Ecosystm RNx: Top 10 Global AI & Automation Vendor Rankings

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Ecosystm RNx is an objective vendor ranking based on in-depth, quantified ratings from technology decision-makers on the Ecosystm platform. In this edition, we rank the Top 10 Global AI & Automation Vendors.

If you are an End-User, you are realising that the right investments in Data & AI now will be the key to your future success. This vendor ranking will help you evaluate your buying decisions based on key evaluation ratings by your peers across a number of key metrics and benchmarks, including customer experience.

If you are an AI & Automation Vendor, it’s an opportunity to understand how your customers rate you on capabilities and their overall customer experience.

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Artificial Intelligence Insights
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Nuance Acquisition Strengthens Microsoft’s Industry & AI Capabilities

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Last week Microsoft announced the acquisition of Nuance for an estimated USD 19.7 billion. This is Microsoft’s second largest acquisition ever, after they acquired LinkedIn in 2016. Nuance is an established name in the Healthcare industry and is said to have a presence in 10,000 healthcare organisations globally. Apart from Healthcare, Nuance has strong capabilities in Conversational AI and speech solutions to support other industries. This acquisition is in line with Microsoft’s go-to-market roadmap and strategies.

Microsoft’s Healthcare Focus

Microsoft announced their Healthcare Cloud last year and this acquisition will bolster their Healthcare offerings and market presence. Nuance’s product portfolio includes clinical speech recognition SaaS offerings – Dragon Ambient eXperience, Dragon Medical One and PowerScribe One for radiology reporting – on Microsoft Azure. The acquisition builds on already existing integrations and partnerships that were in place over the years.

Microsoft Cloud for Healthcare offers its solution capabilities to healthcare providers using a ‘modular’ approach. Given how diverse healthcare providers are in their technology maturity and appetite for change, the more diverse the  ‘modules’, the greater the opportunities for Microsoft. This partnership with Nuance also brings to the table established relationships with EHR vendors, which will be useful for Microsoft globally.  

The Healthcare industry continues to struggle as the world negotiates the challenges of mass vaccination. But on the upside, the ongoing Healthcare crisis has given remote care a much-needed shot in the arm. Clinicians today will be more open to documentation and transcription services for process automation and compliance. The acquisition of Nuance’s Healthcare capabilities will definitely boost Microsoft’s market presence in provider organisations.  

However, Healthcare is not the only industry that Microsoft and Nuance are focused on. The Microsoft Cloud for Retail that was launched earlier this year aims to offer integrated and intelligent capabilities to retailers and brands to improve their end-to-end customer journey. Nuance has omnichannel customer engagement solutions that can be leveraged in Retail and other industries. As Microsoft continues to verticalise their offerings, they will consider more acquisitions that will complement their value proposition.

Microsoft’s Focus on Conversational AI

Microsoft already has several speech recognition offerings, speech to text services, and chatbots; and they continue to invest in the Conversational AI space. They have created an open-source template for creating virtual assistants to help Bot Framework developers. In February, Microsoft announced their industry specific cloud offerings for Financial services, Manufacturing, and Non-Profit, and also introduced a series of AI and natural language features in Microsoft Outlook, Microsoft Teams, Microsoft Office Lens and Microsoft Office mobile to deliver interactive, voice forward assistive experiences.

“There is no slowing down in this space and the acquisition clearly demonstrates the vision that Microsoft is building with Nuance – a vendor that has made speech recognition, text to speech, conversational AI the foundation of the company. This is a brilliant move by Microsoft in the Conversational AI space and a win-win for both companies.

This move could also mark further inroads for Microsoft into the contact centre space. With Teams now being integrated into contact centre technologies, working with large customers using speech and conversational AI, Dynamics 365 could herald the start of more acquisitions for Microsoft to bolster a wider customer engagement vision.

The Conversational AI war is heating up and various other cloud vendors such as Google and AWS are starting to get aggressive and have made investments in recent years to enhance their Conversational AI capabilities. Google Dialogflow has been seeing rapid uptake and they now have deep partnerships with Genesys, Avaya, Cisco and other contact centre players. Microsoft coming into the game and acquiring a company with years of history and IP in the speech space, demonstrates how the cloud battle and the war between Google, Microsoft and AWS is heating up in the Conversational AI. All of a sudden you have Microsoft as a powerhouse in this game.”


