Over a century ago, the advent of commercial flights marked a pivotal moment in globalisation, shrinking the time-distance between cities and nations. Less than a century later, the first video call foreshadowed a future where conversations could span continents in real time, compressing the space-distance between people.
The world feels smaller, not literally, but in how we experience space and time. Messages that once took days to deliver arrive instantly. Distances between cities are now measured in hours, not miles. A product designed in New York is manufactured in Shenzhen and reaches London shelves within weeks. In essence, things traverse the world with far less friction than it once did.
Welcome to The Immediate Economy!
The gap between desire and fulfilment has narrowed, driven by technology’s speed and convenience. This time-space annihilation has ushered in what we now call The Immediate Economy.
Such transformations haven’t gone unnoticed, at the click of a button is now a native (sort of cliché) expression. Amidst all this innovation, a new type of consumer has emerged – one whose attention is fleeting and easy to lose. Modern consumers have compelled industries, especially retail and ecommerce, to evolve, creating experiences that not only capture but also hold their interest.
Beyond Usability: Crafting a Memorable User Experience
Selling a product is no longer about just the product itself; it’s about the lifestyle, the experience, and the rush of dopamine with every interaction. And it’s all because of technology.
In a podcast interview with the American Psychological Association, Professor Gloria Mark from the University of California, Irvine, revealed a significant decline in attention spans on screens, from 150 seconds in 2004 to 40 seconds in the last five years. Social media platforms have spoiled the modern consumer by curating content that caters instantly to desires. Influence spills into the retail sector, compelling retailers to create experiences matching the immediacy and personalisation people now expect.
Modern consumers require modern retail experiences. Take Whole Foods, and their recent partnership with Amazon’s Dash Cart, transforming the mundane act of grocery shopping into a seamless dance of efficiency. Shoppers can now glide through aisles with carts that tally selections and debit totals directly from their accounts, rendering checkout lines obsolete. It’s more than convenience; it reimagines retail – a choreography of consumerism where every step is both effortless and calculated.
Whole Foods can analyse data from their Dash Cart technology to gain valuable insights into shopping patterns. The Immediate Economy revolutionises retail, transforming it into a hyper-efficient, personalised experience.
Retail’s new Reality: The Rise of Experiential Shopping
Just as Netflix queues up a binge-worthy series; retailers create shopping experiences as engaging and addictive as your favourite shows.
It’s been a financially rough year for Nike, but that hasn’t stopped them from expanding their immersive retail experience. Nike’s “House of Innovation” leverages 3D holographic tech. Customers can inspect intricate details of sneakers, including the texture of the fabric, the design of the laces, and the construction of the sole. The holographic display can also adjust to different lighting conditions and present the sneaker in various colours, providing a truly immersive and personalised shopping experience.
Fashion commerce platforms like Farfetch are among many integrating Virtual Try-On (VTO) technology. Leveraging the camera and sensors of customer devices, their AR technology overlays a digital image of a handbag onto a live view of a customer, enabling them to see how different styles and sizes would look on you. This approach to ecommerce enhances experiences, elevating interaction.
The 3D holographic display and the AR tech, are unique and visually appealing ways to showcase products, allowing customers to interact with products in a way that is not possible with traditional displays. Each shopping trip feels like the next episode of retail therapy.
The Evolution of Shopptertainment
The bar for quick content consumption is higher than ever thanks to platforms like TikTok and Instagram.
A prime example of this trend is Styl, a tech startup from two Duke students, with their “Tinder for shopping” application. Styl offers a swipeable interface for discovering and purchasing fashion items, seamlessly integrating the convenience and engagement of social media into the retail experience.
Styl goes beyond a simple swipe. By leveraging AI algorithms, it learns your preferences and curates a personalised feed of clothing items that align with your taste. Streamlining the shopping process, they deliver a tailored experience that caters to the modern consumer’s desire for convenience and personalisation.
Interestingly, Styl isn’t even a retail company; it pools items from websites, redirecting the users with relevant interest. They combine ecommerce with AI, creating the ultimate shopping experience for today’s customer. It’s fast, customised, and changing the way we shop.
Styl is not the first ones to do this, Instagram and TikTok provide individualised suggestions within their marketplace. But they differ by selling an experience, a vibe. That’s what sets them apart.
