Article | April 22, 2020
Ease in doing business.” That is what every C-level execs strive to achieve in their business process and it is no secret that they’ve increasingly turned to Robotic Process Automation (RPA) to streamline enterprise operations.
The first digital computers were invented mostly to calculate tasks but as the technology progressed, we learnt to program hand-code automation through bespoke applications. What brought the RPA into existence was the slow and laborious hand-code automation.
But, as we no longer need to keep our fingers glued to systems to enter data fields and value, there exists some brittleness to the robotic process automation.
Table of Contents:
- What is Robotic Process Automation?
- What ails Robotic Process Automation?
- What is Low-Code Development?
- Why to program in a Low-Code Development environment?
- How does Low-code development help in mitigating RPA implementation?
- Concluding Thoughts
What is Robotic Process Automation?
Deloitte defines RPA as software that “automates repetitive, rules-based processes usually performed by people sitting in front of computers.” Picture your mouse automatically scanning your email for 70 new unread invoices, adding the data to a spreadsheet, and inputting information into your CRM, while sending two outliers to an employee for manual review – all within a fraction of the time it would take a person to do the same tasks and with far fewer errors. RPA workflows are established on logic-based inputs and tasks across applications for the bot to efficiently carry out manual, repetitive tasks with greater accuracy.
Additionally, by separating the uniquely human skills like critical thinking, empathy, and decision making from the manual, repetitive tasks, corporations can provide a more fulfilling and rewarding career for their employees.
Sounds great, right? Of course, it does.
That’s why it’s the fastest-growing market in enterprise software, with 48% of companies saying they are planning to invest in RPA and is projected to be worth nearly USD 4B by 2025. Corporations across industries are buying in to streamline a wide variety of operational tasks, connect legacy systems, and drastically remove errors introduced by humans.
Operations that can benefit from RPA technology include:
Generic office tasks – gathering quarterly cross-department data into an excel sheet, automating CRM inputs, and inventory management.
Back office processes – instead of five people checking for new orders and applying discounts, the tasks are reorganized so the employee is providing a human-level of validation to the order.
Manufacturing – order fulfillment, purchase order processing, and transportation and inventory management.
Retail – product categorization, automated checkout, and delivery tracking.
Customer service – credit checks, account number assignment, and activation tasks can be allocated to bots and employees can speak to a customer and apply empathy and discernment to the situation at hand.
What ails Robotic Process Automation?
The raised fostering of RPA highlights the advantages of the modern technology, however the trip of automation is not without some bumps in advance.
Presently, a bulk of RPA options deal with a typical weak point– a small adjustment to information layouts, service procedures, or application user interface can lead the whole software program to damage down.
By style, RPA is durable software program however that likewise shows its frailty in adjustments.
If anything changes, that can break the automation.
- Jason Bloomberg, Leading IT market expert
For instance, Bloomberg discussed, if an interface component like a switch relocates or transforms dimension, the automation may damage. Or probably the information style adjustments due to the fact that a person included a brand new area. “In other cases, the business requirement for the process logic changes, requiring a rework of the bot.”
RPA functions best with older and also tradition applications powered by regular procedures that go through little adjustment and also secure information layouts. For companies looking to take advantage of the modern technology, the brittleness of RPA might lead to tightening alternatives and also applications in companies.
Financial establishments, as an example, are typically wed to tradition systems and also applications, for which RPA is well matched to aid take care of. However, in a vibrant electronic age– which calls for service dexterity– RPA’s absence of versatility when incorporated can be restricting.
What is Low-Code Development?
Low-code software development could be compared to a car manufacturing assembly line. Both processes automate difficult and time-consuming tasks, in order to increase delivery speed and free up people to focus on high-level tasks.
In technical terms, low-code is a set of tools that developers can use to build applications inside a drag-and-drop visual interface – including complete UI, integrations, data management, and logic.
READ MORE: DISPELLING FIVE MYTHS OF LOW-CODE APP DEVELOPMENT
Why to program in a Low-Code Development environment?
In a quote to address the brittleness of RPA, the arising idea of low-code reveals appealing possibility. Its ability to faster way and also separate software program parts streamlines the design procedure.
For RPA software program that calls for an upgrade, low-code offers the all set-to- code design to convenience the restructuring of systems.
Low-code simplifies the work of developers, whether they be building applications or constructing bots. But even more importantly, low-code empowers developers and business stakeholders to work together more effectively to manage change in the behavior of the software.
- Jason Bloomberg, Leading IT market expert
In significance, low-code opens brand-new opportunities for designers to focus on establishing special software program systems that are matched for particular companies.
READ MORE: BENEFITS OF LOW CODE DEVELOPMENT WITH REUSABLE COMPONENTS
How does Low-code development help in mitigating RPA implementation?
Here’s where low-code development can save the day.
Low-code platforms enable cross-functional teams of professional developers, citizen developers, and functional staff to easily collaborate and connect multiple applications for end-to-end solutions. Because the platforms are built on open standards and are cloud-native, they can easily connect internal legacy and third-party applications in a bot-friendly interface and quickly establish bot workflows that model the real business processes. Enterprise RPA initiatives can get off the ground in a fraction of the time without bringing on additional staff and infrastructure.
