Artificial Intelligence (AI), heralded as a silver bullet solution, has rapidly permeated diverse sectors and the blockchain realm is no exception. Given the Asia-Pacific region’s concurrent leadership in AI development, it comes as no surprise that APAC will become a hotbed for innovative AI-blockchain hybrid solutions.

Despite widespread blockchain adoption in APAC, the level of engagement differs significantly. Vietnam stands out, particularly in fintech — with almost 90 per cent of Vietnamese individuals involved in decentralised finance. However, expertise in one technology does not translate to the mastery of another.

Blockchain enthusiasts may struggle with AI, and AI experts could find blockchain complex. In fact, despite Vietnam’s strong blockchain adoption, only 27 per cent of its organisations are fully prepared to deploy AI.

The varying stages of blockchain and AI development across APAC countries present a unique landscape for collaboration. These synergistic technologies are poised to drive transformative advancements, fostering greater efficiency, security, and transparency across various industries — from finance to supply chain management. As of now, the combined market for these technologies is projected to exceed US$703 million by 2025.

The cloud plays a pivotal role in this evolution, enabling businesses to develop decentralised, resilient blockchain networks capable of scaling to meet the demands of an expanding user base. Yet, challenges around scalability, regulatory compliance, and sustainability must be addressed to fully realise the potential AI-blockchain innovations.

AI’s transformative influence on blockchain

The decentralised nature of blockchain aligns seamlessly with AI’s growing need for autonomy. AI applications – especially those in collaborative industries like logistics, finance and manufacturing – benefit from distributed systems that enable real-time, secure data sharing across multiple entities.

Blockchain’s decentralised architecture enables seamless, secure data sharing between organisations without relying on a central authority, reducing the risk of single points of failure and ensuring AI systems remain resilient in multi-stakeholder environments.

The financial industry, which handles vast quantities of data, is a prime example of how AI and blockchain can be effectively combined. For instance, AI algorithms can detect fraud and money laundering on blockchain data, while blockchain ensures data security.

For startups, this integration presents an opportunity to compete with larger firms on a more level playing field. As startups leverage blockchain to enhance supply chains, they have begun to recognise the limitations of centralised systems. Although these systems initially offered cost efficiency and better visibility, challenges such as decision-making bottlenecks and reduced flexibility were also faced.

The result? Many are now seeking decentralised solutions that can provide greater adaptability and responsiveness. Blockchain’s decentralised, transparent structure empowers startups to streamline their supply chain management, ensuring improved quality control, traceability, and transparency through smart contracts.

Also Read: Strategies for effectively integrating AI into your organisation

With that, businesses are moving towards a future characterised by automation, transparency, and decentralised decision-making, reshaping traditional paradigms and fostering a more collaborative and innovative environment.

Potential pitfalls with AI-Blockchain integration

Despite the excitement around AI within the blockchain ecosystem, the path forward is riddled with challenges that must be addressed.

One of the most significant barriers to AI-blockchain integration is scalability. Blockchain networks, designed with security and decentralisation as their core features, are not optimised for high transaction throughput. When AI is introduced, particularly resource-intensive machine learning models and real-time data processing, the demands placed on the network increase exponentially. Blockchains are not optimised for frequent data writes and reads, a necessity for AI model training, updates, and optimisation.

Moreover, storing large datasets required by AI systems on blockchain can be inefficient and costly. These can lead to slower processing speeds, higher latency, and escalated operational costs, particularly in cloud-based environments where services are billed based on resource consumption.

Furthermore, with the regulatory landscape surrounding AI and blockchain still evolving, organisations struggle to ensure compliance with data protection laws and industry standards. This uncertainty can lead to reluctance in adopting these technologies, hindering innovation and slowing implementation. This is especially acute for startups operating in multiple countries and managing operations across different regions.

Sustainability concerns also loom large over the future of both AI and blockchain technologies, largely due to the substantial energy and data centre resources needed for their operation. In fact, by 2026, the combined energy usage  of AI and blockchain-powered cryptocurrencies could double to over 1,000 terawatt-hours (Twh), roughly equivalent to the annual electricity consumption of Japan. Addressing these sustainability issues is critical, not only to mitigate environmental impact but also to ensure the long-term viability of both technologies.

