LinqAlpha, a New York-headquartered AI startup building intelligence tools for institutional investors, has raised US$22 million in a Series A round backed by a group of global and Asian investors.

The round was anchored by AVP, Atinum Investment, and GFT Ventures. It also drew participation from financial institutions and venture platforms across Singapore, Hong Kong, South Korea, Japan, and India, including SBI Investment, Z Venture Capital, Betatron Venture Group, East Ventures, SV Investment, Samsung Securities, Mirae Asset Venture Investment, Mirae Asset Capital, NH Investment & Securities, Shinhan Venture Investment, Hana Ventures, and NuVentures.

Also Read: From energy to ergonomics: 20 AI startups to watch in Southeast Asia

The company said the capital will be used to expand its global team, deepen integrations with market and alternative datasets, and accelerate deployment of its multi-agent platform across equities, macro, credit, and multi-asset strategies.

Founded by Jacob Choi, Subeen Pang, Jin Kim, and Hojun Choi, LinqAlpha brings together former Goldman Sachs analysts and MIT computer science PhDs. The startup says its platform is already used by more than 70 financial institutions across the US, Europe, and Asia, including sell-side sales, trading, and research teams at investment banks, as well as buy-side firms such as Causeway Capital Management and Schonfeld Strategic Advisors. Its buy-side clients collectively manage more than US$5 trillion in assets.

From search to synthesis

LinqAlpha is entering a market where institutional investors are drowning in data but still struggling to convert information into judgment quickly enough.

For public-market investors, the problem is no longer access. Earnings calls, regulatory filings, broker notes, central-bank commentary, shipping data, satellite imagery, social-media chatter, supply-chain signals, and credit-market movements are all available in some form. The harder task is linking these signals before they become obvious to everyone else.

That challenge is particularly acute in Asia, where market-moving events can cut across geographies within hours. A semiconductor supply-chain disruption in Taiwan or South Korea, a policy decision in China, a currency move in Japan, or a change in commodity demand from Southeast Asia can quickly influence equities, credit, macro trades, and sector views globally.

“The first wave of AI in finance made analysts faster. The next wave changes what they can know,” said Hojun Choi, co-founder and co-CEO of LinqAlpha. “The edge no longer comes from retrieving information; it comes from systems that surface market-moving signals before they are priced in.”

LinqAlpha’s pitch is that generic AI assistants are not enough for professional investors. Instead, the company allows institutional teams to deploy specialised AI agents that learn their investment frameworks, thesis history, research preferences, and feedback loops. These agents are designed to synthesise internal research with external market data, rather than simply retrieve documents or summarise information.

Jacob Choi, co-founder and co-CEO of LinqAlpha, described the product as “a second brain for every investment team”, built to turn accumulated research into actionable insight across liquid public markets.

Why Asian capital is paying attention

The investor syndicate is notable for its Asian depth. Beyond the lead investors, the round includes names from Japan, Korea, India, Hong Kong, and Southeast Asia. East Ventures, one of Southeast Asia’s most active early-stage investors, and Betatron Venture Group, a Hong Kong-based venture platform, are among the backers. SV Investment also brings links across Korea and Southeast Asia.

Also Read: In the age of AI, people matter more than ever

This matters because Asia is both a market opportunity and a stress test for products such as LinqAlpha. The region has complex cross-border capital flows, a fragmented data environment, multilingual markets, and a growing base of institutional investors that need to track global and regional signals simultaneously.

Singapore, in particular, has become a key hub for asset managers, hedge funds, family offices, and private banks. According to the Monetary Authority of Singapore, assets under management in the city-state stood at roughly US$4 trillion in 2023, making it one of Asia’s most important capital-allocation centres. The government has also been pushing the use of AI and data in financial services through regulatory sandboxes, AI governance frameworks, and industry collaborations.

Across Southeast Asia, the broader financial-services sector is also becoming more data-heavy. Digital banks, brokerages, wealth platforms, and institutional trading desks are generating and consuming more real-time information. While much of the region’s fintech investment over the past decade went into payments, lending, and consumer finance, AI infrastructure for financial institutions is now becoming a more serious category.

That shift is being driven by two forces: pressure on financial firms to improve productivity, and the need for differentiated insight in markets where information is increasingly commoditised.

A crowded race in financial AI

LinqAlpha is not alone in chasing this opportunity. The financial information market is already dominated by incumbents such as Bloomberg, LSEG, FactSet, S&P Global, and Moody’s, all of which are embedding generative AI into terminals, data products, and research workflows.