What does low-code and RPA implementation success look like in real life? Just ask Avertra and 2 Sisters Food Group.
Avertra provides technology and consulting solutions for telecom and utility companies, including a modular digital customer experience framework built with the Mendix ecosystem and integrated via API with enterprise solutions like ERP systems, work management applications, and external data sources. Alongside their clients, Avertra establishes which processes to automate, builds user stories, and deploys bots which then follow workflows, transfer data between systems, select appropriate resolution paths, and follow through with documentation and compliance – all within a fraction of the time it takes an individual agent.
Meanwhile, UK poultry supplier 2 Sisters used low-code to implement RPA across 11 accounting transactional processes, moving from 100% manual work to 97% automated within six weeks. They used Mendix to build a data-structuring application that extracts, parses and cleans the data. 2 Sisters was able to reduce their customer invoice verification process from 65% of invoices needing manual data verification to only 8%. Manual data entry was nearly eliminated, save for a few outliers identified by the bots, and employees have more time to analyze the data and costs.
Low-code enables both technical and non-technical users to play an active role in implementing and maintaining RPA initiatives, taking the burden off of the IT team, operating securely within their infrastructure and parameters, and reducing the need for additional developers. Avertra empowered their client’s citizen developers to make workflow iterations in the Mendix platform based on data results and their internal business knowledge. With the assistance of Mendix partner AuraQ, 2 Sisters built 300 unique customer remittance templates in 3 months and over 3,000 have been created to date (and they’re still going).
The beauty of low-code platforms is that applications can be easily adjusted as the business evolves, RPA technology improves, and new automation opportunities are identified, enabling companies to be more agile and competitive. Avertra’s clients have used data insights to produce new and revised resolution paths addressing outlying issues not caught by the RPA framework and 2 Sisters is now analyzing their data to identify their next digital transformation target. Their investments in RPA implementation and low-code development have quickly paid off and will continue to return dividends in the months and years to come.
Low-Code Development is the simpler way to adjust and improve RPA as per the business demands. With the entry of IoT powered by high-speed 5G, low-code programing is touted to be the tool to speedy up RPA innovations. AI has become the most important trend in the low-code RPA market thus making implementation of RPA with low-code quick and agile.
READ MORE: THREE SMART WAYS TO USE LOW-CODE DEVELOPMENT PLATFORMS
Article | February 11, 2020
With the Government investing £250 million into the project, the Lab will consider how to use AI for the benefit of patients – whether this be the deployment of existing AI methods, the development of new technologies or the testing of their safety. Amongst other things, the initiative will aim to deliver earlier diagnoses of cancer. It is estimated that in excess of 50,000 extra patients could see their cancer being detected at an early stage, thus boosting survival rates. More specifically, a study has shown that AI is quicker in identifying brain tumour tissue than a pathologist.This would have a positive knock-on effect in other areas, such as enabling money to be saved (that otherwise would have been spent on further treatment) and reducing the workload of staff (at a time when there is a crisis in NHS workforce numbers).
Article | July 1, 2020
Chatbots have come a long way in the past few years. The improvements in technology have enabled developers to expand on bot capabilities far beyond just functioning as a FAQ. Today, the automation of chatbots can process orders, perform financial transactions, make bookings, and much more. (Check out other intelligent functions here.)
However, as intelligent as bots can be, no chatbot can handle and resolve all your customer queries. It simply cannot answer the infinite number of questions a human may throw at it. The technology is simply not there yet, and it may never truly get there. But perhaps more importantly, brands shouldn’t want a bot to manage every customer query.
A bot working independently of human involvement won’t always deliver the best results for customer or agent. It’s the combination of chatbots and human agents that takes customer service to new heights. What you need is a smart and efficient way of translating your organization’s unique customer service philosophy into appropriate action so that every question is met with an answer in the best way possible – whether that be by bot, human agent, or a blend of both.
To deliver this, you have to pay attention to the who, what, when, and where of customer engagement. You need to know who your highest-value customers are so you can always route them to a human agent, for example. You need to know what they need help with so a simple question can be managed by a bot. And the list goes on.
Here’s why humans need chatbots, and chatbots need humans – and how you can achieve this perfect balance to deliver support that will exceed customer expectations and generate substantial ROI.
Why humans need chatbots
There’s no doubt that supplementing customer-facing roles with automation can yield fantastic results. The launch of McDonald’s self-serve kiosks is a great example of this. By giving customers the option of ordering their meal through a kiosk, or through a cashier, McDonald’s demonstrates the success you can achieve by combining automation with human. Here are just some of the benefits it brought to the customer and employee experience:
1. Automating large portions of simple queries so workers have more time to focus on other, more complex tasks
2. Reducing monotonous, repetitive queries to improve employee experience
3. Catering to customer preferences – choose quick automated service or deeper human engagement
4. Reducing queue times, in turn improving customer experience
5. Lessening the opportunity for human error
6. Generating ROI by reducing staff numbers
These results almost identically mirror the benefits that intelligent chatbots can provide customer service teams. By implementing a bot, a large portion of frontline support can be automatically managed by the bot which:
1. Gives agents more time to handle complex questions
2. Reduces the monotony of answering repetitive questions
3. Allows customer to choose between chatting to a bot or an agent
4. Reduces wait time and queue length (through bot’s ability to handle infinite simultaneous conversations), in turn improving customer satisfaction through quicker resolution
5. Eliminates human error in data entry
6. Generates substantial ROI through lower service costs
See how closely those benefits match?