Building a strong foundation

Cloud-based blockchain solutions merge the decentralised, transparent, and immutable qualities of blockchain with the scalability and accessibility of cloud computing.

Also Read: Data driven healing: The potential of analytics and AI in advancing mental health

The availability of hybrid and multi-cloud options play a critical role in developing blockchain infrastructure, enabling interoperability and decentralisation. They also address data residency concerns for organisations operating across multiple jurisdictions by storing sensitive data within designated geographic regions, minimising the risk of data breaches and regulatory non-compliance.

This must be complemented with high-performance, purpose-built, dedicated servers like bare-metal servers. Unlike virtualised or shared cloud environments, bare-metal servers provide the raw power and memory capacity to ensure optimal performance — crucial for blockchain’s heavy transaction loads, large-scale data storage, and complex consensus mechanisms. For instance, bare-metal servers can be tailored to optimise Graphics Processing Units (GPUs) for AI algorithms used in conjunction with blockchain for predictive analytics and fraud detection.

Given the data-heavy nature of blockchain networks, cost transparency will be vital for the long-term sustainability of blockchain-based cloud services. Businesses which prioritise maximising uptime and cost-efficiency need more transparent pricing models to manage costs effectively. By adopting affordable and fair cloud services, startups can effectively maintain their exchange infrastructure and focus on core business activities.

Lastly, businesses should also prioritise selecting cloud vendors that integrate sustainability into their operations. Choosing cloud vendors with green data centres which balance power efficiency and scalability while reducing environmental impact will be essential.

As AI-blockchain technologies gain traction in APAC, it will be crucial for blockchain businesses to implement robust strategies to scale their networks and deliver reliable, secure services. Fortifying transparent partnerships between blockchain businesses and cloud service providers will be instrumental in propelling the region towards innovation, transparency and trust.

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In an era where Artificial Intelligence (AI) permeates through every facet of our lives, its impact on the creative sectors has become a focal point of both enthusiasm and debate. As a fervent advocate for the metaverse, I find myself deeply entrenched in the fascinating process of creating virtual universes with AI that cater to the imaginative whims of our clients.

The dialogue surrounding AI’s role in creativity and artistic production is vibrant and multifaceted. Within this context, my journey as a startup co-founder aimed at democratising metaverse creation has been both challenging and enlightening.

Our platform leverages design templates to facilitate the seamless deployment of metaverse environments, aiming to inspire a significant increase in their unique creations. However, the preference among numerous enterprises and brands for custom-tailored metaverse designs that reflect their distinct visions and needs has been a consistent trend.

Navigating challenges with AI innovations

Our ambition to scale and expand rapidly is met with several challenges, primarily centred around the visualisation and design process of metaverse concepts:

• Visualisation hurdles: The initial obstacle is the clients’ inability to fully grasp the envisioned metaverse design through mood boards alone.
• Time-intensive modelling: This leads our skilled 3D modellers to undertake the time-consuming task of creating preliminary 3D drafts for each proposal.
• Iterative design process: Finalising a metaverse project often requires extensive collaboration with the client, involving multiple rounds of design revisions.

The incorporation of AI in our design process, particularly through the use of tools like Dall-E, Midjourney, and Stable Diffusion, has revolutionised the speed at which we can produce metaverse design proposals. This technological advancement has not only doubled our proposal generation speed but has also empowered our sales team to use AI to create visual designs, significantly enhancing our sales conversion rates.

Also Read: Rewriting the creation process of ad creatives using generative AI

The question arises: does the advent of AI spell the demise of human creativity in design? Far from it. AI acts not as a replacement but as a catalyst that amplifies the creative process. The initial spark of creativity continues to originate from the human mind, with AI serving as a tool that brings these ideas to fruition more vividly and rapidly.

This synergy underscores the enduring value of human imagination, guiding AI towards generating outcomes that resonate with depth and meaning.

Expanding the creative horizon

Creativity manifests in myriad forms, not solely through the invention of novel ideas but also through the reinterpretation and expansion of existing concepts. AI democratises the creative process, offering a foundation upon which designers can build, refine, and innovate.

