The Education sector is currently facing immense challenges with enabling a remote learning environment and ensuring the safety of staff, employees, and students. This is on top of the usual challenges of resource optimisation, student retention, student recruitment, and so on. Moreover, today’s students are millennials and post-millennials, who are digital natives – pushing educational institutions to adopt technology to attract the right cohort and provide an education that equips the students for the workplace of the future. The industry is being driven to transform, to keep up with student expectations on delivery, access to the resource, and how they choose to communicate with their educators and peers.
Ecosystm Principal Advisor, Dr Alea Fairchild says, “Education administration budgets are not increasing, but the pressure for quick response and more personalised interaction for students, means that administrators need to focus on interaction as the core competency. This requires institutions to automate as much of the volume back-office activity as feasible. The challenge is that individualised course structures mean more complex billing configurations.”
Dr Fairchild, who is active in international education in Belgium, says, “Individual study paths, including Erasmus exchanges, create a need for an audit trail on transfers, exemptions and completions.”
Ecosystm research finds that educational institutions are focused on adopting emerging technologies mainly to improve student services (Figure 1). The processes are being automated to reduce risks, errors and turnaround times for results and application processing, while also removing repetitive tasks so administration can focus on more value-add student-facing activities.
University of Staffordshire Embraces Digital Transformation
The University of Staffordshire is a “connected university” with an emphasis on industry connections and graduate employability. At the heart of Stoke-on-Trent and a regional hub for healthcare education, the university has six schools as well as a well-known degree in computer games design.
The UK-based University has over the years built a reputation for being keen on embracing digital as a way of better management, offering better student services, and serving the larger community. In 2018, Staffordshire University announced plans to build a multi-million-pound apprenticeship hub at its Stoke-on-Trent campus supported by tech giants including Microsoft to equip students with digital skills and to deliver more than 6,500 new apprenticeships over the next decade.
Last year, the University implemented a digital assistant, called Beacon, hosted on Microsoft Azure Cloud that provides support to their students on their learning and on-campus activities, including monitoring their emotional well-being and providing recommendations on groups and societies that they might be interested in. Beacon aims to ease the life of a university student, acting as a digital coach, and to minimise drop-outs due to stress and uncertainty.
Like its peer organisations, in the wake of the pandemic, the university was able to implement a blended learning program – offering courses through digital and remote learning systems from this semester for the entire 2020-21 session.
Focusing on Transformation through Automation
The University of Staffordshire, recently implemented robotic process automation (RPA) as part of its digital transformation plan. Talking about the role of RPA in Education, Dr Fairchild says, “This is a recent trend in higher education, with other new initiatives seen at the University of Auckland and University of Melbourne. RPA as a tool is used in Education to achieve the service levels required to meet both students’ and potential students’ expectations. This includes downloading student applications, processing language waiver requests, and entering academic results. These are all rule-based, high volume applications where automation increases speed and reduces errors.”
The University is using Blue Prism Cloud to access the RPA software and has plans for a automation-led digital transformation roadmap. Dr Fairchild says, “Blue Prism is based on Java and uses a Top-Down approach. It offers a visual designer with no recorders, scripts, or any intervention. Blue Prism is based on process diagrams that utilise core programming concepts and create the operational process flows to analyse, modify and scale business capability.”
The Staffordshire Digital team initially implemented RPA in the Finance department, as it involves a lot of administrative and back-office operations such as management of finance, records, tuition fees details and more. The University’s emphasis is to free up personnel and make them focus on more productive areas. This is beneficial for both the administrative staff’s feeling of personal contribution as well as student service satisfaction levels. “Using RPA gives the opportunity to universities to revisit, redesign, and improve their existing processes in line with expectations from digital native students. For prospective students, the next wave of RPA integration is intelligent machine learning algorithms to help route emails and integrate chatbots to address questions on course selection,” says Dr Fairchild.