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Building Trust in your AI Solutions

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In this blog, our guest author Shameek Kundu talks about the importance of making AI/ machine learning models reliable and safe. “Getting data and algorithms right has always been important, particularly in regulated industries such as banking, insurance, life sciences and healthcare. But the bar is much higher now: more data, from more sources, in more formats, feeding more algorithms, with higher stakes.”

Building trust in algorithms is essential. Not (just) because regulators want it, but because it is good for customers and business. The good news is that with the right approach and tooling, it is also achievable.

Getting data and algorithms right has always been important, particularly in regulated industries such as banking, insurance, life sciences and healthcare. But the bar is much higher now: more data, from more sources, in more formats, feeding more algorithms, with higher stakes. With the increased use of Artificial Intelligence/ Machine Learning (AI/ML), today’s algorithms are also more powerful and difficult to understand.

A false dichotomy

At this point in the conversation, I get one of two reactions. One is of distrust in AI/ML and a belief that it should have little role to play in regulated industries. Another is of nonchalance; after all, most of us feel comfortable using ‘black-boxes’ (e.g., airplanes, smartphones) in our daily lives without being able to explain how they work. Why hold AI/ML to special standards?

Both make valid points. But the skeptics miss out on the very real opportunity cost of not using AI/ML – whether it is living with historical biases in human decision-making or simply not being able to do things that are too complex for a human to do, at scale. For example, the use of alternative data and AI/ML has helped bring financial services to many who have never had access before.

On the other hand, cheerleaders for unfettered use of AI/ML might be overlooking the fact that a human being (often with a limited understanding of AI/ML) is always accountable for and/ or impacted by the algorithm. And fairly or otherwise, AI/ML models do elicit concerns around their opacity – among regulators, senior managers, customers and the broader society. In many situations, ensuring that the human can understand the basis of algorithmic decisions is a necessity, not a luxury.

A way forward

Reconciling these seemingly conflicting requirements is possible. But it requires serious commitment from business and data/ analytics leaders – not (just) because regulators demand it, but because it is good for their customers and their business, and the only way to start capturing the full value from AI/ML.

1. ‘Heart’, not just ‘Head’

It is relatively easy to get people excited about experimenting with AI/ML. But when it comes to actually trusting the model to make decisions for us, we humans are likely to put up our defences. Convincing a loan approver, insurance under-writer, medical doctor or front-line sales-person to trust an AI/ML model – over their own knowledge or intuition – is as much about the ‘heart’ as the ‘head’. Helping them understand, on their own terms, how the alternative is at least as good as their current way of doing things, is crucial.

2. A Broad Church

Even in industries/ organisations that recognise the importance of governing AI/ML, there is a tendency to define it narrowly. For example, in Financial Services, one might argue that “an ML model is just another model” and expect existing Model Risk teams to deal with any incremental risks from AI/ML.

There are two issues with this approach:

First, AI/ML models tend to require a greater focus on model quality (e.g., with respect to stability, overfitting and unjust bias) than their traditional alternatives. The pace at which such models are expected to be introduced and re-calibrated is also much higher, stretching traditional model risk management approaches.

Second, poorly designed AI/ML models create second order risks. While not unique to AI/ML, these risks become accentuated due to model complexity, greater dependence on (high-volume, often non-traditional) data and ubiquitous adoption. One example is poor customer experience (e.g., badly communicated decisions) and unfair treatment (e.g., unfair denial of service, discrimination, misselling, inappropriate investment recommendations). Another is around the stability, integrity and competitiveness of financial markets (e.g., unintended collusion with other market players). Obligations under data privacy, sovereignty and security requirements could also become more challenging.

The only way to respond holistically is to bring together a broad coalition – of data managers and scientists, technologists, specialists from risk, compliance, operations and cyber-security, and business leaders.

3. Automate, Automate, Automate

A key driver for the adoption and effectiveness of AI/ ML is scalability. The techniques used to manage traditional models are often inadequate in the face of more data-hungry, widely used and rapidly refreshed AI/ML models. Whether it is during the development and testing phase, formal assessment/ validation or ongoing post-production monitoring,  it is impossible to govern AI/ML at scale using manual processes alone.

o, somewhat counter-intuitively, we need more automation if we are to build and sustain trust in AI/ML. As humans are accountable for the outcomes of AI/ ML models, we can only be ‘in charge’ if we have the tools to provide us reliable intelligence on them – before and after they go into production. As the recent experience with model performance during COVID-19 suggests, maintaining trust in AI/ML models is an ongoing task.

