In this Insight, our guest author Anupam Verma talks about how the Global Capability Centres (GCCs) in India are poised to become Global Transformation Centres. “In the post-COVID world, industry boundaries are blurring, and business models are being transformed for the digital age. While traditional functions of GCCs will continue to be providing efficiencies, GCCs will be ‘Digital Transformation Centres’ for global businesses.”
India has a lot to offer to the world of technology and transformation. Attracted by the talent pool, enabling policies, digital infrastructure, and competitive cost structure, MNCs have long embraced India as a preferred destination for Global Capability Centres (GCCs). It has been reported that India has more than 1,700 GCCs with an estimated global market share of over 50%.
GCCs employ around 1 million Indian professionals and has an immense impact on the economy, contributing an estimated USD 30 billion. US MNCs have the largest presence in the market and the dominating industries are BSFI, Engineering & Manufacturing, Tech & Consulting.
GCC capabilities have always been evolving
The journey began with MNCs setting up captives for cost optimisation & operational excellence. GCCs started handling operations (such as back-office and business support functions), IT support (such as app development and maintenance, remote IT infrastructure, and help desk) and customer service contact centres for the parent organisation.
In the second phase, MNCs started leveraging GCCs as centers of excellence (CoE). The focus then was product innovation, Engineering Design & R&D. BFSI and Professional Services firms started expanding the scope to cover research, underwriting, and consulting etc. Some global MNCs that have large GCCs in India are Apple, Microsoft, Google, Nissan, Ford, Qualcomm, Cisco, Wells Fargo, Bank of America, Barclays, Standard Chartered, and KPMG.
In the post-COVID world, industry boundaries are blurring, and business models are being transformed for the digital age. While traditional functions of GCCs will continue to be providing efficiencies, GCCs will be “Digital Transformation Centres” for global businesses.
The New Age GCC in the post-COVID world
On one hand, the pandemic broke through cultural barriers that had prevented remote operations and work. The world became remote everything! On the other hand, it accelerated digital adoption in organisations. Businesses are re-imagining customer experiences and fast-tracking digital transformation enabled by technology (Figure 1). High digital adoption and rising customer expectations will also be a big catalyst for change.
In last few years, India has seen a surge in talent pool in emerging technologies such as data analytics, experience design, AI/ML, robotic process automation, IoT, cloud, blockchain and cybersecurity. GCCs in India will leverage this talent pool and play a pivotal role in enabling digital transformation at a global scale. GCCs will have direct and significant impacts on global business performance and top line growth creating long-term stakeholder value – and not be only about cost optimisation.
GCCs in India will also play an important role in digitisation and automation of existing processes, risk management and fraud prevention using data analytics and managing new risks like cybersecurity.
More and more MNCs in traditional businesses will add GCCs in India over the next decade and the existing 1,700 plus GCCs will grow in scale and scope focussing on innovation. Shift of supply chains to India will also be supported by Engineering R & D Centres. GCCs passed the pandemic test with flying colours when an exceptionally large workforce transitioned to the Work from Home model. In a matter of weeks, the resilience, continuity, and efficiency of GCCs returned to pre-pandemic levels with a distributed and remote workforce.
A Final Take
Having said that, I believe the growth spurt in GCCs in India will come from new-age businesses. Consumer-facing platforms (eCommerce marketplaces, Healthtechs, Edtechs, and Fintechs) are creating digital native businesses. As of June 2021, there are more than 700 unicorns trying to solve different problems using technology and data. Currently, very few unicorns have GCCs in India (notable names being Uber, Grab, Gojek). However, this segment will be one of the biggest growth drivers.
Currently, only 10% of the GCCs in India are from Asia Pacific organisations. Some of the prominent names being Hitachi, Rakuten, Panasonic, Samsung, LG, and Foxconn. Asian MNCs have an opportunity to move fast and stay relevant. This segment is also expected to grow disproportionately.
New age GCCs in India have the potential to be the crown jewel for global MNCs. For India, this has a huge potential for job creation and development of Smart City ecosystems. In this decade, growth of GCCs will be one of the core pillars of India’s journey to a USD 5 trillion economy.
The views and opinions mentioned in the article are personal.
Anupam Verma is part of the Senior Leadership team at ICICI Bank and his responsibilities have included leading the Bank’s strategy in South East Asia to play a significant role in capturing Investment, NRI remittance, and trade flows between SEA and India.
