Generative AI, or artificial intelligence, has the power to change how we live and work in so many ways; our creativity is the only limit.

At a recent roundtable discussion with Qlik entitled The Future of Data Analytics in the Age of Generative AI, I shared my thoughts about how the newly released foundation models in language or Large language models (LLMs) as we call them today are reshaping the work landscape. LLMs like GPT4, Claude etc., have been fine-tuned using reinforcement learning with human feedback to enable different categories of uses case, as listed below:

LLM as a language facade

Using LLMs as a layer of communication between humans and machines will make talking to software as easy as talking to another person. Instead of clicking through menus, you would just tell the software what you want and facilitate a seamless flow of information, transforming the way we interact with technology.

LLM as a co-pilot

Envision your digital sidekick enhancing your productivity exponentially, a testament to the possibilities of generative AI. LLMs could aid software programmers, supercharging their efficiency. Similarly, artists could use these models for inspiration, discovering new and creative ideas they haven’t thought of before.

LLM as a role-player

The intricate world model these LLMs acquire through training with trillions of words enables them to don any role and act in character. The power of role-play is limited only by our imagination — they could be coaches, companions, or even therapists.

LLM as an orchestrator

Moving beyond single-step interaction, LLMs could handle a series of tasks, making abstract interactions more concrete. Imagine your digital personal assistant breaking down complex processes into sub-tasks and diligently completing them step by step.

Also Read: How to stay creative in the age of Generative AI and Web3

While we are only at the inception of this transformation, several early use cases have already started to gain traction:

Q&A bot based on a data corpus

In the current search engine paradigm, information synthesis is a manual process. Generative AI models like ChatGPT bridges this gap, internalizing and summarising vast amounts of data, offering succinct and accurate responses. The technology eliminates redundant research, enabling users to devote their time to higher-value tasks. We can also engineer them to cite the sources of information they present to ground them.

Customer service bot

AI models can now imitate a range of communication styles and interact with customers in unique ways, allowing for the customisation of content and its delivery. They can change how they talk, their empathy, and their style based on how they want to talk to the customer.

This allows us to not only personalise what we tell the customer but also how we say it. The depth and reasoning behind every response can be engineered, taking customer service to an unprecedented level.

Coaching

AI coaching assistants can now provide an intelligent, interactive training experience. Be it for sales forces or for children learning new concepts, the models can role-play, ask follow-up questions, and provide feedback, offering a personalised learning experience.

In sales training, the AI bot can pretend to be a customer and ask good follow-up questions, pushing trainees to think about what the customer needs and how to sell to them. In education, these bots can act as personal tutors for kids, helping them understand what they’re learning.

Only the beginning

The power of generative AI lies in its ability to democratise data, bringing unstructured and structured data together to unlock business value in enterprises.

We are actively collaborating with companies like Qlik, for example, to see how we might augment and automate manual tasks involved with data management in order to help companies boost their productivity with quality and governance in mind.

Potential use cases of generative AI with data management

Imagination and innovation would carry us to a future of work that we envisage. The key value of AI is in human augmentation – shifting employees and human labour to higher-value work.

However, a lot of engineering still needs to be done to put safeguards in place to make this technology more robust, safe, and fit for customer interaction.

You can watch my full presentation here.

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This article was first published on June 7, 2023

The post How to unlock new horizons with generative AI appeared first on e27.

Supply chain management software has evolved tremendously over the last decade. With cutting-edge technologies like artificial intelligence (AI) and machine learning being incorporated, these solutions are getting smarter and more intuitive every day.

In this article, we’ll look at how AI and ML are changing the game for supply chain management software and enabling organizations to make better and faster data-driven decisions.

Smooth supply chain management software is key for business success in today’s complex and constantly shifting markets. Companies need to manage their supply chain well to deliver products and services while keeping optimal inventory levels.

This means collecting and analysing tons of data on suppliers, production, inventory, transportation, sales, and more. Trying to make sense of all that information manually is challenging and time-consuming.

This is where AI and ML come in super handy. They give companies advanced tools like demand forecasting, inventory optimisation, supplier relationship management, and logistics routing to uncover patterns and insights from complex data to optimise planning and operations in the supply chain. With supply chain management support from AI and ML, businesses can streamline operations, reduce costs, and better serve customers.

AI and ML for more accurate demand forecasting

Accurate demand forecasting is crucial for efficient inventory and production planning. Old-school forecasting relied on statistical methods like moving averages. However, these have limitations in identifying complex nonlinear patterns. AI and ML models can detect intricate relationships and patterns in historical sales data much better. By analysing bigger datasets with more parameters like promotions, pricing, seasons, events, etc., they provide super accurate demand predictions.

ML techniques like neural networks can continuously learn from new data. This allows real-time refinement of forecasts in response to emerging trends. Companies can react faster to changes in customer preferences. AI also enables automated monitoring of forecast accuracy and exception handling for products with unusual trends. Instead of relying on fixed formulas, AI-enabled systems continuously optimise algorithms and models based on results.

Also Read: Hacking customer engagement in Indonesia’s agri supply chain

For example, if demand rises during holiday seasons, ML models can factor this in automatically. As new products are launched or old ones are discontinued, the system adjusts estimations seamlessly. This level of automation and flexibility is impossible to achieve manually.

Smarter inventory optimisation

Keeping optimal inventory is crucial for customer service and working capital management. Too much stock leads to higher carrying costs and obsolescence risks. Too little causes lost sales and backorders. ML algorithms can consider fluctuating demand, supply uncertainties, logistics delays and other constraints to determine ideal stock levels across the network.

AI can also improve inventory productivity by automating warehouses. Computer vision guides autonomous robots to locate and move inventory efficiently. This accelerates order processing, improves accuracy and allows 24/7 operation.

For example, smart inventory optimisation systems can monitor shelf life, seasonal demand shifts, waste reduction goals and other factors to align stock with business objectives beyond just costs. AI enables leaner, flexible and eco-friendly inventory management.

Dynamic supply chain network optimisation

SCM software with AI capabilities can dynamically optimise supply chain networks in response to changing conditions. The AI engine processes massive data on costs, lead times, risks, transportation lanes, sourcing options, duties, exchange rates, etc. It then uses advanced algorithms to determine the optimal locations and capacity of suppliers, factories, warehouses, cross-docks and outlets to minimise costs and maximise service levels.

As conditions change, the system reruns simulations and adapts the supply chain network for optimal performance. Manual design of such complex global networks can take months. But AI-powered software can crunch through numerous scenarios in minutes to optimise the supply chain in real-time.