***

I have heard people say “AI is too important to be left to the experts”. Perhaps. But I am yet to come across an AI/ML practitioner who is not keenly aware of the importance of making their models reliable and safe. What I have noticed is that they often lack suitable tools – to support them in analysing and monitoring models, and to enable conversations to build trust with stakeholders. If AI is to be adopted at scale, that must change.

Shameek Kundu is Chief Strategy Officer and Head of Financial Services at TruEra Inc. TruEra helps enterprises analyse, improve and monitor quality of machine


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Policy Making in a Pandemic: Use of AI in SupTech

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Artificial Intelligence (AI) is becoming embedded in financial services across consumer interactions and core business processes, including the use of chatbots and natural language processing (NLP) for KYC/AML risk assessment.

But what does AI mean for financial regulators? They are also consuming increasing amounts of data and are now using AI to gain new insights and inform policy decisions. 

The efficiencies that AI offers can be harnessed in support of compliance within both financial regulation (RegTech) and financial supervision (SupTech). Authorities and regulated institutions have both turned to AI to help them manage the increased regulatory requirements that were put in place after the 2008 financial crisis. Ecosystm research finds that compliance is key to financial institutions (Figure 1).

Drivers for Cybersecurity and Regulatory Investments

SupTech is maturing with more robust safeguards and frameworks, enabling the necessary advancements in technology implementation for AI and Machine Learning (ML) to be used for regulatory supervision. The Bank of England and the UK Financial Conduct Authority surveyed the industry in March 2019 to understand how and where AI and ML are being used, and their results indicated 80% of survey respondents were using ML. The most common application of SupTech is ML techniques, and more specifically NLP to create more efficient and effective supervisory processes.

Let us focus on the use of NLP, specifically on how it has been used by banking authorities for policy decision making during the COVID-19 crisis. AI has the potential to read and comprehend significant details from text. NLP, which is an important subset of AI, can be seen to have supported operations to stay updated with the compliance and regulatory policy shifts during this challenging period.

Use of NLP in Policy Making During COVID-19

The Financial Stability Board (FSB) coordinates at the international level, the work of national financial authorities and international standard-setting bodies in order to develop and promote the implementation of effective regulatory, supervisory and other financial sector policies. A recent FSB report delivered to G20 Finance Ministers and Central Bank Governors for their virtual meeting in October 2020 highlighted a number of AI use cases in national institutions.

We illustrate several use cases from their October report to show how NLP has been deployed specifically for the COVID-19 situation. These cases demonstrate AI aiding supervisory team in banks and in automating information extraction from regulatory documents using NLP.

De Nederlandsche Bank (DNB)

The DNB is developing an interactive reporting dashboard to provide insight for supervisors on COVID-19 related risks. The dashboard that is in development, enables supervisors to have different data views as needed (e.g. over time, by bank). Planned SupTech improvements include incorporating public COVID-19 information and/or analysing comment fields with text analysis.

Monetary Authority of Singapore (MAS)

MAS deployed automation tools using NLP to gather international news and stay abreast of COVID-19 related developments. MAS also used NLP to analyse consumer feedback on COVID-19 issues, and monitor vulnerabilities in the different customer and product segments. MAS also collected weekly data from regulated institutions to track the take-up of credit relief measures as the pandemic unfolded. Data aggregation and transformation were automated and visualised for monitoring.

US Federal Reserve Bank Board of Governors

One of the Federal Reserve Banks in the US is currently working on a project to develop an NLP tool used to analyse public websites of supervised regulated institutions to identify information on “work with your customer” programs, in response to the COVID-19 crisis.

Bank of England

The Bank developed a Policy Response Tracker using web scraping (targeted at the English versions of each authority/government website) and NLP for the extraction of key words, topics and actions taken in each jurisdiction. The tracker pulls information daily from the official COVID-19 response pages then runs it through specific criteria (e.g.  user-defined keywords, metrics and risks) to sift and present a summary of the information to supervisors.

Market Implications

Even with its enhanced efficiencies, NLP in SupTech is still an aid to decision making and cannot replace the need for human judgement. NLP in policy decision is performing clearly defined information gathering tasks with greater efficiency and speed. But NLP cannot change the quality of the data provided, so data selection and choice are still critical to effective policy making.  

For authorities, the use of SupTech could improve oversight, surveillance, and analytical capabilities. These efficiency gains and possible improvement in quality arising from automation of previously manual processes could be consideration for adoption.

