The buildings in which people work, live, and play produce a huge amount of carbon emissions; building operations accounted for 30 per cent of global energy consumption in 2021. The building sector, like so many other economic sectors, is on a journey to become more sustainable.

Singapore, along with many other cities, faces the challenge of reducing its carbon footprint; buildings account for over 20 per cent of carbon emissions. Recognising the urgency of the situation, Singapore has pledged to have at least 80 per cent of its buildings certified green under the Green Mark scheme by 2030. This commitment reflects Singapore’s determination to raise the sustainability bar for buildings.

Achieving significant reductions requires a collective commitment to sustainability, including improved energy efficiency in buildings. This involves building owners, industry professionals, policymakers, and other stakeholders transitioning to the best solutions to enhance building performance.

While many stakeholders are making the transition, the question remains: why aren’t all building owners moving more quickly to adopt sustainable solutions that deliver reduced costs and reduced carbon footprints?

The challenges of operating traditional cooling systems

Buildings play a substantial role in greenhouse gas emissions, accounting for a significant portion of both direct and indirect emissions. In 2021, eight per cent of global CO2 emissions came from the use of fossil fuels in buildings, while six per cent were linked to the manufacturing of construction materials. However, the largest share of emissions, amounting to 19 per cent, resulted from the electricity and heating/cooling used in buildings.

Also Read: Propelling SG businesses towards sustainable future: How to inspire emissions plan creation

Building owners must provide comfortable environments for their tenants. In tropical climates, the solution to that traditionally involves purchasing, installing, and operating cooling systems. This process is complex, requiring multiple consultants, contractors, and operators, and comes with substantial upfront, ongoing costs for maintenance and operations, and inefficiencies that increase carbon output.

It is worth noting that despite advancements in cooling equipment performance and the decreasing carbon intensity of electricity production, indirect CO2 emissions from space cooling have experienced rapid growth. These emissions nearly tripled from 1990 to slightly over 1 Gt CO2 in 2022, with emissions in 2022 surpassing those in 2021 by over two per cent. With global temperatures on the rise, the demand for cooling is expected to escalate further.

For building owners and managers, transitioning to smart and sustainable cooling solutions has become a necessity rather than a choice. Governments worldwide are tightening their carbon policies, and Singapore is no exception. In its latest budget, the government announced progressive increases in the carbon tax, which is anticipated to reach US$50 to US$80 per tonne by 2030. This tax applies to all spaces generating 25,000 tonnes or more of greenhouse gas emissions annually, emphasising the need for sustainable practices in cooling and other areas.

AaS (As-a-Service) models for building operations

Drawing inspiration from subscription models like Netflix and Spotify, As-a-Service (AaS) models provide a pathway to addressing the challenges of sustainable building operations. AaS offers on-demand and customised services to meet the unique needs of businesses.

With AaS, businesses can bypass the need for upfront capital expenditure (CAPEX) costs and save money by accessing services. This allows for scaling operations, enhancing efficiency, and allocating resources more effectively.

Introducing CaaS: Cooling as a Service 

Within the As-a-Service model is Cooling-as-a-Service (CaaS), offering building owners a hassle-free approach to cooling solutions without the burdens of ownership. The advantages of CaaS are extensive, beginning with the elimination of upfront capital investments and ongoing maintenance expenses and, through greater efficiencies, reducing carbon footprints.

Also Read: Alt-food revolution: A look at SEA’s growing demand for sustainable food

With CaaS, building owners outsource a non-core yet vital activity. By closely monitoring and controlling the cooling system in real-time and utilising data and artificial intelligence, CaaS maximises cooling performance, eliminates energy waste, and enhances indoor experiences that adapt to changing building conditions.

Embracing CaaS also enables businesses to reduce their carbon emissions and environmental impact. Building owners can specify their cooling requirements and pay a fixed rate based on actual usage. Through comprehensive data analytics, building owners can gain valuable insights into their environmental footprint and identify areas for improvement.

For example, INSEAD Asia Campus and 1Elpro Park, both working with Kaer, have successfully reduced their carbon footprint with CaaS. They use the latest energy-efficient technologies and are powered by 100 per cent renewable energy. Collectively, Kaer saved its larger client portfolio 25,000 metric tonnes of carbon in 2022.