For instance, weather delays, port congestions or other disruptions can frequently alter transportation costs and lead times. AI enables shifting supply paths dynamically to maintain continuity at the lowest cost. Sudden demand surges can be met efficiently by recalibrating inventory deployment and capacities with AI’s help.

Smarter sourcing and procurement

AI is transforming sourcing and procurement through automation and data insights. For example, routine tasks like issuing RFQs, analysing bid responses and preparing contracts can be automated using AI. Chatbots allow natural language interactions to quickly address supplier queries.

Big data analytics uncovers trends like price changes, supply risks, quality issues, etc., to support strategic sourcing decisions. AI determines the right procurement strategies for different spending categories based on value drivers instead of reactive buying. This brings major savings with better supplier terms and reduced maverick spend.

Also Read: Enhancing cyber supply chain resilience: A vision for Singapore

AI can also analyse negotiations with suppliers to continuously improve negotiation strategies and outcomes. It provides insights into which suppliers have higher negotiation room or where bundling spending could get better terms. This allows systematic optimisation of value.

Proactive supply risk management

Supply disruptions can wreak havoc on businesses. ML applies sophisticated pattern recognition and probabilistic modelling on news feeds, financial reports, weather data, transport records, etc., to identify likely disruption causes like natural disasters, trade wars, production issues, strikes, etc.

It analyses the potential impact on capacities, lead times and costs across the network. AI simulation helps mitigate risks proactively through safety stock optimisation, alternate suppliers, route changes, etc. Instead of reacting to disruptions, organisations can get ahead of problems.

AI-driven supply risk management also enhances transparency across tier-two, three and lower-tier suppliers to uncover hidden risks. It enables building contingency plans and scenarios to handle disruptions smoothly. This minimises downtime and customer impact.

Continuous process improvements

AI tracks all supply chain processes and exceptions. It analyses the root causes of inefficiencies like long lead times, quality problems, inaccurate planning, stockouts, etc. AI also estimates the cost impact of process bottlenecks. It uses computer vision to monitor process adherence on factory floors.

The insights allow focused process improvements to enhance productivity. AI also alerts when critical process parameters exceed limits. This enables proactive troubleshooting before issues arise. The continuous feedback cycle sustains gains over the long run.

Also Read: #dltledgers unveils 2023 trends in supply chain digitisation

For instance, AI can track invoice processing times, identify delays from missing information or workload spikes and reroute tasks automatically to improve turnaround. It can adjust warehouse staffing based on order volumes to maintain speed. AI enables self-optimising supply chain processes.

The future with AI and blockchain

Blockchain provides secure, transparent distributed ledger technology to improve end-to-end supply chain traceability. Combining it with AI and ML unlocks more value. AI can analyse blockchain transactions to uncover patterns, risks and insights. Smart contracts enabled by blockchain allow automated workflow execution.

Blockchain establishes a unified data source across networks. AI analyses this data for continuous optimisation. Smart contracts automate execution without conflicts. Together, they enable seamless cross-organisation integration while ensuring trust, security and compliance. This next-gen supply chain architecture minimises inefficiencies, disputes and disruptions.

Final thoughts

With capabilities like machine learning, computer vision and natural language processing, AI-powered SCM solutions help companies achieve new benchmarks in speed, accuracy and efficiency. By leveraging the convergence of AI and blockchain, future supply chains will become intelligent, self-learning networks that maximise value. The possibilities are exciting as AI and ML progress rapidly. Companies that embrace these technologies today will gain a real competitive edge.

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In today’s ever-evolving entrepreneurial landscape, the notion of startup success has transcended the boundaries of Silicon Valley. Aspiring entrepreneurs are no longer limited by geography, and their dreams of building globally successful ventures are now challenging Silicon Valley’s historical dominance as the go-to destination for startups.

Techstars itself began with three simple ideas: entrepreneurs create a better future for everyone, collaboration drives innovation, and great ideas can come from anywhere. So supporting founders looking to create successful startups globally is a mission we have long been engaged in.

Embracing new horizons: The potential of anywhere startups

It has never been easier to launch a startup. Advancements in artificial intelligence and technological tools have significantly simplified the process of starting a business, making it more accessible for entrepreneurs. For instance, the rise of e-commerce platforms has minimised the need for physical stores, enabling businesses to operate online and reach a global customer base.

Digital marketing tools have made it easier to promote products and services, targeting specific audiences with precision. Cloud computing has revolutionised data storage and collaboration, reducing infrastructure costs and facilitating remote work.

Even the entrepreneur’s ability to raise business capital has been somewhat democratised by crowdfunding platforms that give them access to a range of sponsors and investors. These technological advancements have significantly reduced barriers and empowered entrepreneurs to pursue their business ideas with greater ease and efficiency from anywhere in the world.

The untapped potential of ‘anywhere Startups’ has been further reinforced by the COVID-19 pandemic. The advent of remote work and enhanced access to resources have given rise to vibrant startup ecosystems in unexpected corners of the world, making entrepreneurship a global phenomenon.

According to a survey report by McKinsey & Company, the global pandemic accelerated the digitisation of customer interactions with companies by several years.  At the height of the pandemic, several startups were still successfully launched and operated outside of Silicon Valley, showing entrepreneurs that success knows no geographic limits.

With the internet as their powerful ally, entrepreneurs now have the ability to connect with customers, investors, talents, and mentors on a global scale. This newfound freedom empowers them to pursue their dreams and build successful ventures beyond the traditional confines of Silicon Valley.

The role of geography and community

Geography still plays a pivotal role in shaping startup success. Each region possesses unique strengths, resources, regulatory climates, challenges, and market demands that must be navigated by entrepreneurs.

Also Read: Echelon: How increased emphasis on ESG elements in fund management will affect early stage startups

To maximise these strengths and navigate the challenges effectively,  it is necessary to fan the flames of entrepreneurial collaboration and startup community engagement within the region. This is the very reason several governments across the globe are now paying attention to promoting initiatives that bring entrepreneurs together and grow their local ecosystems.

A good example of such initiatives is the Anjal Z Techstars founder catalyst program which is a partnership between the Abu Dhabi Early Childhood Authority and Techstars to help edutech startups from across the globe get localised to Abu Dhabi.

To further support the regional development of startup communities globally, Techstars also offers a startup community catalyst that is a combination of multiple programs aimed at igniting and scaling startup communities from the ground up in partner regions. The fostering of local entrepreneurial communities sparks innovation and collaboration.