Attention will be paid in 2021 to focusing on automation of processes using AI (Figure 2).

Digital Focus for 2021 in Financial Services

Based on a survey done by the FSB of its members (Figure 3), the majority of their respondents had a SupTech innovation or data strategy in place, with the use of such strategies growing significantly since 2016.

Summary

For more mainstream adoption, data standards and use of effective governance frameworks will be important. As seen from the FSB survey, SupTech applications are now used in reporting, data management and virtual assistance. But institutions still send the transaction data history in different reporting formats which results in a slower process of data analysing and data gathering. AI, using NLP, can help with this by streamlining data collection and data analytics. While time and cost savings are obvious benefits, the ability to identify key information (the proverbial needle in the haystack) can be a significant efficiency advantage.


Singapore FinTech Festival 2020: Infrastructure Summit

For more insights, attend the Singapore FinTech Festival 2020: Infrastructure Summit which will cover topics tied to creating infrastructure for a digital economy; and RegTech and SupTech policies to drive innovation and efficiencies in a co-Covid-19 world.

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Conversational AI Gets a Boost – Five9 Acquires Inference Solutions

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Five9, a cloud-based contact centre solutions provider announced the acquisition of intelligent virtual agent (IVA) platform provider, Inference Solutions for about USD 172 million. Five9 and Inference Solutions have been partnering for the last couple of years, with Five9 being a reseller for Inference Solutions’ IVA platform. The acquisition is expected to provide a boost to Five9’s AI portfolio, automate contact centre agent activities and provide AI-based omnichannel self-service solutions.

The need to drive greater automation in the contact centre is high on the agenda, and this acquisition demonstrates how important AI and automation is to contact centre modernisation. The old-fashioned ways of long wait times, being passed on through different menus on the IVR and being asked to repeat yourself through the older speech recognition engines is starting to not only frustrate customers but will become obsolete. Based on Ecosystm’s research, close to 60% of contact centres globally stated that investing in machine learning and AI is a top customer experience priority in the next 12 months.

Inference has come a long way since its inception at Telstra Labs

Inference Solutions (founded in 2005) was spun out of Telstra Labs. It has since expanded to the US and developed a suite of solutions in the IVA segment. They have a good partnership strategy with the leading telecom providers globally as well as the UC/contact centre vendors. Inference Solutions uses resellers such as service providers, UC, and contact centre software providers – and these include AT&T, Cisco (Broadsoft), Momentum Telecom, Nextiva, 8×8 and many others. The Inference Studio solution will see a new release in the next few months where the solution will come pre-built with the ability for the contact centre team to pre-load the contact centre conversations. These can be conversations that have been going on for 6 months or longer. The Studio solution will then be able to analyse and understand the underlying intent of the conversation, match the intent so that it can be used to auto train the bots accurately. That process of matching the intent and training is expensive and if you can automate some elements of that, it will bring the cost of the deployment down. Its solution integrates into NLP engines from Google, AWS, and IBM. In Australia they continue to work on patents in close partnerships with Melbourne University and RMIT. Throughout its journey, Inference has built a good base of customers in the US, UK, and Australia.

Five9 to accelerate on its vision of AI and Cloud

Contact centre modernisation is high on the agenda for many organisations and this will lead them to build AI and automation at the core of their customer strategies. The discussion spans across the CEO, Digital and Innovation, and the Contact Centre teams.

Five9 had acquired Whendu, an iPaaS platform provider empowering businesses and developers with no-code, visual application workflow tool, optimised for contact centres in November 2019, and Virtual Observer, an innovative provider of cloud-based workforce optimisation, also known as Workforce Engagement Management (WEM) in February of this year.

The pandemic has resulted in increased engagement of contact centres with customers. Companies are gradually looking for ways to automate tasks, deliver better communication, speech and text recognition, decipher languages, and implement solutions mimicking humans. As a solution to these challenges, IVAs are being viewed as efficient and effective digital workers for a modern contact centre. IVAs represent increased throughput, more accurate results, and better-informed agents.

Successful use cases have shown that conversational AI can reduce calls and repetitive queries by 70-90%. IVRs with monolithic, complicated menus will start becoming unpopular and force contact centres to embark on a modernisation and automation strategy. If we evaluate the shift in priorities after COVID-19, we see that organisations are ramping up their self-service capabilities and their adopt of AI and machine learning (Figure 1).  