Bloomberg has developed finance-specific AI models and has been adding AI-powered search and summarisation to its ecosystem. FactSet and LSEG are also integrating large language models into analytics and workstation products. S&P Global has Kensho, which applies AI to financial and business data.

On the startup side, companies such as AlphaSense, Hebbia, Rogo, Dataminr, and RavenPack are competing across market intelligence, document search, alternative data, and financial research automation. AlphaSense, for example, has grown into a major player in AI-powered market intelligence by aggregating broker research, transcripts, filings, and expert-call content. Hebbia targets financial and legal professionals with AI agents for complex document analysis. Dataminr and RavenPack focus on real-time event detection and alternative data signals.

The competitive question for LinqAlpha is whether it can move beyond summarisation and search into persistent, investor-specific reasoning. Many AI tools can now condense a transcript or retrieve a filing. Fewer can adapt to how a portfolio manager or analyst builds conviction, tracks prior theses, and weighs contradictory market signals over time.

Manish Agarwal, General Partner at AVP, said LinqAlpha is targeting “a larger opportunity” than retrieval or automation: helping institutional investors discover differentiated insights in markets that reward speed, context, and proprietary judgment.

Execution will decide the outcome

For all the excitement around AI agents, the bar in institutional finance is high. Tools must be accurate, auditable, secure, and deeply integrated into existing workflows. Hallucinations, weak sourcing, or poor data lineage can be costly in investment decision-making. Financial institutions also tend to move slowly when adopting new systems, especially those touching proprietary research and trading processes.

LinqAlpha’s early traction with banks and buy-side firms gives it a stronger starting point than many AI startups selling into finance. But scaling across regions and asset classes will require more than model quality. The company will need robust data partnerships, enterprise-grade controls, and enough customisation to serve different investment styles without becoming a services-heavy business.

Also Read: AI is a game-changer, and here’s how your business can use it to win

The new funding gives LinqAlpha room to build that infrastructure. It also reflects a broader conviction among Asian investors that the next generation of financial technology will not simply digitise transactions, but reshape how capital-market professionals think, research, and act.

For Southeast Asia, where Singapore is increasingly competing as a global asset-management and AI hub, LinqAlpha’s round is another sign that institutional finance AI is becoming a serious investment theme. The winners will be those that can turn fragmented market noise into decision-ready intelligence before the rest of the market catches up.

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Treasurer, an AI financial technology company, used Echelon Singapore 2026 to introduce AlphaLenz, its enterprise AX solution for financial and insurance institutions, as it looks to deepen its footprint across Southeast Asia’s fast-growing fintech and enterprise AI market.

The company presented AlphaLenz at the two-day event, held from June 3 to 4 at the Suntec Singapore Convention & Exhibition Centre, as part of the Seoul Business Agency (SBA) Pavilion. The platform was shown to investors, fintech firms, financial institutions, insurers and enterprise partners, with Treasurer positioning it as a practical AI tool built for the realities of financial workflows rather than a broad, general-purpose assistant.

Built for financial data, not just general AI tasks

AlphaLenz draws on Treasurer’s core strengths in AI financial analytics, market data processing and financial intelligence automation. The solution began as a tool to support investment research and financial data analysis, but is now being expanded into an enterprise AX platform aimed at helping financial and insurance organisations automate repetitive tasks, speed up information processing and reduce the burden on teams handling large volumes of documentation.

Also Read: 5 ways generative AI is transforming the payments ecosystem

For banks, insurers and other financial enterprises, the challenge is rarely a shortage of data. The problem is making sense of it quickly. These firms handle market information, corporate disclosures, product and policy documents, customer-facing materials, internal reports, risk-related records and regulatory information, often across multiple systems and formats.

That means staff spend significant time on manual work such as collecting data, reviewing documents, summarising information, comparing records and preparing reports. Treasurer says AlphaLenz is designed to ease these processes.

Tackling repetitive workflows in finance and insurance

AlphaLenz combines AI agents, structured financial data workflows and industry-specific automation capabilities to support a range of enterprise use cases. These include financial data analysis, document review, market monitoring, report generation, product and policy information structuring, and internal knowledge search.

By automating repetitive analytical tasks, the platform is intended to free analysts, managers and back-office teams to focus on higher-value decision-making. That positioning is likely to resonate in a region where financial firms are under pressure to improve productivity while keeping headcount and operational costs in check.

