With the emergence of blockchain and AI, the digital landscape is starting to change in front of our eyes. Established Web2 business models are starting to get disrupted by Web3 ideas. The emergence of generative AI opens up a lot of business opportunities that were totally
unimaginable before.

Let’s identify the business models poised for disruption in the coming years. Rather than examining each specific model, I want to concentrate on innovative business structures that can transform multiple sectors.

Business models poised for disruption

It has always been clear that blockchain has a huge potential to disrupt traditional business models in finance and beyond. We have experienced different attempts to apply blockchain tech to various business sectors, from logistics to loyalty programs and real estate.

These attempts have been varied in nature; some focused on integrating blockchain into the business infrastructure itself, creating business-specific enterprise blockchains, while others concentrated on bringing more Web3-like business models and tools, such as tokenisation or NFTs, to real-world business.

The enterprise blockchain trend, championed by major corporations like IBM with its Hyperledger blockchain, seems to have lost some momentum. Originally, the idea was to implement closed (permissioned) blockchains in businesses requiring coordination between different units, automated business logic execution, and enhanced transparency.

Also Read: Understanding the role of fintech, blockchain in transitioning to net zero

While this approach has seen moderate success in financial applications, such as interbank settlement networks, its future might hinge on the adoption of Central Bank Digital Currencies (CBDCs).

However, the trend is currently being overshadowed by the use of open blockchain tools and techniques, such as tokenisation, in traditional business models. This shift is quite telling, as the core principle of blockchain technology is openness—an aspect that holds significant potential for improvement.

Areas for disruption: Finance, marketing, and social networking

One of the major areas for disruption is, of course, traditional finance. There’s a tangible improvement when we put finance applications on a blockchain footing. A lot of inefficiencies in traditional finance get immediately eliminated when underpinned by blockchain. The execution of traditional finance business logic, as seen in trade finance, can be fully automated through blockchain smart contracts.

Blockchain also creates financial markets that are available 24×7, which significantly improves on traditional platforms. For the new generation of millennials, it is probably totally unclear why they cannot trade Tesla stock on Sundays (but can trade any crypto token any time they wish).

It is inevitable for finance to move to blockchain rails; it is just a natural development fueled by the obvious improvements that blockchain brings to finance, which will be unfolding in the next decade.

Tokenisation can also be a great marketing tool for businesses. NFTs have already been used by major businesses to engage customers. Triggered by the obvious success of meme coins (tradable crypto assets associated with popular memes), we will see similar instruments being used in traditional business marketing and customer acquisition.

Loyalty reward programs can be tokenised, and token airdrops can be used to onboard new customers. It is important to note that open blockchain tools, which are publicly available, are required for this. No blockchain integration into business structures themselves is needed; rather, traditional Web2 businesses start to absorb Web3 ideas and instruments into their operations.

Also Read: Mastering the art of fundraising: Winning strategies to engage investors

Another major area for disruption is social networking. Web2 has been successful in onboarding billions of users into major social networks, but now their inefficiencies and drawbacks are becoming more apparent. Being a very traditional centralised structure, they can be heavily abused when moderation algorithms fail to deal with bot activity efficiently.

This, in turn, leads to tighter centralised control over the posted content and censorship. Social network users do not pay to use it since they are essentially the product; their private data, shared with the network, is used for targeted advertising and resold without explicit consent from the user. Emerging Web3 social networks allow explicit control of all the data shared by the user, which can be monetised by the users themselves.

We should also mention the (in)famous Metaverse narrative, which is also closely connected to blockchain. Although its successful adoption may be more distant, an immersive andall-encompassing digital experience is something that the digital world is converging towards. Despite its early setbacks, we will definitely see a revival of interest in the Metaverse, fueled by progress in hardware and AI.

Final thoughts

The disruption brought about by AI will be massive, and it is really hard to estimate its scale yet. It really depends on how fast we will be able to achieve Artificial General Intelligence (AGI), whose potential role in human progress can probably only be compared to the invention of the wheel.

