Generative AI is making significant strides across various sectors, particularly in enhancing productivity, efficiency, and customer experience. In this interview with e27, Dipen Mehta, APAC Head of Banking, Financial Services, and Insurance (BFSI), SoftServe, speaks about the common use cases of AI in Singapore today and the challenges that businesses face in adopting the technology and maximising its potential.

In Singapore and globally, businesses are leveraging this technology to streamline operations and improve service delivery. The first major application is increasing productivity by automating tasks and processes, allowing businesses to achieve more with less.

Secondly, AI drives efficiency by reducing customer service costs and optimising business processes. Lastly, AI enhances customer experiences by offering more personalised and responsive interactions.

“You may have heard this term in marketing, but this is used broadly in most industries around hyper-segmentation. Generative AI is one of the technologies that really allow us to do micro-segmentation [of our users and customers],” Mehta says.

“I can now say, here are the characteristics we know about our customers and what might drive a certain behaviour. So you can generate more than just customised text; you can also generate imagery, colour, and the whole template. You can evoke an emotion. This space has a lot of experimentation, but we are seeing that across industries.”

Also Read: How AI enhances market forecasting for tech startups

Remaining challenges in AI adoption for businesses

However, adopting AI technology in businesses presents several challenges. The first of which is the rapid pace of technological advancement. As generative AI evolves quickly, keeping up with the necessary skills within an organisation becomes difficult.

“It is not because the technology is too hard for anyone to understand. But it is moving at a rapid pace; the pace of innovation in Generative AI is very, very fast. There are always new models coming out. The way you interact with the models is changing.”

The second challenge revolves around data readiness. While businesses can quickly develop a proof of concept using AI with their own data, moving beyond this stage to full production is challenging.

“We could work with the customer and get that done in days, showing them a proof of concept with their real data and its use case, but to get them into production and have it integrated across the entire organisation–with production data and all that–is a very difficult proposition,” Mehta says.

Unlike generic consumer AI applications, businesses require AI to work with their specific data, which must be well-managed, up-to-date, and compliant with industry regulations. Many organisations lack the data infrastructure and maturity needed to ensure their data is consistently accessible and secure, which poses a substantial obstacle to AI adoption.

The third barrier is organisational readiness and acceptance of AI technology. Even though AI tools are often user-friendly, they can significantly alter existing workflows, leading to employee resistance.

Consequently, businesses often underestimate the importance of change management in AI adoption. Ensuring the organisation is prepared for these changes and addressing concerns about job security are crucial steps in successfully integrating AI into business operations.

Also Read: Building resilience against cyber attacks in ASEAN through data

Tackling barriers to adoption

So what can businesses do to tackle these barriers to AI adoption?

Education is certainly a key part of it, but Mehta stresses that businesses must take a specific approach. According to him, companies that have successfully adopted AI technologies educate their employees about its usage in a way that fosters innovation instead of simply saying that this technology will make them better employees.

“Some people might feel reluctant and say, ‘Am I not a good employee now?’” he says. “You can educate [them about the] better use of AI. It takes away 30-40 per cent of the stuff you do not want to do anyway, the low-value stuff. You can focus on the higher value things.”

Businesses can also encourage their employees to be creative about it. “Instead of releasing the technology into the operations team of an organisation and giving them some instructional videos and training, we can make it more like a hackathon. Come back in a month’s time with some ideas [about how you want to use the technology].”

Other ways to encourage the use of AI, which is already commonly used by global tech giants, are to integrate the technology into existing products, services, or systems. A well-known example of this approach is Microsoft Copilot.

“Most people already use Microsoft Office. Releasing Copilot was a very innovative way to get people’s exposure to Generative AI without attacking everything you do,” Mehta says.

Also Read: How blockchain can enhance sustainability in fashion

“It is a good example of exposing the technology as an innovation tool rather than a prescriptive of ‘This is going to do your task or your job today.’”

Last but not least, businesses can encourage the adoption of AI by learning from existing use cases.

“We are happy to spend an hour showcasing some demos and things we have seen for your team to get inspired. Then we can go down the route that I said, which is to ask the teams to come up with ideas [of how they want to use this technology].”

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Image Credit: SoftServe

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Businesses and investors should keep their eyes on China’s actions after the Third Plenum concluded without surprises. Perhaps the most telling sign is that the party specified a timeline to achieve the reforms.

The Third Plenum of China’s Communist Party (CCP) concluded in July without new stimulus or deep reforms, but that does not mean there isn’t welcoming news.

While the communique released after the closed-door meeting led by President Xi Jinping contained few surprises, it could also be viewed as maintaining stability and certainty. In a major election year for many countries where we have already seen drastic changes or uncertainties in governments, the less-than-surprising outcome may provide what the market seeks in an uncertain world.