It can also create a nurturing environment that provides easier access to talent, support, and industry-specific knowledge relative to the region.  By understanding the local landscape and building collaborative communities, entrepreneurs in these regions can better leverage their geographic advantages and key into untapped opportunities.

Pre-accelerators and mentorship: Nurturing the entrepreneurial potential

From the initial spark of an idea to the eventual launch of a product and beyond, the entrepreneurial path is filled with challenges and uncertainties. Many founders usually venture in not knowing what to expect. However, pre-accelerator programs and mentorship can help bridge the gap and provide support during these crucial early stages. 

As the world’s largest pre-seed investor,  Techstars knows firsthand the value of mentorship to early-stage entrepreneurs, and that is why our community programs, such as Startup Weekends and Founder Catalysts, are meticulously designed to help founders with the necessary mentorship and support they need through every milestone of their early entrepreneurial journey- that is, from refining their ideas and defining their value proposition to preparing for future investments in our accelerator programs. 

Techstars collaboration: Fostering startup diversity

Diversity and inclusion have become imperative in the startup industry, and Techstars actively collaborates with entrepreneurs from diverse backgrounds and regions to foster a more inclusive ecosystem.

Entrepreneurs and partners that collaborate with Techstars are exposed to global networks and funding opportunities, as well as a supportive community of diverse mentors, entrepreneurs, and investors. These interactions can break down cultural and regional barriers. It also causes founders and partners to be open-minded and embrace global perspectives.

Also Read: Empowering startup entrepreneurs: Harnessing benefits of Web3

A community of diverse entrepreneurs brings fresh ideas, cultural insights, and innovative solutions to the table. The collective expertise and experience shared by mentors and peers empower entrepreneurs to challenge the status quo, disrupt industries, and build scalable startups.

Building the entrepreneurs and thriving communities of the future

Throughout our quest to support founders, we have found that a collaborative and strategic approach is always required when building startup communities. To build the entrepreneurs of tomorrow, we must first start by empowering the children and youth of today, not just in the US.

We can do this by prioritising entrepreneurial education, whether in the form of pre-accelerators, accelerators, and Tech hubs or actively in our schools and universities. This will help young individuals develop an entrepreneurial mindset and equip them with the skills and knowledge needed to navigate the startup landscape. Strengthening our collaborations between academia, industry, and government can drive research and development, further encouraging innovation and breakthrough technologies.

Additionally, fostering a culture of risk-taking and embracing failure as a learning experience is also essential for entrepreneurial growth. Promoting diversity and inclusivity within the entrepreneurial ecosystem is also key to unlocking new perspectives and driving innovation.

Lastly, continuous support and investment in emerging technologies and industries will help create thriving entrepreneurial hubs that shape the future of economies and industries.

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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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This article was first published on June 8, 2023

The post Embracing global entrepreneurship: Redefining startup success beyond Silicon Valley appeared first on e27.

In the dynamic world of Web3 startups, understanding and implementing effective financial models is crucial for achieving long-term success. The emergence of blockchain technology and decentralised finance (DeFi) has revolutionised the way startups operate, presenting unique challenges and opportunities. To navigate this rapidly evolving landscape, entrepreneurs need to adopt innovative approaches to financial planning and management.

In this comprehensive guide, we will explore the guiding principles for developing robust financial models tailored specifically to Web3 startups. From revenue streams to token economics and risk management, we will delve into the key aspects that drive financial success in this exciting domain.

Understanding the Web3 landscape

Before diving into the intricacies of financial modelling for Web3 startups, it is essential to have a comprehensive understanding of the fundamental concepts that define the Web3 landscape.

By familiarising themselves with decentralised finance (DeFi), non-fungible tokens (NFTs), smart contracts, and other essential components of the Web3 ecosystem, startups can align their financial models with the specific dynamics of the decentralised world.

Decentralised finance (DeFi)

Decentralised finance, or DeFi, refers to the use of blockchain technology and smart contracts to create financial applications that operate without intermediaries. Traditional financial services such as lending, borrowing, trading, and asset management are redesigned and decentralised, offering increased transparency, security, and accessibility to users. In the Web3 ecosystem, DeFi protocols enable startups to develop innovative financial products and services while removing traditional gatekeepers.

Web3 startups should explore various DeFi applications, including decentralised exchanges (DEXs), lending platforms, yield farming, and liquidity provision. By understanding the mechanics and potential risks associated with these platforms, startups can strategically incorporate DeFi elements into their financial models, leveraging the benefits they offer while mitigating any associated risks.

Non-fungible tokens (NFTs)

Non-fungible tokens, or NFTs, have gained significant attention in the Web3 world. NFTs are unique digital assets that can represent ownership or proof of authenticity for a wide range of digital and physical items, such as artwork, collectibles, virtual real estate, and more. NFTs are typically built on blockchain platforms like Ethereum, allowing for verifiable ownership and provable scarcity.

Also Read: Sony & UMG join forces with Snowcrash to revive NFTs: Here’s why the digital trend is far from dead

For Web3 startups, NFTs present an exciting avenue for monetisation and user engagement. By incorporating NFTs into their financial models, startups can explore revenue streams such as NFT sales, licensing, fractional ownership, and royalties. Understanding the dynamics of NFT markets, including trends, valuations, and user preferences, will be crucial in designing effective monetisation strategies.

Smart contracts

Smart contracts are self-executing contracts with the terms of the agreement directly written into code. These contracts automatically execute predefined actions when specific conditions are met, eliminating the need for intermediaries and enhancing the security and efficiency of transactions. Smart contracts are a fundamental building block of the Web3 ecosystem, enabling a wide range of applications, including decentralised exchanges, decentralised finance protocols, and more.

Web3 startups should grasp the concept of smart contracts and their potential applications. By leveraging smart contracts in their financial models, startups can automate processes, reduce costs, and ensure trust and transparency in their operations.

Understanding the programming languages used for smart contract development, such as Solidity, and the associated best practices will be essential for startups seeking to harness the full potential of this technology.

Web3 ecosystem and interactions

In addition to DeFi, NFTs, and smart contracts, there are numerous other components within the Web3 ecosystem that startups should be familiar with. These include decentralised storage solutions, identity management systems, oracle services, governance mechanisms, and more.

Understanding the interactions and dependencies between these components will enable startups to design financial models that account for the broader Web3 infrastructure and the potential synergies it offers.

By comprehending the dynamics of the Web3 landscape, startups can leverage the power of decentralised technologies in their financial models. This understanding will allow them to identify relevant revenue streams, incorporate token economics, assess risks and opportunities, and make informed decisions that align with the unique challenges and opportunities of the decentralised world.

