2025

Tuesday, September 23, 2025
7 min read

Solve the legacy challenge for banks in ASEAN

Southeast Asia’s banks are at a pivotal juncture, transitioning from decades-old legacy cores to modern platforms built for the digital age. Most banks in the region still run on aging technology – 90–95% of banks rely on on-premises, mainframe-based cores averaging 20+ years old [1,2]. Only a small minority operate fully cloud-native cores, mainly among digital-only entrants [1]. These systems are expensive to run and hard to change, making modernisation a strategic necessity [1,2].

Banks are ramping up investment in core upgrades and cloud. APAC bank tech spend grew from ~$63B (2022) to ~$68B (2023), with rising allocation to cloud and AI/ML [3]. Over 70% have deployed cloud-based digital apps and around 40% are considering core replacement in the next three years [4]. Usage has gone decisively digital: Singapore adoption has plateaued around 90%; in Malaysia, Indonesia, Philippines, and Vietnam, digital usage rose from about 55% in 2017 to around 88% in 2021 [5]. Several ASEAN markets now report more than 90% of transactions occurring via digital channels [6].

Thailand — Virtual Banks and Infrastructure Acceleration

The Bank of Thailand granted three virtual bank licences in 2024, with launches expected in 2025–26 [7]. The licences were awarded to consortia led by SCBX Group, Kasikornbank Group, and Ascend Money. Ascend Money, well-known for its TrueMoney platform with more than 30 million active users, is backed by CP Group and Ant International. It raised US$195M in 2024 from MUFG and other investors to expand financial services and support its transition into a licensed virtual bank. Its focus is on financial inclusion, offering accessible services to the underbanked, gig workers, and SMEs by leveraging its strong payments ecosystem.

These players will begin operations with cloud-native and API-first foundations, setting new benchmarks for speed, personalisation, and cost efficiency. Their entry is expected to intensify competition, drive innovation, and expand financial inclusion, particularly targeting underbanked and digital-first customers.

Incumbents are responding by accelerating core modernisation budgets to defend market share and reduce time-to-market. SCB (SCB TechX) with Publicis Sapient is an example of progressive modernisation, adopting modular rebuilds, microservices, and parallel-run migrations to reduce cutover risk [8]. Other major banks such as Kasikornbank and Krungthai are enhancing digital cores, adopting cloud, and embedding analytics/AI. A national cloud framework is in development to accelerate adoption [9].

Regulators are balancing innovation with safety. The Bank of Thailand has increased capital requirements from THB 5B to THB 10B and introduced phased operating periods for new banks [10]. Supervisory emphasis includes operational resiliency, consumer protection, and oversight of third-party risks.

Infrastructure investment is also reshaping the market. In January 2025, AWS launched its Asia-Pacific (Thailand) Region with three Availability Zones, committing US$5B over 15 years [16]. This addresses data residency and latency concerns, enabling banks to migrate critical workloads locally.

Singapore — Digital Pioneer with Core Legacy Challenges

The Monetary Authority of Singapore issued four digital bank licences in 2020, with launches in 2022–23 raising expectations for user experience and real-time service [10]. Trust Bank quickly surpassed 600k customers in its first year, underlining consumer appetite for digital-first propositions. Yet, while digital front ends are highly advanced, back-end cores continue to constrain agility.

DBS, OCBC, and UOB have invested deeply in cloud, APIs, agile development, and SRE practices. Despite this, significant legacy code remains, leading banks to pursue hybrid approaches that include API façades, selective workload migration, and microservices refactoring [11]. Outages in 2023 prompted MAS to impose capital surcharges and introduce firmer resiliency expectations [12].

The regulator supports the use of public cloud but with strict risk controls and third-party management guidelines [12]. Supervisory focus is centred on resilience, incident management, and business continuity. Strategies increasingly involve coexistence, where new product engines are deployed on modern cores while stable legacy modules continue to handle settlement.

Vietnam — Rapid Digital Leap and Hybrid-Cloud Adoption

Vietnam’s Decision 810/QD-NHNN sets ambitious digital banking targets through 2025 and 2030 [13]. Account ownership now exceeds 87%, and many banks already process more than 95% of transactions digitally [6]. Banks invested about US\$620M in digital transformation initiatives up to the end of 2022 [6].

HD Bank has emerged as an active participant in the digitalisation drive, focusing on retail and SME clients. It is expanding mobile-first offerings, experimenting with embedded finance, and pursuing partnerships with fintech players. Meanwhile, Techcombank (TCB) continues to lead in large-scale digital transformation with heavy investment in data platforms, customer analytics, and cloud-based infrastructure. Techcombank’s partnership with AWS has enabled new levels of resilience and scalability.

LPBank executed a Temenos core replacement in around seven months, while other incumbents are pursuing scale and resilience improvements [14]. Cloud adoption is growing rapidly, with AWS and Microsoft supporting hybrid migrations.