Tech-Powered Retail: The Heart of the Immediate Economy
History is filled with examples of societal innovation, but the Immediate Economy is transforming retail in exciting ways. In the 21st century, technology is both the catalyst and the consequence of the retail industry transformation. It began by capturing and fragmenting the average consumer’s attention, and now it’s reshaping consumer-brand relationships.
Today’s consumers crave personalised shopping. Whole Foods, with its AI-driven Dash Carts, is redefining convenience. Nike and Farfetch, through immersive AR and 3D tech, is making shopping an interactive adventure. Meanwhile, startups like Styl are leveraging AI to bring personalized fashion choices directly to consumers’ smartphones. The world is shrinking, not just in miles, but in the milliseconds it takes to satisfy a desire. From the aisles of Whole Foods to the virtual showrooms of Farfetch, The Immediate Economy offers an immersive world, where time and space bend to technology’s will, and instant gratification is no longer a perk; it’s an expectation. The Immediate Economy is here, and it’s changing how we interact with the world around us. Welcome to the future of retail, and everything else.
AI has become a business necessity today, catalysing innovation, efficiency, and growth by transforming extensive data into actionable insights, automating tasks, improving decision-making, boosting productivity, and enabling the creation of new products and services.
Generative AI stole the limelight in 2023 given its remarkable advancements and potential to automate various cognitive processes. However, now the real opportunity lies in leveraging this increased focus and attention to shine the AI lens on all business processes and capabilities. As organisations grasp the potential for productivity enhancements, accelerated operations, improved customer outcomes, and enhanced business performance, investment in AI capabilities is expected to surge.
In this eBook, Ecosystm VP Research Tim Sheedy and Vinod Bijlani and Aman Deep from HPE APAC share their insights on why it is crucial to establish tailored AI capabilities within the organisation.
The tech industry tends to move in waves, driven by the significant, disruptive changes in technology, such as cloud and smartphones. Sometimes, it is driven by external events that bring tech buyers into sync – such as Y2K and the more recent pandemic. Some tech providers, such as SAP and Microsoft, are big enough to create their own industry waves. The two primary factors shaping the current tech landscape are AI and the consequential layoffs triggered by AI advancements.
While many of the AI startups have been around for over five years, this will be the year they emerge as legitimate solutions providers to organisations. Amidst the acceleration of AI-driven layoffs, individuals from these startups will go on to start new companies, creating the next round of startups that will add value to businesses in the future.
Tech Sourcing Strategies Need to Change
The increase in startups implies a change in the way businesses manage and source their tech solutions. Many organisations are trying to reduce tech debt, by typically consolidating the number of providers and tech platforms. However, leveraging the numerous AI capabilities may mean looking beyond current providers towards some of the many AI startups that are emerging in the region and globally.
The ripple effect of these decisions is significant. If organisations opt to enhance the complexity of their technology architecture and increase the number of vendors under management, the business case must be watertight. There will be less of the trial-and-error approach towards AI from 2023, with a heightened emphasis on clear and measurable value.
AI Startups Worth Monitoring
Here is a selection of AI startups that are already starting to make waves across Asia Pacific and the globe.
- ADVANCE.AI provides digital transformation, fraud prevention, and process automation solutions for enterprise clients. The company offers services in security and compliance, digital identity verification, and biometric solutions. They partner with over 1,000 enterprise clients across Southeast Asia and India across sectors, such as Banking, Fintech, Retail, and eCommerce.
- Megvii is a technology company based in China that specialises in AI, particularly deep learning. The company offers full-stack solutions integrating algorithms, software, hardware, and AI-empowered IoT devices. Products include facial recognition software, image recognition, and deep learning technology for applications such as consumer IoT, city IoT, and supply chain IoT.
- I’mCloud is based in South Korea and specialises in AI, big data, and cloud storage solutions. The company has become a significant player in the AI and big data industry in South Korea. They offer high-quality AI-powered chatbots, including for call centres and interactive educational services.
- H2O.ai provides an AI platform, the H2O AI Cloud, to help businesses, government entities, non-profits, and academic institutions create, deploy, monitor, and share data models or AI applications for various use cases. The platform offers automated machine learning capabilities powered by H2O-3, H2O Hydrogen Torch, and Driverless AI, and is designed to help organisations work more efficiently on their AI projects.
- Frame AI provides an AI-powered customer intelligence platform. The software analyses human interactions and uses AI to understand the driving factors of business outcomes within customer service. It aims to assist executives in making real-time decisions about the customer experience by combining data about customer interactions across various platforms, such as helpdesks, contact centres, and CRM transcripts.