Initial designs generated by AI, though not flawless, serve as springboards for further creative exploration, enabling designers to infuse additional layers of sophistication and personalisation into their work.

The integration of AI into creative workflows represents a pivotal moment in the evolution of the creative industries. It heralds a new era where technology and creativity converge, opening up a realm of possibilities for innovation and expression. The key to harnessing the full potential of this convergence lies in embracing AI not as a threat but as an invaluable ally in the creative journey.

The interplay between AI and creativity in the development of the metaverse is a testament to the symbiotic relationship between technology and human imagination. As we navigate this exciting frontier, it becomes clear that AI serves not to eclipse creativity but to enrich it, offering new avenues for exploration and expression.

The future of creativity in the metaverse and beyond is not just about adapting to the wave of AI but riding it to unlock new dimensions of imaginative possibility. In this journey, the fusion of AI and human creativity heralds a promising horizon for the creative industry, one brimming with opportunities for growth, innovation, and unparalleled artistic expression.

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This article was first published on April 4, 2024

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As organisations navigate their digital transformation, protecting data becomes increasingly vital as it’s the lifeblood of their business operations. However, in an era of escalating cyber threats, safeguarding is no longer just about security — it’s about ensuring data availability and integrity in the face of disasters.

This is where artificial intelligence (AI) is revolutionising modern backup and disaster recovery (DR) strategies.

But does the rise of AI signal the end of traditional backup and disaster recovery methods?

Limitations of traditional backup and recovery

Historically, disaster recovery strategies revolved around physical backup sites or periodic data backups to tapes or external drives. While these methods were once effective, they are increasingly inadequate in today’s cloud-based environment. They struggle to address modern cyber threats like malware, ransomware, and other malicious attacks, which can compromise data long before it’s even backed up.

In its first Cybersecurity Health Report, the Cyber Security Agency of Singapore (CSA) revealed that more than eight in ten organisations experienced a cybersecurity incident in a year, with nearly half encountering it several times.

Consider this scenario: many organisations follow strict daily backup protocols. Yet, when disaster strikes, they discover their backups are compromised because malware had infiltrated their systems long before the threat was detected. Malware can lie dormant for weeks or even months, infecting backup files unnoticed, only to resurface when compromised data is restored during recovery.

This underscores a crucial evolution in backup strategies. Regularly backing up data is no longer enough; we need to ensure that the backed-up is secure, clean, and recoverable. This is where AI becomes indispensable.

AI’s role in modern backup and disaster recovery

AI’s strength lies in its ability to analyse vast amounts of data in real-time, detect anomalies, and respond to potential threats. In the context of backup and disaster recovery, AI-driven solutions can perform real-time malware scans before any data is backed up. This allows AI to detect and isolate threats, ensuring that backups remain secure and reliable for future recovery.

Also Read: Coded in your DNA: How Singapore can help avert a global data storage crisis

Moreover, AI-powered disaster recovery integrates Managed Detection and Response (MDR), where AI continuously monitors systems for potential threats and takes pre-emptive action before those threats escalate. As ransomware attacks become more frequent and severe, such proactive measures are invaluable.

In the past, organisations invested heavily in physical disaster recovery sites that were often left unused until disaster struck. Today, cloud-based data recovery powered AI offers a more agile, cost-effective, and scalable solution.

The shift from reactive to proactive disaster recovery

One of the key advantages AI brings to disaster recovery is the shift from reactive to proactive strategies. Traditionally, organisations would only test their disaster recovery plans sporadically, often after a disaster had already occurred. With AI, continuous monitoring becomes a reality, allowing organisations to identify vulnerabilities before they escalate into larger issues.

For example, AI-integrated MDR solutions can constantly monitor systems for suspicious activity, flagging and containing risks before they can spread. This real-time capability is crucial as cyberattacks become more sophisticated.

In the event of an attack, AI also accelerates response times. Instead of waiting for a disaster to occur and then scrambling to restore data, AI can automate the responses, significantly reducing downtime and minimising business disruptions.