Treasurer argues that this makes AlphaLenz different from off-the-shelf AI tools. Rather than being built for generic productivity, it has been designed around financial data structures and workflows specific to the sector.

Also Read: AI’s transformative role: Making insurance accessible and affordable globally

Its underlying technology is powered by Treasurer’s financial analytics engine, which processes Korean and Asian market data, company information, disclosures and industry trends. That foundation, the company says, allows AlphaLenz to deliver more relevant automation for enterprises operating in finance, insurance and capital markets.

Strong interest from Southeast Asia’s enterprise AI market

At Echelon Singapore 2026, Treasurer met with investors, fintech executives, financial institutions, insurance stakeholders and innovation teams. The company said it found strong interest in AI-driven workflow automation, cost-efficient financial data processing and enterprise AI adoption among organisations seeking to boost productivity with limited resources.

The reception highlights a broader trend across Southeast Asia, where financial institutions are increasingly exploring AI not just for customer service or front-end digital experiences, but for the less visible work that keeps operations moving: document handling, information retrieval, internal knowledge management and compliance-related processing.

For many firms, that back-office layer remains a major cost centre. Tools like AlphaLenz are being pitched as a way to streamline those functions without requiring organisations to rebuild their entire technology stack.

Treasurer eyes regional expansion

“Echelon Singapore 2026 was a meaningful opportunity for us to introduce AlphaLenz not only as an AI financial analytics platform, but as an enterprise AX solution for financial and insurance institutions,” said Ted Kim, CEO of Treasurer. “Financial companies are under increasing pressure to process more data, generate insights faster, and improve productivity with limited resources. AlphaLenz is designed to help these organisations automate repetitive workflows and make better use of their internal and external data.”

He added that Treasurer’s strength lies in its ability to structure complex financial information into actionable intelligence.

“Treasurer’s strength lies in AI-based financial analysis technology and the ability to structure complex financial data into actionable intelligence. By applying these capabilities to enterprise AX solutions, we aim to help financial and insurance companies improve work efficiency, reduce resource costs, and accelerate digital transformation across their organisations,” Kim said.

Treasurer joined the event through the SBA Pavilion, a programme aimed at helping promising Seoul-based startups expand into global markets. The company took part in exhibition showcases, demo presentations, private pitching sessions and AI-powered business matching activities, creating opportunities to connect with potential investors, partners and enterprise customers in Singapore and the wider region.

Next steps for AlphaLenz

Following the event, Treasurer plans to continue discussions with financial institutions, insurance companies, fintech firms and enterprise partners in Singapore and Southeast Asia. The company also intends to further develop AlphaLenz’s data coverage, AI agent capabilities and industry-specific workflows as it works towards broader adoption.

Also Read: AI agents are already inside your systems, but who’s controlling them?

The move comes as enterprises across the region increasingly look for AI systems that can do more than generate text or answer simple queries. In finance and insurance especially, the real value lies in tools that can read, compare, organise and act on large and fragmented data sets with speed and consistency.

For Treasurer, AlphaLenz represents a bet that the next wave of enterprise AI in Southeast Asia will be less about flashy demos and more about practical automation. In that sense, the platform’s pitch is straightforward: less manual drudgery, more informed decision-making, and a smoother path towards digital transformation.

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As FitnessForce folds into US-based Daxko, the more interesting story is not the deal itself but what happens after it. Can a company built around the messy realities of India and the Gulf keep its product edge inside a larger US software group?

In this edited Q&A with e27, Hadi Curtay, former CEO of FitnessForce and now Managing Director at Daxko, argues that the company’s value was never about hype. It was built around localisation, operational detail, and software for fitness operators running across multiple sites and markets. He says India and the GCC remain the company’s strongest territories, while Southeast Asia is promising but uneven.

Also Read: The rise of ‘Strava Jockeys’: How Indonesia’s vanity economy is hacking the fitness tech ecosystem

Curtay also addresses a familiar founder anxiety after acquisitions: the team stays, the knowledge gets transferred, and the product slowly fades into somebody else’s roadmap.

The fundraising journey in emerging markets can be rough. Did regional VCs really understand the problem you were solving?

Not really, and that shaped how we built FitnessForce. Most investors understood the broad market story. It was not hard to see that fitness in India, the Middle East, and Southeast Asia was growing on the back of rising incomes, urbanisation, and greater health awareness. The harder part was explaining the kind of company we were building.