The pandemic crisis has rapidly accelerated digitalisation across all industries. Organisations have been forced to digitalise entire processes more rapidly, as face-to-face engagement becomes restricted or even impossible.
The most visible areas where face-to-face activity is being swiftly replaced by digital alternatives include conferencing and collaboration, and the use of digital channels to engage with customers, suppliers, and other stakeholders.
For example, the crisis has made it difficult – even impossible, sometimes – for contact centre agents to physically work in contact centres, and they often do not have the tools to work effectively from home. This challenge is particularly apparent for offshore contact centres in the Philippines and India. The creation of chatbots has reduced the need for customer service staff and enabled data to by entered into front-office systems, and analysed immediately.
Less visible are back-office processes which are commonly inefficient and labour-intensive. Remote working makes some back-office workflows challenging or impossible. For example, some essential finance and accounting workflows involve a mix of digital communications, printing, scanning, copying and storage of physical documents – making these workflows inefficient, difficult to scale and labour-intensive. This has been highlighted during the pandemic. RPA adoption has grown faster than expected as organisations seek to resolve these and other challenges – often caused by inefficient workflows being scrambled by the crisis.
The RPA Market in Asia Pacific
There are many definitions of the RPA market, but it can broadly be defined as the use of software bots to execute processes which involve high volumes of repeatable tasks, that were previously executed by humans. When processes are automated, the physical location of employees and other stakeholders becomes less important. RPA makes these processes more agile and flexible and makes businesses more resilient. It can also increase operational efficiency, drive business growth, and enhance customer and employee experience.
RPA is a comparatively new and fast-growing market – this is leading to rapid change. In its infancy, it was basically the digitalisation of BPO. It was viewed as a way of automating repetitive tasks, many of which had been outsourced. While its cost saving benefits remain important as with BPOs, customers are now seeking more. They want RPA to help them to improve or transform front-office, back-office and industry-specific processes throughout the organisation. RPA vendors are addressing these enhanced requirements by blending RPA with AI and re-branding their offerings as intelligent automation or hyper-automation.
Asia Pacific organisations have been relatively slow to adopt RPA, but this is changing fast. The findings of the Ecosystm Digital Priorities in the New Normal study show that in the next 12 months, organisations will continue to focus on digital technologies for process automation (Figure 1).
The market is growing rapidly with large global RPA specialists such as UiPath, Automation Anywhere, Blue Prism and AntWorks experiencing high rates of growth in the region.
RPA vendors in Asia Pacific, are typically addressing immediate, short-term requirements. For example, healthcare companies are automating the reporting of COVID-19 tests and ordering supplies. Chatbots are being widely used to address unprecedented call centre volumes for airlines, travel companies, banks and telecom providers. Administrative tasks increasingly require automation as workflows become disrupted by remote working.
Companies can also be expected to scale their current deployments and increase the rate at which AI capabilities are integrated into their offerings
RPA often works in conjunction with major software products provided by companies such as Salesforce, SAP, Microsoft and IBM. For example, some invoicing processes involve the use of Salesforce, SAP and Microsoft products. Rather than having an operative enter data into multiple systems, a bot can be created to do this.
Large software vendors such as IBM, Microsoft, Salesforce and SAP are taking advantage of this opportunity by trying to own entire workflows. They are increasingly integrating RPA into their offerings as well as competing directly in the RPA market with pureplay RPA vendors. RPA may soon be integrated into larger enterprise applications, unless pureplay RPA vendors can innovate and continually differentiate their offerings.
Artificial intelligence (AI) is perhaps the most electrifying and controversial of the so-called “disruptive” technologies. As AI becomes more sophisticated and the technology evolves, it will increasingly help to perform more complex tasks whether for personal or commercial use.
Today’s AI machines can replicate certain elements of intellectual ability and they are constantly striving to achieve more. This includes applications of autonomous vehicles, domestic and industrial robots, surveillance and security, automation, personal assistants, forecasting, data analysis and more.