The broad goals emphasised in the plenum have not changed — economic recovery, technological innovation, and market reforms.

Despite its economic challenges, China seems more cognisant that any drastic measures may introduce volatility and potentially derail plans in achieving its 5 per cent growth target for 2024.

Long-term shift vs short-term stimulus

A long-term shift is what the world’s second-largest economy is gunning for, when the drivers of its meteoric rise have been slowing down.

Before the plenum, official data showed the Chinese economy expanded by just 4.7 per cent in the second quarter this year, among its slowest growth in recent years. When combined with the first quarter data of 5.3 per cent growth, China may still be on track to its year-end target.

But for the market, it may be cutting too close for comfort, given the short-term challenges ahead. This may explain why the plenum has focused on the long term, and placed emphasis on enhancing people’s quality of life as one of the means to boost overall consumption which some economists have been urging for, among others.

Also Read: Succeeding in e-commerce in China: Building AI-powered chatbots that know how to close a sale

Still, the plenum offered some positive signals for businesses.

One of the most notable aspects of the communique was the emphasis on giving “fuller play to the role of market mechanisms”, which could increase efficiency and competition, reduce bureaucracy and regulatory hurdles, and enhance the role of the private sector.

Businesses could have more freedom to innovate and compete. Better products and services for consumers could be a more reliable way of addressing sluggish domestic consumption and shift to consumption-led growth.

In addition, the aim of unleashing the consumption power of people in rural areas and giving them access to urban public services may have a trickle effect on boosting overall consumer demand in the country.

Fewer regulatory hurdles could allow quicker adaptation to market demands, crucial in tech-heavy sectors that Beijing is targeting as “future industries” for more innovation-led growth.

That said, China also aims to “build a high-level socialist market economy system”, so the pace of reform and growth may not be at the same pace that other markets are accustomed to.  The silver lining is that the plenum affirmed the need to address market inefficiencies, which can be taken as a sign that the country acknowledges there is work to be done to bridge the expectations gaps.

Uncertainty in implementation

But the lack of detailed implementation plans creates uncertainty for businesses at a time when foreign direct investment shrank 29.1 per cent year-on-year to US$49.9 billion in June 2024.

For tech companies, the emphasis on innovation and self-reliance is a double-edged sword: It opens up opportunities for growth, but the absence of detailed policies on funding and regulatory support needed to make informed investment decisions can hinder long-term planning.

Similarly, financial institutions are left guessing about the specifics of financial market reforms and regulatory changes, which are crucial for future investments and risk management.

The property market, already facing a significant slump, remains volatile without clear policies to stabilise prices and address debt issues. Similarly, infrastructure projects depend heavily on local government funding and stability. Suggestions of local government tax reforms may sound intuitive, but the lack of concrete policies or directives at this stage may complicate long-term planning and investment in public projects, affecting sectors reliant on local incentives and infrastructure development.

Consumer goods companies involved in export-import activities face uncertainty regarding tariffs, trade agreements, and market access, impacting their global operations.

Watch what China does, not just what it says

That said, businesses and investors should watch what China does, not just what it says.

Also Read: Ecosystem Roundup: US VCs’ exodus from China; JALA completes US$13.1M funding round

The plenum stated that it is focused on creating a “beautiful China”, by accelerating green transformation and pursuing green, low-carbon development, among others. This alone may not say much.

But if taken together with its directions of improving the mechanism for modern infrastructure construction and the resilience and security of industrial supply chains, the opportunities in environmental sustainability could be vast.

For instance, the plenum proposed deeper reforms of its railway system and further develop the “low-altitude economy” (such as drones and flying cars). This means demand for renewable energy will likely increase. The need to secure the supply chain in the production of these assets would be critical.

Perhaps the most telling sign that businesses should keep their eyes on China’s actions is that the plenum specified a timeline to achieve the reforms. In past plenums, the CCP has not set such timelines.

This time, unusually, Beijing has given clear indication of a 2029 deadline – an important year as the People’s Republic of China celebrates its 80^th anniversary. Some analysts see this as an effort to focus the CCP on implementing the reforms.

This in a way provides a direction for businesses and markets on China’s roadmap.

While the road ahead is fraught with challenges, businesses that can navigate these complexities and leverage emerging opportunities will be well-positioned to thrive.

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From fear of failure, fear of losing a stable income, work-life balance concerns and more, many factors can hold someone back from starting their own business.

However, one recurring reason I noticed was the lack of CTO or programming skills holding those with a business idea back. This is probably why so many Facebook groups and community events exist to help people meet their co-founders, and even incubators like Antler have popped up to help with the problem.

Having someone who codes is so critical to the success of your startup. That is why so often, we hear VCs say they need to see the Hustler, the Hacker and the Hipster before investing.