Organisations have found that it is not always desirable to send data to the cloud due to concerns about latency, connectivity, energy, privacy and security. So why not create learning processes at the Edge?
What challenges does IoT bring?
Sensors are now generating such an increasing volume of data that it is not practical that all of it be sent to the cloud for processing. From a data privacy perspective, some sensor data is sensitive and sending data and images to the cloud will be subject to privacy and security constraints.
Regardless of the speed of communications, there will always be a demand for more data from more sensors – along with more security checks and higher levels of encryption – causing the potential for communication bottlenecks.
As the network hardware itself consumes power, sending a constant stream of data to the cloud can be taxing for sensor devices. The lag caused by the roundtrip to the cloud can be prohibitive in applications that require real-time response inputs.
Machine learning (ML) at the Edge should be prioritised to leverage that constant flow of data and address the requirement for real-time responses based on that data. This should be aided by both new types of ML algorithms and by visual processing units (VPUs) being added to the network.
By leveraging ML on Edge networks in production facilities, for example, companies can look out for potential warning signs and do scheduled maintenance to avoid any nasty surprises. Remember many sensors are linked intrinsically to public safety concerns such as water processing, supply of gas or oil, and public transportation such as metros or trains.
Ecosystm research shows that deploying IoT has its set of challenges (Figure 1) – many of these challenges can be mitigated by processing data at the Edge.
Predictive analytics is a fundamental value proposition for IoT, where responding faster to issues or taking action before issues occur, is key to a high return on investment. So, using edge computing for machine learning located within or close to the point of data gathering can in some cases be a more practical or socially beneficial approach.
In IoT the role of an edge computer is to pre-process data and act before the data is passed on to the main server. This allows a faster, low latency response and minimal traffic between the cloud server processing and the Edge. However, a better understanding of the benefits of edge computing is required if it has to be beneficial for a number of outcomes.
If we can get machine learning happening in the field, at the Edge, then we reduce the time lag and also create an extra trusted layer in unmanned production or automated utilities situations. This can create more trusted environments in terms of possible threats to public services.
What kind of examples of machine learning in the field can we see?
Healthcare
Health systems can improve hospital patient flow through machine learning (ML) at the Edge. ML offers predictive models to assist decision-makers with complex hospital patient flow information based on near real-time data.
For example, an academic medical centre created an ML pipeline that leveraged all its data – patient administration, EHR and clinical and claims data – to create learnings that could predict length of stay, emergency department (ED) arrival models, ED admissions, aggregate discharges, and total bed census. These predictive models proved effective as the medical centre reduced patient wait times and staff overtime and was able to demonstrate improved patient outcomes. And for a medical centre that use sensors to monitor patients and gather requests for medicine or assistance, Edge processing means keeping private healthcare data in-house rather than sending it off to cloud servers.
Retail
A retail store could use numerous cameras for self-checkout and inventory management and to monitor foot traffic. Such specific interaction details could slow down a network and can be replaced by an on-site Edge server with lower latency and a lower total cost. This is useful for standalone grocery pop-up sites such as in Sweden and Germany.
In Retail, k-nearest neighbours is often used in ML for abnormal activity analysis – this learning algorithm can also be used for visual pattern recognition used as part of retailers’ loss prevention tactics.
Summary
Working with the data locally on the Edge, creates reduced latency, reduced cloud usage and costs, independence from a network connection, more secure data, and increased data privacy.
Cloud and Edge computing that uses machine learning can together provide the best of both worlds: decentralised local storage, processing and reaction, and then uploading to the cloud, enabling additional insights, data backups (redundancy), and remote access.
Last week I wrote about the need to remove hype from reality when it comes to AI. But what will ensure that your AI projects succeed?
It is quite obvious that success is determined by human aspects rather than technological factors. We have identified four key organisational actions that enable successful AI implementation at scale (Figure 1).
#1 Establish a Data Culture
The traditional focus for companies has been on ensuring access to good, clean data sets and the proper use of that data. Ecosystm research shows that only 28% of organisations focused on customer service, also focus on creating a data-driven organisational culture. But our experience has shown that culture is more critical than having the data. Does the organisation have a culture of using data to drive decisions? Does every level of the organisation understand and use data insights to do their day-to-day jobs? Is decision-making data-driven and decentralised, needing to be escalated only when there is ambiguity or need for strategic clarity? Do business teams push for new data sources when they are not able to get the insights they need?