Principles of financial modelling for Web3 startups

Understanding blockchain economics

Web3 startups are built upon blockchain technology. The financial model for such startups must reflect an understanding of the underlying blockchain economics. Factors like gas fees (transaction costs on a blockchain), mining rewards, and tokenomics (economic system around the token of a specific blockchain) will have significant implications on the startup’s financial dynamics.

Incorporating tokenisation

Web3 startups often use tokens as a mode of value exchange within their ecosystem. These tokens can serve various functions like utility tokens (providing users with access to a product or service) or security tokens (representing ownership in an asset). Their volatility in value needs to be factored into financial projections, and possible capital gain scenarios must be accounted for.

Handling regulatory uncertainty

Given the relatively novel nature of Web3 and the ensuing regulatory ambiguities, startups in this domain need to model the potential financial impacts of regulatory changes. This could include costs for compliance, penalties, or changes in user behaviour resulting from such regulatory decisions.

Forecasting user growth

User adoption and growth are vital to Web3 startups, with direct implications on financial performance. The financial model should consider different growth scenarios and examine the corresponding impacts on revenues and costs.

Accounting for network effects

The value of Web3 startups often grows as the network expands. This phenomenon, called network effects, should be incorporated into financial projections, including the impact of growth on value and costs.

Building a financial model for a Web3 startup

Now, let’s walk through a simplified version of building a financial model for a Web3 startup.

Revenue estimation

For most Web3 startups, revenues may come from transaction fees, staking rewards, or selling tokens. It’s crucial to forecast revenues based on estimated growth, token value changes, and market dynamics.

Also Read: Web3 startups: The next big thing investors are flocking to

Cost projection

On the expense side, typical costs include development, operations, and marketing. Additionally, costs unique to Web3, like gas fees or smart contract audits, must be accounted for.

Financial statements

Build the traditional profit and loss statement, balance sheet, and cash flow statement. However, these will likely need modifications. For example, balance sheets might need to include token reserves, while the cash flow statement needs to account for cryptocurrency flows.

Scenario analysis

Given the volatility and uncertainty in the Web3 space, it’s critical to model different scenarios to understand potential outcomes and risks.

Valuation

Valuing a Web3 startup is challenging, given the scarcity of comparable companies, token price volatility, and regulatory risks. Techniques like Discounted Cash Flow (DCF), token economy valuations, or using multiples from a few existing similar companies can provide some guidance.

Final thoughts

Developing robust financial models is essential for the success of Web3 startups. By understanding the principles of financial modelling specific to the Web3 ecosystem, entrepreneurs can make informed decisions, attract investors, and navigate the challenges and opportunities in this dynamic landscape.

Incorporating elements such as blockchain economics, tokenisation, regulatory considerations, user growth forecasting, and network effects will enable startups to build comprehensive financial models that drive sustainable growth and long-term success. Through diligent research, analysis, and scenario planning, Web3 startups can optimise their financial strategies and position themselves for success in this exciting and rapidly evolving domain.

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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

Join our e27 Telegram group, FB community, or like the e27 Facebook page

Image credit: Canva Pro

The article was first published on June 6, 2023

The post Financial models for Web3 startups: Guiding principles for success appeared first on e27.

Dear reader,

In 2023, Grab solidified its dominance in Southeast Asia’s food delivery market, capturing a remarkable 55% of the gross merchandise value, as reported by Momentum Works. While Grab witnessed a robust 6.8% y-o-y GMV growth, competitors Foodpanda and Gojek faced a decline of 12.9% and 10%, respectively. Despite Grab serving only 5% of the region’s 600 million population as monthly transacting customers, the report emphasises an extensive untapped user base for food delivery apps in Southeast Asia.

The region’s food delivery expenditure reached a noteworthy US$17.1 billion, with a 5% y-o-y increase. Vietnam emerged as the growth leader, boasting a substantial 28% GMV surge, followed by Malaysia, Thailand, and Indonesia, while Singapore’s growth stagnated.

F&B spending rebounded beyond pre-pandemic levels, but the report linked closures and cost-cutting among premium brands to macro uncertainties. Amid this landscape, companies are diversifying strategies, with consolidation remaining a common thread.

Notably, Chinese F&B brands like Luckin Coffee and Cotti Coffee are entering the region, anticipating further growth opportunities. Jianggan Li, CEO of Momentum Works, sees ample room for expansion in Southeast Asia’s food delivery sector, citing robust F&B consumption, low delivery penetration, and ongoing consolidation as key growth drivers.

Sainul,
Editor.

NEWS ARTICLES

Grab captured 55% of SEA’s food delivery market in 2023: report
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SoftBank Ventures Asia changes name to SBVA after closing US$150M fund
The new Alpha Korea Fund’s LPs include Korea Development Bank, SoftBank Group, and Hanwha Life; This comes after Singapore-based The Edgeof acquired SoftBank Ventures Asia last year.

Vertex Growth leads Korean startup Elice’s US$15M funding round
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InnoVen Capital launches second China fund, aiming for US$250M
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Singapore’s university launches US$3.2M micro-innovation fund for students
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Singapore healthtech startup Mesh Bio raised funding
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Flipkart co-founder Binny Bansal leaves board
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Singapore, Google launch initiatives to foster local AI tech scene
One of these initiatives is the second edition of AI Trailblazers, which provides organisations with access to Google Cloud’s unified AI stack and its Innovation Sandbox programme to create and implement their own generative AI solutions.

ChatGPT is violating Europe’s privacy laws, Italian DPA tells OpenAI
The firm has been told it’s suspected of violating EU privacy, following a multi-month investigation of ChatGPT by Italy’s data protection authority; OpenAI has been given 30 days to respond with a defence against the allegations.

Byju’s cuts valuation ask by 99% in rights issue amid cash crunch
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The post Ecosystem Roundup: Grab dominated SEA’s food delivery market in 2023 | SBVA closes new US$150M fund appeared first on e27.

During her inaugural Committee of Supply (COS) speech in Parliament on March 4, 2022, Josephine Teo, the Minister for Communications and Information and Minister-in-Charge of Cybersecurity, announced the enforcement of increased maximum financial penalties for data breaches by organisations, as outlined in the 2020 amendments to the Personal Data Protection Act 2012 (PDPA). These changes are scheduled to be effective from October 1, 2022.

According to the updated regulations, organisations with an annual turnover in Singapore exceeding SG$10 million may face a maximum financial penalty of 10 per cent of their annual turnover, while in other cases, the maximum penalty is set at SG$1 million.

