Frequently Asked Questions

Robotic Process Automation or RPA refers to the use of software bots that emulates a person executing manual, repetitive tasks. Just like a human, the bot is able to work with different systems and applications through the User Interface (UI), although RPA bots are increasing automating tasks via Application Programming Interfaces (APIs) as well. In addition, these bots are able to make decisions based on pre-defined business rules or logic.

The key benefits of RPA include:

• Increased productivity as the RPA bots work 24/7 with minimal downtime
• Reduced manpower costs as each RPA bot costs only a fractional of a FTE (whether onshore or offshore)
• Improved customer experience with faster and always-on service
• Improved employee engagement and retention by eliminating mundane, menial tasks and enabling them to focus on more interesting, varied work
• Reduce compliance costs and risks by eliminating human errors
• Greater operational agility and ability to handle variations in business volumes

While RPA can be a great way to automate routine and repetitive tasks, it is important to be mindful of the following limitations:

• RPA works best for structured and well-defined processes. Tasks that require human judgement and involve unstructured data are not suitable for RPA unless when combined with Artificial Intelligence (AI) or Machine Learning (ML).
• RPA bots are very sensitive to the operating environment, and the applications' User Interfaces (UIs) in particular. Any changes in the UIs (e.g. due to software upgrades) might cause the bot to break and the automation to fail.
• RPA implementations can require a substantial investment, especially when outsourced to expensive consultancies. If you are looking for a cost-effective RPA expert, why not consider us?

Calculating the Return on Investment (ROI) for a Robotic Process Automation (RPA) process involves comparing the cost of implementing RPA against the benefits gained from automating the process.

1. Identify costs — The typical costs includes software subscription fees, IT infrastructural costs, implementation and other professional services, maintenance and support costs, and training.
2. Quantify benefits — The most obvious and quantifiable benefit is manpower savings, which is calculated by multiplying the number of hours saved per automation with the hourly rate of the employees performing the tasks previously. Other benefits include reduced errors and rework costs, improved compliance or avoidance of regulatory penalties, increased productivity and throughput, and greater customer satisfaction, though it may not be possible to easily quantify some of these benefits.
3. Calculate the ROI — Use this formula to determine the ROI: (Total Benefits - Total Automation Costs) / Total Automation Costs * 100%. The higher the ROI, the more beneficial the automation becomes.

In attended RPA, the bot serves as a digital assistant to the employees. The processes are manually triggered by the employees, and generally require some human intervention during the execution. Attended RPA are commonly associated with automating front-office tasks. Unattended RPA, on the other hand, acts more like a digital worker that runs 24/7 based on a defined work schedule or triggers. These bots do not require any human intervention, and are usually used to handle back-office tasks.

The RPA software market is quite fragmented and competitive, with a large number of vendors providing solutions in this space. According to market research firm Gartner, the leaders in RPA software include Automation Anywhere, Microsoft Power Automate, Nice, SS&C Blue Prism and UiPath. However, it is important to note that every software has its pros and cons, so it is much more important to perform a detailed evaluation and select one that fits your unique requirements and budget.  

Well, yes and no. The UiPath Community Edition is free to use, but it comes with certain limitations:

• Non-commercial use — It’s designed solely for internal non-commercial purposes, such as education and individual or institutional research.
• Small businesses — Only small businesses, defined as having less than 250 users or machines, or less than USD 5 million in annual revenues, may use the software for internal business purposes.
• Limited capabilities — The Community Edition comes with certain limitations in terms of capabilities and features as compared to the Enterprise Edition

To learn more, do check out the Community Agreement which may be amended from time to time.

The identification and selection of RPA use cases is a 2-step process that ensures both the technical and commercial feasibility of the automation. The first step is to identify processes that are well suited for RPA. Typically, processes that are rule-based, have high transaction volumes, have low exception rates, are stable and well-defined, have high system stability, and deal with structured and electronic data are good candidates for automation. The second step is to establish the return on investment (ROI) which considers both the costs of implementation (i.e. software, service and support) as well as the benefits of automation (e.g. time and cost savings, error reduction, better customer service, etc.).   