Without this kind of culture, it may be possible to implement individual pieces of automation in a specific area or process, applying brute force to see it through. In order to transform the business and truly extract the power of AI, we advise organisations to build a culture of data-driven decision-making first. That organisational mindset, will make you capable implementing AI at scale. Focusing on changing the organisational culture will deliver greater returns than trying to implement piecemeal AI projects – even in the short to mid-term.
#2 Ingrain a Digital-First Mindset
Assuming a firm has passed the data culture hurdle, it needs to consider whether it has adopted a digital-first mindset. AI is one of many technologies that impact businesses, along with AR/VR, IoT, 5G, cloud and Blockchain to name a few. Today’s environment requires firms to be capable of utilising a variety of these technologies – often together – and possessing a workforce capable of using these digital tools.
A workforce with the digital-first mindset looks for a digital solution to problems wherever appropriate. They have a good understanding of digital technologies relevant to their space and understand key digital methodologies – such as Customer 360 to deliver a truly superior customer experience or Agile methodologies to successfully manage AI at scale.
AI needs business managers at the operational levels to work with IT or AI tech teams to pinpoint processes that are right for AI. They need to make an estimation based on historical data of what specific problems require an AI solution. This is enabled by the digital-first mindset.
#3 Demystify AI
The next step is to get business leaders, functional leaders, and business operational teams – not just those who work with AI – to acquire a basic understanding of AI.
They do not need to learn the intricacies of programming or how to create neural networks or anything nearly as technical in nature. However, all levels from the leadership down should have a solid understanding of what AI can do, the basics of how it works, how the process of training data results in improved outcomes and so on. They need to understand the continuous learning nature of AI solutions, getting better over time. While AI tools may recommend an answer, human insight is often needed to make a correct decision off this recommendation.
#4 Drive Implementation Bottom-Up
AI projects need alignment, objectives, strategy – and leadership and executive buy-in. But a very important aspect of an AI-driven organisation that is able to build scalable AI, is letting projects run bottom up.
As an example, a reputed Life Sciences company embarked on a multi-year AI project to improve productivity. They wanted to use NLP, Discovery, Cognitive Assist and ML to augment clinical proficiency of doctors and expected significant benefits in drug discovery and clinical trials by leveraging the immense dataset that was built over the last 20 years.
The company ran this like any other transformation project, with a central program management team taking the lead with the help of an AI Centre of Competency. These two teams developed a compelling business case, and identified initial pilots aligned with the long-term objectives of the program. However, after 18 months, they had very few tangible outcomes. Everyone including doctors, research scientists, technicians, and administrators, who participated in the program had their own interpretation of what AI was not able to do.
Discussion revealed that the doctors and researchers felt that they were training AI to replace themselves. Seeing a tool trying to mimic the same access and understanding of numerous documents baffled them at best. They were not ready to work with AI programs step-by-step to help AI tools learn and discover new insights.
At this point, we suggested approaching the project bottom-up – wherein the participating teams would decide specific projects to take up. This developed a culture where teams collaborated as well as competed with each other, to find new ways to use AI. Employees were shown a roadmap of how their jobs would be enhanced by offloading routine decisions to AI. They were shown that AI tools augment the employees’ cognitive capabilities and made them more effective.
The team working on critical trials found these tools extremely useful and were able to collaborate with other organisations specialising in similar trials. They created the metadata and used ML algorithms to discover new insights. Working bottom-up led to a very successful AI deployment.
We have seen time and again that while leadership may set the strategy and objectives, it is best to let the teams work bottom-up to come up with the projects to implement.
#5 Invest in Upskilling
The four “keys” are important to build an AI-powered, future-proof enterprise. They are all human related – and when they come together to work as a winning formula is when organisations invest in upskilling. Upskilling is the common glue and each factor requires specific kinds of upskilling (Figure 2).
Upskilling needs vary by organisational level and the key being addressed. The bottom line is that upskilling is a universal requirement for driving AI at scale, successfully. And many organisations are realising it fast – Bosch and DBS Bank are some of the notable examples.
How much is your organisation invested in upskilling for AI implementation at scale? Share your stories in the comment box below.
Written with contributions from Ravi Pattamatta and Ratnesh Prasad
You know AI is the absolute next biggest thing. You know it is going to change our world!! It is the little technology trick start-ups use to disrupt industries. It enables crazy applications we have never thought of before! A few days ago, we were dazzled to learn of an AI app that promises to give one a credit rating score based on reading your face – essentially from just a photograph it can tell a prospective financier what the likelihood of your paying back the loan is!