Transition to a low-carbon economy with CaaS

CaaS presents a golden opportunity to reduce carbon footprints, achieve cost savings, and streamline operations.

This CaaS transition not only accelerates the journey towards carbon neutrality and a climate-resilient future but also enables the handover of operating and maintaining cooling systems to experts. This allows businesses to focus on their core operations while enjoying the financial and environmental advantages associated with the ‘as-a-service’ economy.

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In the world of marketing, especially in the recent situation where brands are often tsunami’d with negative impacts due to morality issues, the golden ticket to success isn’t sugar-coated gimmicks but authenticity. Much like Willy Wonka’s coveted golden tickets, consumers today seek brands offering an authentic experience. In the realm of brand communication morality, the power of authenticity emerges as the sweetest treat in the marketing mix.

Willy Wonka, the eccentric chocolatier from the recent Wonka movie, which originated from Roald Dahl’s classic, Charlie and the Chocolate Factory, understood the allure of authenticity. His magical factory showcased the wonders of genuine passion and creativity intertwined. In the marketing realm, where messages often entice and persuade, the importance of authenticity cannot be overstated.

However, much like the young Wonka, most of us marketers and communications professionals are chock-full of ideas and determined to impress the world one delectable idea at a time – proving that the best way to put our brands out there is through creative storytelling and if we are lucky enough to perform the way the young Wonka would, we might just hit the jackpot.

The everlasting gobstopper of trust

In Wonka’s factory, the Everlasting Gobstopper represented a confectionery marvel — a treat designed to last forever. In the marketing world, trust is the Everlasting Gobstopper, and authenticity is the recipe for crafting it. Brands that consistently communicate their values and stay true to their promises create a foundation of trust that endures over time and potentially gets over tough times with minimal scratches.

Consumers today are more discerning than ever. They can spot a marketing ploy from a mile away, much like the way Charlie Bucket recognised the genuine nature of Wonka’s chocolate. Authenticity in brand communication is about going beyond the glossy exterior and revealing the core values that resonate with the audience.

The Oompa Loompas of ethical framework

Willy Wonka’s Oompa Loompas served as industrious workers behind the scenes, ensuring the seamless operation of the chocolate factory. Similarly, in the realm of marketing morality, it is imperative for a brand to establish a framework that assesses its practices and performance, serving as a guide for the brand to be socially accountable to itself, its stakeholders, and the public. This role acts as the ethical framework’s equivalent of Oompa Loompas for a brand.

Also Read: Barbie-fy your business with the power of PR

Brands that incorporate this framework into their identity demonstrate a dedication to making a positive impact beyond mere profit margins. Much like the Oompa Loompas singing moral lessons in response to misbehaving children, brands with a robust ethical presence utilise their platform to advocate for social and environmental causes. This isn’t merely a philanthropic gesture; it’s a manifestation of a brand’s commitment to being a responsible and conscientious member of society.

Navigating the chocolate river of cultural and religious sensitivity

In Wonka’s factory, the chocolate river was a mesmerising spectacle, but navigating its currents required skill and understanding. Similarly, in the diverse and culturally rich landscape of the market, brands must navigate the chocolate river of cultural or religious sensitivity.

Missteps in this area can lead to a sour taste in consumers’ mouths, and the consequences can be as swift and unpredictable as the currents of Wonka’s river. This has undoubtedly proven true when many brands find themselves associated with groups perpetuating harm to the innocent, facing the repercussions of such affiliations.

Authenticity in brand communication involves more than just crafting messages that resonate; it requires a deep understanding of the cultural nuances and values of the audience. Brands that recognise and celebrate diversity authentically not only avoid the pitfalls of cultural insensitivity but also create a stronger connection with their audience.

The golden goose of transparency

In Charlie and the Chocolate Factory, the Golden Goose laid eggs that held the promise of unimaginable wealth. In the real world, transparency is the Golden Goose that lays the eggs of consumer trust. 

Willy Wonka, with his mysterious persona, understood the allure of keeping an element of surprise. However, in the real world of brand communication, transparency is the key to fostering trust. Brands that are open about their practices acknowledge mistakes and communicate openly with their audience to build a reservoir of goodwill that can withstand challenges.