RPA and finance are a match made in heaven. Why? RPA technology tends to work best when automating high volume, repetitive tasks with structured data and little need for human judgement, and the finance and accounting department is full of such tasks. RPA bots’ rule based, consistent behavior also enhances accuracy and compliance that are critical in F&A work. On top of that, RPA’s non invasive nature means it can be quickly deployed on almost any existing IT system, often without involvement from the IT team. Below are some common use cases that you can consider.

Accounts Payable: 

Vendor setup & maintenance

Automate workflow and approval processes

Invoice data capture & processing

Payment preparation

Accounts Receivable:

Customer master file maintenance

Information gathering for credit approvals

Payment status tracking

Sending late notices

Instalment tracking, sending instalment invoices

Data Entry to Accounting System:

Transactions posting

Standard journal entries

Invoices

Operational Finance & Accounting:

Calculating and processing rebates

Download sales data and calculate commissions

Automated price reviews based on contracts and price lists

Reconciliations:

Bank reconciliations

Accounts reconciliations

Intercompany reconciliations

Financial Reporting:

Daily P&L preparation

Pre-populate reports or slide decks for financial reviews

Regulatory reporting

Taxes:

Extract information from systems

Automate inputting and filling up of tax forms

Data validations and roll forwards

Ensuring the right attachments are included in tax filing

The Human Resources (HR) function involves plenty of manual, repetitive tasks that are gobbling up the HR professionals’ time — time much better spent elsewhere, doing something more valuable. The time consuming and repetitive nature of these administrative tasks in addition to the likelihood of human errors means that software robots are often better candidates to perform these tasks. HR executives should instead be doing tasks that humans do well — speaking with candidates, checking in with colleagues, strategizing , and much more. Below are some common use cases that you can consider.

Employee Onboarding/Offboarding:

Collecting and checking employee documentation

Automate creating/cancelling system credentials and access

Send welcome email with onboarding resources

Payroll:

Calculation of shift allowances

Leave tracking / absence management

Payroll processing

Automate distribution of payslips

Administration:

Update employee particulars in the system directly from information in email

Automatically update employee data across different systems for new hires, termination, promotion, etc.

Expenses & Budgeting:

Expense management

Submission of government claims (e.g. government-paid leave, training grants)

Position budget management

Spend analysis & reporting

Talent acquisition:

Post job postings on multiple sites at one go

Comb through large numbers of candidates from various platforms

Employee background checks

Preparing and sending offer letters

OD & Training:

Tracking of certification documentation and validity with automated reminders

Performance review administration, analysis, and reporting

Create performance dashboards & score cards from various data sources

Employee survey administration, analysis, and reporting

The process complexity is influenced by several factors including:

• The number of discrete process steps
• The number of applications or systems to interface with
• The types of automation required, e.g. User Interface (UI) versus Application Programming Interface (API) automations, foreground versus background automations, etc
• The number of data sources
• The type and quality of data, e.g. structured, semi-structured or unstructured
• The complexity of the business logic or rules
• Whether human intervention is required, i.e. Human-in-the-Loop
• Availability of process documentation or Standard Operating Procedures (SOPs)
• The number of process variations and exceptions
• Any additional security, compliance and governance requirements

To determine if a process can and should be automated with RPA, consider the following criteria:

• Rule-based — The process should be governed by clear, well-defined business rules and logic, with minimal need for human judgment or discretion.
• Repetitive — The process should involve repetitive tasks that are performed in the same manner each and every time, making them suitable for automation.
• High transaction volumes — Processes that are high in volume or frequency benefit most from automation, leading to significant time and cost savings.
• Structured data — Processes which handle structured data in the digital format lend themselves best to automation.
• Stable and well-defined processes — The process should be stable with well-documented steps and minimal changes over time, reducing the need for frequent bot updates.
• Stable applications — The applications that the RPA bot interacts with should be stable and with minimal changes over time, reducing the need for frequent bot updates.
• Low exceptions — Processes that have little variations or exceptions will help to reduce the complexity of the RPA implementation.
• Clear ROI: There should be a justifiable return on investment, with tangible and intangible benefits that can be clearly demonstrated.