We were not another consumer app chasing millions of users. We were building operational software for fitness businesses with multiple locations, franchise layers, and a lot of moving parts. At the time, that did not fit neatly into the frameworks many investors were using.

The people who got it fastest were usually from adjacent industries such as hospitality, franchising, or multi-site operations. They understood how difficult it is to run one location well, let alone hundreds across countries. In the end, we spent more energy building with customers than chasing capital, and that discipline probably helped us.

Founders often worry about the acqui-hire trap. How did you avoid becoming just another team absorbed into a larger platform?

I think that concern is completely fair. I have seen it happen. A founder joins, the knowledge gets absorbed, the roadmap quietly loses momentum, and a year later the product is little more than a feature buried in a bigger system.

My co-founder, Quaid Jawadwala, and I were very clear from the beginning that we were not looking for a conventional exit. We wanted a partner that would let us keep building and scale the business further, not one that simply wanted our team or customer list. That ruled out a lot of conversations quite quickly.

What stood out with Daxko was that it did not approach the business as something to flatten into one generic stack. It runs purpose-built platforms and puts real operators in charge of them. My role is an operating role, not a ceremonial title. Quaid is also deeply involved in expansion, engineering, and product. We did not join to hand over the keys. We joined to keep building.

India is a fragmented market. Where are you genuinely strongest, and how big can that opportunity become?

We are strongest in the mid-market and premium end of the fitness industry in India. That ranges from ambitious single-site operators to large franchise groups and multi-location brands. Those businesses do not want software built for some entirely different market and then awkwardly forced on to them.

What matters in India is not just feature depth but operational fit. Payments, taxation, compliance, franchise structures, and customer behaviour all have local specifics. Our product has done well because it handles those realities while still supporting global operating standards. That matters more as international brands enter India and need systems that respect how they already run elsewhere but can still adapt locally.

Also Read: Healthtech in South and Southeast Asia – Seeing beyond the “obvious”

We have also invested heavily in areas such as multi-location management, central reporting, automation, franchise operations, and governance. Those capabilities become more valuable as operators go from a handful of sites to dozens or more. I still think the market is early, especially as organised chains continue to expand.

Can you point to one market where getting local details right really made or broke a customer relationship?

Saudi Arabia is the clearest example for me. It taught us early that localisation is not a translation exercise. A lot of software companies thought adding Arabic and local payment support was enough. It was not.

Operators there needed ZATCA-compliant invoicing. They needed proper right-to-left workflows, not translated labels dropped into a product designed for left-to-right use. They also relied heavily on WhatsApp, not as a secondary tool but as a core channel for renewals, confirmations, receipts, and day-to-day member communication.

I remember one customer coming from a European platform where staff were effectively running key workflows outside the software. Renewals were tracked manually, confirmations were sent separately, and management had poor visibility because too much was happening in disconnected processes.

We approached that differently. We built WhatsApp into the membership journey, strengthened Arabic and right-to-left experiences, and backed it with local-language support teams. Adoption moved quickly because we were supporting how operators already worked rather than asking them to adapt to somebody else’s assumptions.

Which markets do you lead in today, and where are you still earlier in the journey?

India and the GCC are where we have the strongest footing today. In India, the product has been shaped by a very fragmented and operationally demanding market, which forced us to become practical and resilient. In the GCC, especially in markets such as the UAE, Saudi Arabia, Kuwait, and Bahrain, we earned traction by localising properly across language, tax, payments, communication habits, and support.

Australia and Southeast Asia are more mixed. We are making progress, but I would still describe them as earlier compared with India and the GCC. The demand is there. Operators are becoming more sophisticated, and franchise growth is creating a clearer need for enterprise-grade software. But those markets are not all moving at the same speed or in the same way.

The US is different again. It is a mature market with higher expectations and tougher competition, but Daxko gives us a much stronger position there than we would have had on our own.

What does the Southeast Asia go-to-market strategy actually look like? Direct sales, partners, or following global franchises into the region?

It is a mix, and the balance changes by country. One of the fastest paths into Southeast Asia is through global franchise brands already expanding across the region. Those operators want a platform that can move with them across borders without forcing a fresh implementation every time they enter a new market. That plays to our strengths.