As a group expert for the Facebook group Business Questions Answered for Entrepreneurs & Startups: Singapore by Sleek.sg, very often, I see entrepreneurs or aspiring entrepreneurs posting questions about how to go about starting the tech side of the business or finding the right help to do so.

I was invited to the group to touch on finance and loan topics mainly. However, as someone who doesn’t code but still managed to build a fairly technically complex loan marketplace, I thought sharing my experience could be helpful after seeing three members asking about this subject this week alone. Disclaimer: I did do a term of coding while in poly but that was two decades ago and I had largely returned everything to the lecturer.

Very often to start a tech business, the following three options are available to someone who doesn’t code:

• Get a CTO
• Engage a web agency or freelancer
• Hire someone either remotely or locally

As someone who has tried all three options, here are my experiences followed by some of the trade-offs between each route, and 10 tips that I hope will help you in ensuring the delivery of your project.

Web agency or freelancer

Hiring a web agency or freelancer seems to be the most common route, especially for those with little financial resources and who come upon a business idea by themselves instead of with a group of friends with one who happens to code.

But after taking this route and spending over one year with little to show for it, I began talking to other friends who also took it. That was when I noticed something no one had written about — a failure rate of 10 out of 10!

First of all, I don’t mean that everyone I spoke to failed to produce the app or website that they wanted by engaging a web agency. However, they had to change firm and had at least one dispute or non-delivery before they got it built. I myself changed three agencies before I started digging more into the subject and asking around, with one friend changing seven before something that even barely resembled what he had envisioned was finally made. While it was a small group and hardly statistically representative, still a 10 out of 10 ratio should warrant anyone considering this route to want to understand what it means better.

Also, it is not as simple as just hiring, delivering, paying, starting the business and underestimating what it means to engage a firm. For some, the repeated failure could even cause them to run out of funds or time and they had to give up eventually.

Here is why it can be so problematic. When you want to build an app or a website, you need to know it is not one thing but layers of many things such as the choice of programming stack frontend and backend, APIs, and UXUI.  

Also Read: Why finding your co-founder is a lot like meeting your soulmate

In any area, there are tons of places where there can be corner-cutting, miscommunications, and prioritising quantity and speed over quality that when they come together, it just wouldn’t work.  Let me use an analogy of building a house to illustrate it further.

Even if someone built something that looks identical for you, what construction techniques were used? What materials were used? Any fire and safety procedures? Just because it looked like what you wanted on paper doesn’t mean you can live in it safely.

Take just the material used. Okay, you know enough to ask for steel but then what sort of steel and how was the quality of the material used? Depending on how tech-based your startup is, there can be so many things that one without a tech background wouldn’t even know what matters and what to look out for.

I have many friends who run web agencies and while not their clients directly knowing them and understanding what they do, I believe they delivered many quality projects. So identifying the gems among the many agencies can be an art.

I’m not trying to put any industry down. But if you think about it mathematically on average, the number of people that is above average cannot be higher than those that are below average. So If you have a 50 per cent chance of meeting the right firm, then factor in that even established firms like banks and scale-ups do outsource their projects from time to time, and with their highly technical and competent people having the skills to pick and meet the right firms, you are more likely than not to meet the wrong firm.

That is also why I’m trying to disrupt my industry and take the unprofessional loan brokers out of the equation after hearing the horrible things that they have done. While there is AI, I don’t think developers will be disrupted anytime soon. Sure, it can help you write some small snippets of code but it is unlikely for a non-coder to put everything together unless you are building something really small, and in such case, choosing the right firm would be less critical anyway.

Hiring in-house

So what about hiring in-house whether it’s local or remote?

Fundamentally, agencies want to serve as many clients as they can and complete as many projects as possible. That way, they can earn more in a month and that is something that will certainly encourage corner-cutting, miscommunications, prioritising quantity and speed over quality.

In theory, a staff will think long-term as they are incentivised differently. But how do you assess the right skills and chemistry as a non-tech person? Without any technical background, it can also be a challenge to communicate exactly what you want to him or her and vice versa.

CTO

So, naturally just having a CTO is something many people lean towards. However, it is not going to magically solve the problem. You may have someone you trust, but unless you have worked together before, you just wouldn’t know what problems would pop up. Also, with dozens of programming languages available and many areas to look at, most startups probably wouldn’t be able to afford a CTO well-versed in all your programming needs. And if he or she is so competent, then why you and your idea?

Additionally, if the project is not small, he/she might be unable to code it by himself/herself. He/she is more likely to be a project manager than a CTO whose role includes R&D to stay ahead of technological trends and drive innovation within the company, as well as development and implementation of new technologies and systems to ensure they align with the company’s business goals — something that might be too strategic when you have more pressing tactical needs.