A Process Definition Document (PDD) is a comprehensive document that outlines the details of a business process targeted for automation. It serves as a blueprint for the RPA development team and typically includes the following elements:

• Process Overview — A high-level description of the process, its objectives, and its importance to the business.
• Process Flow — Detailed step-by-step workflow of the entire process, often accompanied by flowcharts or diagrams.
• Inputs and Outputs — Specific details about the data inputs required by the process and the expected outputs.
• Business Rules — The rules, conditions, and logic that govern the process, including exceptions and variations.
• System Interactions — Information about the systems, applications, and interfaces the process interacts with, including how data is exchanged.
• Process Triggers — Conditions or events that initiate the process, such as a specific time, a user action, or an incoming file.
• Exceptions Handling — Procedures for managing exceptions, errors, or unexpected scenarios within the process.
• Key Performance Indicators (KPIs) — Metrics to evaluate the success and efficiency of the automated process.

The PDD is critical for ensuring that the RPA development aligns with business requirements and accurately reflects the process to be automated.

The 3 critical success factors for any RPA implementation are People, Process and Technology.

People — Many companies underestimate the critical role that their people play in the success of the RPA implementation. Some of the common mistakes made include not getting the buy-in of all the stakeholders, inadequate change management, and failure to upskill or reskill the employees' whose work are affected by automation.

Process — It is important to choose the right process to automate from both the technical and commercial feasibility standpoint. In addition, given that the RPA bots will simply replicate what they have been instructed to do, it is vital to involve the process' Subject Matter Experts (SMEs) during the implementation stage, as well as to optimize the process prior to automation.

Technology — Choosing the right RPA software that fits into your existing technology stack and budget is critical. Furthermore, it is important to audit the RPA bots to ensure that they are reliable and robust enough before pushing them into production.

The truth about low-code/no-code technology (RPA included) is that they are simple, but not easy to implement well. Indeed, according to research, as much as 30-50% of initial RPA projects fail. The root cause of these failures is choosing the wrong processes to automate. While RPA is a great tool for automating mundane, repetitive tasks, it is important to understand that not all processes are suitable for RPA. The greatest mistake is to use RPA as a hammer and treat all processes as if they were nails.

As with any project implementation, change management is often a critical success factor. This is perhaps even more so for RPA projects as automation often entails changes to the way work is being done, and this affects multiple stakeholders including both the customers and employees. For the latter in particular, automation anxiety is a real concern as many fear that they might lose their jobs to automation. Allaying such concerns and getting their buy-in can often make or break a project.  

Yes, Robotic Process Automation is still in high demand. According to market research firm Gartner, the RPA market grew by 22% in 2022. Many companies are still facing challenges hiring and retaining employees. This acute lack of manpower is forcing many businesses to turn to RPA bots to automate mundane and menial tasks that their employees cannot or will not do. In addition, the emergence of generative Artificial Intelligence (gen AI) and Large Language Models (LLMs) are providing a boost to demand as firms re-imagine the art of the possible with LLMs as the brain and RPA bots the hands and legs.

As with any disruptive technology, it is unfortunately inevitable that there will be some impact on existing job functions. However, it is important to understand that RPA cannot entirely automate all the different tasks that an employee does, especially those that require human empathy or ingenuity. In any case, the most successfully deployments of RPA involves using the technology to assist and augment employees, rather than to replace them altogether.  

A typical RPA project consists of 3 cost components: RPA software licenses, implementation services and maintenance support. The actual cost will vary depending on the complexities and number of processes that you are automating. For budgeting purposes, the cost of automating a process usually ranges from as low as $1,000 to as high as $10,000. There are also strategies to minimize the total cost outlay, including using open source RPA software and opting for RPA-as-a-Service where you essentially pay for performance or outcomes only.

When implementing an RPA project, do consider the following costs:

• Software — The annual RPA software subscription, the cost of which depends on various factors including the chosen platform, the number of bots required, the platform capabilities required, etc.
• Services — This refers to the one-time professional services required for the entire project lifecycle, such as consultancy, process discovery, solution design, development, testing, training, documentation, and deployment.
• Support — Ongoing costs required for the maintenance and enhancement of the RPA bots in production.