Ethical considerations and the human factor

As AI becomes more pervasive in disaster recovery, new challenges arise, particularly around ethics and governance. The race between cybercriminals and defenders is intensifying, as bad actors also adopt AI to bolster their attacks. To stay ahead, businesses must implement clear ethical guidelines for AI usage in their disaster recovery strategies, ensuring transparency, accountability, and minimising risks, especially concerning sensitive customer data.

However, AI is only as effective as the humans managing it. Regular staff training on cyber threats and data protection remains crucial. Employees are integral to the AI learning model — the more they understand about risks and how to mitigate them, the stronger the organisation’s security posture becomes.

Changing mindset: Treat AI as your partner

One of the ongoing challenges in adopting AI for backup and disaster recovery is that many business leaders still view AI as a complex and expensive tool rather than a critical partner in safeguarding their organisation’s data. To foster AI adoption, this mindset needs to change.

Also Read: Bursting the big data bubble: Why we don’t need more data scientists

AI should be seen as a trusted partner — one that continuously learns and adapts to the organisation’s needs, much like an employee. For AI to reach its full potential, organisations must implement governance frameworks and ethical structures that ensure AI acts in the company’s best interest.

As data volume surges, the need for sophisticated data protection and recovery methods will continue to grow. Experts predict that by 2050, AI will be fully integrated into most business operations, from customer service to public services and beyond.

Many have likened cybersecurity to a game of cat and mouse, where cybercriminals constantly evolve, and solution providers and enforcement agencies are perpetually trying to catchup. AI has the potential to level the playing field. Regardless of any organisation’s reluctance or ignorance over AI, cybercriminals are already utilising it to launch more complex and effective attacks. As a way forward, the consensus among cybersecurity experts is to deploy AI-enabled defences.

With AI, organisations can not only ensure their data is backed up but that it remains secure and recoverable in the face of evolving cyber threats and disasters. However, adapting AI requires a proactive approach: implementing ethical frameworks, training staff, and embracing AI as the critical business partner it is.

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As COVID-19 continues to impact most parts of Asia Pacific, digitising the food system has never been more important. Food safety, hygiene and storage management had already been fundamental to the success of food supply chain systems prior to the pandemic.

With the recent surge of COVID-19 cases in Southeast Asia, consumers have become even more concerned with the source, quality, and safety of their food, leading to an increased need for food safety and accountability.

Consumers across the region have been rethinking their eating habits after the pandemic and shifting away from an ‘on-the-go lifestyle’ to more of a ‘safe in-home consumption’ trend.

The Zebra Technologies’ Food Safety Supply Chain Vision Study found that more than half of the consumers (51 per cent) cite the fear of food borne illness and disease as the reason to learn more about where their food comes from, especially with Singapore researchers recently reporting that the COVID-19 virus can survive in frozen meat for up to three weeks.

As a result, food manufacturers are confronted with the issues of food supply chain transparency. At the same time, they need to meet food safety standards, avoid recalls, maintain compliance, and earn customer trust and loyalty.

Food supply chains will need to bear increased pressure to deliver quality and safe food from the farm, to the factory and finally to the consumer’s table. Concurrently, countries like Singapore, an emerging food tech hub of Asia, must quickly address these issues.

Due to continually increasing consumer demands, food safety will need to be taken more seriously, with increased collaboration between the food industry, regulators, and tech companies to create a safer, more traceable food system.

In Singapore, the government recently launched the Food Manufacturing Industry Digital Plan to help food manufacturers use digital tools to ensure food safety and traceability, amongst other objectives.

Many of these changes will be led by technology-enabled solutions that can garner additional trust and ease business operations by tracing each food item throughout the supply chain.

Also Read: Fixing food waste problem means less hungry people and a great economy

Ultimately, this increased traceability will reduce recalls and food waste, protect consumers by preventing lapses in food safety, and speed up crackdowns on contaminated food. This will provide consumers with the peace of mind in knowing where their food was manufactured, prepared and handled, as a greater number turn toward preparing food at home.