Large Language Models and generative AI will be disruptive in changing roles in the digital world; most mundane tasks will be handled by LLMs, which can free up a lot of human resources to deal with the tasks that AI can’t handle yet.

Despite the current rather authoritarian trends in the world, which are also partially enabled by IT technology, we can see that the ideas of openness that blockchain brought about are finding their way into the business world, and this trend will be growing.

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The world of cryptocurrency is wild. It’s full of crazy ideas, high risk, and yes, even some laughs. Lately, meme coins, digital currencies based on internet jokes and pop culture have been all the rage. They’ve drawn in investors with their wild price swings and passionate online communities.

Dogecoin, the Shiba Inu dog that started it all, might have begun as a lighthearted jab at Bitcoin, but some meme coins have skyrocketed in value. This leaves many wondering: how do you invest in this wacky but risky corner of the crypto market?

The truth is, there’s no guaranteed way to win with meme coins. Their value depends on a weird mix of things, so the usual ways of judging investments don’t apply as much here. A strong community and lots of trading can be good signs, but you need to look deeper when it comes to these crypto jokesters. Here are some key things to consider, along with a healthy dose of caution:

Looking beyond the hype: A strong community

A big and enthusiastic online following on Reddit, Discord, or Telegram can be a good thing but don’t just look at the surface. Here’s what you really need to see:

• Real talk, not just memes: A good community talks about the memecoin’s future plans, how it might be used for more than just laughs, and how it might work with other projects. Look for people who genuinely care about the coin’s future, not just those mindlessly cheering it on.
• Coders on the case: A dedicated team actively working on the tech behind the meme coin is a good sign. Look for frequent updates, code posted on platforms like Github, and clear ways to talk to the developers.
• Keeping things clean: A well-moderated online community helps get rid of negativity, false information, and scams where people try to pump up the price and then dump their coins for a quick profit. Look for active moderators who keep the conversation healthy.

Trading volume: A double-edged sword

Lots of trading means there’s a lot of interest in the meme coin, which can make the price go up in the short term. But be careful:

• Fake pumps: Beware of sudden spikes in trading that come out of nowhere. These could be the work of “whales” (people with huge amounts of coins) trying to drive the price up so they can sell for a quick profit.

Also Read: How your business can benefit from the NFT phenomenon

• Slow and steady wins the race: Look for trading that gradually increases over time. This suggests real growth, not just a temporary burst of excitement.
• Big exchanges are good: Being on well-known cryptocurrency exchanges makes the meme coin more visible and easier to trade, which can lead to higher trading volume.

Beyond the basics: The x-factors

While a strong community and active trading are important, there are other things that can affect a meme coin’s success:

• Celebrity tweets: A tweet from a big name like Elon Musk can send a meme coin’s price through the roof (remember Dogecoin?). However, relying on celebrities is risky because their interest can fade fast. Ideally, the celebrity actually holds and believes in the meme coin.
• Real-world use: Memecoins that have a real-world purpose, like being used in online games or making payments, are more likely to stick around for the long haul than those that are just hype.
• Fear of missing out (FOMO): This is when people buy something because they’re scared they’ll be left behind if they don’t. Be careful of buying sprees fueled by FOMO, and always do your own research before investing. We’ve seen this happen a lot with meme coins on Solana lately. Hopefully, they’ll show more stable growth later this year.

Laughter is great, but don’t invest based on it

Memecoins can be a fun and interesting part of the crypto world. They create a sense of community and offer the chance to make a lot of money (or lose it all). But if you only invest in them because they’re funny or because there’s a lot of buzz online, you’re setting yourself up for disaster.

By looking at data like how engaged the community is, trading volume, and other important factors, you can approach meme coins with a bit more caution and maybe even some success (without the tears).

Remember, a good meme might make you laugh, but it shouldn’t be the only reason you invest your hard-earned money. And hey, maybe someday we’ll even get that Dogecoin ETF!