If all of the above seems like they can come with problems, does it mean you cannot start a tech startup unless you know how to code yourself?

Of course not. If I can do it, so could you.

As the saying goes, good and fast cannot be cheap, cheap and fast cannot be good, and cheap and good cannot be fast. For each, there are trade-offs and no one route is better than the other without factoring the internal needs and circumstances. I hope that helping you understand what the trade-offs are for each will help you better pick the path that works best for you, as well as help you anticipate the challenge ahead.

“If you think hiring a professional is expensive, wait till you hire an amateur,” Red Adair.

Also, it is only natural to kiss a few frogs before you meet your prince. So do not be deterred about going with the wrong route but rather, examine what went wrong and what you can improve on. Otherwise, why do some companies have 7 rounds of interviews and still no HR can ensure a 100 per cent fit and cannot do away with the probation period? Just because one of the above paths has failed you, doesn’t mean it doesn’t work. But reflect and adjust your plan.

Also Read: Startup funding in SEA declined 68% to US$129M in July: Tracxn

This is how I would summarise the pros and cons of the three options: cost vs speed vs risk.

• CTO: The cost for a CTO is the highest in the long term. If the company succeeds, the equity you gave up will cost more than hiring but your upfront cost is lower. However, if you have someone capable, it can greatly reduce the risk of a project failure and the time needed. Using vesting agreements with a cliff period can help reduce your risk of getting stuck with someone who might not be a long-term match.
• Hiring: The cost of this route will be the highest upfront. If you do not have a good interview process and have to replace the candidate, your cost and timeline are going to increase as well. However, if you make a good hire, your speed and risk should be lower than a web agency’s. But even if you are experienced in hiring and managing people, without a tech background, you may find hiring and managing a tech person to be very different.
• Web agencies: This is usually the cheapest, especially if you are tapping on firms in developing countries. But as mentioned earlier, many things can go wrong, and your odds of picking the wrong firm are more likely than the right firm, therefore the risk is the highest. Having to work with someone from a different culture may add to the risk due to miscommunication.

Speed-wise, it can be rather polarising. If you have to change firms, you will face delays in your project. A medium to large firm may cost more than a smaller firm, but will have worked on many projects and has employees who are familiar with the various programming languages, stacks and areas.

This means employees assigned to your project can tap on their colleagues for help. If you can find the right firm, you will find this route to be the fastest for you. A good firm will also require less managing on your end, allowing you to focus on other aspects of the business.

That doesn’t mean you shouldn’t consider smaller firms. A smaller but highly resourceful firm can tap into their network for help. A custom-built project will still involve using many third-party tools and APIs. For instance, we use reconciliation tools to inform the system when we receive payments from our lenders so that the system can pay our referrers right away. If someone hasn’t integrated something similar but has a colleague who has done so to assist, that will shave off tens of per cent of the time required for it. Add dozens of integrations and that could mean weeks to months faster overall.

Likewise for hiring. Even if someone claims to be full-stack, usually he/she is stronger at either front-end or back-end and that is just spitting the many areas and layers of a website into two large categories. If he is resourceful, that will mean less time taken to figure things out.

Here are some tips that I hope could help you with choosing the right agency/freelancer and ensure the development goes as smoothly as possible.

• Don’t bother about reviews. Too often, I hear people rely on them. Unless someone is the worst of the worst, he/she would have still delivered a successful project for a client and got a good review. If you want to use reviews as a guide, you should read 3rd-party reviews from websites like clutch.com.
• Talk to some of their past clients, about why they chose them, and relying on their judgements, especially if they are more experienced- could help too. The more time you invest upfront to filter out the wrong agencies, the greater the risk you can reduce for yourself.
• When hiring in-house, getting a technical friend to help you do some technical tests on the candidate could increase your odds of hiring someone with the right skills. While I believe you don’t always need a tech cofounder, it pays to build allies with some. For example, I mentored a CTO turned prop-tech founder who is strong in AWS deployment and I was lucky to be able to point my developers to my friend for assistance. The right fit is subjective. Sometimes, it could be you that is the problem. Making an effort to learn some programming aspects could help you to navigate your product’s journey smoothly. Understanding the various components that go into it will also let you better understand the time, effort, and cost that go into it so you can factor that into your business plan, as well as allow you to communicate with your programmers more easily.
• Owning as many things as possible directly under your name will make it harder for them to hold you ransom. Even if they don’t go as far as doing so, the more you rely on them, the easier it is for them to lure you in with a low price but jack up the pricing as time passes. However, once they have proven themselves, I think it is okay to raise the price a little. In that sense, they are just reducing the initial cost to make it easier for you to decide to go with them. But if it jumps by multiple times, that feels more like a clickbait and they are just luring you in. Speaking of clickbait, here’s another article about clickbaits in other industries. Owning under your name also allows you to switch providers and agencies more easily without having to start from scratch.