Prioritising consumer care and trust

Only two in 10 consumers surveyed in the Zebra study place complete trust in the industry to ensure food and beverage safety. This severe trust gap stems from various factors, with restaurant kitchen staff hygiene and the fear of food borne illnesses or allergies listed as top food safety concerns for consumers.

Additionally, 60 per cent of surveyed consumers would never return to a restaurant if they contracted a food borne illness from eating its food.

Tracking and traceability also protect brands from damage to their reputations following a food safety incident. Preventing food borne illnesses and product spoilage is a constant challenge in the industry, as one mistake in supply chain management can lead to dangers on store shelves and in restaurants.

With COVID-19, heightened consumer concerns are likely to permanently increase the demand for information and transparency regarding food safety.

Therefore, industry decision-makers can look to technology solutions to ease the strain of curb side and e-commerce deliveries, and at every touchpoint, by improving traceability, safeguarding food items, and mitigating food supply disruptions.

Enhancing traceability in the supply chain

The Zebra’s study also found that 69 per cent of industry decision-makers trust food enterprises’ ability to manage traceability and transparency, with only 35 per cent of consumers stating their assurance.

To address this discrepancy, industry decision-makers can help collect comprehensive data and make that information available to consumers.

Moreover, the study indicates that nine out of 10 decision-makers believe safety and traceability-focused technology can give them a competitive advantage.

Areas that could benefit from devices and technologies include: compliance with food safety and quality guidelines; ensuring proper food handling, transportation and storage; tracking product perishability; intake of raw materials and ingredients; and faster and more efficient lot recall.

Technologies such as RFID tags, rugged handheld mobile computers with scanners, and thermal printers, can track items quickly throughout the supply chain and help increase food product traceability.

Also Read: The spotlight on foodtech: Why we believe that what we put on our plate will determine the future

The implementation of these solutions is projected to rise, with 93 per cent of industry decision-makers surveyed stating that they are planning to increase investment in food monitoring tools within the next year.  It is apparent that companies are recognising the benefits of including these technologies in their operations.

Predictive analytics powered by the visibility provided by these technologies will also allow decision-makers to improve their strategies, optimise transportation efficiency, and tighten loopholes in tracking and traceability.

Future-proof the supply chain with robust digital solutions

Improving food safety is now more challenging more than ever due to the increasing demand and rise of consumers’ expectations. Globalisation also brings new challenges to food supply chain optimisation.

As international trade grows, particularly in a post-pandemic world, so does the necessity for consistent data, reporting and transparency throughout the supply chain.

With traceability and transparency, the future of food safety and food supply seems bright. Comprehensive information and transparency will help eliminate supply chain blind spots.

Companies in Southeast Asia that can demonstrate robust and effective traceability and transparency capabilities will increase business efficiency, protect their consumers and businesses, and ultimately improve customer confidence and loyalty, giving them an edge over their competitors in this rapidly evolving market.

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This article was first published on June 28, 2021

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For most e-commerce companies, the path to profitability will hardly be a straight line. It is a journey that will twist and turn within the entire organisation and always work in tandem with the constantly shifting consumer demands. 

A fast and cost-efficient fulfilment process is the mainstay of every e-commerce company. However, recent consumer expectations like free and expedited shipping often have entrepreneurs barely coping with logistics costs.

Therefore, lowering logistics expenses can be a potential saving site for e-commerce companies in India and worldwide.

According to a report by Ti, the global e-commerce logistics market is expected to grow at 8.6 per cent CAGR between 2020 – 2025. Globally, e-commerce logistics costs make up anywhere between a massive 15 to 30 per cent of total costs and about 20 per cent of product prices depending on the size of the business, industry, national economy, and other factors. 

Last-mile delivery operations are the most significant contributor to global e-commerce logistics costs, making up as much as 53 per cent of total e-commerce logistics costs.

It is closely followed by fulfilment services (warehousing and inventory management) at 47 per cent. Ti’s report shows Amazon’s logistics costs for order fulfilment have risen from 17.8% in 2011 to 30.1% in 2020. 

The cost of e-commerce logistics in India

A report by APAC shows shipment costs were about US$1.40 per package in the past. But as volumes increased and technology improved, this figure declined about 25 per cent, just over a dollar.