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

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As we approach the latter half of 2024, the cryptocurrency landscape is poised for significant transformation. Among the myriad of emerging trends, one stands out as particularly revolutionary: the intersection of artificial intelligence (AI) and blockchain technology. This convergence promises to redefine the crypto ecosystem, offering unprecedented opportunities and challenges.

In this opinion piece, I will delve into why this trend is set to dominate the crypto space, backed by data, expert insights, and a personal perspective on its potential impact.

The convergence of AI and blockchain: A new frontier

The integration of AI and blockchain is not merely a speculative trend; it is a burgeoning reality that is already beginning to reshape various sectors. AI, with its ability to process vast amounts of data and learn from it, complements blockchain’s decentralised, transparent, and secure nature. Together, they form a powerful synergy that can address some of the most pressing issues in the digital world.

One of the most compelling aspects of this convergence is its potential to revolutionise smart contracts. Traditional smart contracts, while innovative, are limited by their static nature. AI can enhance these contracts by making them dynamic and adaptive, capable of learning from past transactions and optimising future ones. This could lead to more efficient and secure decentralised finance (DeFi) applications, reducing the risk of bugs, hacks, and errors that have plagued the sector.

Market data and expert insights

The market’s response to the integration of AI and blockchain has been overwhelmingly positive. According to a report by Gemini, AI-related tokens have seen a notable surge in prices, signalling growing interest and confidence in this emerging trend. This is further corroborated by data from CoinMarketCap, which highlights a significant increase in institutional investments in AI and blockchain projects.

Also Read: Understanding the role of fintech, blockchain in transitioning to net zero

Experts in the field are equally optimistic. Scott Tripp, CEO of Neurai, an AI Startup based in Singapore, notes that the combination of AI and blockchain is leading to innovative projects that merge web3 monetisation, provenance tracking, and digital content attributions. He predicts that AI agents will soon handle most on-chain payments, interfacing with blockchain’s user experience and presenting transactions in a human-friendly manner.

Anndy Lian, a best-selling book author, echoes this sentiment, emphasising the potential of AI and blockchain to create decentralised compute protocols and marketplaces for AI outputs. He believes that while early activity may be driven by hype, the long-term promise of this combination is immense.

Real-world applications and use cases

The practical applications of AI and blockchain are vast and varied. One of the most promising areas is in the realm of secure data solutions. AI can enhance blockchain’s ability to provide secure, transparent, and tamper-proof records, making it ideal for industries such as healthcare, finance, and supply chain management.

In healthcare, for instance, AI can analyse patient data stored on a blockchain to provide personalised treatment plans, predict disease outbreaks, and streamline administrative processes. This not only improves patient outcomes but also reduces costs and inefficiencies.

In finance, AI-powered blockchain platforms can offer more accurate risk assessments, fraud detection, and automated compliance, making financial services more secure and accessible. The integration of AI can also enable more sophisticated trading algorithms, leading to better investment strategies and higher returns.

Supply chain management is another area where AI and blockchain can have a transformative impact. By combining AI’s predictive analytics with blockchain’s transparency, companies can optimise their supply chains, reduce waste, and ensure the authenticity of products. This is particularly important in industries such as pharmaceuticals and luxury goods, where counterfeiting is a major concern.

The role of regulation and security

As with any emerging technology, the integration of AI and blockchain is not without its challenges. One of the primary concerns is regulation. The decentralised nature of blockchain and the autonomous capabilities of AI pose significant regulatory hurdles. Governments and regulatory bodies will need to develop new frameworks to address issues such as data privacy, security, and ethical considerations.

Also Read: Boosting efficiency and care: How AI is transforming medical records

Security is another critical concern. While blockchain is inherently secure, the addition of AI introduces new vulnerabilities. AI algorithms can be manipulated, and the data they rely on can be corrupted. Ensuring the security and integrity of AI-powered blockchain systems will require robust encryption, continuous monitoring, and advanced threat detection mechanisms.