To provide context, the Personal Data Protection (Amendment) Bill was passed in Parliament on November 2, 2020, following its introduction for the first reading on October 5, 2020. The Personal Data Protection (Amendment) Act 2020 (“Amendment Act”) was gazetted on December 10, 2020. The Amendment Act commenced partially on February 1, 2021, implementing mandatory data breach notification requirements and introducing offences related to the mishandling of personal data. Provisions concerning data portability, higher financial penalties, and certain consequential amendments are set to take effect at a later date.

After this amendment, other ASEAN countries in the region have followed suit.

In August 2022, Malaysia announced that it would be introducing a New Cybersecurity Bill in development by the National Cyber Security Agency (NACSA) to be tabled in early 2024.

In September 2022, after a series of high-profile data breaches in recent months, Indonesia enacted the Personal Data Protection Law (PDP Law). The PDP Law places responsibility on both domestic enterprises and global corporations for mishandling the information of Indonesian customers.

Also Read: Holiday cybersecurity: Safeguarding businesses amidst increased cyber threats

Companies can be subject to a corporate penalty amounting to a maximum of two per cent of their annual revenue in the event of data breaches. Furthermore, individuals may be fined up to IDR6 billion (equivalent to US$400,000) for contravening the provisions outlined in the PDP Law.

Proactive measures for businesses

The announced increase in maximum financial penalties for data breaches by countries across ASEAN can have significant implications for businesses in the region, and they must respond proactively to ensure that they are sufficiently protected.

Here are key considerations and actions that businesses should take:

• Review data protection policies: Businesses should review and update their data protection policies and procedures to ensure alignment with the amended PDPA regulations. This includes incorporating measures to prevent data breaches and outlining procedures for handling and reporting incidents.
• Reassess cybersecurity measures: Evaluate and strengthen security measures within the organisation. This may involve implementing endpoint protection, email protection, patch management, and other safeguards to protect sensitive information. Conduct regular security audits and assessments to identify and address vulnerabilities.
• Data breach response plan: Develop or update a comprehensive data breach response plan. This plan should outline the steps to be taken in the event of a data breach, including notification procedures, communication strategies, and coordination with regulatory authorities.
• Employee training: Provide training to employees on data protection best practices and updated regulations. Employees should be aware of their roles in preventing data breaches and understand the importance of promptly reporting any potential security incidents.
• Conduct risk assessments: Regularly assess the risks associated with the processing of personal data within the organisation. This includes identifying and addressing potential vulnerabilities in systems, processes, and third-party relationships that could lead to data breaches.

In Conclusion

The consequences of a cyber data breach are no longer just a ‘slap on the wrist’ but have significant financial, reputation, and business continuity consequences. By taking a proactive approach to cybersecurity and user compliance, businesses can reduce the risk of data breaches, demonstrate accountability, and minimise the potential consequences.

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Food waste in Singapore accounts for approximately 11 per cent of the total waste generated. In neighbouring Malaysia, a staggering 8.3 million metric tonnes of food waste is generated annually. This is alarming and causing severe damage to the planet we live in.

Two Malaysian entrepreneurs are trying to address this problem with direct-to-consumer vacuum-packed, ready-to-cook meals for individual and business customers.

Launched in 2020 by Brandon Lim and Khiara Mia, Meals in Minutes aims to simplify cooking for individuals leading busy lives, offering them the opportunity to enjoy high-quality, clean, and nutritious meals without the associated hassle and requisite culinary skills.

“Our mission is to become a global household meal kit brand,” states Lim.

Meals In Minutes enables consumers to prepare a gourmet dining experience in 15 minutes or less, said Lim. Each meal is meticulously flash-frozen, ensuring optimal freshness while conveniently portioning and a long freezer shelf life to minimise potential food waste.

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

The meals contain no genetically modified ingredients or artificial additives. “Furthermore, we hold HACCP, HALAL and ISO 22000 certifications, guaranteeing the highest standards of food safety and adherence to Islamic dietary guidelines and international-level food compliance,” he claimed.

To place the order, customers can visit its website and pick and choose from a variety of proteins and staples like brown rice or quinoa, or they can order a side to come with the meal. The meal kit is delivered directly to their doorstep.

“It only takes less than 15 minutes to prepare a gourmet meal using our meal kit. All they have to do is either ‘sous-vide’ or microwave our individually packed meals, or go ‘Chef Mode’ by removing it from the vacuum pack and pan-frying, putting it into the oven, steaming or adding other ingredients to the mix,” said Mia.

Tackling food wastage

All the meals are flash-frozen at their freshest point and are stored in a frozen state until ready to be consumed. This enables Meals In Minutes to achieve a shelf life of 18 months from production, reducing food wastage and maintaining its freshness.

Moreover, the meals are individually packed, meaning individuals can better control their portion sizes, resulting in less leftover food that would otherwise go to waste. “By serving our portioned meals, restaurants can more accurately estimate the food needed for each order, minimising excess production and subsequent waste. Furthermore, individually packed meals allow for better inventory management, as leftovers can be easily repurposed or donated, reducing food waste even further,” Lim elaborated.

Relying on a changing food habit

With more Singaporeans prioritising healthy food choices and F&B businesses adding healthy menu options, Meals In Minutes sees massive growth opportunities.

“While manpower shortages remain a challenge in Singapore for F&B businesses to scale and improve, consumers are demanding higher quality and consistency in the meals provided to them. This is also similarly witnessed by their neighbouring country, Malaysia,” said Mia.
“We will see an upward trend in our B2B consumers where more restaurants or cafe owners will approach us to supply them with our advanced meal kit that is healthy, quick and easy to prepare, addressing the challenging demands of the Singapore and Malaysian markets.”

Also Read: MAEKO converts food waste into compost. Greta Thunberg should feel happy

The firm plans to maximise the accessibility of Meals in Minutes in Malaysia and Singapore. This involves expanding into additional stores and establishing a more substantial presence in various regional food outlets. It will also reach out to the hospitality and healthcare industries. The company recently received US$1.5 million in funding from an undisclosed investor to work toward this mission.

In addition to Singapore and Malaysia, Meals In Minutes also targets the UK market for expansion. The UK is a more mature market for ready-to-cook food, with 29 per cent of consumers consuming ready-to-eat meals at least once per week. The meal kit market is worth approximately one billion pounds annually and is forecast to grow by 8 per cent yearly.