Last-mile delivery costs can be broken down into three main categories—first, shipping, which covers the cost of any human resources involved in delivering your package.

Second, operating hubs, where packages are stored before being delivered or picked up by customers themselves (think of drop off points). And third, incentives are offered to drivers who take these last few steps for their clients.

The cost of operating a warehouse or sort centre, whether by the company’s own or third-party logistics firm, is about 10 rupees per shipment. This brings the total for your business’ shipments at INR 75  excluding warehousing fees which can add another 20 -25 per cent.

India’s post-COVID economy is steadily improving with the rise of the e-commerce and warehouse market. Some logistics startups have even acquired deals worth US$425 million. The e-commerce warehousing industry has also seen a boom (expected to achieve a CAGR of 12.6 per cent in the next five years).

It has started investing in Automatic Guided Vehicle (AGVs), Automatic Storage and Retrieval (ASRs), and robotic arms. Such technological advancements will help lower e-commerce delivery costs by 40 per cent.

Also Read: E-commerce logistics is at a tipping point in India as Delhivery raises over US$100M from Carlye, Tiger Global

Some factors contributing to India’s high e-commerce logistics costs are lack of multimodal and intermodal transportation systems, heavy reliance on road transport, fragmented storage infrastructure, bad condition of roads, and most importantly, slow technological adoption.

To improve matters, the government has allotted 18 per cent of the US$1.4 trillion capital expenditure of the National Infrastructure Pipeline till 2025 to develop roads.

Tech to the rescue

Here are seven areas in the supply chain where technology is driving the maximum impact:

• Warehouse automation and data analytics can help lower freight costs the need for human resources and allow you to monitor logistics operations closely. Harnessing the power of blockchain technology provides businesses with the much-needed data to create a clear year-on-year comparison of expenditure, profits, and losses. WMS can improve shipping speeds by making order pickups much faster. 
• Inventory management systems can update businesses on real-time inventory status. This will significantly lower customers’ problem of ordering “out of stock” items because the inventory is not updated in real-time. 
• Real-time tracking: Ecommerce giants like 1mg, Nykaa, and others rely on live order tracking updates to keep customers happy about their post-purchase experience. They can also send automated order status updates to customers via SMS, WhatsApp and emails or redirect them to a fully branded tracking page where customers can enter their tracking IDs to find out the status of their order. The white-labelled tracking page also provides online retailers with great cross-selling and upselling opportunities.
• Returns: E-commerce is notorious for product returns. Tech-enabled reverse logistics or returns management saves businesses hundreds of dollars by reducing churn and increasing Customer Lifetime Value (CLV). It can potentially lower the returns processing costs by 25 per cent while boosting net profit by 2-5 per cent. Some logistics platforms also provide a self-serve returns portal where customers place return requests and enter feedback.

Also Read: Why e-commerce startups will revolutionise the supply chain in Southeast Asia

• Robotics is now becoming one of the key differentiators in e-commerce shipping. It uses drones, robots, automated forklifts, scanners, etc., to reduce time spent on every order processing stage. Some of India’s most prominent e-commerce players, like Flipkart, DTDC, Myntra, etc., use robots to manage orders at large warehouses and distribution centres.
• Automating processes like cargo or freight audits, tracking carrier performance, appropriate carrier allocation, etc., can serve as significant risk mitigators by eliminating inaccuracies, streamlining operations, and preventing delays or shipping exceptions. Technology can prove especially helpful in load planning, driver selection, and route optimisation. It can help strategise delivery routes, order prioritisation, and load planning for optimum fuel and fleet usage. Such technology proves exceptionally useful in countries of the Middle East that lack a National Address system.
  
• Omnichannel fulfilment: With the help of logistics technology, many e-commerce companies have even ventured into adopting the omnichannel fulfilment route. Brands like Aditya Birla, Zara, H&M, and others provide customers with the BOPIS (Buy Online, Pickup In-Store) model. The omnichannel method allows for better utilisation of resources by turning the physical store into fulfilment centres. Brands can then choose to fulfil orders from the nearest brick-and-mortar store.

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This article was first published on February 7, 2022

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