The future of AI and blockchain

Looking ahead, the future of AI and blockchain appears bright. The potential for these technologies to transform industries and create new economic opportunities is immense. However, realising this potential will require collaboration between technologists, regulators, and industry stakeholders.

One of the key drivers of this trend will be the development of AI-powered decentralised applications (dApps). These applications can leverage the strengths of both AI and blockchain to offer innovative solutions in areas such as finance, healthcare, and supply chain management. For instance, AI-powered dApps can provide personalised financial advice, automate complex supply chain processes, and offer real-time health monitoring and diagnostics.

Another important aspect of this trend is the role of AI in enhancing blockchain’s scalability. One of the main challenges facing blockchain technology is its limited scalability, which restricts its ability to handle large volumes of transactions. AI can help address this issue by optimising transaction processing and improving consensus mechanisms, making blockchain more efficient and scalable.

Personal perspective

From a personal perspective, the convergence of AI and blockchain represents a significant leap forward in the evolution of technology. As someone who has closely followed the development of both AI and blockchain, I am excited about the possibilities that this integration offers. The potential to create more secure, efficient, and transparent systems is truly transformative.

However, it is important to approach this trend with a balanced perspective. While the potential benefits are immense, there are also significant challenges that need to be addressed. Ensuring the security and integrity of AI-powered blockchain systems, developing appropriate regulatory frameworks, and addressing ethical considerations will be critical to the success of this trend.

In conclusion, the intersection of AI and blockchain is set to be the standout trend in the crypto space in the latter half of 2024. This convergence promises to revolutionise industries, create new economic opportunities, and address some of the most pressing issues in the digital world.

By leveraging the strengths of both technologies, we can create more secure, efficient, and transparent systems that have the potential to transform our world. As we move forward, it will be essential to address the challenges and ensure that this trend is developed in a responsible and ethical manner.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

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As artificial intelligence (AI) continues to revolutionise industries worldwide, Southeast Asia (SEA) finds itself at a critical juncture.

A recent study by Deloitte states that developing economies in the APAC region are actively implementing generative AI at a faster pace, with a 30 per cent higher share of gen AI users embracing AI with more enthusiasm compared to developed nations with more digitally native workers.

The report cautions that businesses that fail to adopt AI will feel the impact; CEOs and senior leaders should not only focus on integrating generative AI to enhance efficiency but also reconsider their processes to adapt to the AI surge and avoid disruption. 

Interestingly, despite the increased usage of generative AI by employees, businesses may not be maximising the full benefits of their investments in AI. In fact, only half of surveyed employees felt they were fully utilising the potential of generative AI. Out of Searce’s 200+ customers in South East Asia, only five per cent of them have Gen AI use cases deployed in production. 

With companies jumping onto the AI bandwagon, it’s imperative that they are clear on their strategies for AI, maximising their investments to achieve business results and to generate revenue. 

We’ve observed  four categories of companies on the AI journey:

• AI-Explorers: Companies exploring initial use cases with varying degrees of data readiness to harness the power of AI and machine learning (ML) 
• AI-Augmented:  Companies seeking to drive operational efficiencies, using AI & ML to support their operations, with AI secondary to go-to-market (GTM) strategies
• AI-Powered: Companies utilising AI for competitive advantage, using large language models (LLM) to power their primary GTM offerings with products/services seamlessly embedded with AI
• AI-Disrupters: Companies creating new markets, producing LLMs or core AI products for external parties to utilise

We observe that many enterprises fall under the AI Explorer category while most of the investment is going to AI Disrupter organisations. This has created an immediate urgency for organisations to adapt to products/services built by AI Disrupters, but there is a lack of clarity on impactful use cases. 

Adoption framework

To drive the strategic adoption of  AI amongst our clients, we have been deploying the above framework to drive the strategic adoption of AI in businesses. The journey begins with the Discovery phase, where organisations define use cases, conduct design thinking workshops, and create innovation prototypes. This lays the groundwork for understanding how AI can address specific business needs.

