One size fits all approach doesn’t necessarily work for organizations in today’s dynamic business environment. An ideal digital transformation platform gives you the flexibility to smoothly cater to your business requirements. Therefore, it is important for you to understand your customer requirements and evaluate how digital transformation platforms fit in.
Digital transformation, if done correctly, dramatically changes the competitive landscape by unlocking simplicity in how business is conducted and how customers experience it.
Choosing an Ideal Digital Transformation Platform-Six Considerable Criteria
1. Intelligent Business Process for Connected Enterprises
You should look for a platform that can bridge organization silos while connecting your processes, people, and content via low code process automation
2. RPA with BPM for a Holistic Customer Journey
You should choose a digital transformation platform that automates end-to-end customer outcomes with customer-centric processes leveraging robotic process automation (RPA). It helps you identify, analyze, classify opportunities and complaints, make intelligent decisions, and strengthen customer relationship
3. Omni-channel and Cross-channel Engagement for Enhanced Experience
You need to look for a platform that has cognitive intelligence, natural language processing, machine learning (ML), and digital sensing capabilities which provide human experience to employees, customers, and partners. It can predict future actions enabling omnichannel and cross-channel engagement, with smart, speedy, and contextually accurate responses
4. Contextual Content Service for Informed Decision Making
You must choose a platform that enables you with contextual content services capability, thereby creating a workplace without boundaries with anytime-anywhere content access and document management
5. Analytics for Continuous Process Improvement
You must choose a platform that deploys cognitive bots and content analytics to process all incoming contents, understands customer behavior, converts them into actionable insights, and facilitates continuous process improvement
6. Low Code for Rapid Application Development
Per Forrester, low-code development platforms have the potential to make software development as much as 10 times faster than traditional methods.
Increasing demand for application development while trying to manage inadequate IT resources is a perennial business challenge. You need to look at a low-code development platform that empowers your users to develop applications within the IT guardrails. It should provide low code/no code facility for developing mobile apps, process automation, straight-through processing, collaboration, business rules configurations, easy customer communication, and contextual content services
Organizational leaders who think they’re done with digital transformation and is a dusted thing for them, it certainly means they did not understand what it really means. To stay ahead of time, it is significant for you to keep innovating and be at par with the changing expectations of your customers and the competitive marketplace.
Hyperautomate with Process Insights and Artificial Intelligence for Efficient Processes
In the first blog of this series, I had shared one half of the hyperautomation journey—how RPA and BPM can work in harmony to automate incredibly complex business tasks.
In this one, let’s delve into the second half—how other complementary technologies, such as process insights and artificial intelligence (AI)—are crucial parts of hyperautomation, as they enable rapid, end-to-end business process automation and accelerate digital transformation.
Process Insights: Discover, Monitor, and Improve Workflows
Process insights are created by leveraging event logs, generated by enterprise systems including ERP, BPM, CRM, human capital management, and supply chain management, to rebuild a virtual view of your business processes. These insights are designed for you to discover, monitor, and improve real processes by extracting knowledge available within application systems.
Process mining is one of the multiple stages in the process automation lifecycle, which analyzes the extent to which RPA can be implemented across legacy systems. Furthermore, it enables monitoring and analysis of process performance for continuous improvement. Robust process mining tools can blend data mining with AI and machine learning (ML) to generate data-based analytics. This can help you explore the state of your business processes and identify new opportunities and bottlenecks for optimization and automation.
Artificial Intelligence: Automate Repetitive-to-Cognitive Processes the Smart Way
AI enables bots to intelligently perform tasks, such as reading, understanding,?and processing data, thus making it an essential ingredient of hyperautomation. Cognitive technologies, such as?ML,?natural language processing (NLP), optical character recognition?(OCR),?and?AI, integrate with RPA to increase process efficiency and accuracy. You should deploy these technologies in tandem to realize business value and deliver specific, measurable outcomes for targeted use cases.