• System — Infrastructural expenses related to the hardware components and cloud services necessary to support the RPA environment.

Here are some ways to reduce the costs of your RPA software licenses:

• Monitor the utilization of your RPA licenses and only subscribe for what you need.
• Consider using attended bots instead of unattended bots for your automation as the former tends to be much cheaper (even though the functionality is about the same 😅).
• Explore alternative RPA software vendors, not just the popular ones. For example, there are 'free' RPA software like Microsoft Power Automate for desktop as well as open source ones like Open RPA or TagUI.
• While the most common licensing model is based on the number of named users, there are some RPA software vendors (e.g. Robocorp) that offers a consumption-based pricing model that may be more viable for processes that run infrequently.
• Adopt a multi-vendor strategy so that you will not be at the mercy of a single supplier
• Consider migrating your legacy RPA platform to a more modern, cost-friendly platform altogether.

There is no magic formula for determining the standard duration of a RPA project. The time required for implementation mostly depends on the complexity of the process to be automated. As a rough rule of thumb, simple processes can be completed in a matter of days, whereas complex processes can take up to 3-6 months. It is important to note that there could be additional delays due to factors such as lack of clear requirements or unavailability of the Subject Matter Experts (SMEs).

The RPA project lifecycle typically consists of the following stages:

1. Discovery — Identify automation opportunities, perform business analysis and select use case
2. Documentation — Document all process steps and generate comprehensive, step-by-step documentations
3. Design & Development — Design and develop RPA solution to automate the selected use case in accordance with best practices
4. Detect — Perform user acceptance testing to ensure robustness, especially for exceptions handling
5. Deploy — Commission the RPA bot into production, conduct knowledge transfer and present results to management.

In addition, it is important to note that the RPA bots do require ongoing maintenance support and continuous improvements post-deployment to ensure they remain fit for purpose.

Requirement gathering revolves around meticulously documenting and analyzing the business processes, and identifying repetitive tasks that are suitable for automation. This is a critical step to ensure that the automation solution aligns with business needs and delivers the expected outcomes. Here’s how to perform it effectively:

1. Stakeholder Interviews:
   • Conduct interviews with key stakeholders, including process owners, end-users, and IT staff, to understand their needs, expectations, and pain points.
   • Capture both business and technical requirements from different perspectives.
2. Process Observation:
   • Observe the current manual process in action to gain firsthand understanding of the workflow, steps, and nuances.
   • Identify potential automation opportunities and challenges that may not be evident from documentation alone.
3. Documentation Review:
   • Review existing process documentation, such as standard operating procedures (SOPs), flowcharts, and reports, to understand the process flow, inputs, outputs, and business rules.
   • Identify any gaps or inconsistencies in the documentation that need clarification.
4. Workshops and Focus Groups:
   • Organize workshops or focus groups with stakeholders to discuss the process in detail, clarify requirements, and prioritize features.
   • Use these sessions to brainstorm potential automation opportunities and address concerns.
5. Surveys and Questionnaires:
   • Use surveys or questionnaires to gather input from a larger group of stakeholders, especially when dealing with processes that affect multiple teams or departments.
   • This method can help capture broader insights and identify common issues.
6. Use Case Identification:
   • Identify specific use cases for automation, detailing the process steps, exceptions, decision points, and interactions with other systems.
   • Prioritize use cases based on complexity, business impact, and ease of automation.
7. Data Analysis:
   • Analyze data related to the process, such as transaction volumes, error rates, and processing times, to quantify the potential benefits of automation.
   • Use this data to support the business case for RPA.
8. Process Mapping:
   • Create detailed process maps to visualize the workflow, decision points, and interactions between different systems.
   • This helps in identifying areas where automation can be applied and where human intervention is necessary.
9. Define Success Criteria:
   • Work with stakeholders to define clear success criteria and key performance indicators (KPIs) for the RPA project.
   • Ensure that these criteria align with business goals and provide a measurable way to assess the automation's effectiveness.
10. Document Requirements:
    • Compile all gathered information into a comprehensive requirements document that includes functional and non-functional requirements, business rules, and any constraints.
    • Ensure the document is reviewed and approved by all relevant stakeholders before proceeding to the design phase.

