ServiceNow Acquires RPA Vendor

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ServiceNow announced their intention to acquire robotic process automation (RPA) provider, Intellibot, for an undisclosed sum. Intellibot is a significant tier 2 player in the RPA market, that is rapidly consolidating into the hands of the big three – UiPath, Automation Everywhere, and Blue Prism – and other acquisition-hungry software providers. This is unlikely to be the last RPA acquisition that we see this year with smaller players looking to either go niche or sell out while the market is hot.

Expanding AI/Automation Capabilities

Intellibot is the latest in a string of purchases by ServiceNow that reveals their intention to embed AI and machine learning into offerings. In 2020, they acquired Loom Systems, Passage AI (both January), Sweagle (June), and Element AI (November) in addition to Attivio in 2019. These acquisitions were integrated into the latest version of their Now Platform, code-named Quebec, which was launched earlier this month. As a result, Predictive AIOps and AI Search were newly added to the platform while the low-code tools were expanded upon and became Creator Workflows. This means ServiceNow now offers four primary solutions – IT Workflows, Employee Workflows, Customer Workflows, and Creator Workflows – demonstrating the importance they are placing on low-code and RPA.

ServiceNow was quick to remind the market that although they will be able to offer RPA functionality natively once Intellibot is integrated into their platform, they are still willing to work with competitors. They specifically highlighted that they would continue partnering with UiPath, Automation Anywhere, and Blue Prism, suggesting they plan to use RPA as a complementary technology to their current offerings rather than going head-to-head with the Big Three. Only a month ago, UiPath announced deeper integration with ServiceNow, by expanding automation capabilities for Test Management 2.0 and Agile Development projects.

Expansion in India

The acquisition of Intellibot, based in Hyderabad, is part of ServiceNow’s expansion strategy in India – one of their fastest growing markets. The country is already home to their largest R&D centre outside of the US and they intend to launch a couple of data centres there by March 2022. The company plans to double their local staff levels by 2024, having already tripled the number of employees there in the last two years. The expansion in India means they can increasingly offer services from there to global customers.

Market Consolidation Accelerates

In the Ecosystm Predicts: The Top 5 AI & AUTOMATION Trends for 2021, Ecosystm had talked about technology vendors adding RPA functionality either organically or through acquisitions, this year.

“Buyers will find that many of the automation capabilities that they currently purchase separately will increasingly be integrated in their enterprise applications. This will resolve integration challenges and will be more cost-effective.”

ServiceNow’s purchase is one of several recent examples of low-code vendors acquiring their way into the RPA space. Last year, Appian acquired Novayre Solutions for their Jidoka product and Microsoft snapped up Softomotive. Speculation continues to build that Salesforce could also be assessing RPA targets. Considering RPA market leader, UiPath recently announced that their Series F funding round values the company at USD 35 billion, there is pressure on acquirers to gobble up the remaining smaller players before they are all gone or become prohibitively expensive.

The cloud hyperscalers are also likely to play a growing role in the RPA market over the next year. Microsoft and IBM have already entered the market, coming from the angle of office productivity and business process management (BPM), respectively. Google announced just last week that they will work closely with Automation Anywhere to integrate RPA into their cloud offerings, such as Apigee, AppSheet, and AI Platform. More interestingly, they plan to co-develop new solutions, which might for now satisfy Google’s appetite for RPA rather than requiring an acquisition.


Here are some of the trends to watch for RPA, AI and Automation in 2021. Signup for Free to download Ecosystm’s Top 5 AI & Automation Trends Report.

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The Value of the Human Touch in 2021

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Authored by Alea Fairchild and Audrey William

There is a lot of hope on AI and automation to create intellectual wealth, efficiency, and support for some level of process stability. After all, can’t we just ask Siri or Alexa and get answers so we can make a decision and carry on?

Automation has been touted as the wonder formula for workplace process optimisation. In reality it’s not the quick fix that many business leaders desire.  But we keep raising the bar on expectations from automation. Investments in voice technologies, intelligent assistants, augmented reality and touchscreens are changing customer experience (Figure 1). Chatbots are ubiquitous, and everything has the potential to be personalised. But will they solve our problems?