Also Read: Transforming tech performance: A brain-friendly growth approach

Avoiding the Vermicious Knid of manipulation

Consumers today are savvy and can spot when they’re being manipulated. Brands that prioritise authenticity over manipulative tactics not only build stronger connections with their audience but also avoid the long-term damage to their reputation that can result from deceitful practices.

Finding the golden ticket in authenticity

In the enchanting world of Willy Wonka’s chocolate factory, the journey to find the golden ticket was a metaphorical quest for something rare and extraordinary. In the world of brand communication, the golden ticket to success lies in authenticity.

Brands that weave genuine narratives, embrace ethical practices, navigate cultural sensitivity, prioritise transparency, and eschew manipulative practices find themselves holding the golden ticket to consumer trust and loyalty.

Much like the timeless appeal of Willy Wonka’s chocolate, authenticity in brand communication is the secret ingredient that keeps consumers coming back for more. As we continue navigating the ever-evolving landscape of marketing morality, let us remember the enduring lessons from the world of Wonka — the power of authenticity is not just a fleeting trend; it’s the golden ticket to sweet, everlasting success.

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The Human Managed platform operates on the edges of technology-forward enterprises in the essential services sectors. Some questions at the forefront of every decision-makers’ mind are: 

• How do I get more value from data using AI?   
• How do I empower and scale my team’s decision-making with AI?  
• How do I improve business resiliency with AI?  

These are complex questions to answer. But a great silver lining is that you can solve all these problems and more with distributed and scalable data operations. We pick the three ops decisions with the highest impact and value for businesses of all sizes and industries as we enter the age of AI headfirst.

DataOps: Feeding AI with AI-ready data

In today’s digital world, data directly impacts your organisation’s top and bottom lines. The last few years have seen explosive growth of AI tools and services accessible to players of all sizes and sectors. Within this fast-changing environment, businesses should focus on the value they can generate from data. The faster, more agile and scalable your dataops is, the bigger your AI-powered opportunities are. For enterprises, this means operationalising any data from anywhere. 

Challenges: Now you have the data, what do you do with it?  

Businesses are usually adept at identifying the business problems they want to solve, what data is essential for the problem, and where it will come from. The real challenge comes after.  

For example, say your cyber goal is to control exposures impacting your most critical service, such as a banking app server. Some key sources that generate data related to assets, violations, and vulnerabilities are managed endpoint detection tools, vulnerability management SaaS, and external threat databases. These data sources could be sensors deployed on-premise, in the public cloud, or in your software provider’s cloud. The data from these sources vary in volume, format, schema, integration methods, etc.  

Getting all the relevant data from multiple sources, synthesising them, and processing them through a unified pipeline is infamously challenging to build and scale across numerous use cases. This limits the depth of analysis businesses can run on their data and drives siloed or tool-driven approaches to problem-solving.

Opportunities: Distributed data engineering as input for AI models  

The Human Managed DataOps platform ensures that whatever data you want to analyse is continuously collected, processed, and stored to be AI-ready.  

One of our customers, a leading ASEAN conglomerate, approached us with a widely shared problem in cyber operations: effective prioritisation. They had struggled with siloed asset databases for 20+ years and managing disparate cybersecurity tools across the public cloud, software vendor cloud, and on-premise. This resulted in manual and slow cyber operations, where many issues slipped through. 

Also Read: AI companies raised record US$50B in 2023 globally: data shows

The goal was to automatically contextualise and prioritise our customer’s cybersecurity issues as and when the alerts are generated. The customer’s job was completed when they chose 10 data sources to provide us with the required input (alerts, logs, metrics from SaaS and on-prem systems) and context (asset databases, strategies, and business logic).

The HM platform onboarded the customer’s data for continuous cyber operations in less than a month. We catalogued their assets, controls and attributes and structured their cybersecurity alerts, logs and metrics under one data schema and model. 

Our solution meant that the variable components — the ingredients — for any analysis by any kind of computing, whether rule-based programming or AI/ML models, were ready.