The State Bank of Vietnam has expanded fintech sandboxes, eKYC, interoperability frameworks, and open-banking initiatives [15]. Regulatory priorities include driving cashless payments, ensuring consumer protection, and strengthening cybersecurity.

Cross-Market Outlook

In Malaysia, five digital bank licences were awarded in 2022, with surveys indicating that about 95% of banks will be cloud-ready by 2025 [9]. Maybank, CIMB, and RHB are engaged in multi-year core upgrades and data platform modernisation.

The Philippines has issued six digital bank licences, and strong mobile adoption is pushing banks towards cloud-native cores and instant payments. The government’s “Cloud First” policy and investment in real-time payment infrastructure are accelerating transformation [9].

Indonesia, with over 180 million internet users, is experiencing rapid digital adoption. Digital banks such as Jago, Jenius, and SeaBank are scaling quickly. Regulatory reforms by OJK are evolving to support digital-only operations, while incumbents are modernising cores progressively.

Defining a Future-Proof Core Banking Systems

A future-proof core must operate as a real-time, event-driven ledger with strong reconciliation controls. It should provide a product factory for rapid configuration of offerings, and support an API-first, open ecosystem for embedded finance and partner distribution. Resilience is achieved through cloud-native design, including multi-AZ deployment, autoscaling, and robust observability. Security and compliance must be built in from the start, with data residency and auditability features. Finally, migration should be progressive, relying on parallel runs, strangler-fig approaches, and dual-write models to mitigate execution risk.

Role of System Integrators

Global system integrators such as Accenture, Deloitte, and GFT play a critical role in large-scale core transformations, ensuring regulatory compliance and smooth delivery. Specialists like Audax and Constantinople focus on greenfield digital bank builds and operating model design. Their value lies in providing predictable migration outcomes, accelerating time-to-market, and ensuring resilience.

Conclusion — Neo-Core or Bust

Modernising the core is no longer optional. It is now foundational to growth, resilience, and innovation across ASEAN. Banks that execute core replacement progressively will unlock faster product launches, embedded partnerships, and lower unit costs. Those that delay face higher operational risks, escalating costs, and customer attrition. By 2030, the region’s leading banks will be operating on cloud-native, modular cores that support regional expansion and long-term competitiveness.

References

Tuesday, September 23, 2025
in zh
3 min read

解決東盟銀行的核心系統挑戰

東南亞銀行正處於一個關鍵轉折點，從沿用數十年的核心系統過渡到為數位時代打造的現代平台。該地區大多數銀行仍依賴老化的技術——90–95% 的銀行依靠平均超過 20 年歷史的本地部署、主機架構的核心系統 [1,2]。僅有少數銀行（主要是純數位新進者）運行完全雲原生的核心系統 [1]。這些老舊系統運行成本高且難以更改，使得現代化成為戰略必然 [1,2]。

銀行正在加快對核心升級和雲端的投資。亞太地區銀行科技支出從 2022 年的約 630 億美元增至 2023 年的約 680 億美元，並且雲端與 AI/ML 的配置持續上升 [3]。超過 70% 的銀行已部署雲端數位應用，大約 40% 計畫在三年內考慮核心替換 [4]。數位使用已成為主流：新加坡的採用率約 90% 並趨於穩定；馬來西亞、印尼、菲律賓和越南的數位使用率則從 2017 年的約 55% 上升至 2021 年的約 88% [5]。部分東盟市場現已超過 90% 的交易透過數位管道完成 [6]。

泰國 — 虛擬銀行與基礎設施加速

泰國銀行在 2024 年發放了三張虛擬銀行執照，預計將於 2025–26 年啟動營運 [7]。獲牌財團分別由 SCBX 集團、Kasikornbank 集團以及 Ascend Money 主導。Ascend Money 以其擁有超過 3000 萬活躍用戶的 TrueMoney 平台廣為人知，背後由正大集團與螞蟻國際支持。它在 2024 年獲得來自三菱日聯銀行等投資者的 1.95 億美元資金，用以擴展金融服務並支持其轉型為持牌虛擬銀行。其重點在於推動金融普惠，利用強大的支付生態系統，為未受充分服務的群體、零工經濟工作者和中小企提供便捷的服務。

這些新進者將以雲原生和 API 優先的基礎起步，為速度、個人化與成本效率設立新標準。他們的進入將加劇競爭、推動創新並擴展金融普惠，特別針對數位優先及未受充分服務的客群。

傳統銀行則透過加速核心系統現代化的預算來捍衛市佔率並縮短上市時間。SCB（SCB TechX 與 Publicis Sapient 合作）便是進步式現代化的例子，採用模組化重建、微服務以及平行運行遷移，來降低系統切換風險 [8]。其他如 Kasikornbank 與 Krungthai 等大型銀行，也在強化數位核心、採用雲端並導入分析與人工智慧。一個全國雲端框架也正在制定，以加快採用速度 [9]。