Some of the things you should own or set up under your name:

• Your domain: E.g FindTheLoan.com. Purchase it yourself and grant them the right to manage it.
• Github: This is where all your codes sit. They should push their codes to your account and you should back it up from time to time to prevent them from threatening to wipe everything.
• If you use any third party APIs (for example, we use Google map API to help anyone applying for renovation loans etc to auto-complete their address, some OCR solutions to extract certain information from uploaded documents to reduce manual form filling), those sign-ups should be in your name too. Otherwise, they will still have the ability to disrupt the smooth working of your website.

• If you are finding developers from websites like freelancer.com or Upwork.com, ensure to use the escrow feature. Once, an agency had non-delivery but accused me of non-payment. The fact that they threatened to wipe everything and tried to hold me for ransom made it easy for the platform to rule in my favour.
• Have everything in black and white. If things are discussed in a meeting, have them send a summary of the key points, agreed-upon deliverables and timeline. This will protect you in the event of a dispute, as well as make it easier for you to bring on another firm when they can easily understand what’s been built, and how it’s being built and jump into the project from where it was left off.
• For larger agencies, note that the actual project manager and the salesperson pitching to you are often not the same person. So no matter how much you like the salesperson, it probably doesn’t matter. If he or she has overpromised, the project manager is stuck between you and the salesperson.
• Break things down into as many milestones as you can. That way, you don’t have to wait months to realise something is wrong.
• Never pay upfront. I think a lot of agencies are going to hate me for this. To be fair, this would mean vice versa: if they meet a bad client, they are the one at risk. Thus, my suggestion is to make the first few milestones smaller and correspond them to the payment, such as five per cent, 10 per cent, then 20 per cent and progressively increase it, until both sides can trust each other better to take the next milestone together.
• Whatever timeline they promised you, double it or more, especially if you have other operational areas that depend on certain milestones of your project being made before you can work on it.

Unless your project is small and you are building very common features, it can be very hard to determine how long it will take exactly to build a feature and then fit it into the existing codes without bugs or conflicts. They might not be trying to overpromise you but at times, it’s just hard to pinpoint where problems may arise when codes come together, especially for projects where multiple developers are working on it concurrently.

Just like everyone will write this article differently, everyone will write their codes differently. If I had split this article between three people to write different parts of it, chances are it would have been harder to gel them into one article without having to amend a lot of things.

Bonus tips

I had people asking me to learn coding after I lamented to them the challenges I faced. This to me is the most stupid advice ever. As a startup founder, while getting the product out is the key thing now, there are many other challenges you will face along the way as the company grows, and new areas you will need to look into. So are you going to get a CFA, an HR & SEO certification and pass the bar as well?

Learn just enough to be dangerous. As a founder, I believe that knowing how to assess and convince the right candidate is probably one of the most important skills to have. However, knowing some aspects of an operational area would not just help you communicate with your employees better, but also understand their challenges, making you a better employer to work with.

So, don’t learn to code unless you have a passion for it. But, at least understand the various components that need to go into producing the product you want. Spending a few days to read up on what front-end and back-end, APIs, UXUI, hosting, servers, syntax, and logic mean will allow you to communicate with your programmers more easily too.

Spending a few days to learn what a Scope Of Work is, Wireframe and how to prepare one, would also reduce the disputes you have with agencies and freelancers, as well as communicating with any employees what you hope to build.

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Singapore’s vibrant food scene is at a crossroads of innovation and tradition. As the Food & Beverage (F&B) industry faces unprecedented challenges and opportunities, the integration of cutting-edge technologies, particularly from Korean startups, could be the key to unlocking a new era of growth and efficiency.

The AI transformation era

While digital transformation has been the buzzword for years, we’re now entering the age of AI transformation. Generative AI (Gen AI) is the hottest trend in the IT sector, and the F&B industry must embrace this technology to stay competitive. From menu creation to customer service, Gen AI can revolutionise every aspect of restaurant operations.

Korean startups are at the forefront of this AI revolution, offering solutions that can be seamlessly integrated into Singapore’s F&B landscape. These AI-powered tools can help with:

• Personalised menu recommendations based on customer preferences and dietary restrictions
• AI-driven chatbots for customer service and order-taking
• Predictive analytics for inventory management and demand forecasting
• Automated content creation for marketing materials and social media

Data-driven decision making: The foundation of success

The modern restaurant landscape demands a data-centric approach. Korean data analytics technologies can provide Singapore’s restaurants with deep insights into:

• Customer behaviour patterns and preferences
• Menu optimisation based on popularity and profitability
• Targeted marketing campaigns
• Efficient inventory management

By leveraging these tools, restaurants can make informed decisions that drive growth and customer satisfaction.