Meals in Minutes Co-Founders Brandon Lim (L) and Khiara Mia

However, a recent study found that purchasing ready meals is more expensive and unhealthy than cooking from scratch due to the added ingredients or preservatives, which can deter consumers from purchasing such meals. “Hence, for the UK, providing high-quality, healthy and easy-to-prepare meal kits are the key factors startups looking to reap the opportunities in this market should consider,” she stated.

“Our strategic plan includes launching distributor markets and strategically positioning Meals In Minutes for broader availability and influence. The UK market serves as our initial entry point into the broader European market, marking the beginning of our journey to introduce the convenience and excellence of Meals in Minutes to an international audience,” Lim said.

Singapore has a thriving F&B sector. The city-state experienced a notable surge of 13.6 per cent in annual growth between 2022 and 2023. Despite the challenges, mainly driven by the lack of manpower and increased competitiveness, the F&B industry is expected to generate a CAGR of 4.56% between 2023-2028.

“These challenges can be overcome through Artificial Intelligence and logistics delivery. Self-ordering kiosks are becoming increasingly popular, and QR code mobile ordering is also gradually becoming the norm in restaurants, all of which are targeted to overcome manpower shortages,” noted Lim.

Addressing plastic waste problem

Meals In Minutes has collaborated with Geman firm CleanHub to address environmental concerns. “Through this collaboration, we take proactive measures to tackle the issue by collecting plastic waste for each product sold,” Mia revealed. “Our brand also champions a minimalist packaging approach to diminish unnecessary waste and mitigate environmental impact significantly.”

Having said that, all our packaging will be fully recyclable, and in the coming months, we will launch the UK market with these materials. “As we move into the future, we are constantly looking for new innovative & sustainable materials to incorporate into our business to widen our impact,” Mia concluded.

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We often see AI and blockchain portrayed as competitors for the spotlight and investor funds. Yet, it’s more fitting to see them as partners in innovation. When these two groundbreaking technologies come together, they have the potential to create a powerful synergy, overcoming some of the inherent limitations in their respective paths. 

Watching AI and blockchain develop is like watching the popular kids in school. Both have significantly influenced our vision of the future and have been trying to outshine each other since the start of the year. Blockchain, despite being in the limelight for over a decade and promising financial empowerment to individuals, has faced challenges, including unfulfilled promises and numerous hacks.

On the other hand, AI, the newer star of the class, has attracted billions in investments and is growing in popularity, but it has its bag of issues, such as concerns about its impact on our lives and the difficulty of distinguishing real from AI-generated content. 

Some have argued cryptocurrency is yesterday’s news and AI is the next big thing. But instead of putting them in a face-off, it’s evident when delving into their core capabilities that they can enhance each other’s strengths and offset limitations. Together, they lay the foundation for a secure and efficient digital realm.

AI’s rapid rise and its trust challenges

AI, once just a futuristic concept, rapidly rose to dominance with ChatGPT hitting 1 million daily users in just five days and a projected over 8.4 billion AI devices globally by 2024.

However, this rapid ascent caught many unprepared. With AI’s increasing use, people often question if the content they encounter is genuine or AI-generated, leading to widespread distrust in online information, with over 75 per cent expressing concerns about misinformation.

Also Read: AI in mobile advertising: Transforming relevance, efficiency, and immersive experiences

Recently, governments worldwide are rapidly developing AI regulations, with China setting security standards for AI-powered services and the EU focusing on rules for AI use in biometric surveillance and systems like ChatGPT.

The AI landscape faces another critical challenge: centralisation in development. This is a cause for concern because it could lead to a situation similar to what happened with search engines and social media, where large companies Microsoft, OpenAI, and Google ended up controlling the vast amounts of user data that had been accumulated.

Relying on centralised models widens the gap between resource-rich companies and the broader market. Consequently, this not only perpetuates a ‘rich get richer’ cycle, where these companies have the resources and expertise to reap AI rewards, but it also places too much power and control in the hands of a few. All of these trends combined are causing a growing distrust in AI. 

Blockchain can help

People should be able to use AI tools without worrying about encountering fakes, scams, or data accuracy issues. AI tools should be easily comprehensible, impartial, unbiased, and transparent. Blockchain technology can make this happen. 

Blockchains, rooted in cryptography and security, establish trust through a decentralised, immutable, and continuously growing digital ledger. This ledger comprises ordered records (blocks) linked with cryptographic hashes, timestamps, and transaction data. Altering any part of it requires changing all subsequent blocks and gaining network consensus.

Similar to how a certificate authority verifies website security, blockchain can serve as a certifying agent for digital creations in real-time, providing assurance in a world where authenticity is crucial. 

In the 2016 US presidential election, X (formerly Twitter) bots disseminated false information by targeting influential users. Centralised websites also posted articles that weren’t fact-checked and were vulnerable to hacking and misinformation.

However, blockchain can effectively address this problem. For example, in 2020, the Associated Press used blockchain oracles to track US presidential election race calls on the chain for the first time. The AP published election results on Ethereum, creating an unhackable and permanent record of state-by-state results.

Also Read: How the blockchain could change the way the government works

On top of that, it released the smart contract address to allow readers to use block explorers like Etherscan to track results in real-time. This approach will become even more critical as AI now makes it easier to spread misinformation and attempt hacking.

Furthermore, blockchain can address AI’s centralisation issue, as seen in projects like Ocean Protocol, which utilise blockchain and tokens along with its compute-to-data technology. It enables data providers to monetise their data while maintaining privacy and control and for consumers to purchase previously inaccessible data. This decentralises AI workflows, as AI and its machine learning algorithm need data to work.

Blockchain needs AI for an accessibility upgrade

AI technology has risen to prominence for good reasons. AI tools, such as ChatGPT, are user-friendly, with no downloads or signups required. Also, developers find it accessible through APIs from major players like Google and AWS.

On the flip side, the blockchain world seems like it’s still finding its way, with a notable gap between hype and reality. Its intricate user interface poses difficulties in welcoming new users, while those already involved must navigate through untested products and over-optimistic business models. This situation exposes them to public scrutiny and encourages criticism and scepticism. 

Imagine if the integration of AI into blockchain projects could facilitate the onboarding of the next wave of users, offering a simplified user experience, a clearer understanding of projects, and a more developer-friendly environment.

AI can simplify user experiences in the Web3 space by assisting in understanding crypto-specific terms like “smart contracts,” “seed phrases,” and “wallets”. Blockchain projects are able to deploy virtual AI assistants trained on Web3 knowledge and built-in search prompts to guide users through actions and explanations.