Contact Centres Conversational AI

The acquisition will give Five9 a foothold in the Asia Pacific region with an initial focus on the Australia market. The Australia market is by far the most advanced cloud contact centre market in the Asia Pacific. Five9 gains a team of staff that will help them fuel the contact centre modernisation discussion across the Asia Pacific. As the region has a complex market, the need to work with local carriers and partners will be critical for further expansion. Five9 has made an important acquisition in building in IVA capability into its CCaaS solution.


Click below to access insights from the Ecosystm Contact Centre Study on visibility into organisations’ priorities when running a Contact Centre (both in-house and outsourced models) and the technologies implemented and being evaluated

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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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A New State of Equilibrium: Thoughts on Post-COVID Predictions

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I recently came across an article which makes 7 predictions for a post COVID world. Upon reflection, I agree with the predictions to varying degrees and decided to comment further.

First, let me share a couple of general observations. Currently, we are still in the eye of the storm. Many are unable to see any light at the end of the tunnel. There is quite a bit of negative sentiments, and some fail to see that the situation will ever improve. I am sure similar thoughts occurred during other crises: the 1918 Pandemic (Spanish Flu); the Great Depression of the 1930s; the Dot.com bust of 2001; SARS in 2003; and the Global Financial Crisis/Great US Recession of 2007. During each of these events, a sense of impending Armageddon came over much of the population. Certainly, in each instance, people did experience some personal and social permanent changes, with which they learned to adapt and cope. But, inevitably, the world did go on and Armageddon did not occur.

One of the basic truths I believe, is that humans require and crave interaction with other humans. Think about the videoconferencing applications. The use of these apps grew exponentially as the main communication channel. Instead of just audio, it was audio and video. These mediums greatly assisted society in coping and adapting. Mankind, and the Natural World, will always find a way.

Here are the predictions from the article:

  • Companies that traffic in digital services and e-commerce will make immediate and lasting gains
  • Remote work will become the default
  • Many jobs will be automated, and the rest will be made remote-capable
  • Telemedicine will become the new normal, signaling an explosion in med-tech innovation
  • The nationwide student debt crisis will finally abate as higher education begins to move online
  • Goods and people will move less often and less freely across national and regional borders
  • After an initial wave of isolationism, multilateral cooperation may flourish

I very much agree with the author’s first prediction. This one is fairly obvious, as it has proven true throughout the crisis with providers such as Amazon, Zoom and others. It is expected to continue into the post COVID world. This is also evident from the findings of the Ecosystm research on the impacts of COVID-19. Organisations intend to continue to use digital technologies, even after the immediate crisis is over (Figure 1).

Top Measures to be retained by organisations Post COVID-19

A Natural State of Equilibrium will Emerge

I believe for each of the areas described in the predictions, there will be various levels of long-term modification. None of them will return to their pre COVID-19 state, as we have all experienced going down the rabbit hole. During the pandemic, due largely to the lockdowns, the pendulum swung significantly towards one side. Many times, when people predict a new view, the current state is considered the New Normal. For me, the relevant question is: Will things stay as they are now, or will there be a new natural state of equilibrium? If so, what will it look like, in each of these areas? I don’t believe there is one answer, or one New Normal for all the dimensions being discussed. I believe a new normal state will potentially be different for each individual, each company/entity and each condition. In a post COVID-19 world there could be 50 shades of grey in each of these areas. 

One of the predictions states that remote work will become the default. It must be remembered that part of work is a collaborative effort. While video conferencing has enabled collaborative efforts, the importance of the accidental interaction at the break room, printer, etc. can’t be under-estimated. It is these unscheduled interactions that enable accidental collaboration which can lead to great solutions. Thus, there will be many shades to the Future of Work – there will not be one absolute. 

A similar example is a prediction for higher education. Part of the learning process a university offers is interacting with people who are not similar to your background or beliefs. That is one of the benefits of a diverse university. Similar to the corporate environment, many different types of learning environments will enable a person to gain great experiences from the time at university.

The advantage of all these alternatives will be the additional options and benefits to people post COVID compared to the pre COVID-19 world. It will present many great opportunities for entrepreneurs and innovators, as well as end-users and consumers. It will create new and iterative ‘middle spaces’. It will be possible for a David to emerge and challenge a Goliath(s).