Elevating customer experience: The power of personalisation

Today’s diners expect more than just good food; they crave personalised, convenient experiences. Korean restaurant technologies excel in this area, offering:

• AI-powered recommendation systems
• Seamless ordering platforms across multiple channels
• Contactless payment solutions
• Data-driven loyalty programs

These customer-centric innovations can significantly enhance the dining experience in Singapore’s restaurants.

Also Read: What is circular economy and why F&B companies should care

Operational efficiency: Automation and AI at work

Korean restaurant automation technologies can dramatically improve operational efficiency:

• Kitchen automation with AI-powered cooking robots
• Smart inventory management systems
• AI-based staff scheduling and management
• Optimised delivery routing using machine learning

These solutions can help Singapore’s restaurants reduce costs while improving service quality.

Sustainability: A core focus

As environmental concerns grow, Korean eco-friendly restaurant technologies offer sustainable solutions:

• AI-driven food waste reduction
• Energy-efficient smart systems
• Sustainable packaging innovations
• Blockchain-powered supply chain management for local sourcing

Implementing these technologies can appeal to environmentally conscious customers while reducing operational costs.

The unique advantage of Korean restaurant tech

While Singapore boasts its own impressive array of restaurant technologies, Korean solutions offer unique advantages:

• Diversity: Korean startups provide a wide variety of solutions, covering every aspect of restaurant operations.
• Customer-driven approach: Korean technologies are often developed with a strong focus on enhancing customer experience, aligning well with Singapore’s service-oriented culture.
• K-food compatibility: As Korean cuisine gains popularity in Singapore, adopting technologies designed for K-food restaurants can provide a competitive edge.

The rise of K-food and tech synergy

The growing popularity of Korean cuisine in Singapore creates a perfect opportunity to adopt Korean restaurant technologies. These solutions are often designed with K-food preparation and service in mind, making them ideal for:

• Korean barbecue restaurants requiring specialised equipment
• Boba tea shops needing efficient ordering systems
• Korean fried chicken outlets looking for delivery optimisation

By integrating these technologies, Singapore’s K-food establishments can offer authentic experiences while maximising efficiency.

Conclusion: Collaboration for innovation

The digital and AI transformation of Singapore’s F&B industry is not just a challenge, but an exciting opportunity. Korean restaurant technologies, with their diverse, customer-driven, and K-food compatible solutions, can play a crucial role in this evolution.

As we move forward, fostering collaboration between Singaporean and Korean companies will be key to creating a more innovative, efficient, and sustainable F&B ecosystem. By combining Singapore’s renowned food culture with Korea’s technological prowess, we can usher in a new era of smart dining that delights customers and drives business success.

The future of dining is here, and it’s powered by data, driven by AI, and enhanced by the unique synergy between Singaporean cuisine and Korean technology. It’s time for Singapore’s F&B industry to embrace this transformation and serve up a future that’s as exciting as it is delicious.

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In today’s competitive business environment, maximising customer lifetime value (CLV) is more critical than ever, especially under tight budgets.

The Echelon X keynote speech, titled ‘The Efficiency Conundrum: Unlocking Customer Lifetime Value under Tight Budgets,’ explored innovative strategies and technologies that businesses can employ to optimise the value of each customer throughout their relationship with the brand.

Led by Marc-Antoine Hager, Regional Head (SEA) at CleverTap, the session provided invaluable insights into identifying, nurturing, and retaining high-value customers, as well as effective strategies for enhancing customer engagement and loyalty.

The keynote speech offered a comprehensive roadmap for businesses seeking to unlock the full potential of their customer relationships. Hager’s insights underscored the importance of leveraging data-driven strategies and advanced technologies to maximise customer lifetime value, even under constrained budgets.

By focusing on customer identification, nurturing, and retention, businesses can foster deeper engagement and loyalty, ultimately driving sustainable growth and success in an increasingly competitive market. The session highlighted the transformative power of efficient customer management practices, setting a clear path for businesses to thrive in the modern landscape.

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Market forecasting is imperative to any startup’s growth. The ability to accurately predict how economic factors influence future supply and demand dynamics is an underrated advantage, especially in today’s increasingly competitive tech landscape. 

Historically, forecasting heavily relied on manual efforts and archival data, but market changes and complexity highlight the need for more agile and data-driven approaches. Artificial intelligence holds significant promise for tackling this issue. 

AI systems can process vast amounts of data at unprecedented speeds. Successful companies have found innovative ways to leverage this functionality to generate precise forecasts, faster insights and scalable prediction models.

How does AI help with market forecasting?