MLOps: Tuning AI models personalised for your business

Although AI, machine learning, and neural networks are not new, what drove the now-familiar explosion of new AI-powered capabilities is the underlying pre-trained models that picked up speed coined in 2021 as foundation models. Foundation models (sometimes called general-purpose AI or GPAI) are AI neural networks trained on massive raw, unstructured data, often with unsupervised learning, that can be adapted to perform a wide range of general tasks without human intervention.   

Foundation model, including generative AI-powered apps beyond our wildest imaginations like ChatGPT, can understand language, generate text and images, and converse in natural language. As AI’s capabilities continue to capture consumers’ hearts and minds, businesses are in a race to decide how best they can adapt AI models in their operations.  

Challenges: Now you have the AI models, how do you make it work for your business context?  

Foundation models are called ‘foundation’ because they act as the base to build apps that solve different problems. Open and commercial AI models are trained on generalised and unlabelled data, such as data scraped from the Internet or untraceable databases.

Generic AI models, no matter how advanced, will not magically produce accurate and precise outputs suited for your unique business context. For machine learning to be operational, AI models must be trained, tuned, and improved with data, logic, and patterns unique to your business.

Opportunities: Decision models combining tribal knowledge and trending knowledge

The Human Managed MLOps platform tunes foundation AI models with a knowledge base that is unique to each customer’s business context and incorporates a human-in-the-loop feedback cycle to continuously improve and measure the performance of the customer’s personalised AI model. This way, our customers get the best of both worlds: trending knowledge that builds foundation models and tribal knowledge that builds their own context models.  

One of the business-critical use cases we apply MLOps for is fraud detection and management for a global banking customer, where we continuously tune AI models with customers’ personalised data to build their own contextualised fraud model that classifies, predicts, or indicates suspicious fraudulent activities.  

Over decades of operations, our customer has amassed tribal knowledge and experiences on their fraud landscape and wanted an automated and scalable solution to increase their detection accuracy and decrease their response time.

Also Read: 2024 cloud trends: AI-powered machine learning, distributed databases, and more

Examples of their tribal knowledge include indicators of different types of fraud (e.g. repeated withdrawals from the same account on the same day), when a detection alert should be triggered (e.g. withdrawal amount is higher than US$50,000), and what needs to be done when there is a detection (e.g. send a critical alert to fraud investigation unit to investigate suspicious transaction). 

All these data points form the unique context of our customer, such as their business logics, prioritised assets, and historical patterns. The Human Managed MLOps platform transforms these data points into structured data and code to form features and labels that tune the customer’s contextualised fraud model.  

Once the input data, AI/ML processes, and desired output are aligned, it’s a virtuous circle of human and machine collaboration because ML models improve with more training and feedback. Our customer continues to add more datasets, rules and conditions while the platform continues to learn from data to improve the accuracy of their ransomware model.

IntelOps: Applying AI for better and faster decisions and actions

In today’s turbulent digital world, where speed and agility have become a necessity rather than an aspiration, leaders should pay attention to how they can make their businesses resilient. Business resiliency is about sustaining during unknown conditions and improving and coming out stronger from VUCA (volatile, uncertain, complex, ambiguous) conditions.

Resiliency, when executed right, can turn challenges into opportunities to protect their assets better, create more revenue, and make customers happier. Achieving business resiliency through data is highly impactful for many reasons, not least to understand the changing environment in which you operate, as well as generate intel to make better and faster decisions and actions.  

Challenges: Now you have the intel, how do you apply it with the right priorities?  

By 2025, it has been predicted that data will be embedded in every decision, interaction, and process (Source: McKinsey, The data-driven enterprise of 2025). 

We’ve seen how context can be built through data and models. DataOps prepares the data to generate valuable intel, and MLOps improves AI models to learn from contextualised data.  

Getting the proper intel consistently is a challenging feat. However, the benefits of the right intel are limited if you do not apply it to the right problem at the right time. How do businesses ensure this? Even with the best intel made available through DataOps and MLOps, if it is not served to the right audience at the right time, the value of that intel is not realised, and the window of opportunity closes. The challenge here is to bring DataOps and MLOps processes together in a consistent and scalable operational cycle across the business, which we call IntelOps.

Opportunities: Data-driven resiliency with I.DE.A. 

Acting with speed and accurate prioritisation is critical to business resiliency. The Human Managed platform services are designed to empower the end-to-end decision-making process, from generating personalised intel to ranked decisions and prescriptive actions. We call this the IDEA Model (Intelligence Decision Action).  