The global Ecosystm AI study reveals top drivers of AI adoption. Organisations are trying to incorporate AI in their existing processes for better competitor analysis and insights, cost-effectiveness, deeper customer engagement to provide personalised service/product offerings, and for process redesign or automation.
AI supporting technologies
AI is driving important technologies and processes and driving better, faster and more accurate decisions which help processes run more effectively and efficiently. With AI insights, business strategies will be more information-driven, efficient and consistent.
There are certainly many benefits that organisations are deriving from AI. Ecosystm AI study reveals that organisations implementing AI are using it to drive various business solutions including billing management, supply chain optimisation, predictive maintenance, enhancing operations and more.
Below is a list of some hot technologies driven by AI.
Advanced Analytics
The proliferation of Big Data has led to the creation of massive data sets that can only be effectively analysed with AI tools and statistical models. AI can spot complex patterns in the data which is difficult for humans to understand. AI’s usefulness as an analytics tool is highly gaining importance in the use of predictive analytics and decision automation. Once sufficient data is available for use by AI, which is further filtered and processed thoroughly, the system can suggest actions or outcomes based on various parameters such as trends, patterns, historical information, frequency and more.
For example, the financial services industry is using advanced analytics to evaluate how customers earn, invest, spend and make financial decisions, which is useful for the organisations to customise their customers’ preference and offerings accordingly.
Natural Language Processing (NLP) and speech recognition
NLP involves the learning of languages by machine through the means of interaction between computers and unstructured speech/text. NLP requires massive processing power and complex algorithms to reinforce learning mechanisms. To help in NLP, AI generates models, which are further improved to create NLP and speech simulations. Nowadays, Natural language is being implemented and used in various conversational interfaces, such as those with bots, artificial learning agents that can generalise to new environments, and autonomous vehicles.
Cognitive processing
Otherwise known as semantic computing, refers to a digital processing that attempts to mimic the operation of the human brain. In general, semantics means the meaning and interpretation of words and sentence structure and how words relate to other words. So, how is semantic related and what is the semantic analysis used for in AI? Semantic technology processes the logical structure of sentences to identify the most relevant elements in the text to understand the topic. It is especially suited to the analysis of large unstructured datasets with high efficiency.
Vantagepoint has an artificial intelligence tool to improve their trading results. It has a patented tool which can forecast stocks, futures, commodities, Forex and ETFs and claims an accuracy of up to 86%. The tool can predict changes in market trend direction up to three days in advance thus enabling traders to get in and out of trades at optimal times with confidence.
Robotic Process Automation (RPA)
RPA has grown out of Business Process Automation (BPA) and refers to the use of AI to automate workflow and business processes. The advantages of RPA demonstrate it to be a solid tool in attaining higher quality output at lower costs which is much quicker than traditional methods. RPA can be used in IT support processes, back-office work, and workflow processes. The rules are programmed, and bots extract structured inputs from applications like Excel and enter them into other software such as CRM, SCM or accounting. A good example is the use of NLP to scan incoming emails and undertake the appropriate action, such as generating an invoice or flagging a complaint in an automated manner.
Machine Learning
Machine learning is an application of artificial intelligence (AI) which involves a combination of raw computing power and logic-based models to simulate the human learning process. Machine Learning is proving to be a successful approach to AI. When humans learn, they alter the way they relate information and the world, similarly when machines learn, they alter the data and form it into a piece of information.
An example, Image recognition is a popular application of machine learning in which images are fed into an algorithm, which attempts to recognise the contents of the image based on patterns. For instance, Yelp’s machine learning algorithms help the company’s human staff deal with tens of millions of photos to compile, categorise, and label the images more efficiently.