AI-powered forecasting encompasses numerous methods tailored to different goals and applications. 

Automated data collection and processing

Effective market research and forecasting begin with collating and analysing data to derive actionable insights. AI can automate the entire process by simultaneously gathering real-time data from multiple sources, such as social media, user reviews and transactional databases. 

Advanced algorithms can sift through this information efficiently, transforming raw datasets into useful knowledge much quicker than any manual method. These technologies also ensure businesses always have the most recent information at their disposal.

Adaptive insight generation

Machine learning (ML) systems are the analytical engines processing and interpreting data, but that’s just scratching the surface. Modern ML configurations draw on their large dataset training modules to identify patterns and relationships in the data that may have otherwise gone unnoticed. 

ML systems also possess unprecedented adaptability. Once trained, these algorithms apply their learned knowledge to new data and refine their predictions accordingly. This feature ensures the insights generated remain accurate and relevant in today’s dynamic tech landscape, where market behaviour is always changing. 

Demand forecasting

AI’s predictive analytics functionality can help tech companies improve their operations and competitiveness by identifying patterns in customer behaviour to drive decision-making. 

Across Southeast Asia, e-commerce platforms Alibaba and Lazada rely heavily on AI for demand forecasting to offer highly personalised product recommendations. Their sophisticated AI models analyse critical data like historical sales, seasonality and external factors to predict future demand. 

Also Read: Soft skills, learning ability get increasingly important for hiring managers as AI transforms the workplace: LinkedIn

Similarly, Grab, the region’s most popular ride-hailing app, harnesses AI algorithms to analyse consumer preferences and anticipate booking surges. The service uses these insights to forecast demand patterns and optimise driver allocation. 

Customer churn prediction

One critical challenge startups face is customer churn. The tech space is increasingly saturated as more companies adopt managed service models. According to research, IT and computer software services command 12 per cent and 14 per cent average churn rates, respectively. 

Machine learning models can combat this issue by analysing diverse data streams concurrently to spot early signs of potential churn. For example, algorithms can identify customers showing reduced usage patterns and dissatisfied customer service interactions. These are telltale indications that a user may be considering moving to another vendor. 

Benefits of AI-based business forecasting

The use of AI in business has grown exponentially, transforming how organisations operate and innovate. These applications yield numerous benefits for startups with the right foundational frameworks of AI forecasting integration. 

Enhanced precision

AI algorithms can crunch massive datasets with cutting-edge precision, empowering startups to make data-driven decisions with confidence. This enhanced forecasting accuracy has a ripple effect across the startup ecosystem, including preventing out-of-stock issues and supply chain network errors. 

More refined responses

AI systems continuously evolve, incorporating advanced technologies like natural language processing and deep learning to enhance market forecasting capabilities.

For instance, advances in neuro-symbolic AI have resulted in ML models with neural networks capable of making rational arguments and responding to emotional nuances. Another exciting development is Causal AI, a powerful model that can learn real-world causal relationships. 

Also Read: Generative AI: Unprecedented adoption rates in 2024

These innovations will expand the reasoning scope of AI predictive algorithms and improve the reliability of responses since there’s greater assurance that the system comprehends nuanced queries. 

Adaptive segmentation

As newer classes of consumers emerge, the global tech market will become more segmented. This would complicate forecasting methods, given the additional dataset inclusions and considerations for mapping a startup’s target audience. 

AI systems can segment markets quicker and more effectively based on individual preferences and behaviours. Businesses can use these personalised insights to drive targeted marketing campaigns and tailor their offerings to specific customer segments. 

Challenges and limitations 

Since AI became mainstream, its biggest challenge has been data quality. A predictive ML model is only as good as the data used to train it. Feeding the wrong input will cause the system to generate inaccurate responses, creating problems for the organisations using them. 

These limitations also increase the risk of bias and AI hallucinations. For example, 22 per cent of the data used to train the first ChatGPT version originated from Reddit links. This caused the model to produce clearly biased answers. 

Another issue to consider is the dwindling quantity of training data. AI systems have long relied on information from thousands of public web domains. However, increasingly restrictive service terms limit access to high-quality sources, effectively drying up the training dataset pool. According to a recent study, as much as 45 per cent of website data in a sampled set are no longer accessible to AI models as a training resource. 

Despite these challenges, AI’s impact and potential in today’s business environment are undeniable. As many as 75 per cent of large enterprises across the Asian Pacific region plan to incorporate these systems to enhance business processes by 2026. 

Leverage AI for improved market forecasting 

Merging AI and forecasting has proved to be a critical resource in navigating the complex business landscape. The emergence of no-code AI platforms further simplifies the integration of these systems into existing market analytics frameworks. At the very least, it allows startups to better understand their markets without spending a fortune on data science resources.