Important customer touchpoints

100 percent automation is not effective

Let’s first consider using automation to replace face-to-face interactions. There was a time when people were raving about the check-in experience at some of the hotels in Japan where robots and automated systems would take care of the check-in, in-stay and check-out processes. Sounds simple and good? Till 2019, if you checked into the Henn-na Hotel in Japan, you would be served and taken care of by 243 robots. It was viewed by many as a template for what a fully automated hotel could look like in the future.

The hotel had an in-room voice assistant called Churi. It could cope with basic commands, such as turning the lights on and off, but it was found to be deficient when guests started asking questions about places to visit or other more sophisticated queries. It was not surprising that the hotel decided to retire their robots. In the end it created more work for the hotel staff on-site.

People love the personal touch when they are in a hotel; and talking to someone at the front desk, requesting assistance from hotel staff, or even just a short chat over breakfast are some of the small nuances of why the emotional connection matters. Many quarantine hotels today use robots for food delivery, but the hotel staff is still widely available for questions. That automation is good, but you need the human intervention. So, getting the balance right is key.

Empathy plays a big role in delivering great Customer Experience

Similarly, there was a time when many industry observers and technology providers said that a contact centre will be fully automated, reducing the number of agents. While technologies such as Conversational AI have come along where you can now automate common or repetitive questions and with higher accuracy levels, the human agent still plays a critical role in answering the more complex queries. When the customer has a complicated question or request, then they will WANT to speak to an agent.

When it reaches a point where the conversation with the chatbot starts getting complicated and the customers need more help there should be the option – within the app, website or any other channel – to escalate the call seamlessly to a human agent. Sometimes, a chat is where the good experience happens – the emotional side of the conversation, the laughter, the detailed explanation. This human touch cannot be replaced by machines. Disgruntled customers are happier when an agent shows empathy. Front line staff and human agents act as the face of a company’s brand. Complete automation will not allow the individual to understand the culture of the company. These can be attained through conversations.

Humans as supervisors for AI – The New Workplace

Empathy, intuitiveness, and creativity are all human elements in the intelligence equation. Workers in the future will need to make their niche in a fluid and unpredictable environment; and translating data into action in a non-replicable way is one of the values of human input. The essence of engineering is the capacity to design around human limitations. This requires an understanding of how humans behave and what they want. We call that empathy. It is the difference between the engineer who designs a product, and the engineer who delivers a solution. We don’t teach our computer scientists and engineering students a formula for empathy. But we do try to teach them respect for both the people and the process.

For efficiency, we turn to automation of processes, such as RPA. This is designed to try to eradicate human error and assist us in doing our job better, faster and at a lower cost by automating routine processes. If we design it right, humans take the role of monitoring or supervisory controlling, rather than active participation.

At present, AI is not seen as a replacement for our ingenuity and knowledge, but as a support tool. The value in AI is in understanding and translating human preferences. Humans-in-the-loop AI system building puts humans in the decision loop. They also shift pressure away from building “perfect” algorithms. Having humans involved in the ethical norms of the decision allows the backstop of overly orchestrated algorithms.

That being said, the astute use of AI can deepen insights into what truly makes us human and can humanise experiences by setting a better tone and a more trusted engagement. Using things like sentiment analysis can de-escalate customer service encounters to regain customer loyalty.

The next transformational activity for renovating work is to advance interactions with customers by interpreting what they are asking for and humanising the experience of acquiring it which may include actually dealing with a human contact centre agent – decisions that are supported at the edge by automation, but at the core by a human being.

Implications

Ecosystm research shows that process automation will be a key priority for technology investments in 2021 (Figure 2).

Digital Technology focus for 2021

With AI and automation, a priority in 2021, it will be important to keep these considerations in mind:

  1. Making empathy and the human connection the core of customer experiences will bring success.
  2. Rigorous, outcome-based testing will be required when process automation solutions are being evaluated. In areas where there are unsatisfactory results, human interactions cannot – and should not – be replaced.
  3. It may be easy to achieve 90% automation for dealing with common, repetitive questions and processes. But there should always be room for human intervention in the event of an issue – and it should be immediate and not 24 hours later!
  4. Employees can drive greater value by working alongside the chatbot, robot or machine.