Artificial Intelligence is real and has started becoming mainstream – chatbots using AI to answer queries are everywhere. AI is being used in stock trades, contact centre applications, bank loans processing, crop harvests, self-driving vehicles, and streaming entertainment. It is now part of boardroom discussions and strategic initiatives of CEOs. McKinsey predicts AI will add USD 13 trillion to the global economy by 2030.
Hype vs Reality
So much to like – but why then do we often find leaders shrugging their shoulders? Despite all the good news above there is also another side to AI. For all the green indicators, there are also some red flags (Figure 1). In fact, if one googles “Hype vs reality” the majority of the results returned are to do with AI!!!!
Our experience shows that broad swaths of executives are skeptical of AI. Leaders in a variety of businesses from large multinational banks, consumer packaged goods companies to appliance makers have privately expressed their disappointment at not being able to make AI work for them. They cannot bridge the gap between the AI hype and reality in their businesses.
The data available also bears this out – VentureBeat estimates that 87% of ML projects never make it into production. Ecosystm research suggests that only 7% of organisations have an AI centre of excellence (CoE) – while the remaining depend on ad-hoc implementations. There are several challenges that organisations face in procuring and implementing a successful AI solution – both technology and business (Figure 2).
Visible Patterns Emerge from Successful AI Use Cases
This brings us to an interesting dichotomy – the reality of failed implementations versus the hype surrounding AI. Digital native companies or early adopters of AI form most of the success stories. Traditional companies find it tougher to embark on a successful AI journey. There have been studies that show a staggering gap in the ROI of AI projects between early adopters versus others, and the gulf between the high performers and the rest when using AI.
If we look back to figure 2 and analyse the challenges, we will see certain common themes – many of which are now commonplace wisdom, if not trite. Leadership alignment around AI strategy is the most common one. Getting clean data, aligning strategy with execution, and building the capabilities to use AI are all touted as critical requirements for successful execution. These themes all point to the insight that it is the human element that is more critical – not the technology.
As practitioners we have come across numerous examples of AI projects which go off-track because of human issues. Let’s take the example of an organisation that had enhancing call centre capabilities and capacity using RPA tools, as a key business mandate. There was strong leadership support and enthusiasm. It was clear that a large number of basic level tickets raised by the centre could be resolved using digital agents. This would result in substantial gains in customer experience, through faster ticket resolution and higher employee productivity – it was estimated to be above 30%. However, after two months of launching the pilot only a very small percentage of cases were identified for migration to digital agents.
Very soon, it became clear that these tools were being perceived as a replacement for human skills, rather than to augment their capabilities. The most vocal proponent of the initiative – the head of the customer experience team – became its critic, as he felt that the small savings were not worth the risk of higher agent turnover rates due to perceived job insecurity.
This was turned around by a three-day workshop focused on demonstrating how the job responsibility of agents could be enhanced as portions of their job got automated. The processes were redesigned to isolate parts which could be fully automated and to club non-automated components together driving more responsibility and discretion for agents. Once enhanced responsibility of the call centre staff was identified, managers felt more comfortable and were willing to support the initiative. In the end, the goals set at the start of the project were all met.
In my next blog I will share with you what we consider the winning formula for a successful AI deployment. In the meantime, share with us your AI stories – both of your challenges and successes.
Written with contributions from Ravi Pattamatta and Ratnesh Prasad
Since the start of this millennium, no region has transformed as much as Asia. There has been significant paradigm shifts in the region and the perception that innovation starts in the US or in Europe and percolates through to Asia after a time lag, has been shattered. Asia is constantly demonstrating how dynamic, and technology-focused it is. This is getting fueled by the impact of the growing middle class on consumerism and the spirit of innovation across the region. The region has also seen a surge in new and upcoming business leaders who are embracing change and looking beyond success to creating impact.
What is Driving Innovation in Asia?
The “If you ain’t got it, build it” attitude. One of the key drivers of this shift is the age of the average population in Asia. According to the UN the Asia Pacific region has nearly 60% of the world’s youth population (between the age of 17-24). With youth comes dynamism, a desire to change the world, and innovation. As this age group enters the workforce, they will transform their lives and the companies they work in. They are already showing a spirit of agility when it comes to solving challenges – they will build what they do not have.