Adhering to the following key design principles are vital in ensuring that the automation solutions built are robust and reliable:

1. Auditability and Transparency — Ensure that all bot actions are logged and traceable, providing clear audit trails that can be reviewed for compliance and process improvement
2. Efficiency and Performance — Optimize the bot’s performance by simplifying and standardizing the processing steps wherever possible
3. Flexibility and Extensibility — Design bots to be flexible enough to accommodate future changes in the process or the underlying systems without requiring significant redesign
4. Maintainability — Write clean, well-documented code that follows coding standards and best practices, so that the scripts can be easily updated or modified as business processes evolve
5. Minimal Human Intervention — Aim for end-to-end automation with minimal need for human intervention, focusing on automating all repetitive, rule-based tasks
6. Modularity and Reusability — Design automation workflows in modular components that can be reused across different processes, reducing development time and improving maintainability
7. Reliability — Implement comprehensive error and exception handling to ensure the bot can handle unexpected or edge scenarios gracefully without manual intervention
8. Scalability — Ensure the solution can handle increased workload or complexity by designing bots that can be easily scaled up, e.g. by adding additional bots for parallel processing
9. Security and Compliance — Design the automation with security best practices in mind, ensuring sensitive data is protected and compliance requirements are met, such as logging, auditing, and data encryption
10. User-Centric Design — Design bots with the end-user in mind, ensuring the automation enhances the user experience and integrates smoothly with existing workflows

5S is defined as a methodology that results in a workplace that is clean, uncluttered, safe, and well organized to help reduce waste and optimize productivity. Commonly adopted in manufacturing, applying lean’s 5S principles to RPA helps create automations that are effective and efficient. Here's how you can apply each of the 5S principles to RPA:

1. Sort (Seiri):
   • Identify and eliminate unnecessary tasks: Analyze existing processes to determine which tasks are essential for automation and which can be eliminated. Focus on automating tasks that add value and remove redundant steps, thus simplifying the processes to be automated.
   • Prioritize high-impact automations: Sort through potential automation opportunities and prioritize those that offer the highest business impact and/or commercial value.
2. Set in Order (Seiton):
   • Ensure code maintainability: Write clean, well-documented RPA scripts that follows coding standards and best practices. This enables the scripts to be easily updated or modified as business processes evolve.
   • Standardize bot development and deployment: Establish Standard Operating Procedures (SOPs) for bot development, testing, and deployment to ensure consistency and efficiency across the entire RPA lifecycle.
3. Shine (Seiso):
   • Maintain clean and error-free code: Regularly review and clean up automation scripts to remove any redundant or obsolete code. Ensure that bots are running error-free and that logs are clear and well-organized.
   • Perform regular audit: Perform routine maintenance and audit checks to keep bots running smoothly, including updating them to handle changes in underlying systems, data formats or processes.
4. Standardize (Seiketsu):
   • Develop reusable components and best practices: Create reusable components for common automation tasks and establish best practices for bot development. This helps reduce development time and ensures maintainability.
   • Ensure adequate documentation: Maintain up-to-date and comprehensive documentation for all RPA processes, including process maps, design documents, and user guides. This standardization facilitates better understanding and easier troubleshooting.
5. Sustain (Shitsuke):
   • Embed continuous improvement: Foster a culture of continuous improvement by regularly reviewing and optimizing RPA processes. Encourage teams to identify areas for improvement and implement changes as needed.
   • Train and educate: Provide ongoing training for RPA developers and end-users to ensure they adhere to the established standards and best practices. Keep the team updated on new tools, techniques, and industry trends.

It is a common misperception that RPA is suitable for large companies only. In fact, the reverse is true. Research have shown that SMEs have much lower productivity levels compared to large enterprises. So if anything, it is the SMEs who stand to benefit more from automation. The challenge is how to help the smaller companies to better understand the technology and how/where best to deploy it.