Also Read: Transforming customer service: AI’s ‘artificial empathy’ holds the key

The second phase focuses on establishing a solid data foundation and building a compelling business case. This involves checking and upgrading the data infrastructure, calculating ROI, defining expected outcomes, and establishing AI foundations.

The third phase is about execution, with MVP launches, A/B testing, and business case validation. Finally, the framework culminates in scaling and optimising AI solutions, building machine learning operations (MLOps) end-to-end, and scaling business cases.

By following this structured approach, companies can mitigate risks, align AI initiatives with business objectives, and avoid costly missteps in their AI adoption journey.

Cost levers during adoption

The adoption of AI and machine learning technologies involves several key cost levers that organisations must consider for effective budgeting and implementation. 

Technology Costs encompass the core infrastructure needed to run AI systems. This includes expenses for LLMs, CPUs, GPUs, and SaaS subscriptions. 

Additionally, organisations need to factor in costs for API gateways, data acquisition, storage, and processing. The implementation of testing frameworks and MLOps pipelines also falls under this category. Security and Compliance form another crucial cost centre, covering data privacy measures, regulatory approvals, and potential issues such as legal pushback and litigation.

This area also includes the development of ethical and explainable AI systems, which is becoming increasingly important. Lastly, organisational costs involve addressing the skills gap through training, managing change within the company, facilitating cultural shifts, and adapting business models and GTM strategies.

By understanding and planning for these diverse cost levers, organisations can create a comprehensive framework for assessing and managing the financial implications of AI adoption, ensuring a more strategic and cost-effective implementation.

Measuring the ROI 

The framework presented in this image outlines a systematic approach to calculating the ROI of AI adoption, which can be crucial for understanding and managing the costs associated with implementing AI solutions. 

The journey begins with reimagining existing processes through an AI lens, identifying key impact KPIs and their current costs, and mapping out potential business cases for the project. This lays the foundation for a comprehensive understanding of where AI can add value. The identified KPIs and associated costs will anchor the overall ROI calculations. 

Also Read: These Artificial Intelligence startups are proving to be industry game-changers

The second step involves selecting appropriate technologies, and benchmarking KPIs with AI to create best and worst-case scenarios, which may involve proofs of concept, pilots, or high-impact, low-effort (HumanInTheLoop) initiatives. Partnering with the right set of consulting organisations and using the right technology vendor has a significant impact. 

The third step focuses on mapping costs across all three cost levers, identifying both one-time and recurring expenses, and validating the business case by mapping these costs to an ROI model benchmarked against the identified KPI costs.  

The final step is about execution with tight governance, continuous monitoring of benchmarked KPIs, and leveraging MLOps to improve metrics continually. This structured approach ensures that organisations not only understand the full spectrum of costs associated with AI adoption but also have a clear path to measuring and optimising their return on investment.

Overall, the adoption of AI should ideally lead to lower operations costs, increased revenue, or innovation that supports growth metrics for the organisation. 

Managing your AI deployments 

AI adoption involves more than just initial implementation — ongoing management is crucial for maintaining performance and value. AI models, once trained, remain static, but they operate in a dynamic world where reality continuously diverges from the initial training data. This mismatch leads to a gradual decrease in model accuracy over time. To counter this, periodic retraining becomes necessary to maintain performance levels.

Effective AI management requires continuous monitoring and reinforcement. Organisations need to regularly assess model outputs, collect new data, and prepare it for model updates. This ongoing process is essential for keeping AI solutions relevant and accurate.

These guidelines emphasise the importance of viewing AI adoption as a continuous journey rather than a one-time deployment. By recognising the need for persistent maintenance and adaptation, companies can better prepare for the long-term commitment required to maximise the benefits of their AI investments.

The path forward

For SEA businesses, the path forward involves embracing AI not just as a tool but as a strategic asset. This requires a commitment to continuous learning and adaptation. Companies must invest in training their workforce, ensuring they have the skills and knowledge to harness AI’s full potential. This investment pays off in the form of increased efficiency, innovation, and competitive advantage.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

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