The Need to enable Foundational Shifts. The younger generation is more aware of environmental, social and governance issues that the world continues to face. Many of the countries in the region are emerging economies, where these issues become more apparent. COVID-19 has also inculcated an empathy in people and they are thinking of future success in terms of impact. The desire to enable foundational shifts is giving direction to the transformation journey in the region. The wonderful new paradigm that is the Digital Economy allows us to cut across all segments; and technology and its advancements has immense potential to create a more sustainable and inclusive future for the world.
Realising the Power of Momentum. The pandemic has caused major disruptions in the region. But every crisis also presents an opportunity to perhaps re-imagine a brighter world through a digital lens. The other thing that the pandemic has done is made people and organisations realise that to succeed they need to be open to change – and that momentum is important. As organisations had to pivot fast, they realised what I have been saying for years – we shouldn’t “let perfect get in the way of better”. This adaptability and the readiness to fail fast and learn from the mistakes early for eventual success, is leading to faster and more agile transformation journeys.
Where are we seeing the most impact?
Industries are Transforming. There are industries such as Healthcare and Education that had to transform out of a necessity and urgency brought about by the COVID-19 pandemic. This has led to a greater impetus for change and optimism in these industries. These industries will continue to transform as governments focus significantly on creating “Social Safety Nets” and technology plays a key role in enabling critical services across Health, Education and Food Security. Then there are industries, such as the Financial Services and Retail, that had a strong customer focus and were well on their digital journeys before the pandemic. The pandemic boosted these efforts.
But these are not the only industries that are transforming. There are industries that have been impacted more than others. There are several instances of how organisations in these industries are demonstrating not only resilience but innovation. The Travel & Hospitality industry has had several such instances. As business models evolve the industry will see significant changes in digital channels to market, booking engines, corporate service offerings and others, as the overall Digital Strategy is overhauled.
Technologies are Evolving. Organisations depended on their tech partners to help them make the fast pivot required to survive and succeed in the last year – and tech companies have not disappointed. They have evolved their capabilities and continue to offer innovative solutions that can solve many of the ongoing business challenges that organisations face in their innovation journey. More and more technologies such as AI, machine learning, robotics, and digital twins are getting enmeshed together to offer better options for business growth, process efficiency and customer engagement. And the 5G rollouts will only accelerate that. The initial benefits being realized from early adoption of 5G has been for consumers. But there is a much bigger impact that is waiting to be realised as 5G empowers governments and businesses to make critical decisions at the edge.
Tech Start-ups are Flourishing. There are immense opportunities for technology start-ups to grow their market presence through innovative products and services. To succeed these companies need to have a strong investment roadmap; maintain a strong focus on customer engagement; and offer technology solutions that can fulfil the global needs of their customers. Technologies that promote efficiency and eliminate mundane tasks for humans are the need of the hour. However, as the reliance on technology-led transformation increases, tech vendors are becoming acutely aware that they cannot be best-in-class across the different technologies that an organisation will require to transform. Here is where having a robust partner ecosystem helps. Partnerships are bringing innovation to scale in Asia.
We can expect Asia to emerge as a powerhouse as businesses continue to innovate, embed technology in their product and service offerings – and as tech start-ups continue to support their innovation journeys.
Ecosystm CEO Amit Gupta gets face to face with Garrett Ilg, President Asia Pacific & Japan, Oracle to discuss the rise of the Asia Digital economies, the impact of the growing middle class on consumerism and the spirit of innovation across the region.
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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2020 was a strange year for retail. Businesses witnessed significant disruption to supply chains, significant swings in demand for products (toilet paper, puzzles, bikes etc!) and then sometimes incredible growth – as disposable income increased as many consumers are no longer taking expensive holidays. Overall, it was a mixed year, with many retailers closing down and others reporting record sales. The grocery sector boomed – with many restaurants and fast-food providers closed, sometimes the supermarkets were some of the few remaining open retailers.
For many retailers, technology has become a key enabler to their transformation, survival and success (Figure 1).
Woolworths, Australia’s largest retailer, operates across the grocery, department store, drinks, and hospitality sectors. They hold a significant market share in most markets that they operate in. The company had a strong 2019/20 (financial year runs from July 2019 to June 2020) with sales up 8% – and in the first half of the 2020/21 financial year, sales were up nearly 11%. But the company is not resting on its laurels – one of its 6 key priorities is to “Accelerate Digital, eCom and convenience for our increasingly connected customers”. This requires more than just a deep technology investment, but a new culture, new skills, and new ways of working.