At the same time, I do not think you can treat Southeast Asia as one market. The regulatory environment, payment rails, and fitness culture vary significantly from country to country. That is where a lot of expansion plans go wrong. People talk about the region as if it behaves like a single operating market, and it simply does not.

So yes, global franchise expansion is an important entry point, but local execution still matters. You need to respect what is different in each market rather than applying one template and hoping for the best.

FitnessForce built more than 1,100 APIs and leaned into a headless architecture. Was that strategic, or was it forced on you by customer complexity?

It was both, but the strategic view came first. Quaid had a strong conviction very early that the platform had to be API-first. That was not a pitch-deck decision. It came from the belief that serious operators would eventually need flexibility, integrations, custom member journeys, and the ability to connect systems across markets.

Also Read: The most-funded healthtech startups in Southeast Asia: A decade in review

Then reality reinforced the point. Once you support operators across India, the GCC, Southeast Asia, Australia, and beyond, you quickly run into different payment systems, compliance requirements, communication habits, and workflows. A closed platform would have meant saying no far too often.

So the API depth grew from both vision and necessity. A principle we kept was that the APIs we use internally should also be available to customers. Headless should not mean a few token integrations on the side. It should mean the product is genuinely extensible.

What is the honest version of what could still go wrong after the deal?

The honest answer is that the biggest risk is not hidden in a clause somewhere. It is a values mismatch. In most acquisitions, if the two sides think differently about customers, products, people, or decision-making, the legal protections only carry you so far.

That was the main thing I paid attention to. We spent a lot of time understanding how Daxko makes decisions, how it treats customers, and what kind of company it wants to be. The more time we spent together, the more aligned we seemed.

That does not mean there is no risk. Any integration has execution risk. Priorities can drift, communication can break down, and good intentions do not automatically produce good outcomes. I am just less worried about the classic founder fear of being absorbed and sidelined, because this did not feel like joining a company that wanted to erase what made us work in the first place.

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San Diego, June 22, 2026 — Qualcomm Technologies, Inc. announced the 15 shortlisted startups selected to advance in the Qualcomm® AI Program for Innovators (QAIPI) 2026, recognising startups developing next-generation AI solutions across industries. Representing Japan, Singapore, and South Korea, the selected teams were recognised for their innovative AI technologies and solutions to address real-world challenges across industries. The shortlisted startups will advance to the program’s Mentorship Phase and showcase their AI innovations at a Demo Day planned for later this year.

A program built for edge AI

QAIPI 2026 empowers startups across the Asia-Pacific region to develop scalable edge AI solutions using Qualcomm® platforms. Participants will gain access to advanced development tools, including Qualcomm Dragonwing and Snapdragon® platforms, and the new Arduino® UNO Q development board. Participants can also leverage the Qualcomm® AI Hub, technical resources, training, and mentorship to build optimised end-to-end AI use cases across mobile, compute, and IoT.

The fields they’re working in

The 15 shortlisted teams are developing AI solutions across a wide range of fields, including aerospace, agriculture, drones, healthcare, robotics, smart infrastructure, and smart industry. These fields reflect the growing demand for real-time, power-efficient AI at the edge, as well as physical AI systems that connect intelligent computing with real-world environments. The selected startups are (in alphabetical order):

• Japan: APTO, Inc., KanjuTech Kabushiki Kaisha, KimPax, MY ROBOTS K.K., XNOVA Inc.
• Singapore: AIPLUX TECHNOLOGY, QuikBot Technologies, RED DOT DRONE PTE. LTD., Refined Robotics, Zebrid Pte. Ltd.
• South Korea: CLIKA, ENERZAi Inc., Plaid Labs Inc., Undermilli Inc., WITHROBOT Inc.

 

Also Read: Qualcomm expands AI R&D with acquisition of MovianAI from Vietnam’s Vingroup

What the shortlisted startups receive

Over the next six months, the selected startups will participate in the Mentorship Phase, during which they will receive tailored support from Qualcomm Technologies. This includes 1:1 mentorship with Qualcomm subject matter experts, access to a hardware development platform based on products of Qualcomm Technologies and/or its affiliates, up to US$2,500 support for product development, and eligibility for a patent filing incentive of up to the equivalent of US$5,000. Startups that successfully complete the program will receive a grant of up to US$10,000. The program will culminate with a Demo Day in Q4 2026, where the startups will present their AI solutions to industry leaders, system integrators and investors to enhance visibility and support their future business success.

