Automation in silos is not enough. If you are looking for ways to handle multiple disjointed applications, drive contextual engagement, meet changing business demands, and provide an omnichannel customer experience, workflow automation software would be the perfect fit.
Workflow automation software helps you develop applications rapidly, automate end-to-end customer journeys without losing context across all digital touchpoints, and drive continuous process development. The software provides various tools and technologies to simplify your business processes, increase productivity, reduce operational costs, and improve customer experience.
What are the typical Enterprise Challenges?
Overcoming Obstacles
Given below is how workflow automation software can help overcome enterprise-wide challenges and overcome process inefficiencies.
Simplifying Application Development
Workflow automation tools like point-and-click and drag-and-drop simplify the processes of designing, developing, and delivering responsive, enterprise-grade applications for web and mobile. It automates your simple departmental functions to complex business-wide operations, bringing agility to the business workflow. Workflow automation software provides user interface-designing and low-code application development to meet changing business requirements.
Per Gartner, “by 2025, 70% of new applications developed by enterprises will use low-code or no-code technologies, up from less than 25% in 2020.”
Automating Intelligent Business Processes
Workflow leverages robotic process automation (RPA) to manage and streamline end-to-end complex and content-centric business processes. It automates repetitive and mundane tasks allowing your skilled employees to perform more critical tasks. Workflow automation software empowers your employees with dynamic case management capabilities to collaborate and respond to real-time as well as unanticipated situations.
Creating Contextual Engagement
Workflow automation captures, manages, and derives context from information generated across various sources. Based on context derived from data and information, your employees deliver personalized communications to customers across multiple user-interface. It also increases the conversion rate.
Facilitating Continuous Improvement
Improvement is an ongoing process. Workflow software uses artificial intelligence and multi-experience applications to evaluate historical data, analyze the trends, predict the impact of strategic changes. It uncovers business risks makes intelligent recommendations to improve employee productivity and reduce operational costs.
In conclusion
In a nutshell, workflow automation helps you with low code application development, business rules management, dynamic case management, and process insights across your organization. It brings in cost benefits, enhances employees’ productivity, minimizes human-prone errors, ensures continuous process improvement, reduces turnaround times, and enhances customer satisfaction.
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
In Conclusion
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.
Are you looking to reduce your underwriting expenses and transform your claims process? Are technological advancements and cut-throat competition driving you to analyze and optimize your existing processes?
For a long while, process automation has been at the forefront in the insurance landscape, but now, the future of insurance will be shaped by behavioral intelligence and predictive analytics. In order to maintain a competitive edge, you must transform your traditional, rule-based framework into a data-driven, intelligent, and predictive system.
Let’s take an example of a modern insurance company that is disrupting the industry landscape. The company offers homeowners and renters insurance. It targets tech-savvy millennials—people with basic coverage needs, looking for a completely digital experience.
They hit the nail on the head by building a business model powered by artificial intelligence (AI) and predictive behavioral analytics. The insurer uses behavioral intelligence to measure their customers’ “digital body language” when they begin the application process all the way through to filing a claim. This incredible amount of data is leveraged to provide a world-class experience to its customers.
So, if you’re looking to transform your processes and tap into your target market share, predictive analytics is the answer.
Here are five areas where predictive analytics is projected to be influential:
Underwriting
Predictive analytics acts as underwriters’ virtual assistants. It analyzes historical data to rank risk parameters according to their significance and weightage. It also provides data-driven reports in a snapshot for efficient decision-making
Insurance Pricing
With predictive analytics, you can dynamically adjust quoted premiums. By monitoring variables—such as claim history in an area, construction costs, and weather patterns—you can predict risk and set prices more accurately
Claims Processing
Decision-making support through analytics can help you in accurately adjudicating claims. This will also facilitate in expediting the process and reducing errors
Preventing Claims Fraud
Input claim parameters—such as a surge in claims during a specific month, previous matching claim amounts, the same surveyor being involved in multiple claims from the same area, etc.—can be compared with past records and an alert can be raised if anything unusual is detected. You must take advantage of any available data and convert it into actionable intelligence
Improving Customer Loyalty
Predictive analytics can be used to anticipate the needs of your customers by analyzing their history and behavior. This information can also help you offer personalized products, better suited to their specific needs
Transform Data into Future Insights!