Ecosystm Predicts: The Top 5 Customer Experience Trends for 2021

Download Ecosystm’s complimentary report detailing the top 5 customer experience trends for 2021 that your company should pay attention to along with tips on how to stay ahead of the curve.

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Consumers at the Core of the Digital Financial Ecosystem

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The disruption that we faced in 2020 has created a new appetite for adoption of technology and digital in a shorter period. Crises often present opportunities – and the FinTech and Financial Services industries benefitted from the high adoption of digital financial services and eCommerce. In 2021, there will be several drivers to the transformation of the Financial Services industry – the rise of the gig economy will give access to a larger talent pool; the challenges of government aid disbursement will be mitigated through tech adoption; compliance will come sharply back into focus after a year of ad-hoc technology deployments; and social and environmental awareness will create a greater appetite for green financing. However, the overarching driver will be the heightened focus on the individual consumer (Figure 1).

2021 will finally see consumers at the core of the digital financial ecosystem.

Ecosystm Advisors Dr. Alea Fairchild, Amit Gupta and Dheeraj Chowdhry present the top 5 Ecosystm predictions for FinTech in 2021 – written in collaboration with the Singapore FinTech Festival. This is a summary of the predictions; the full report (including the implications) is available to download for free on the Ecosystm platform.

The Top 5 FinTech Trends for 2021

 #1 The New Decade of the ‘Empowered’ Consumer Will Propel Green Finance and Sustainability Considerations Beyond Regulators and Corporates

We have seen multiple countries set regulations and implement Emissions Trading Systems (ETS) and 2021 will see Environmental, Social and Governance (ESG) considerations growing in importance in the investment decisions for asset managers and hedge funds. Efforts for ESG standards for risk measurement will benefit and support that effort.

The primary driver will not only be regulatory frameworks – rather it will be further propelled by consumer preferences. The increased interest in climate change, sustainable business investments and ESG metrics will be an integral part of the reaction of the society to assist in the global transition to a greener and more humane economy in the post-COVID era. Individuals and consumers will demand FinTech solutions that empower them to be more environmentally and socially responsible. The performance of companies on their ESG ratings will become a key consideration for consumers making investment decisions. We will see corporate focus on ESG become a mainstay as a result – driven by regulatory frameworks and the consumer’s desire to place significant important on ESG as an investment criterion.

#2 Consumers Will Truly Be ‘Front and Centre’ in Reshaping the Financial Services Digital Ecosystems  

Consumers will also shape the market because of the way they exercise their choices when it comes to transactional finance. They will opt for more discrete solutions – like microfinance, micro-insurances, multiple digital wallets and so on. Even long-standing customers will no longer be completely loyal to their main financial institutions. This will in effect take away traditional business from established financial institutions. Digital transformation will need to go beyond just a digital Customer Experience and will go hand-in-hand with digital offerings driven by consumer choice.

As a result, we will see the emergence of stronger digital ecosystems and partnerships between traditional financial institutions and like-minded FinTechs. As an example, platforms such as the API Exchange (APIX) will get a significant boost and play a crucial role in this emerging collaborative ecosystem. APIX was launched by AFIN, a non-profit organisation established in 2018 by the ASEAN Bankers Association (ABA), International Finance Corporation (IFC), a member of the World Bank Group, and the Monetary Authority of Singapore (MAS). Such platforms will create a level playing field across all tiers of the Financial Services innovation ecosystem by allowing industry participants to Discover, Design and rapidly Deploy innovative digital solutions and offerings.

#3 APIfication of Banking Will Become Mainstream

2020 was the year when banks accepted FinTechs into their product and services offerings – 2021 will see FinTech more established and their technology offerings becoming more sophisticated and consumer-led. These cutting-edge apps will have financial institutions seeking to establish partnerships with them, licensing their technologies and leveraging them to benefit and expand their customer base. This is already being called the “APIficiation” of banking. There will be more emphasis on the partnerships with regulated licensed banking entities in 2021, to gain access to the underlying financial products and services for a seamless customer experience.