- Uizard offers a rapid, AI-powered UI design tool for designing wireframes, mockups, and prototypes in minutes. The company’s mission is to democratise design and empower non-designers to build digital, interactive products. Uizard’s AI features allow users to generate UI designs from text prompts, convert hand-drawn sketches into wireframes, and transform screenshots into editable designs.
- Moveworks provides an AI platform that is designed to automate employee support. The platform helps employees to automate tasks, find information, query data, receive notifications, and create content across multiple business applications.
- Tome develops a storytelling tool designed to reduce the time required for creating slides. The company’s online platform creates or emphasises points with narration or adds interactive embeds with live data or content from anywhere on the web, 3D renderings, and prototypes.
- Jasper is an AI writing tool designed to assist in generating marketing copy, such as blog posts, product descriptions, company bios, ad copy, and social media captions. It offers features such as text and image AI generation, integration with Grammarly and other Chrome extensions, revision history, auto-save, document sharing, multi-user login, and a plagiarism checker.
- Eightfold AI provides an AI-powered Talent Intelligence Platform to help organisations recruit, retain, and grow a diverse global workforce. The platform uses AI to match the right people to the right projects, based on their skills, potential, and learning ability, enabling organisations to make informed talent decisions. They also offer solutions for diversity, equity, and inclusion (DEI), skills intelligence, and governance, among others.
- Arthur provides a centralised platform for model monitoring. The company’s platform is model and platform agnostic, and monitors machine learning models to ensure they deliver accurate, transparent, and fair results. They also offer services for explainability and bias mitigation.
- DNSFilter is a cloud-based, AI-driven content filtering and threat protection service, that can be deployed and configured within minutes, requiring no software installation.
- Spot AI specialises in building a modern AI Camera System to create safer workplaces and smarter operations for every organisation. The company’s AI Camera System combines cloud and edge computing to make video footage actionable, allowing customers to instantly surface and resolve problems. They offer intelligent video recorders, IP cameras, cloud dashboards, and advanced AI alerts to proactively deliver insights without the need to manually review video footage.
- People.ai is an AI-powered revenue intelligence platform that helps customers win more revenue by providing sales, RevOps, marketing, enablement, and customer success teams with valuable insights. The company’s platform is designed to speed up complex enterprise sales cycles by engaging the right people in the right accounts, ultimately helping teams to sell more and faster with the same headcount.
These examples highlight a few startups worth considering, but the landscape is rich with innovative options for organisations to explore. Similar to other emerging tech sectors, the AI startup market will undergo consolidation over time, and incumbent providers will continue to improve and innovate their own AI capabilities. Till then, these startups will continue to influence enterprise technology adoption and challenge established providers in the market.
Generative AI is seeing enterprise interest and early adoption enhancing efficiency, fostering innovation, and pushing the boundaries of possibility. It has the potential of reshaping industries – and fast!
However, alongside its immense potential, Generative AI also raises concerns. Ethical considerations surrounding data privacy and security come to the forefront, as powerful AI systems handle vast amounts of sensitive information.
Addressing these concerns through responsible AI development and thoughtful regulation will be crucial to harnessing the full transformative power of Generative AI.
Read on to find out the key challenges faced in implementing Generative AI and explore emerging use cases in industries such as Financial Services, Retail, Manufacturing, and Healthcare.
Download ‘Generative AI: Industry Adoption’ as a PDF
The Retail industry has faced significant challenges in recent times. Retailers have had to deliver digital experiences and delivery models; navigate global supply chain disruptions; accommodate the remote work needs of their employees; and keep up with rapidly changing customer expectations. To remain competitive, many retailers have made significant investments in technology.
However, despite these investments, many retailers have struggled to create market differentiation. The need for innovation and constant evolution remains.
As retailers cope with hypersonalisation trends, supply chain vulnerabilities, and the rise of ESG consciousness, the industry is seeing several instances on innovation.
Read on to find out how brands such as Clinique, Gucci, Tommy Hilfiger, Nike, Woolworths, Prada, Levi Strauss, Mahsenei Hashuk and Instacart are using emerging technologies such as the Metaverse and Generative AI to create the much-needed market edge.
Download “The Future of Retail” as a PDF
It is true that the Retail industry is being forced to evolve the experiences they deliver to their customers. However, if Retail organisations are only focused on creating digital experiences, they are not creating the differentiation that will be required to leap ahead of the competition.