Additionally, AI can analyse on-chain data for optimal strategies. Instead of sifting through resources like CoinGecko and DeFiLlama, AI tools can provide quick access to information like Bitcoin’s highest price this month or top-performing tokens with a market cap of over US$100 million. This speeds up individual research to the level of larger teams with bigger resources and budgets for more hires. 

Also Read: SGTech launches GenAI jobs and skills guide in response to Singapore’s National AI Strategy 2.0

Similarly, developers who often have to deal with a scattered assortment of technical resources will also find AI advantageous for streamlining product development. For instance, AI can look at blockchain transaction movements and assist in comprehending the flow of funds, especially following security breaches. 

Looking ahead

Recently, we’ve witnessed significant progress in recognising and implementing collaborative blockchain and AI solutions: Congressman Tom Emmer has acknowledged the role of blockchain in content authentication, and Microsoft partners with Aptos to deploy AI and blockchain offerings. From politicians to major tech companies, there’s growing recognition, and more efforts are being made to bring AI and blockchain closer together.

The symbiotic relationship of AI and blockchain is not a matter of “if” but “when.” As our reliance on AI continues to grow, so does the need for trust in digital information. Blockchain is an effective solution to address the trust challenges in the digital age. Its ability to securely record data origins provides the much-needed proof of authenticity required in the era of AI.

Simultaneously, AI can enhance blockchain with user-friendly interfaces and streamline technical resources for developers. We’re optimistic about this fusion and its potential to shape a more transparent and reliable Web3 future.

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Prompt: Create an image that blends the innovative use of AI in business with a vibrant, Gen Z-inspired aesthetic. Picture a dynamic, colourful workspace filled with young professionals engaged in creative brainstorming, surrounded by holographic displays of AI interfaces, emoji-filled chat bubbles, and playful data visualisations. Add elements like quirky desk decorations, trendy tech gadgets, and a backdrop of digital art, all under a neon glow. The scene should buzz with energy and a sense of fun, reflecting a workspace where work and play merge seamlessly, embodying the spirit of Gen Z’s approach to productivity and innovation.

I remember this time last year, I was fascinated with the new world of AI. Like many of us, I learned how to use ChatGPT, and I was already super excited with the answer it gave me by what was then ChatGPT 3.5. Fast forward a year later, and we’ve seen so many AI tools in the market and so many new innovations. But how do we know which AI tools to use and which will be useful for our business?

The right questions to ask as a leader: What are the immediate AI use cases to unlock creativity and free up resources? How can I use AI to focus on my genius zone and delegate dreadful parts of my tasks? How can I use AI to be a better leader?

As a Partner and CMO of Remote Skills Academy, I run this non-profit organisation that focuses on empowering individuals with digital skills to allow them to work remotely and live life on their own terms. I’m not only overseeing marketing but also managing the organisation’s day-to-day operations, including building the team. We’re a lean team of five people, and half of them are not full-time. We will need all the extra help to do our work in an effective and efficient way.

Also Read: The rise of generative AI in digital mental health solution

Prompting principles

Understanding how to prompt is crucial in using generative AI. The quality of your output is as good as the quality of your input. Some of the things I make sure are there while prompting with ChatGPT or other generative AI tools:

• Context: Give ChatGPT context by providing details about yourself or your business. This can be your business goals, target audience, etc., condensed into paragraphs.
• Roleplay: You can ask ChatGPT to act as a subject expert to give you more precise results
• Iteration: Working with ChatGPT is all about iteration. If ChatGPT give answers you’re not satisfied with, ask it to:
  • Add more explanations
  • Provide feedback on what you like
  • Tell it what you didn’t like about the results
  • And what do you want to see instead
• Training: LLMs (Large Language Models) such as ChatGPT are powerful because they can provide responses without needing specific examples (zero-shot prompt), but you can also give examples of similar content that you love so they can use the reference (one-shot prompt), or you can provide multiple examples (multiple shots prompt). This is the game-changing part.

Use cases

After understanding the prompting principles, we’re already halfway there to reach our goal. I’ll show you the ways I use AI tools in my daily operations.

Ask AI to write like me

In my job, I write a lot, especially to build thought leadership and attract inbound leads through content creation on LinkedIn. To save time, I train ChatGPT to understand my writing style. I gave some examples of my writing and asked them to remember my writing in Lia Sadia’s style. Then I will ask it to write a post for me. Usually, it will be a great foundation that I can edit, work on, and add stories and a human touch to it.

Content editing and repurposing

In the world of digital marketing, content is king, but it’s not just about creating new content—it’s about making the most of what you already have. That’s where content repurposing comes in, and AI plays an important role in speeding up this process.

By leveraging AI tools, I’ve been able to take our existing content and transform it into various formats suitable for different platforms and audiences. For instance, an audio/video interview with a media can be turned into a detailed blog post, a series of social media posts, or even a short TikTok video. You can even ask AI to suggest hooks you can use to capture your audience’s attention in five seconds.

Some of the tools I use for this are Capsule and Otter.

Create stock photos for social media

Finding the right visuals for social media can be a daunting task, especially when you’re looking for something very specific, like stock photos with Indonesian faces and certain hand gestures. This is where AI tools like Midjourney help.

Also Read: How AI and blockchain collaborate for a transparent Web3 future

By inputting detailed descriptions, I can generate custom stock photos that meet our exact needs, all within a minute. This capability not only saves time but also enhances the authenticity and relatability of our social media content, making it resonate more with our target audience.

Chat with your emails and your documents

With Bard, you can now connect it to your Gmail and Google Drive. You can chat and ask questions about your email and documents, summarise email threads, and draft responses. This not only streamlines our workflow but also ensures that we can focus more on strategic tasks rather than getting bogged down by administrative duties.

Create presentation visuals in minutes

Creating compelling visuals for presentations is crucial for engaging our audience, whether it’s for internal team meetings or external stakeholder engagements. With AI-powered presentation tools, like Gamma.app, we can generate stunning presentations in a matter of minutes, tailored to the theme and content of our presentation. These tools understand the context and suggest designs that enhance our message, making our presentations more impactful and memorable.

Give insights from your data

Data is at the heart of informed decision-making, but analysing vast amounts of data is not easy, especially if we’re not familiar with the process. AI tools excel in sifting through data to provide actionable insights, whether it’s identifying trends in our marketing campaigns or uncovering efficiency gaps in our operations. We can make data-driven decisions quickly, ensuring that our strategies are aligned with our goals and the market’s demands.