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As Malaysia continues to invest in technology, foster innovation, and build a resilient economy, it is poised to play a pivotal role in shaping the future of Southeast Asia.

The Echelon X panel discussion, titled ‘Driving Impactful Innovations in the New Economy in Malaysia,’ explored the nation’s efforts to embrace digital transformation, foster innovation, and build a sustainable economy in the post-pandemic era.

Moderated by Justin Chin, Head of Business Development at e27, the panel featured esteemed speakers:

• Gil Carmo, CEO & Founder of iMotorbike.com
• Richard Ker, Chief Storyteller & Founder of Richard Ker Digital
• Khairool Adzelan Aman, Manager for Ecosystem Development & Digital Innovation at Sarawak Digital Economy Corporation
• Mydiana Madzlan, Head of Ecosystem Building at Iskandar Investment Berhad.

The panel delved into the strategic initiatives and policies that Malaysia is implementing to accelerate its digital transformation. Speakers highlighted the importance of fostering a supportive ecosystem for startups and innovators, emphasising the need for collaboration between the public and private sectors underscoring the country’s potential to become a regional hub for digital transformation and sustainable growth.

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Fundraising or preparing your startup for fundraising? Build your investor network, search from 400+ SEA investors on e27, and get connected or get insights regarding fundraising. Try e27 Pro for free today.

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The Echelon X panel discussion titled ‘Transforming SEA’s Healthtech Sector: How are Startups and Investors Innovating to Enable Access, Build Infrastructure and Provide Affordable Enablement of Healthcare Services?’ delved into the dynamic landscape of Southeast Asia’s healthtech sector.

The session explored how startups and investors were driving transformative change, with a focus on enabling access, building infrastructure, and providing affordable healthcare services.

The panel was moderated by Pauline Erica Tay, Director at the National Health Innovation Centre (NHIC). Joining her were esteemed speakers:

• Ram N Kumar, CEO and Founder of NirogStreet
• Margaret Wang, CEO of Rhea Fertility
• Wai Chiew Chik, CEO and Executive Director of Heritas Capital

These experts shared their insights on the current state of the healthtech sector in Southeast Asia, discussing the innovative approaches and successful strategies that were shaping the future of healthcare in the region.The discussion highlighted challenges and opportunities in Southeast Asia’s healthtech sector, focusing on how startups and investors are enhancing access, infrastructure, and affordability.

Speakers showcased innovative strategies transforming healthcare and emphasised the importance of collaboration, innovation, and investment. The panel provided valuable insights into the future of healthtech, guiding efforts to drive meaningful change in the region’s healthcare landscape.

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Fundraising or preparing your startup for fundraising? Build your investor network, search from 400+ SEA investors on e27, and get connected or get insights regarding fundraising. Try e27 Pro for free today.

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Tech startups in Southeast Asia raised US$129 million in July 2024, a significant 68.38 per cent decline from the previous month. The drop is 78.82 per cent from the same month last year.

Like the past several months, seed-stage rounds dominated venture capital investments in July this year, with 15 deals recorded, followed by early-stage (9) and late-stage (2) fundings.

With US$21.4 million raised, Carsome reported the largest financing round in July, followed by Chainbase (US$15 million), Hive Health (US$6.5 million), and PEXX (US$4.5 million).

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Bering Lab, an AI-powered legal translation startup in South Korea, has announced a US$2.3 million pre-Series A fundraise led by SBVA (formerly SoftBank Ventures Asia), with participation from The MBA Fund.

The company plans to leverage the new funding to enhance user experience and accelerate global expansion.

Bering Lab has already established a presence in 15 countries, including the US, Hong Kong, Singapore, and Japan.

Also Read: Generative AI: Unprecedented adoption rates in 2024

Founded in 2020 by co-CEOs Jae-Yoon Kim and Seong Moon, Bering Lab offers domain-specific AI translation engines to handle complex legal document translations.

Its flagship translation platform is BeringAI. According to the startup, BeringAI+, which combines AI technology with expert review by over 500 lawyers and 800 professional translators across over 30 countries, achieves 99 per cent translation accuracy.

Bering Lab claims it serves over 300 clients worldwide, including over 140 law firms.

The company stated that it plans to expand its services beyond legal and patent translations into other specialised areas like finance, life sciences, and IT. It also develops customised enterprise solutions tailored to specific industries, enabling businesses to overcome language barriers and operate more efficiently in a data-secure environment.

Also Read: 5 dimensions of responsible AI: Enhancing societal needs with blockchain

Jae-Yoon Kim, co-CEO of Bering Lab, said, “We are redefining the translation industry with our unique technology and expertise. Our focus remains on empowering professionals across various fields to overcome language barriers and operate seamlessly.”

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