The two Chinese characters for the word ‘crisis’ are “danger” and “opportunity”. Just as we are in a dangerous time now, it has also presented new and different opportunities. Those opportunities will continue to exist even when the danger has passed. I am also reminded of the old expression “May you live in interesting times”. It very much applies to all of us now and in the future. I wish the same for all of you.

Stay Safe. Stay Healthy. Stay Mentally Positive.


More insights on the impact of the COVID-19 pandemic and technology areas that will see transformation post COVID, as organisations get into the recovery phase, can be found in the Ecosystm Digital Priorities in the New Normal Study
Ecosystm COVID-19 Research Data

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DBS and AWS Collaborate to Upskill Employees

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Organisations are on a fast track to digitalisation. The Ecosystm Digital Priorities in the New Normal study finds that 60% of organisations anticipate increased use of digital technologies for process automation, even after the COVID-19 restrictions are lifted. One of the key challenges that these organisations will face is the lack of internal digital skills – especially in emerging technologies. One of the success metrics of any technology adoption is employee uptake. Without the necessary skills or understanding of the benefits of emerging technology, employees will largely shy away from digital offerings, even the ones that will make their work more efficient and their lives easier.

Organisations are realising the value of making their workforce future ready.

DBS Instilling Company-Wide Digital Culture

Far-sighted companies are collaborating with technology vendors and professional training providers to promote tech awareness and education to futureproof their workforce. DBS Bank in Singapore has collaborated with AWS to train and upskill 3,000 employees – including the leadership team – with AI and machine learning skills through gamification in a DBS x AWS DeepRacer League.

The AWS DeepRacer Leagues have been previously organised in several parts of the world, but the DBS x AWS DeepRacer will be the first to be organised at this scale. The league will enable DBS employees to get their hands-on AI and machine learning tutorials online. They will then have the opportunity to test out their new skills in programming a 3D racing simulator and iteratively fine-tune their models and compete with each other. The learning program is entirely cloud-based and aims to ingrain digital skills in the workforce.

DBS has won several accolades for their digital transformation and innovation initiatives, and they continue to experiment with emerging technologies. In 2019, DBS digitalised and simplified end-to-end credit processing, setting the foundation for advanced credit risk management using data analytics and machine learning. They have also deployed an AI-powered engine for self-service digital options to its retail banking customers. Taking their employees along with them on this journey is a wise move.

Ecosystm Principal Advisor, Ravi Bhogaraju says, “With the increasing use of automation, AI and machine learning, the nature of work and businesses is transforming rapidly. This is creating opportunities for processes to be automated and increasing the use of AI and Deep Learning into the business processes of the organisation. Industry value chains are transforming – AI and machine learning is adding automation, analytics and predictive intelligence to the portfolio. The recent news of DBS and AWS partnering to upskill the bank’s workforce underscores the value of creating a future ready workforce.”  

“Such upskilling efforts add industry-specific context to make them more effective. BCG refers to this as ‘Human + AI’. A recent study from BCG and MIT shows that 18% of companies in the world that are pioneering AI are making money with it. Those companies focus 80% of their AI initiatives on effectiveness and growth, taking better decisions – not replacing humans with AI to save costs.” 

Government Focus on Digital Skills Upgrade

This week, Singapore also saw another initiative to bridge digital skills gaps – this time from the public sector. In 2018, the Government launched its Smart Nation Scholarship program to attract and nurture talent, and later involve them in various departments to drive Singapore’s Smart Nation initiatives. The most recent Smart Nation Scholarship program 2020 attracted 723 applicants (17% more than the previous year). This is a slightly different approach, aimed at attracting digital native employees and mentoring them for digital leadership. After completing their studies, the 15 scholarship recipients are set to join public sector agencies such as Cyber Security Agency of Singapore (CSA), Government Technology Agency (GovTech), and Infocomm Media Development Authority (IMDA), to give the younger generation an opportunity to co-create the country’s Smart Nation vision.   

Bhagaraju says, “Both private and government institutions are working to enhance workforce skills, improve marketability and making the workforce future ready. Industry 4.0 and the digital revolution have created the need to address the skill gaps that have arisen. Government programs such as the Skills Future program in Singapore, Malaysia’s HRD upskilling program, and the EU-28 European Digital initiative are all making a sustained effort to promote lifelong learning and acquisition/upgrading of skills for their respective citizens with quite successful results, that will have long-term impacts.”