There are many reasons why companies are increasingly looking to migrate their existing RPA platform in spite of the challenges associated with RPA migration. Some of the top reasons include:

• Leverage better hyperautomation platform capabilities and features
• Realize greater ROI from their RPA investments
• Ensure better compatibility with their enterprise architecture
• Reduce the licensing fees and ultimately, the Total Cost of Ownership (TCO)
• Reduce technical complexities and make RPA more accessible to citizen developers
• Achieve automation at scale, i.e. widespread adoption and utilization
• Reduce maintenance and support issues, e.g. bot breakages
• Consolidate multiple RPA tools into one
• Issues and limitations encountered with existing RPA tools

The bad news is that most, if not all, RPA software are proprietary and there are no common standards for how the RPA bots are being developed. The good news is that we have experience helping our clients migrate from one RPA platform to another (typically due to cost considerations). We have developed a set of migration tools that significantly reduces the efforts and risks involved in such migration.  

• Costs associated with RPA migration
• Need to rebuilt existing processes for the new RPA platform
• Knowledge gaps in existing automations due to factors like employee churn, lack of proper documentations, etc.
• Need for specialized technical resources, e.g. familiarity with both the existing and new RPA tools
• Need to retrain existing developers and users on the new RPA platform
• Organizational inertia and pushback

RPA is a low-code or no-code technology that almost everyone can learn, even for non-IT business users with no programming background. Having said that, there are a few reasons why you might want to consider engaging a RPA specialist to help with your implementation:

• RPA is simple, but not easy. It may be relatively straightforward to build a RPA bot to automate the "happy path", but creating a reliable and robust bot that is able to handle errors and exceptions is both an art and science.
• People tend to underestimate the amount of time and effort required to design and develop a high performing bot. Given limited resources, it might make more sense to devote your resources to your core business, and let your RPA vendor handle the automations for you.
• The RPA technology is evolving rapidly with new features and functionality being added all that time. It can be pretty overwhelming to keep up with all the changes unless you are prepared to invest substantial resources into it. An experienced RPA vendor can also bring new perspectives to your business, especially in the areas of process optimization and business process redesign.
• All the bots that you deployed into production requires tender loving care, i.e. maintenance and support. Most in-house RPA developers hate supporting existing automations, and would much prefer to spend their time building new bots. Therefore, it makes sense to outsource the maintenance support to a external vendor.

Consider the following sobering statistics:

• More than 3 hours per work day on average is spent on manual, repetitive computer tasks
• 67% of employees struggle to finish work on time due to time spent on manual, repetitive digital administration tasks
• Almost 50% find such digital administration boring and a poor use of skills
• More than 80% indicated that they would be attracted to work at a company that embraces automation

Taking the robot out of humans is therefore the use of RPA technology to automate those tasks that humans neither like nor are well suited to perform. This allows employees to focus instead on more interesting and value-added tasks, and results in happier, more engaged employees as well.

A robot for every person is a mindset or belief that human capital is scarce and valuable. Hence, for a company to generate sustainable value in a competitive marketplace, they need to deploy their human resources in the most value-accretive way. That means equipping each and every employee with a personal digital assistant who can take care of the mundane, menial tasks. This would improve the overall productivity of the company as the employees are now freed up to pursue innovation and exercise their human ingenuity.

RPA offers significant productivity gains for businesses, but it also introduces security risks such as data breaches, unauthorized access, and compliance issues. The following is a list of the top security issues that any RPA Center of Excellence (CoE) needs to pay attention to:

• Data leakages and exposure of sensitive or confidential information
• Tampering of RPA scripts to perform unauthorized actions
• Theft of hard-coded credentials to gain unauthorized access
• Inadequate logging and monitoring
• Lack of governance and visibility into robotic operations

Understanding these top security concerns is critical for effectively managing and mitigating potential threats in RPA implementations.