This will see the growth of financial institutions’ dependence on third-party developers that have access to – and knowledge of – the financial institutions’ business models and data. But this also gives them an opportunity to leverage the existent Fintech innovations especially for enhanced customer engagement capabilities (Prediction #2).   

#4 AI & Automation Will Proliferate in Back-Office Operations

From quicker loan origination to heightened surveillance against fraud and money laundering, financial institutions will push their focus on back-office automation using machine learning, AI and RPA tools (Figure 3). This is not only to improve efficiency and lower risks, but to further enhance the customer experience. AI is already being rolled out in customer-facing operations, but banks will actively be consolidating and automating their mid and back-office procedures for efficiency and automation transition in the post COVID-19 environment. This includes using AI for automating credit operations, policy making and data audits and using RPA for reducing the introduction of errors in datasets and processes.

There is enormous economic pressure to deliver cost savings and reduce risks through the adoption of technology. Financial Services leaders believe that insights gathered from compliance should help other areas of the business, and this requires a completely different mindset. Given the manual and semi-automated nature of current AML compliance, human-only efforts slow down processing timelines and impact business productivity. KYC will leverage AI and real-time environmental data (current accounts, mortgage payment status) and integration of third-party data to make the knowledge richer and timelier in this adaptive economic environment. This will make lending risk assessment more relevant.

#5 Driven by Post Pandemic Recovery, Collaboration Will Shape FinTech Regulation

Travel corridors across border controls have started to push the boundaries. Just as countries develop new processes and policies based on shared learning from other countries, FinTech regulators will collaborate to harmonise regulations that are similar in nature. These collaborative regulators will accelerate FinTech proliferation and osmosis i.e. proliferation of FinTechs into geographies with lower digital adoption.

Data corridors between countries will be the other outcome of this collaboration of FinTech regulators. Sharing of data in a regulated environment will advance data science and machine learning to new heights assisting credit models, AI, and innovations in general. The resulting ‘borderless nature’ of FinTech and the acceleration of policy convergence across several previously siloed regulators will result in new digital innovations. These Trusted Data Corridors between economies will be further driven by the desire for progressive governments to boost the Digital Economy in order to help the post-pandemic recovery.

Ecosystm Predicts: The Top 5 FinTech Trends for 2021

The full findings and implications of the Top 5 FinTech Trends for 2021 are available for download from the Ecosystm platform. Create your free account to access more from the Ecosystm Predicts Series, and many other reports, on the Ecosystm platform

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Ecosystm Predicts: The Top 5 AI & Automation Trends for 2021

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Ecosystm had predicted that in 2020, AI and analytics would be a top priority for organisations as they embarked or continued on their Digital Transformation journeys. What we saw instead was organisations collecting the right data – but handling more pressing matters this year. They focused more on cybersecurity frameworks, enabling remote employees and the shifts in product and service delivery. In 2021, as organisations work their way to recovery, they will re-evaluate their AI and automation roadmaps, more actively. Ecosystm Advisors Alea Fairchild, Andrew Milroy and Tim Sheedy present the top 5 Ecosystm predictions for AI & Automation in 2021.

This is a summary of the AI & Automation predictions, the full report (including the implications) is available to download for free on the Ecosystm platform here.

The Top 5 AI & Automation Trends for 2021

  1. AI Will Move from a Competitive Advantage to a Must-Have

The best practices and leading-edge technology-centric implementations, over the years gives a very good indication of market trends. In 2018 and 2019 AI-centric engagements were few and far between – they were still in the “innovation stage” as trials and small projects. In 2020, AI was mentioned in most applications, showcased as best practices. AI is currently a competitive advantage for businesses. CIOs and their businesses are using AI to get ahead of their competitors and highlighting these practices for external recognition.

That also means that it is a matter of time before AI becomes a standard practice – processes are smart “out-of-the-box”; intelligent applications are an expectation, not the exception; systems learn because that is how they were designed, not as an overlay. If your competitors are using AI today to get ahead of you, then you need to also use AI to catch up and keep up. In 2021, having a smart business will not get you ahead of the pack – it will move you into it.