What Qualcomm’s leaders say

“We are pleased to once again support high-potential startups across the Asia-Pacific region through QAIPI this year,” said O.H. Kwon, Senior Vice President & President, Qualcomm APAC. “2026 is the year of AI agents, powered by a highly connected and distributed computing environment. Spanning across devices, edge systems, and the cloud, these agentic experiences embody the shift toward what Qualcomm defines as a unified compute continuum. As AI technologies continue to advance rapidly, the Asia-Pacific region’s hardware and software capabilities have become even more critical to the global technology landscape. The momentum of the startup ecosystem is a key force in driving the next wave of technological progress. This year’s shortlisted startups demonstrate the growing relevance of edge AI across practical industry use cases. Through QAIPI, Qualcomm aims to help these companies accelerate the path from technology development to commercialisation, while further strengthening the startup ecosystem across APAC.”

“In the second year of QAIPI across Japan, Singapore, and South Korea, we are seeing AI move decisively from prototypes to deployed infrastructure,” said Sudeepto Roy, Vice President of Engineering, Qualcomm Incorporated, and Lead of Qualcomm’s Global Ecosystem Development Program. “Selected from over 100 applications, this cohort stands out for its use of agentic AI and edge intelligence built for the physical world, spanning robotics and healthcare to drones, industrial safety, smart infrastructure, and secure multilingual workflows. Through QAIPI, Qualcomm is proud to provide equity-free mentorship, advanced platforms, product guidance, and patent incentives that help these teams turn promising prototypes into scalable, protected products.”

 

Meet the 2026 cohort

The 15 shortlisted teams are working on a diverse range of applications including smart healthcare, robotics, retail, audio, and transportation, each responding to urgent, localised challenges across Asia. These innovations reflect the growing demand for real-time, power-efficient AI at the on-device level. From Japan’s drive to humanise robotics and decarbonise maritime logistics, to Singapore’s multilingual AI and maternal health tech, to South Korea’s push for hyper-personalised, federated AI — these startups exemplify how regional needs are shaping globally relevant solutions.

Japan: Facing labour shortages and knowledge gaps driven by demographic change, Japan is exploring the potential of Physical AI and Edge AI to transform industrial systems, preserve human expertise, and advance real-world AI applications.

• APTO, Inc. (株式会社APTO) — An AI data company providing high-quality multimodal datasets and data operations solutions that help enterprises build, train, and deploy reliable AI systems at scale.
• KanjuTech KK (KanjuTech株式会社) — Builds brain-inspired adaptive AI for physical systems, helping machines learn on-device and remain reliable as real-world conditions change.
• KimPax Inc. (KimPax株式会社) — Transforms master farmer expertise into trusted digital knowledge assets that help farmers reduce operational risk, improve profitability, and create premium agricultural value.
• MY ROBOTS K.K. (MY ROBOTS株式会社) — Builds physician-reviewed surgical knowledge infrastructure that turns surgical video and audio into reusable clinical knowledge for training and documentation.
• XNOVA Inc. (株式会社XNOVA) — A Japan-based physical AI startup building autonomous robotic systems for the construction industry through robotics, spatial intelligence, and edge AI technologies.

Singapore: As a garden city with highly connected urban and smart infrastructure, Singapore provides an ideal environment for real-world AI deployment. Startups from Singapore are showcasing innovations in Physical AI that interact with the physical world, alongside advancements in edge computing, offline AI, and data security, enabling intelligent solutions that operate efficiently and securely in real-world environments.

• Aiplux Technology Co., Pte. Ltd. — An AI infrastructure company delivering on-device multilingual IP intelligence, helping enterprises run secure, auditable patent and legal workflows while keeping sensitive data under control.
• QuikBot Technologies Pte Ltd — A Singapore-based physical AI infrastructure company. Its QuikSync platform connects, orchestrates, governs, and records robotic and smart infrastructure operations to power the future of smart cities.
• RED DOT DRONE PTE. LTD. — A drone software company specialising in remote and autonomous drone operations, enabling intelligent drone missions through cloud and edge technologies.
• Refined Robotics Pte Ltd. — Builds spatial and physical AI that allows legged robots to operate in unstructured real-world environments.
• Zebrid Pte. Ltd. — An AI-native deeptech company, Zebrid builds a trust layer for orbital compute through autonomous, radiation-resilient infrastructure that helps keep edge AI data trusted, recoverable, and mission-grade.