 1. Ensure accountability for bot actions

• Assign a unique identity to each RPA bot and process
• Implement two-factor human-to-system authentication along with the username and password
• Frequently rotate bot credentials

2. Avoid abuse and fraud

• Restrict RPA access to what each bot strictly needs to conduct the assigned task
• Introduce session management capabilities such as screenshots or video surveillance

3. Protect log integrity

• Ensure that the RPA tool provides a complete, system-generated log without any gaps

4. Enable secure RPA development

• Engage in regular, proactive dialogues between the security team and the line-of-business team
• Establish a risk framework that evaluates RPA implementation as a whole
• Periodically review and test RPA scripts
  

An RPA Center of Excellence (CoE) is a dedicated team or framework within an organization that oversees the implementation, governance, and management of RPA initiatives. It defines best practices, sets standards, and provides guidance to ensure effective, efficient, and sustainable automation efforts across the entire business. The CoE also manages resources, monitors performance, and identifies opportunities for new automations, ensuring that the RPA initiatives align with the overall business strategy. It serves as a hub for knowledge sharing, training, and continuous improvement of automation processes.

In a centralized RPA operating model, all automation development, governance, and control are managed by a central Centre of Excellence (CoE) team, ensuring consistency and scalability. In contrast, a federated model allows individual business units or departments to manage their own RPA initiatives, with shared governance and best practices from the CoE team. The centralized model offers more control, while the federated model promotes flexibility and faster deployment within specific units.

In RPA, the terms “foreground automation” and “background automation” refer to how the RPA bots interact with the underlying systems and business applications.

In foreground automation, the RPA bot interact with the applications mainly via the user interface or UI. During execution, the users can see the bots performing various actions like clicking and typing. Such automations are sometimes referred to as UI automation, attended automation, desktop automation or front-office automation. Usually, there is an element of user intervention or interaction with the RPA bot as well.

In background automation, the automation executes silently in the background and the actions are not visible at all. The RPA bot typically interacts with applications via Application Programming Interfaces (API) or headless browsers. Such automations are also referred to as API automation, unattended automation or back-office automation. Unlike foreground automation, there is no user intervention or interaction at all.

The choice of foreground or background automation usually depends on the specific use case, as well as the technologies of the applications involved.

In UI automation, the interaction with software applications is through its Graphical User Interface (GUI), mimicking human actions like clicking buttons and typing. It is typically used for automating repetitive tasks in third party or legacy applications where direct code access isn’t available.

On the other hand, API automation interacts with applications via its backend Application Programming Interfaces (APIs), allowing for direct integration between systems without relying on the user interface. Compared to UI automation, API automation tends to be faster and more reliable.

While API automation is generally preferred, both methods can be combined depending on the specific use case.

Within the context of Intelligent Automation or IA, RPA and AI serve 2 different, but complementary purposes.

RPA involves the use of software bots to automate repetitive, rule-based tasks. Fundamentally, RPA is about automating actions that are previously performed manually by a human. RPA is deterministic in nature, and the outputs of such RPA scripts can be predicted in advance. For RPA to succeed, both the process and the accompanying data need to be structured and well-defined.

AI, on the other hand, is the simulation of human intelligence by machines. Equipped with cognitive capabilities like reasoning, learning and decision-making, AI is able to perform tasks that typically require human intelligence, including natural language processing and image recognition. Implementing AI usually requires substantial data which are typically unstructured or semi-structured. And unlike RPA, the outputs of AI are typically non-deterministic.

By combining RPA with AI, one is able to automate a wider range of use cases, as well as to achieve straight-through automation with minimal human intervention. It is also worth noting that many automation platforms now embed the use of AI in areas like Intelligent Document Processing (IDP) and Process Mining.

Both RPA and Excel Macros are used to automate tasks, but they differ significantly in terms of scope. Excel Macros are limited to automating repetitive activities within Excel through the use of the Macro Recorder or Visual Basic for Applications (VBA) scripts. RPA, on the other hand, is capable of automating tasks across different applications and systems through User Interface (UI) and Application Programming Interface (API) automations. Most RPA software also offer the ability to automate Excel activities. Hence, you can also view Excel Macros as a subset of RPA software.

Task mining involves the capture and analysis of the detailed actions taken by users while performing specific tasks on their computers. It aims to identify the manual, repetitive tasks that can be automated using RPA to improve user efficiencies.