Also Read: Running on empty: What happens when AI models run out of data?

Some of our most subscribed services are data-driven asset management, attack surface management, fraud, and security posture management. For every service, our DataOps platform ensures that all of our customers’ data gets processed and analysed to create contextualised insights, which then act as input for the MLOps platform to continuously tune the AI model for more accurate and precise intel. Finally, our IntelOps platform takes contextualised intel to generate ranked decisions and prescriptive actions based on calculated priorities and impact.    

One of our customers subscribed to our network security posture management service to move the needle on fixing 40,000 violations that have been open and unresolved for over two years. Contextualised intel on the violations — no matter how organised and easy to understand — did not get their operations team to decide on the next steps because the number of issues was so high.  

To make progress on the cyber posture issue, the customer asked for decisions and actions that would have the “biggest bang for the buck”, which is precisely what we delivered. Our IntelOps platform ran computation on 40,000 reported violations on 1000 network segments, protecting around 50,000 assets accessed by 40,000 employees. As an output, we generated four ranked decisions and 16 prescriptive actions that would remove 40 per cent of all violations and improve 100 per cent of customer’s critical assets. The prescriptive actions were delivered through a nudge-based dispatch system to the users who could affect the change.

Get the data right, apply to unique business context, build business resiliency

In conclusion, a business can harness the potential of AI when it has a complete understanding and control of its enterprise data across all sources. Data must be continuously collected, processed and stored to be AI-ready.

For machine learning to be operational, AI models must be trained, tuned, and improved with data, logic, and patterns unique to your business. Finally, applying AI for better and faster decisions means ensuring the company stays resilient against changing VUCA (volatile, uncertain, complex, ambiguous) conditions.  

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Singapore-based customer data platform Meiro has secured US$3 million in an over-subscribed pre-Series A funding round led by Wavemaker Partners.

Several angel investors from Angel Central also joined the round.

Also Read: How to put customer experience at the heart of digital acceleration journey

Meiro, with offices in Central Europe, aims to fast-track its product development, bolster its team, and expand its presence in Europe and Southeast Asia. Additionally, it plans to enter new markets, starting with Dubai in 2024.

“We are gearing up to penetrate the hugely promising Middle Eastern market after launching our initial partnerships and securing our first clients there in late 2023,” said Pavel Bukowski, Chief Product Officer and Co-founder of Meiro.

Evolving data privacy regulations have resulted in the decreased use of third-party cookies that help brands understand customer behaviour and preferences. This trend and soaring marketing costs present a considerable challenge for brands.

Founded in 2018 by Pavel Bulowski, Jana Marlé-Zizková, and Vojtěch Kurka, Meiro’s Customer Data Platform (CDP) empowers brands to better understand customer preferences and behaviours across various touchpoints. Through Meiro, brands can use data to improve customer experience and marketing campaign performance, ultimately maximising customer satisfaction and business profitability.

Also Read: What makes a great customer experience?

Going beyond the traditional boundaries of CRM, Meiro’s platform collects, cleans, and manages data from virtually any source online and offline and provides personalised marketing tools to enhance business ROI.

“In 2024, we plan to complete the integration of additional communication channels and build GenAI use cases into our platform. We will also introduce a brand new product.

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As Artificial Intelligence (AI) becomes even more popular, companies around the world are exploring its implementation in the various aspects of business operations. For Singapore-based Helport, customer service is the area that they are looking at.

In 2018, the company said that Singapore reported four per cent growth in the contact centre outsourcing market, a number that is expected to cross US$77.7 million by 2025. Helport aims to tap into this opportunity by providing AI solutions that can reduce customer-related error rates by 60 per cent and cost efficiencies of up to 17.5 per cent for their clients.

Helport’s main offerings include the AI-Assist, which aims to improve service quality while reducing training and onboarding time. Its Helphub platform utilises AI to help manage customer relationships, business, data, and outbound calls to enable companies to scale globally.

Established in 2020, with a team of over 100 staff globally, the company has a presence in Singapore, the US, the Philippines, and China. This year, Helport has a major plan to expand in the US while maintaining its leading position in Southeast Asia (SEA).