  1. AI Will Thrive in Areas where the Cost of Failure is Low

While organisations will be forced to adopt AI to remain competitive, initial exploration of AI solutions will be in areas that they consider low risk. The Financial Services, Retail, and other transaction-oriented industries will use AI to drive improved personalisation, increase customer retention, and improve their ability to lower risk and combat fraud. These are process-driven areas, where manual processes are being enhanced and enriched by AI. Although machine learning and other AI technologies will help improve the speed and quality of services, they will not be a replacement for many of the more complex business practices that companies and their employees frequently overlook to automate. The ‘low hanging fruit’ to add AI to will come first, with various degrees of success.

There will be industries and processes where organisations will be more skeptical about adopting AI. If Google finds a wrong translation or gives a wrong link, it is not a big concern, unlike a wrong diagnosis or wrong medication. In areas that are crucial to our well-being – such as healthcare – AI does not yet have the trust for acceptance of society. There are still questions around ethics and algorithm concerns.

  1. Technology Providers Will Stop Talking about AI

Technology vendors highlight what they consider their key differentiators, that show that they are ahead of the game. When every piece of software and hardware is intelligent, vendors will stop talking about the fact that they are intelligent. This may not fully happen in 2021 – but ENOUGH technology will be intelligent for those who have not yet made their software smart to understand that they cannot talk about its intelligent capabilities as that just shows they are behind the market.

The good news is that the less we hear about AI, the more intelligent applications will become. AI is quickly becoming a core capability and a base expectation. Systems that learn and adapt will be standard very soon – but be wary, as significant market changes can break these systems! Many companies learned that the pandemic broke their algorithms as times were no longer “normal”.

  1. Enterprises Will Seek Hyperautomation Solutions

RPA will increasingly become part of large enterprise application implementations. Technology vendors are adding RPA functionality either organically or through acquisitions to their enterprise application suites. RPA often works in conjunction with major software products provided by companies such as Salesforce, SAP, Microsoft, and IBM. Rather than having an operative enter data into multiple systems, a bot can be created to do this. Large software vendors 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.

As the RPA offerings continue to mature, enterprises seek to scale implementations and to automate non-repetitive processes, which require more intelligence. They will seek to automate more processes at scale. They will demand solutions that process unstructured data, handle exceptions, and continuously learn, further increasing productivity. Intelligent automation typically incorporates AI, particularly voice and vision capabilities and uses machine learning to optimise processes. Hyperautomation turbo charges intelligent automation by automating multiple processes at scale – and will become core to digital transformation initiatives in 2021.

  1. Businesses Will Put “Automation Targets” in Place

2020 was the year that many businesses started seeing some broad and tangible benefits from their automation initiatives. Automation was one of the big winners of the year, as many businesses took extra steps to take humans out of processes – particularly those humans that had to be in a specific location, such as a warehouse, the finance team, the front desk and so on (because of the pandemic, they were often working at home instead). Senior management is seeing the benefits of automation, and they will start to ask their teams why more processes are not automated Therefore we will start to see managers put targets around a certain percentage of tasks automated in an area – e.g. 70% of contact centre processes will be automated, 90% of the digital customer experience for a certain outcome will be automated and so on. Achieving these numbers may not be easy, but the targets will change the mindset of people designing, implementing, and improving processes.


Download Ecosystm Predicts: The Top 5 AI & Automation Trends for 2021

The full findings and implications of The Top 5 AI & Automation Trends for 2021 are available for download from the Ecosystm platform. Signup for Free to download the report.

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Automation Drives Digital Transformation at the University of Staffordshire

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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.

Top Tech Priorities in Education

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.


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RPA Adoption Accelerates in Asia Pacific – but the Future is Cloudy

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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).

Measures to be retained by organisations after COVID-19

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.


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How AI is changing the business landscape

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No ratings yet. 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.Drivers of AI Adoption

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.

benefits that organisations are deriving from AI

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.

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Automation Versus AI – Building the Business Case for 70% Accuracy

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5/5 (2) 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

 

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