ThougtSpot, Tableau and SiSense
Workflow governance - Mulesoft, Turbotic
Data governance - Splunk, Datadog
Many enterprise IT and operations leaders will build their enterprise architecture on top of legacy ERP or CRM solutions. Intelligent automation tools compliment this strategy. Most of the modern intelligent automation solutions integrate with or compliment the core enterprise data systems, such as the SAP or Oracle ERP data foundation, or provide easy ways to export data from one system to another through data exports, integrations or APIs.