South Korea: Building on its strengths in industrial manufacturing and digital infrastructure, South Korea is advancing AI solutions that address model optimisation, energy-efficient computing, industrial safety, and real-time multilingual communication.

• CLIKA, Inc. (클리카) — Auto-compresses AI models into optimised versions for target devices, reducing memory, heat, and cost while enabling efficient edge deployment across diverse hardware platforms.
• ENERZAi Inc. (에너자이) — Delivers ternary audio and language AI, including STT, TTS, LLMs, and translation, with end-to-end expertise from model design to compiler optimisation to reduce memory and power consumption and boost inference speed.
• Plaid Labs Inc. (플래드랩스 주식회사) — Builds NUVION, an AI vision inspection solution for small manufacturers, using on-device AI and a subscription model to make quality automation affordable and easy to deploy.
• Undermilli Inc. (주식회사 언더밀리) — Builds Maloha, a real-time voice-to-voice interpretation AI that originated in healthcare and now serves broader industries, delivering on-device speech translation across 23 languages.
• WITHROBOT Inc. (위드로봇 주식회사) — Develops AI robots, algorithms, and edge boards for industrial safety, delivering automated multimodal monitoring solutions tailored for harsh environments.

For more information on the Qualcomm AI Program for Innovators, visit qualcomm.com.

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Featured Image Credit: Qualcomm

About Qualcomm

Qualcomm is a global computing leader at the center of the AI era, enabling intelligence to scale from the most personal devices to large-scale infrastructure. Building on more than four decades of innovation, we develop platforms and solutions that bring together advanced AI, high-performance low-power computing, and industry-leading connectivity — powering products and services used around the world. Snapdragon® platforms power consumer and personal computing experiences, while our Qualcomm Dragonwing and Qualcomm Dragonfly portfolios support enterprise, industrial, automotive, networking, and data center applications. At Qualcomm, we are engineering human progress.

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Modern workplaces are increasingly shaped by visibility and accountability. When employees know their work is being monitored, expectations around leadership naturally change. Managers are no longer judged only by results, but by how they lead people in environments where oversight is part of daily work.

This shift has measurable consequences. According to the APA’s 2024 Work in America survey, 51 per cent of employees who know they are monitored report feeling tense or stressed during a typical workday — compared to 38 per cent of those who are not monitored. That 13-point gap illustrates precisely why leadership quality matters more, not less, in environments built around oversight.

The data on management quality reinforces this further. Gallup’s Global Workplace Report found that employees in companies with ineffective management practices are nearly 60 per cent more likely to experience stress than those working under effective managers. Without strong leadership, monitoring can create pressure, anxiety, and disengagement. With the right leadership approach, it can coexist with trust, motivation, and autonomy.

That’s why soft skills such as emotional intelligence, active listening, and transparent communication now define effective leadership in monitored workplaces. The importance of leadership skills for managers lies in their ability to balance accountability with empathy and control with autonomy. Research shows that employees with supportive managers are 70 per cent less likely to experience burnout — a finding that underscores that people, not tools, metrics, or processes, remain at the heart of performance, even in monitored environments.

In this article, we explore seven essential leadership skills every manager needs to lead effectively in a monitored workplace, build trust, and sustain high performance.

Active listening

Employees may hesitate to speak openly about challenges, workload concerns, or stress when they know their work is being closely observed. Without strong listening skills, managers risk missing valuable insights and damaging trust.

Active listening is not only about hearing what employees say. It involves giving full attention, asking thoughtful questions, and responding with clarity and respect. Managers who practice active listening create an environment where employees feel safe expressing their perspectives, even in settings where performance is closely evaluated.

In a monitored workplace, employees want reassurance that their voices matter as much as their metrics. Managers who listen demonstrate fairness and transparency. Regular one-on-one conversations, open forums for feedback, and follow-through on concerns all reinforce the message that leadership is people-centred, not data-driven.

Relationship building

When employees feel that leadership is distant or overly performance-focused, monitoring can amplify feelings of pressure and detachment. Managers who invest in relationships help counter this by creating a culture of trust and belonging.

Effective leaders take the time to understand their teams beyond tasks and metrics. They build rapport through regular check-ins, genuine interest, and consistent support. These relationships make employees feel valued as individuals, not just contributors to output. As a result, teams remain engaged and motivated, even in environments where work is closely observed.