More insights on the impact of the COVID-19 pandemic and technology areas that will see transformation, as organisations get into the recovery phase, can be found in the Digital Priorities in the New Normal Study
Ecosystm COVID-19 Research Data

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Global Leaders Partner with Tech Start-ups to Create an Agile Supply Chain

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Global supply chains were impacted early and badly by the COVID-19 pandemic. The fact that the pandemic started in China – the leader in the Manufacturing industry – meant that many enterprises globally had to re-evaluate their supply chain and logistics. This was compounded by the impact on demand – for some sectors the demand went down significantly, while in others, especially for items required to fight the crisis, there was an unexpected spike in demand. There was also the need for many manufacturers and retailers to shift to eCommerce, to directly access the market and sustain their businesses. These sudden shifts that were required of the industry, opened up the need for a global supply chain that is more integrated, agile and responsive.    

Last week, global heavyweights with a stake in the global supply chain, joined a consortium to work on creating that agility. This includes PepsiCo, BMW, Shopify, DHL, and the United States Postal Service and some emerging tech companies. The alliance will actively work on solutions to embed automation and digitalisation in the logistics and supply chain systems. While this consortium was formed last year, recent events have accelerated the need to fix a global problem.

Co-Creation and Innovation

LINK is a collaborative ecosystem, co-founded by Innovation Endeavors and Sidewalk Infrastructure Partners (SIP) to bring together emerging tech start-ups, institutions and global organisations to innovate and make supply chains resilient. The tech start-ups involved include the likes of Fabric, that has large automated micro-fulfillment centres for faster deliveries, and Third Wave Automation, that has developed automated forklifts with enhanced safety measures.  

LINK aims to transform global supply chains, with the use of technologies such as automation, IoT, AI, and Robotics. The solutions developed by the start-ups will be tested in real-life situations, often in large organisations with complex operations. On the other hand, the start-ups will have access to the internal systems of these large organisations to understand the data and their organisational needs.

Ecosystm Principal Advisor, Kaushik Ghatak says, “COVID-19 has brought the need for supply chain agility and resilience to a completely new level of criticality. Companies in the ‘New Normal’ will need higher levels of nimbleness and flexibility to be able to recover from this crisis quickly and sustain in an increasing disruptive world. Increased ability to sense and respond to disruptions will be key to success. It will require better visibility of their entire supply chain, increasing efficiencies, building necessary redundancies (in form of inventory and capacity) where they are required the most – redundancy comes at a cost – and being flexible and innovative to cater to the rapid market and supply-side changes. Rapid digitalisation to build such capabilities will be a key to success.” 

“Managing such rapid changes is usually a struggle for organisations with large and complex supply chains, because of the years of past practices, systems and culture. For them Innovation is a must, but the path to innovation is difficult. The LINK collaboration model is the right step towards addressing that challenge. Collaborating with start-ups can infuse new ideas, more innovative ways of solving a problem and rapid testing of use cases in the areas of IoT, AI and automation.”

Involving Start-ups for Innovation

This initiative is a great example of how larger enterprises are looking to leverage innovations by the start-up community. The Financial Services industry has been an early beneficiary, when it stopped competing with Fintech organisations, partnering with them instead. Other industries have started to recognise the benefits of fast pivots and the role start-ups can play. 

Ecosystm Principal Advisor, Ravi Bhogaraju says, “Bringing together companies that have complementary and unique capabilities to solve industry issues is a great way to speed up experimentation and innovation.”

However, he recognises that forming alliances such as this, comes with its own set of challenges. “One of the key things to recognise in such a construct is that the team members from different possessions bring with them their unique belief systems, organisational and country cultural constructs. Expectations on how things should work, can become quite tricky to navigate. The talent and expertise in such an environment need to be facilitated be able to deliver high quality outcomes.”

Talking about how these constructs can work successfully, delivering what started out to deliver, Bhogaraju says, “An agile team setup can help tremendously as it uses two key principles – People and Interactions over processes; as well as Working models over documentation.”

“A clear expectation setting through contracting at the beginning of the project cycle can help establish the ways of working and rules of engagement. Increased regular feedback and problem solving should continuously fine tune the ways of working. This way teams can get through the norming process at pace and scale and eventually focus on outcomes, rather than fumble over each other and/or have ego flareups.”

“The key is to get to creative problem-solving working cohesively – the intent being to challenge the status quo – stepping outside the box and using all capabilities within the team. Blending the subcultures together using agile way of working and principles, can be a fantastic way to make that happen – failing which you have the challenge of trying to somehow bring together different work products, people and preferences.”

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