Recommended reading: Chatbot ROI Calculator
Why chatbots need humans
The relationship between bots and humans isn’t a one-way street. While agents need bots to provide more effective and efficient support, bots need agents to provide the personal, ‘human’ touch that many situations call for. In our latest 2020 Live Chat Benchmark Report, we found that chatbots handle 68.9% of their chats from start to finish – although an impressive stat, it still shows that many queries require an agent’s touch.
Recommended reading – 2020 Live Chat Benchmark Report
There are always going to be situations that call for human assistance: canceling a subscription, reporting a lost or stolen credit card, or registering a serious complaint. Or maybe the topic is sensitive, and your customer would feel more comfortable explaining their situation to an agent. Similarly, some (though increasingly less: stat?) people are still wary or reluctant to communicate with bots and prefer to only speak with a live agent. To cater to these customer preferences, it’s vital that these customers can be routed past or transferred from your chatbot to human agent without effort and without having to repeat themselves.
It’s important to note however, that transferring from bot to agent isn’t always just in the interest of the customer – it can often benefit the customer service team too. This is because not all queries are equal. For example, if a customer reaches out asking about a bank’s opening times, this can be easily managed by a bot. However, when the same customer asks about a loan, this high-value interaction may dictate that – according to your unique customer service view – a human agent takes over immediately to ensure the customer receives the best experience and you close the deal as quickly and effortlessly as possible. If your chatbot can’t do this, turn it off and find a chatbot that can (we can help with that).
How to create the perfect chatbot – human (agent) balance
To begin creating the right balance between chatbot and human, you need a bot that’s widely accessible to today’s digital-first consumers; your bot needs to be where they are, wherever they are. Comm100’s AI Chatbot can serve customers on web, in-app, Facebook, Twitter, WeChat, WhatsApp for Business, and SMS. You also don’t need to build separate chatbots for each channel. Simply select the channels you want your bot to be available on (hint: all of them!) and you’re off.
Although your customers will know they are speaking to a bot (and you should make this clear to them to set expectations), you need a bot that understands natural human language. Comm100’s AI Chatbot harnesses the world’s most advanced NLP engine so that it can understand your customers’ goals and provide the answers they’re looking for. Better still, add a large range of off-the-shelf integrations to this, and the Comm100 bot can begin performing actions on behalf of your customers – from tracking an order and paying a bill, to booking a flight.
By resolving a large portion of your frontline customer service questions, your agents will have more time to focus on higher-value queries and customers that matter most to your bottom line.
Recommending reading: Comm100 Chatbot Resolves 91% of Assigned Live Chats for Tangerine
As we’ve discussed earlier, there will be times when you or a customer would rather connect with an agent than a bot. It’s crucial that your bot offers this flexibility.
Firstly, your bot should be able to give the customer the option to speak to an agent at any time. Eighty-six percent of consumers believe they should always have the option to transfer to a live agent when dealing with a chatbot. You can easily set this option up within the Comm100 AI Chatbot.
Next, you need a bot that can automatically identify the conversations that you want an agent to manage. This requires training your bot on the topics – ‘intents’, in bot lingo – that your customers will bring up. If there are specific intents that are of high value to you, you can tag them so when a customer mentions it, the bot recognizes it and automatically transfers the chat to the appropriate agent or department. The bot can also be trained to notify an agent or escalate the conversation when asked a question it can’t answer or if a visitor is clearly frustrated. As a failsafe, your agents should also be able to monitor bot conversations and take them over in these situations.
Chatbots will never replace whole customer service teams, and nor should they. The ‘human touch’ is still essential to customer support, and we are a long way off until this changes. However, if implemented intelligently, bots can resolve a great portion of customer queries without any human involvement, allowing team sizes to reduce, or remain the same in the face of increased support volume.
Take Tangerine, an Australian telecom company, for example. They experienced rapid growth, which in turn produced a surge in chat requests. By implementing Comm100’s AI Chatbot, up to 91% of assigned live chats were resolved by the bot without any agent involvement. As a result, Tangerine could manage the increase in chat volume without hiring and training more agents. And when high-value customers reached out, their agents were free to provide them with the best experience.
Article | April 1, 2020
Most of the time, collaborating on a software project means working with tools like Git—taking turns making modifications, then reconciling the final product into a single codebase. But live collaboration on code—two or more people working on the same file in real time—has become far more viable in recent years. You’ll still want to have one person sign off on the final code, but being able to see other people’s edits as they happen is a great boon for distance learning, crunch-time work, and peer review.