In this interview with Helport CEO Li Guanghai, we find out more about the expansion plan and other details about the company. The following is an edited excerpt of the interview with e27:

How does your company aim to make a difference with your solutions? Why is it better than existing solutions?

Helport distinguishes itself with a three-fold approach:

Empowering Human Talent: Unlike solutions aimed at replacing humans, Helport’s AI contact centre solution focuses on improving the productivity and efficiency of customer representatives by reducing their workload and boosting their sales.

Also Read: How Transparently.AI uses Artificial Intelligence to detect accounting manipulation, fraud

Unique Technological Advantage: Helport’s technology is rooted in over a decade of direct contact centre operational experience and continuous self-improvement in AI and big data applications. This has resulted in a unique blend of over 100 business scenario knowledge bases, algorithm models, and training tools that combine business expertise and scenario know-how with advanced operations research and AI for powerful application outcomes.

This approach significantly surpasses emerging AI tech companies’ stability, applicability, and domain knowledge.

Market-Validated Products and Business Model: Helport is less than three years old but has already achieved continuous profitability and market validation. Its AI assistant product is SaaS-based, allowing for rapid and flexible deployment across various contact centres, typically going live within one to four weeks and showing performance improvements within two to three months.

Currently, nearly ten thousand contact centre agents use Helport’s AI assistant, with rapidly growing user numbers, in contrast to many AI peers still in the investment stage with unproven business models and products.

Can you tell us about your product development process?

Our technology and products originated from the founding team’s decade-plus experience in operating contact centres and their best practices, as well as the accumulation of both hardware and software capabilities development through continuous digital upgrading and exploration of AI and big data applications. After three years of refining and iterating in real customer application scenarios, we have now developed a standardised SaaS product.

Currently, our R&D efforts are driven by two main factors: customer and market needs feedback, and technological advancement. We closely monitor the latest developments in artificial intelligence, communication technology, big data technology, and operations research, invest in related R&D, and integrate these advancements into our products.

What notable milestones have you made recently? What lessons have you got from this?

Recently, we have made significant strides in enhancing our AI software capabilities, particularly in AI speech and real-time monitoring. These advancements have improved service efficiency and reduced the time and resources required for agent training.

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

One key lesson we have learned is the importance of continuous improvement and adaptation to meet evolving market needs and technological advancements.

What do you think will be key to a successful AI implementation across industries in SEA?

Helport firmly believes that, especially in the B2B domain, artificial intelligence technology must be combined with industry experience, domain knowledge, and best business practices to unleash its immense commercial value and truly solve complex business problems. With its large population, diverse scenarios, and varied customer needs, SEA presents unique challenges.

The key to successful AI implementation across SEA industries is customisation and understanding local market nuances. This involves tailoring AI solutions to meet each industry’s specific needs and challenges, taking into account cultural, linguistic, and regulatory differences, and integrating this with global and local business experts. Additionally, building robust partnerships and collaborating with local entities will be crucial for gaining market acceptance and ensuring the practical applicability of AI solutions.

Can you tell us about your expansion plan to the US? How do you plan to achieve this? Any specific strategy to acquire users?

North America is the largest market for contact centre services, and it is also our target market. We have already opened our office in San Diego, California. We will accelerate our market expansion in North America through direct market development, strategic cooperation, and mergers and acquisitions. Currently, all of these are actively progressing.

What is your big plan for 2024?

In 2024, we will accelerate our development in the global market. Our business in mainland China is experiencing rapid growth in scale, and we anticipate fast development in both North America and Southeast Asia.

At the same time, as an AI technology company, we will continue to invest in technology research and development this year. We aim to optimise product features and enhance product competitiveness. Our core R&D directions include knowledge base construction technology, AI speech technology, advanced operations research algorithms, multi-channel communication, AI knowledge base production and training technology based on large language models (LLM), and so on.

Also Read: RevComm’s MiiTel, Cloud IP phone powered by artificial intelligence, is changing how businesses engage customers

One of our key R&D areas is the application of general AI. Over the past few years, the Helport team has closely followed the progress of LLM technology and was among the earliest to apply it in our R&D. We have currently made significant strides in knowledge extraction and knowledge base construction with the LLM.

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Image Credit: Helport

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