Chatbots and virtual assistants
Chatbots are robotic processes which simulate human conversation and automate functions. The technology is also used for so-called ‘virtual assistants’, which uses AI to interact with humans and aid with specific queries. They are increasingly being used to handle simple conversation and tasks in B2B and B2C environments. The addition of chatbots reduces human assistants and they can work throughout the clock. Chatbots and Virtual assistants improve with AI and can be trained to review conversations, past transactions and to draft a response based on context. If the user interacts with the bot through voice, then the chatbot requires a speech recognition engine.
Chatbots have been used in instant messaging (IM) applications and online interactive platforms. To exemplify, chatbots are deployed to assist online shoppers by answering noncomplex product questions, pricing, FAQ’s, order processing steps or forwarding information to human agents on complicated questions such as shipping delays or faults.
With AI technology evolving and improving so rapidly, many organisations are looking to use AI in their business, but there are still many questions to which adopters are seeking answers such as how to integrate AI into their existing systems, how to get access to data that will enable AI as well as the persistent technology concerns around cybersecurity and cost. The goal of many AI providers is to reach a stage where AI will support humans, control machines for us and automate repetitive tasks and processes.
I ran several roundtables over the past few weeks speaking to business and technology leaders about their AI investments – and one factor came up many times – that it is hard to build a business case for AI because 70% accuracy was not good enough…
What this means is that companies have thousands of things to automate. Most of those automations in the short-medium term will deliver 100% accuracy using RPA and other simple automation tools. Every time you run that process you know the outcome.
Along Comes AI and Machine Learning
These dumb processes can now learn – they can be smart. But originally they won’t deliver 100% accuracy. They might only deliver 60-70% to start with – climbing perhaps to 90%. The benefits of these smart, learning processes can amaze – costs can fall, processes can improve, outcomes can accelerate. But traditionally we have built technology business cases delivering 100% accuracy and outcomes.
So we need a new way to think about AI and a different language to use about the way it works. The people who sign off on the business cases might not understand AI – they will come to the business case with the same lens they use for all technology investments (and evidently – all business investments). We also need to be better at selling the benefits to our leaders. CEOs and Managing Directors in the roundtables are surprised to hear that AI won’t deliver 100% accuracy – they said unless they know more about the capability, savings and outcomes that the solution might drive, they are unlikely to fund it.
Make Your Dumb Processes Smart
I take this as good news. It means we have moved beyond the hype of AI – the need to “do AI in our business” that drove many of the poorer chatbots and machine learning projects. It means that businesses review AI investments in the same way as any business investment. But it also means we can’t over-promise or under-deliver on AI. Woodside did this with their initial foray into AI, and they are still playing catch up today.
While there are many opportunities to use “dumb automation” and save money, reduce or redeploy headcount – or have employees focus on higher value activities or make real differences to customer experiences – there are as many opportunities to make dumb processes smart. Being able to automatically read PDF or paper-based invoices – processes usually done by humans – could be a huge saving for your business. OK – maybe you can’t redeploy 100% of the staff, but 70% is still a big saving. Being able to take human error out of processes will often help to save money at two steps on the process – automating the human input function up front and also getting rid of the need to fix the mistake.
Start Your AI Journey With The Low Hanging Fruit
Ecosystm’s Global Ongoing AI study has shown that most businesses are focusing their AI investments on internal initiatives – on reducing process time, cost savings and driving productivity – which makes the most sense today. They are the easier business cases to build and the easiest benefits to explain.
Perhaps AI is also a chance for businesses to acknowledge that “efficient” does not always mean “good”. Many of the processes we automated or coded to ensure 100% compliance don’t give customers or employees what they are looking for. And maybe making the customer happy 70% of the time is better than not making them happy at all…
If you’d like to dig deeper into Ecosystm’s reports exploring the data from our ongoing AI study – check them out here (you’ll need to register if you have not already – it is free to register, but some content is premium):
4 Vendors Emerge as Leaders: Understanding the AI Vendor landscape
Use Cases Drive AI Software Adoption: Understanding The Industry Landscape