Understanding Hyperautomation with a Use Case
Let’s suppose you are automating an anti-money laundering process and implementing a fraud detection algorithm. You may need to understand the interfaces between your AI components and other automation tools. Many of these processes involve non-routine tasks, intelligent decision making, and human judgment.
In this case, your system would execute the following steps:
An intelligent business process management suite manages the decision-driven workflow/orchestration of your process
It triggers an RPA bot to perform data collection, and other routine work, to validate your customer records
The consolidated data is fed into the fraud detection algorithm, built on an ML model, to identify patterns. This process can sometimes involve human intervention, in case a formal approval or e-signature is required
Subsequently, another RPA bot is triggered to perform follow-up actions and update transactional systems, such as ERP, CRM, and other applications
The Time for Action is Now!
Hyperautomation is the key to adapt to the ever-changing business environment and achieving unprecedented levels of quality and efficiency. RPA, BPM, and process insights, and AI will enable your organization to achieve scale and flexibility in operations and allow your employees to focus on more value-added tasks.
Hyperautomate with BPM and RPA for Optimized Operations
With the evolution in the automation industry, business leaders, like you have encountered a dilemma on what is the right technology to invest for end-to-end automation. But, over the years I’ve learnt from my experience in the automation space that no single tool can entirely replace human workforce. In such a scenario, hyperautomation is gaining momentum as it combines disruptive technologies, including intelligent process automation (BPM), robotic process automation (RPA), process mining, artificial intelligence (AI), and machine learning (ML) to create an end-to-end automated solution for business users. Hyperautomation augments human workforce in ways that are significantly more effective than isolated automation tools.
As your first step towards the hyperautomation journey, let’s explore how to make BPM and RPA work together in harmony.
The First Approach: Process-driven RPA
Consider order management, an automated process that uses a BPM system, but lacks integration with the shipping vendor’s system.
In order to complete the “ship order” activity shown in the process above, a user needs to work on multiple systems to complete different tasks:
Search and open order details
Copy and paste all the required data from the order management system to the shipping system
Ship the order and copy tracking number from the shipping system back into the order management system
Mark order as shipped in the order management system
If you get one or two orders a day, these tasks might not seem like a big deal, but if this is happening multiple times a day, you end up spending too much time on mundane activities!
The idea behind the process-driven RPA approach is that your process keeps running inside a BPM system without any major modifications. You place bots in the BPM workflow to automate repeatable tasks. In the order management process specifically, once the transaction reaches the “ship order” stage, a trained bot can execute all the tasks, eliminating human intervention.
The Second Approach: RPA-initiated Process
A bot is very useful when you have rule-based, repeatable tasks, but what happens when there are data inconsistencies or errors? It is impossible to train a bot on how to deal with all the possible exception cases.
To understand this approach better, let’s look at the trade reconciliation process. This process usually happens at the end of a trading day, and the goal is to make sure that the balance is accurate across two or more systems.
To ensure reconciliation, an agent has to perform a number of different tasks:
Search and select the customer in the trade management system
Search and select the customer in the broker system
Verify that the end-of-day balance in both systems matches
What if the balance does not match in both systems? In this exceptional case, an agent will need to intervene and perform follow-up tasks, such as calling the client and broker to discern a reason for the mismatch.
In such cases, a bot can be trained to perform the daily recurring tasks of checking the balance across two systems, but teaching it to address all exception scenarios, perform follow-ups, and execute follow-up actions may be impossible. This is where a BPM system comes to the rescue.
The idea behind the RPA-initiated process approach is that when a bot has not been trained to handle exceptional cases, a human agent can intervene. When a bot finds anomalies, the transaction can be routed to a human queue, with the help of BPM, so that they can follow-up and resolve the issue manually.
In a nutshell, what your organization needs is the flexibility to use RPA and BPM in tandem with other complementary technologies. Stay tuned for the next blog where I will share how process mining and AI can further accelerate your hyperautomation journey.