Process mining, on the other hand, focuses on entire business processes or workflows as opposed to individual user tasks. It involves the collection and analysis of data, typically event logs, from systems and business applications such as ERP. It aims to facilitate the discovery, understanding, and monitoring of business processes through the visual representation of end-to-end process flows. The goal of process mining is to achieve process excellence, e.g. by identifying deviations and bottlenecks, performing root cause analysis, checking compliance, and optimizing processes.

Hyperautomation, a term coined by Gartner, refers to a business-driven, disciplined approach that organizations use to rapidly identify, prioritize, and automate as many business processes as possible.

Hyperautomation involves the orchestrated use of multiple technologies, including artificial intelligence (AI), machine learning (ML), intelligent document processing (IDP), 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, task mining, process mining, and other types of decision, process, and task automation tools.

Hyperautomation goes beyond individual task automation by focusing on the seamless automation of end-to-end processes. This holistic approach ensures that automation efforts are scalable, flexible, and can adapt to the evolving needs of the organization. By integrating these technologies, businesses can achieve higher efficiency and effectiveness, reduce operational costs, improve customer and employee satisfaction, and enhance overall productivity.

Contrary to popular opinion, hyperautomation is not just about technology. The success of any hyperautomation initiative hinges on the triumvirate of people, processes, and technology. It demands a cultural shift within the organization, promoting continuous improvement and encouraging employees to embrace automation as a tool for driving innovation and growth. By doing so, organizations can remain competitive in a rapidly changing business landscape while delivering better value to their stakeholders.

Citizen developers are non-technical or business users who develop business applications and automations, typically through the use of low-code or no-code (LCNC) technology platforms. Many enterprises are now embracing citizen developers due to a variety of reasons, including:

• Shortage of in-house IT talent and an overburdened IT department
• Increasing popularity of LCNC platforms which make it easy to build applications without the need to code
• To leverage the domain expertise of the end-users to build solutions that actually address business issues.

While citizen development can lead to empowerment and business agility, one needs to be mindful of the risks as well. This includes the rise of shadow IT, accumulation of technical debt, and siloed implementations.

In today’s rapidly evolving digital landscape, businesses are leveraging various automation technologies to streamline operations, improve efficiency, and reduce costs. These technologies cater to different automation needs, from automating routine tasks to integrating complex processes across systems. Below is a list of the most prominent automation technologies available in the market today.

• Agentic Workflows — Agentic workflows refer to automated processes that are powered by autonomous agents. These agents often leverage AI and machine learning technologies to handle complex tasks that traditionally require human intervention, such as task planning, decision-making, data processing, and interaction with multiple systems.
• Business Process Management (BPM) — BPM focuses on improving business processes by modeling, analyzing, and optimizing workflows. It typically involves end-to-end process orchestration rather than just task automation.
• Integration Platform as a Service (iPaaS) — iPaaS allows businesses to integrate applications and data across different environments (both cloud and on-premise) to automate workflows.
• Low Code Application Platforms (LCAP) — LCAP platforms enables business users to build web and mobile applications with embedded automation using drag-and-drop tools.
• Robotic Process Automation (RPA) — RPA is typically used to automate repetitive, rule-based tasks by mimicking human interactions with applications at the User Interface (UI) level.

It is important to note that there are significant overlaps in automation capabilities across the different technologies. In addition, an organization may deploy multiple technologies in order to meet different needs.

Business Orchestration and Automation Technology (BOAT) is an emerging class of software technologies that enables businesses to automate and orchestrate end-to-end processes while integrating multiple enterprise systems. BOAT platforms consolidate various automation technologies, including Business Process Automation (BPA), Robotic Process Automation (RPA), Integration Platform as a Service (iPaaS), and Low-Code Application Platforms (LCAP).

Key features of BOAT include:

• Process orchestration: Facilitates long-running workflows with complex rules.
• Intelligent automation: Incorporates AI-powered agents for autonomous task execution.
• Data processing: Uses tools like Intelligent Document Processing (IDP) and process mining to handle unstructured data.
• Generative AI: Enables autonomous orchestration, intelligent agents, and prompt-driven workflow designs.

BOAT offers businesses a unified platform to manage multiple automation needs, enhancing efficiency through integrated AI capabilities.