Woolworths’ Employee Focus
Woolworths has committed to invest AUD 50 million in upskilling and reskilling their employees in areas such as digital, data analytics, machine learning and robotics over the next three years. The move comes as a response to the way the Retail industry has been disrupted and the need to futureproof to stay relevant and successful. The training will be provided through online platforms and through collaborations with key learning institutions.
The supermarket giant is one of Australia’s largest private employers with more than 200,000 employees. Under Woolworths’ ‘Future of Work Fund’ their staff will be trained across supply chain, store operations, and support functions to enhance delivery and decision-making processes. The retailer will also create an online learning platform that will be accessible by Woolworths employees as well as by other retail and service companies to support the ecosystem. Woolworths has plans to upskill their staff in customer service abilities, leadership skills and agile ways of working.
Woolworths’ upskilling program will also support employees who were impacted by Woolworths planned closures of Minchinbury, Yennora, and Mulgrave distribution centres due in 2025.
Woolworths’ Tech Focus
Woolworths has been ramping up their technology investments and having tech-savvy employees will be key to their future success. In October 2020, Woolworths deployed micro automation technology to revamp their eCommerce facility in Melbourne to speed up the fulfilment of online grocery orders, and front and back-end operations. Woolworths also partnered with Dell Technologies in November 2020 to bring together their private and public cloud onto a single platform to improve mission-critical processes, applications and support inventory management operations across its retail stores.
Future of Work
For many years, Ecosystm has been advising our clients to invest more in the skills of the business. Every business will be using more cloud next year than they are this year; they will suffer more cybersecurity incidents; they will use more AI and machine learning; they will automate more processes than are automated today. More of their customer engagements will be digital, and more insight will be required to drive better outcomes for customers and employees. This all needs new skills – or more people trained on skills that some in the business already understand. But too many businesses don’t train in advance – instead waiting for the need and paying external consultants or expensive new hires for their skills. Empowered businesses – ones that are creating a future-ready, agile business – invest in their people, work environment, business processes and technology to create an environment where innovation, transformation and business change are accepted and encouraged (Figure 2).
Empowered businesses can adapt to new challenges, new market conditions and respond to new competitive threats. By taking these steps to upskill and empower their employees, Woolworths is building towards empowering their own business for long term success.
Transform and be better prepared for future disruption, and the ever-changing competitive environment and customer, employee or partner demands in 2021. Download Ecosystm Predicts: The top 5 Future of Work Trends For 2021.
Many opinions exist on how automation and machine learning will help our return to the office environment. Removing physical touchpoints and leveraging machine learning to trace employee behaviour can help with the transition back to the workplace. But will people trust the office’s automated suggestions on where to work in the building, or help themselves to alternative workspaces?
Processes & Trust for People Engagement
Organisations such as Disney and Amazon understand what kinds of processes and trust it takes to engage people. These organisations took their time to create a vision of the contactless trusted experience before developing an implementation plan. The RFID wristbands at Disney that open hotel doors and get you on to rides involve many elements of trust and privacy. The automated order and delivery tracking of Amazon, along with suggestions and buying patterns, require the person to opt-in and share information to make happen.
So for your company, once employees re-enter the workplace, how will your company create those processes, that level of trust and faith, that would allow movements and health status to be tracked by office automation? For example, how often should employees overtly be aware of their temperature being scanned?
Abilities of Buildings to Manage
Facilities management is trending towards intelligent building management systems (iBMS) which know about room occupancy, room hygiene and are tracking who has been where and with whom. Elevators will limit occupancy and direct users to the correct lift going to the correct location. I have already seen this in our city hospital where you get directed to the correct lift once you have entered information on your destination. This combines user interface devices such as touchless pads, system hardware, and access control management software.
The building can also possibly direct you via a building app to request a place to work. You could swipe your personnel card and then be shown several options based on your personal profile and job role, including private quiet rooms, communal areas, and outside meeting tables. Previous occupants can be noted to share hygiene tracing if necessary. Intelligent buildings already offer direct support to the employees who interact with them for HVAC, lighting control, and occupation sensor. They have the ability to reduce user friction while raising workplace experience metrics to create a measured environment.
User Trust & Participation
Users should be willing to participate to get access. To create the trust that is required for employees to be willing to participate in the process, companies need to share policies and demonstrate stewardship of the data accessed. Who is holding my locational data, for how long, and for what purpose?