It’s time for you to focus on what the future holds for your organization. Predictive analytics has never been more important for insurers, and time is of the essence. Technology, and implementing it in a timely manner, is the best way for you to boost customer loyalty, increase market share, and thrive in a highly competitive market.
To learn more about how Newgen’s predictive analytics helps insurers, like you, contact us here.
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 Summary
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.
As a service delivery representative, I recently had to collaborate with the global in-house centers (GICs) of one of my customers. By definition, GICs are the offshore centers that perform designated functions for large organizations to minimize overall costs, enable access to better talent, and help operate their business smoothly.
The customer’s GICs were established across different regions, including Asia Pacific (APAC), Europe, the Middle East, Africa (EMEA), and North America/Latin America (NA/LA). Having their GICs spread across various regions, they lacked a unified view of business transactions. This pushed their management team to take an enterprise-wide decision to merge the Middle East and Asia Pacific GICs. The resulting accounting groups were Asia Pacific/Middle East (APME) and Europe/Africa (EA).
What got them stuck?
The middle eastern currencies were not getting mapped to the new APME centers while performing order fulfillment/realization and incident management (internal processes). The decision-makers couldn’t see the amounts being entered in the system. The business owner, who was working from the NA/LA region, did not flag the process properly, and so the issue lasted almost a month after the quarter closure, stalling the company’s complete accounting closure.
The organization faced some major challenges, including the lack of visibility and communication gap amongst users. Additionally, the IT infrastructure of the organization, which was set up in such a way that the order management/realization process and the incident management process were hosted in two different applications with no linking interface—thereby turning the processes opaque.
Plugging the gap!
In order to combat such challenges, a low code automation platform, integrated with intelligent digital automation (BPM) capabilities comes to rescue.
In practice, a better solution to the above-stated case would have been the implementation of a tracking mechanism, which could monitor the flow of orders in the middle eastern currencies at the APME GIC systems. In case any orders/revenue was not being processed in those currencies, the system would have proactively raised an alarm to the right stakeholders, requesting them to log an incident using the same platform.
As BPM brings together all the tools and technologies to help break through silos and connect organizational resources better, it helps in enabling a holistic process experience. This is one of the major reasons why the BPM industry thrives today. The emerging trends of 2019 in the BPM space highlight the inclusion of intelligent process modeling—essentially, leveraging a data-driven, process modeling approach to build a process seamlessly and across departments, interfaces, and geographical space.
In a commissioned study by Forrester on procurement transformation, one of the strategic objectives for the chief experience officers (CXOs) was to support better decision making by improving data analysis and insights. Further, the same study highlighted that insight-driven firms were more likely to report year over year revenue growth of at least 15%.
Another survey by KPMG & Harvey Nash highlighted that nearly 60% of digital leaders could not effectively analyze the data available to them without the right tools. Intelligent analytics, embedded into their processes, shed more light on process analysis and bottleneck detection, enabling CXOs to make informed and data-driven decisions.
Insight-driven platform to maximize operational excellence
While investment into an integrated management approach is a step in the right direction, enterprise resource planning is not able to deliver the levels of omniscience and value it promises in the context of business processes. At least not alone.
This is largely because of the conventional drill down/postmortem identification of issues. However, a comprehensive platform, coupled with data-driven insights, drives an alert-up approach while helping users take proactive measures.
The value of process insights is based on the macroeconomic concept—time value of money. The insight-driven platform not only reflects the existing process discrepancies, but it also involves a continuous learning module to aid informed decision making.
In conclusion
In the past, CXOs were unaware of the process inefficiencies bogging down their companies at the elementary level. However, with an insight-driven platform, they can know-it-all, from the very beginning. A robust platform, complemented by a data insight module, would be a game-changer in bridging silos, increasing organization-wide visibility, and maximizing operational effectiveness for companies of all sizes.
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