The leadership qualities of great leaders are often reflected in their ability to foster strong interpersonal connections. Relationship-focused managers create trust organically through respect and reliability, rather than authority. By prioritising relationship building, managers create teams that are more resilient, communicative, and willing to perform at their best, regardless of oversight.

Also Read: Your AI strategy isn’t broken, your leadership structure is

Effective communication

Effective communication is one of the most critical leadership skills in management, especially when employees are operating in environments with increased visibility and evaluation. In monitored workplaces, unclear instructions or inconsistent messaging can lead to confusion, frustration, and disengagement.

Strong leaders communicate expectations, goals, and feedback clearly and consistently. They ensure that employees understand not only what is expected of them, but also why it matters. Effective communication reduces uncertainty and aligns individual efforts with organisational objectives.

Managers who communicate effectively focus on guidance and improvement rather than criticism. This approach encourages openness and helps employees view feedback as constructive rather than threatening. By maintaining clear, honest, and purposeful communication, managers keep teams aligned and sustain productivity.

Emotional intelligence

When employees are aware that their performance is being observed, emotions such as stress, anxiety, or self-doubt can surface more easily. Leaders who lack emotional awareness may overlook these signals, while emotionally intelligent managers know how to recognise and respond to them.

At its core, emotional intelligence involves self-awareness, empathy, and emotional regulation. Managers who understand their own reactions lead calmly and fairly, even in high-pressure environments. Empathy lets them understand how monitoring impacts different individuals in different ways. Not every employee responds to oversight in the same manner, and great leaders adjust their approach accordingly.

The leadership qualities of managers are often defined by how well they connect with people, not just how well they manage performance. In monitored workplaces, emotionally intelligent managers facilitate psychological safety by acknowledging concerns, validating effort, and supporting well-being. This helps employees feel respected rather than scrutinised.

Agility and adaptability

Agility and adaptability are essential leadership skills for modern workplaces, where expectations, workflows, and employee needs continue to evolve. In monitored working environments, rigid leadership approaches often fall short. Employees respond differently to oversight, and effective managers recognise the need to adapt their leadership style accordingly.

Agile leaders are open to change and willing to adjust strategies when circumstances shift. They respond thoughtfully to challenges rather than relying on fixed rules or assumptions. This flexibility allows managers to support diverse working styles while maintaining accountability and performance standards.

Adaptable leaders help teams navigate change with confidence by remaining approachable, responsive, and solution-oriented. This leadership style encourages resilience and continuous improvement. By embracing agility and adaptability, managers create environments where employees feel supported rather than restricted.

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Critical thinking

When leaders are surrounded by performance data and observations, the ability to analyse situations thoughtfully becomes more important than reacting quickly. Critical thinking enables managers to interpret information accurately, weigh multiple perspectives, and make sound judgments under pressure.

Managers with strong critical thinking skills look beyond surface-level results. They consider context, identify patterns, and question assumptions before drawing conclusions. This approach prevents misinterpretation and ensures that leadership decisions are informed rather than impulsive.

Critical thinking helps managers balance insight with empathy, allowing them to address challenges constructively rather than relying on rigid judgments. By applying critical thinking, leaders make fairer assessments and foster a culture of reflection and continuous improvement.

Decision making

In monitored workplaces where outcomes are closely scrutinised, employees look to their managers for direction, consistency, and confidence, especially when expectations are high. Effective leaders make timely, balanced decisions aligned with both organisational goals and employee well-being.

Strong decision-making involves weighing information carefully, considering potential impacts, and communicating outcomes clearly. This reassures employees and reinforces confidence in leadership. Decisive leaders provide clarity and stability, helping teams move forward with purpose even in complex environments.

By demonstrating sound decision-making skills, managers establish credibility and direction. This leadership capability ensures that teams remain focused, motivated, and aligned in monitored workplaces.

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Conclusion

Leadership in a monitored workplace demands a strong, people-centred approach. As visibility and accountability become part of everyday work, managers need to shift from oversight to intentional guidance, support, and empowerment.

Effective leadership skills help managers balance accountability with empathy and structure with flexibility. Their real value lies in building trust, sustaining engagement, and driving performance without relying on control. When leaders focus on these core capabilities, they are able to navigate complexity, address challenges thoughtfully, and strengthen team resilience.

In monitored workplaces, strong leadership is not about watching more closely, it’s about leading thoughtfully. By prioritising essential leadership skills, managers can create environments where employees are supported, confident, and motivated to perform at their best.

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