Trust facilitates successful data sharing, which in turn reinforces trust. Trust is built when the purpose of data sharing is made clear, and when those involved in the process know each other, understand each other’s expectations, and carry out their commitments as agreed. Trust increases the likelihood of further collaboration and improves core surveillance capacity by supporting surveillance networks.
Conclusion
Will we put our trust in buildings and facilities management on our return to the office? If communication is clear and policy well articulated, the building can play a role in engaging users to return to some standards of in-office participation. But if communication is muddy and policy not made clear, people will make their own way to safety – potentially impacting the environment of others.
Transform and be better prepared for future disruption, and the ever-changing competitive environment and customer, employee or partner demands in 2021. Download Ecosystm Predicts: The top 5 Future of Work Trends For 2021.
The past twelve months have been tough. Most businesses in Singapore (68%) still haven’t seen revenue recover to pre-pandemic levels. Many budgets are down and you are likely to have a long list of spending options that might help you grow revenue and pull your business out of the pandemic-induced slump. Even if your business is doing well, the pressure on budgets is real.
Increasing your CX Spend
Despite the pressure on budgets Ecosystm data makes a strong case to not cut your customer experience (CX) spend! Businesses in Singapore that are cutting their CX spend are less likely to return to growth, more likely to be competing on price (hence cutting margins), not focused on their digital and omnichannel customers, and have lower levels of innovation. Funnily enough, these are also the businesses with complex, legacy systems which need more focus to provide an improved CX! To be quite frank, businesses in Singapore who are cutting CX spend are setting themselves up for failure. With other businesses increasing CX spend, the gap between the customer experiences will grow to a point where customers will leave and it will be hard to catch up.
Prioritising your CX Spend
So now that you have secured your CX spend, where will you get the biggest bang for your buck? Let’s look at where businesses in Singapore are focusing their CX initiatives in 2021.
Offering an omnichannel experience. Your customers expect more than just a great digital experience – they want the right experience at the right touchpoint. The CX leaders in Singapore (who, unsurprisingly are often the market leaders) are already offering great omnichannel experiences, so this is quickly becoming about catching up – and not about getting ahead. Providing a consistent, personalised, and optimised experience across your digital touchpoints needs to be a top priority for your business today. If you are not offering conversational commerce solutions, start that strategy as soon as possible – you need to be where your customers are today. Extending this to physical channels and broader ecosystem partners should also be on your agenda.
Improving knowledge systems. Your knowledge systems don’t do what they say on the box. They don’t provide answers to questions – for employees or customers. In fact, if your customer service agents get asked a question they don’t know the answer to, their number one source for answers is actually their colleagues or team leaders – NOT the knowledge management system! Start investing in systems – or ideally a single system – that help your employees get better, faster answers to questions. Make sure that the system is providing the same answers to both your employees and your customers across all touchpoints – physical and digital.
Migrating customer service platforms to the cloud. Over half the businesses in Singapore that we assessed have this as a top CX priority. Cloud solutions offer faster time to value, lower management costs, give access to more regular improvements and often provide the ability to easily integrate with partners who offer product extensions and customisations. This trend will continue in 2021 and 2022 as more businesses realise that their legacy customer service or contact centre platform is inhibiting their ability to innovate their customer experience. These systems also help businesses to stay compliant and reduce the reliance on internal IT – which has traditionally struggled to keep up with the fast-changing nature of the contact centre and customer service teams.
Investing in AI and machine learning. Many businesses are using AI to provide the personalised and optimised customer experiences they aspire to. AI and machine learning are allowing businesses to create personalised offers, offer a next-best action and automate services. Advanced banks in Singapore can create interest rate offers for each individual customer based on their credit profile and history. 46% of businesses in Singapore are already using AI to offer recommendations for customer service agents, 44% to optimise or test messaging and campaigns and 43% to provide faster, more accurate access to information and knowledge. 18 months ago, AI was a business differentiator – allowing your business to create a stand-out CX. Today AI is quickly becoming a standard practice – the battle now is around using AI to create personalised and optimised experiences.
A great customer experience will be the most important factor in lifting your business to pre-pandemic growth levels and helping your business remain competitive in today’s tough business conditions. When it comes to CX, there is no such thing as “saving your way to growth”.
Your opportunity to drive greater business success lies in your ability to better win, serve and retain your customers. Refresh your customer strategy and capability today to make 2021 an exceptional year for your business.