Be careful not to give too much context on your data when you’re asking for insights because most of the existing AI tools are designed for personal use. Research more to get a corporate package of the tools, which will give you a feature to protect your data.

Help automate your process

Efficiency is key in running a lean team, and process automation is a game-changer. AI tools can automate repetitive tasks, in our case, from events management to the student enrolment process. We use the Zapier plugin at ChatGPT, which can give ideas on how to set up the automation and then actually create automation for us.

Fast learning with YouTube summary

The wealth of knowledge available on YouTube is incredible, but sitting through long videos to find relevant information can be time-consuming. AI tools have revolutionised the way we learn by providing concise summaries of lengthy YouTube videos.

I used Glasp YouTube Summary to capture the transcription and asked ChatGPT to provide the summaries. I can also immediately share this information with our team, who can quickly grasp the key points of a video, which is particularly useful for staying up-to-date with the latest trends and skills in digital marketing and remote work. This accelerated learning process ensures that we remain agile and informed, ready to adapt to new challenges and opportunities.

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

Strategic business partner

Beyond just a tool for operational tasks, AI has become a strategic business partner. I will have self-reflection conversations with ChatGPT, solving problems and challenges within the organisation and giving me ideas to better recognise the needs of my team and fulfil them. AI helps me to dream and broaden my vision.

What are we going to do with our free time?

These AI automation have freed up our team’s time, allowing us to focus on creative and strategic tasks that require a human touch. By integrating AI into our processes, we’ve not only increased our productivity but also improved our job satisfaction, as we’re able to dedicate more time to more impactful work. We also have time to not only do deep work but also deep learning. Deciding a topic that we wanted to dive into, and become the best at it to serve the organisation and community.

I can feel it has already enhanced the quality of life at work and made our time more fulfilling. Now, it’s your turn. How have you been using AI tools in your business operations?

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I’ve always been deeply fascinated by psychology — the scientific exploration of the human mind and its influence on behaviour. This fascination also encompasses affective computing, a field that merges insights from computer science, psychology, and cognitive science.

The increasing burden of mental health issues, along with advancements in technology and a deeper understanding of the biopsychosocial model of health, has spurred interdisciplinary research to enhance our understanding of mental health disorders, which have profound effects on our communities globally.

The global mental health landscape

According to the World Health Organisation:

• Mental, neurological, and substance use disorders account for over 10 per cent of the global disease burden (approximately 280 million people have depression globally)
• In countries with low to middle-income levels, a staggering 85 per cent of individuals suffering from mental health conditions go untreated.
• The lost productivity resulting from depression and anxiety, two of the most common mental disorders, costs the global economy US$1 trillion each year. 

The China Brain Project, a 15-year project targeting major scientific discovery and technological development for early diagnosis and intervention of brain diseases and brain-machine intelligence technology by 2030, estimates that if no effective treatments for brain diseases emerge in the coming decades, the global medical care system is likely to collapse by 2050.

Also Read: Strengthening mental healthcare in Asia through local data that enhances efficacy

This intersection where technology meets healthcare is where its disruptive nature transforms into a lifeline for humanity. Technological advancements, particularly in AI, neuroscience, and psychology, are not just reshaping industries; they’re pioneering the development of innovative diagnostic tools and treatment methods for brain diseases. These tools include obtaining data on emotions at scale through apps to mapping and coding out brain activity using AI devices.

My journey and explorations

As someone who has worked in high-growth tech startups for over a decade, my journey mirrors this larger narrative of technology’s capacity to both disrupt and heal. Growing a tech startup is all about relentless execution, and it came at the expense of my mental health.

Despite the constant challenges, this experience has led me on a lifelong path of personal growth and reinforced my commitment to contributing to solving some of humanity’s global mental health burden with technologically sustainable solutions, both personally and professionally.

Beyond my work in tech startups, I embarked on both an academic and practicum path by pursuing a Professional Diploma in Psychotherapy, Counseling and Positive Psychology with The School of Positive Psychology (TSPP), taking night classes every weeknight after work for two years.

Deep down, I want to explore how I can consciously evolve into the best version of myself as a human being and how people can engage with me on a new level of openness and emotional vulnerability with my personal growth work and mental health advocacy through technology. 

During the COVID-19 lockdown, I co-founded a startup called Ministry For Good, which seeks to raise awareness of mental health issues and how technology can be used to improve access to mental health care and help scale other social impact causes. Our first project was raising awareness of the symptoms of dementia and exploring how AR/VR technology can help with reminiscence therapy.

In the broader spectrum of my tech roles, I became a super user of digital mental health solutions, testing out current product offerings in the market on myself, which extended to generative AI solutions. Although generative AI models cannot experience emotion as humans do, these models can be programmed to recognise emotional cues from text, speech, or facial expressions and adjust their responses accordingly, mimicking how emotions affect human thought and behaviour.

This is often used in fields like affective computing, where AI is designed to detect and respond to human emotions, enabling an empathetic response from a chatbot to potential early detection of mental health issues.

Also Read: From chatbots to therapists: How AI break ground in bridging the mental health care divide

Rana El Kaliouby, CEO and Co-founder of Affectiva, an emotion AI startup, writes in her book Girl Decoded, “I was also struck by the vital role of emotion in enabling people to make sound decisions. At the time, I believed that the best decisions were based on cold, calculated logic that didn’t let feelings get in the way. In fact, as I learned, decades of neuroscience showed just the opposite to be true. Your “feelings” don’t get in the way. They improve your thought processes.”

AI in mental health: Enriching emotional intelligence

This understanding underscores the potential for AI to not just automate tasks but to enrich our emotional intelligence. In this light, digital mental health solutions emerge as conduits between the analytical capabilities of AI and the nuanced realm of human emotions, fostering an environment where technology supports and enhances mental well-being.

I envision a collaborative future where digital mental health solutions evolve to become more empathetic, advanced, and interactive, revolutionising mental health care. In the future, mental health professionals leverage the efficiency of AI in routine tasks such as diagnostics, monitoring, and research, thereby enhancing their productivity.

This allows them to dedicate more time to activities that truly set them apart from machines: their emotional intelligence, creativity, and deep interpersonal connections. Meanwhile, individuals can engage with AI-powered tools like chatbots or virtual assistants, which offer simulated scenarios to encourage positive thought patterns and behaviours.

However, the integration of AI into mental health care requires ethical, practical, and clinical considerations. It is crucial that governments intervene with well-thought-out mental health policies that ensure the ethical application of AI while fostering innovation, ensuring a balance that benefits all stakeholders in the mental health ecosystem.

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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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