Business leaders, like you, will agree that robotic process automation (RPA) is no longer just a buzzword. Organizations are adopting RPA at a faster pace and reaping its benefits. This is further reinforced by Forrester’s prediction that the industry will grow from $250 million in 2016 to $2.9 billion in 2021. However, the question is how to achieve a significant return on investment (ROI) with RPA?
In this blog series – How to get the most out of your RPA investment, let’s explore ways that can help you to effectively measure ROI.
How Do You Measure the ROI of RPA?
First things first, you must have a clear picture of what you are expecting to achieve with RPA- better customer experience, innovation, enhanced productivity, improved quality, reduced costs, or even compliance.
Once you have clarity on the desired outcome, the next step is to calculate the difference in effort needed to accomplish tasks, from your employees versus bots. Below are a few parameters that are useful in drawing this comparison:
Process Execution Speed
You can start by recording the time it takes your employees to complete a particular process and compare it with the time taken by bots. This will give you a fair estimate of the time you can save to perform a task.
Resource Versus Bot Productivity
Typically, your employees work for 8 hours a day. So, you can keep tabs on the number of transactions that your employees complete in a day versus the amount completed by bots. However, bots offer an advantage here as they can operate 24/7, thereby enhancing overall productivity.
We cannot ignore the fact that humans can make errors during data entry or can miss a business rule while performing rule-based calculations. Alternatively, with bots, you can ensure data accuracy as they are trained to run business logic per the execution script.
To calculate the return, you should measure process accuracy before and after RPA deployment for a quality comparison.
Ensuring compliance with various regulatory requirements is a top priority for organizations, like yours. With RPA, you can maintain 100% compliance. And so, you must track compliance issues post-RPA deployment and compare your results with earlier records in order to calculate ROI.
Overall Process Cost
Finally, you need to deep dive into the cost required to manage and execute a process and compare it with the overall cost involved in your RPA deployment, including the recurring costs. Try to compare and estimate ROI values for the next five years to understand how beneficial the RPA deployment will be for your organization in the coming years.
To get positive results from your RPA implementation, you need to be focused on measuring the ROI and identifying process candidates for automation. For this, it is recommended to have an in-house centre of excellence (COE) team that can perform in-depth research and help you identify the right business processes for successful RPA implementation.
In my next blog, I will share how a COE team can guide you throughout the RPA implementation process and ensure that you get the most out of your investment.
When we were chalking out business plans in the board rooms, COVID-19 struck, affecting businesses globally and disrupting manual or partially automated processes. By now, we have learned to live with the pandemic around us. We’ve embraced the new normal of remote working. But are we as effective as before? Are manual processes hurting our business operations and customer service?
Thanks to technologies like robotic process automation (RPA) that came to our rescue during these testing times when survival meant the rapid adoption of digital. Automation of routine, mundane, and template-based processes with bots acted as a quick-fix in enabling smooth remote operations and maintaining business continuity.
RPA has made it easier for organizations to go digital and deliver uninterrupted services without compromising their speed and quality. Bots can work round the clock tirelessly and accurately, freeing up our valuable resources to take charge and talk strategy.
scaling operations based on varying supply chain demands
Let’s take a look at three industry-specific use cases where RPA is helping organizations survive and thrive in the new normal.
Faster loan disbursement
Financial institutions are leveraging bots to automate their loan qualification and validation processes, making disbursements more efficient. This is also helping them streamline their inbound loan processing and existing underwriting approvals.
Streamlined employee health screening
Government agencies and private organizations alike are deploying bots to send out periodic surveys through multiple channels with questions about their employees’ health and travel history, among others. Based on the responses, the bot creates a risk score to assess whether a person is fit to physically attend the office or not.
Hospitals are setting up bots to maintain a log with details about healthcare workers who are in good health, need quarantine, or have been exposed to or infected with COVID-19. This helps them allocate resources efficiently and ensure timely support for both the medical staff as well as the patients.
This is just a glimpse of how RPA has been enabling a secure, touchless environment to help businesses stay afloat through this crisis. RPA is the clear choice for organizations to stay current and gear up for the digital-only world.