It is time for Retail organisations to leverage data to empower multiple roles across the organisation to prepare for the different ways customers want to engage with their brands.
So what are the phases of customer engagement? How are companies such as Singapore Airlines and TikTok preparing for the future of Retail?
Customer Experience teams are focused on creating a great omnichannel experience for their customers – allowing customers to choose their preferred channel or touchpoint. And many of these teams are aware of the challenges of omnichannel – often trying to prise the experience from one channel into another. Too often we create sub-optimal experiences, forcing customers to work harder for the outcome than if they were using other channels.
I know there have been times when I have found it easier to jump in the car and drive to a store or service centre, rather than filling in a convoluted online form or navigating a complex online buying process. I constantly crave larger screens as full web experiences are often better than mobile web experiences (although perhaps that is my ageing eyes!).
One of the factors that came out in a study conducted by Ecosystm and Sitecore is that customers don’t just want personalised experiences – they want optimised experiences. They want to have the right experience on the right device or touchpoint. It is not about the same experience everywhere – the focus should be on optimising experiences for each channel.
We call this “opti-channel”.
Use an Opti-Channel Strategy to Guide Investment and Effort
This is what you are probably doing already – but by accident. I suggest you formalise that strategy. Design customer experiences that are optimised for the right channel or touchpoint – and personalised for each customer. Stop forcing customers into sub-optimal experiences because you were told to make every customer experience an omnichannel one.
The move towards opti-channel accelerates your ability to provide the best experience for each customer, as you ask the important question “Does this channel suit this experience for this customer?” before the fact – not after the experience has been designed. It also eliminates the rework of existing experiences for new channels and provides clear guidance on the next-best action for each employee.
There Will be Conflict Between Opti-Channel and Personalisation
The challenge for opti-channel strategies will be to align them to your personalisation strategy. How will it work when you have analytics driving your personalisation strategy that say customer X wants a fully digital experience but your opti-channel strategy says part of the digital experience is sub-standard? And the answer to this lies in understanding the scope of your experience creation – are you trying to improve the existing experience or are you looking to create a new improved experience?
- If you are improving the existing experience, then you have less license to shift transactions and customer between channels – even if it is a better experience.
- If you are creating a new experience, you have the opportunity to start again with the overall experience and prove to customers that the new experience is actually a better one.
For example, when airlines moved away from in-person check-in to self-check-in kiosks, there was an initial uproar from customers who had not yet experienced it – claiming that it was less personal and less human. But the reality is that the airlines took the check-in screen that the agents were using and made it customer-facing. Travellers can now see the seats and configuration and select what is best for them.
This experience was reinvented again when the check-in moved to web and mobile. By turning the screen around to the customer, the experience actually felt more human and personal – not less. And by scattering agents around the screens and including a human check-in desk for the “exceptions”, the airlines could continue to optimise AND personalise the experience as required.
Opti-Channel Opens Many New Business Opportunities
Your end-state experience should consider what is the best channel or touchpoint for each step in a journey – then determine the logic or ability to shift channels. Pushing customers from a chatbot to web chat is easy. Moving from in-store to online might be harder, but there are currently some retailers looking to merge the in-store and digital experience – from endless aisle solutions to nearly 100% digital in-store. Some shoe and clothing stores offer digital foot and body scans in-store that help customers choose the right size when they shop online. And we are beginning to see the rollout of “magic mirrors” – such as one retailer who has installed them in fitting rooms and you can virtually try different colours of the same item without actually getting them off the shelf.
Businesses are trying to change customer behaviour – whether it is getting them into stores or mainly shopping online or encouraging them to call the contact centre or to even visit a service centre. Creating reasons for why that might be a better option, while also providing scaled-back omnichannel options is a great way to meet the needs of existing customers, create brand loyalty and attract new customers to your company or brand.
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.
Industries continue to innovate and disrupt to create and maintain a competitive edge – and their technology partners evolve their solution offerings to empower them.
We bring to you latest industry news from the Healthcare, Financial Services, Retail, Travel & Hospitality and Entertainment & Media industries to show you how organisations are leveraging technology. Find out more about organisations such as Services Australia, Paypal, Walmart, Zara and Amex – and how tech providers such as IBM, Oracle, Google and Uplift are supporting organisations across industries.
View the latest Ecosystm Bytes on Industries of the Future below, and reach out to our experts if you have questions.
For more ‘byte sized’ insights click on the link below