監管機構則在創新與安全之間尋求平衡。泰國銀行已將資本要求從 50 億泰銖提高至 100 億泰銖，並為新銀行引入分階段營運期 [10]。監管重點包括營運韌性、消費者保護以及第三方風險的管理。

基礎設施投資同樣正在重塑市場。2025 年 1 月，AWS 在泰國推出了亞太（泰國）區域，設有三個可用區，並承諾 15 年內投資 50 億美元 [16]。這解決了數據在地性與延遲問題，使銀行能將關鍵工作負載本地化遷移。

新加坡 — 數位先鋒卻仍受核心遺留問題制約

新加坡金融管理局於 2020 年發放了四張數位銀行執照，並於 2022–23 年間陸續啟動，顯著提升了市場對用戶體驗與即時服務的期望 [10]。Trust Bank 在首年便突破 60 萬客戶，突顯消費者對數位優先方案的強烈需求。然而，雖然前端數位應用高度先進，後端核心卻依然限制了靈活性。

星展、華僑與大華銀行在雲端、API、敏捷開發與 SRE 實踐上都有深厚投入。儘管如此，大量的遺留程式碼仍存在，促使銀行採取混合策略，包括 API 外掛層、選擇性工作負載遷移與微服務重構 [11]。2023 年的系統中斷事件，讓金管局對銀行施加資本附加要求，並制定更嚴格的韌性標準 [12]。

監管機構支持公共雲的使用，但設下嚴格的風險控制與第三方管理指引 [12]。監管重點圍繞韌性、事故管理與業務持續性。銀行策略愈發傾向「共存模式」：新產品引擎運行於現代核心上，而穩定的遺留模組則繼續處理清算。

越南 — 快速數位躍升與混合雲採用

越南《810/QD-NHNN 決議》為 2025 年及 2030 年制定了雄心勃勃的數位銀行目標 [13]。帳戶持有率現已超過 87%，許多銀行已有超過 95% 的交易透過數位渠道處理 [6]。截至 2022 年底，銀行在數位轉型上的投資約達 6.2 億美元 [6]。

HD Bank 在數位化推進中表現活躍，專注於零售及中小企客戶。該行正在擴展「行動優先」的產品，嘗試嵌入式金融，並積極尋求與金融科技合作。同時，Techcombank（TCB）持續引領大規模數位轉型，重金投入數據平台、客戶分析與雲端基礎設施。其與 AWS 的合作，使其在韌性與可擴展性上達到新高度。

LPBank 在約七個月內完成了 Temenos 核心替換，而其他傳統銀行則專注於提升規模與韌性 [14]。雲端採用正在迅速增長，AWS 與 Microsoft 正支持混合遷移。

越南國家銀行則擴展了金融科技沙盒、電子身份驗證（eKYC）、互通框架與開放銀行計劃 [15]。監管重點包括推動無現金支付、保障消費者權益以及強化網絡安全。

跨市場展望

在馬來西亞，2022 年發放了五張數位銀行執照，調查顯示約 95% 的銀行將在 2025 年前具備雲端就緒能力 [9]。馬來亞銀行、聯昌銀行與興業銀行均展開多年期核心升級與數據平台現代化。

菲律賓則已發出六張數位銀行執照，強勁的行動用戶採用率正在推動銀行轉向雲原生核心與即時支付。政府的「Cloud First」政策與即時支付基礎設施投資正加速轉型 [9]。

印尼擁有超過 1.8 億網民，數位採用快速增長。Jago、Jenius 與 SeaBank 等數位銀行正在迅速擴張。印尼金融服務管理局（OJK）的監管改革則持續演進，以支持純數位營運，而傳統銀行則逐步現代化核心系統。

定義未來防禦型核心銀行系統

未來防禦型核心必須作為即時、事件驅動的分類帳運行，具備強大的對賬控制。它應提供一個「產品工廠」，能快速配置新產品，並支持 API 優先的開放生態系統，以促進嵌入式金融與合作夥伴分銷。韌性需透過雲原生設計實現，包括多可用區部署、自動擴展與健全的可觀測性。安全與合規必須從一開始便內建，涵蓋數據在地化與可稽核性功能。最後，遷移策略應循序漸進，依靠平行運行、「絞殺者樹」方法與雙寫模式來降低執行風險。

系統整合商的角色

埃森哲、德勤與 GFT 等全球系統整合商在大規模核心轉型中扮演關鍵角色，確保合規並順利交付。Audax 與 Constantinople 等專業公司則專注於新建數位銀行與營運模式設計。他們的價值在於提供可預測的遷移成果、加快上市時間並確保韌性。

結論 — 不革新核心，便走向沒落

核心現代化已不再是可選項，而是成長、韌性與創新的基礎。能夠循序漸進完成核心替換的銀行，將解鎖更快的產品上市、更深入的嵌入式合作夥伴關係，以及更低的單位成本。而那些拖延的銀行，將面臨更高的營運風險、上升的成本以及客戶流失。到 2030 年，區域內的領先銀行將運行於雲原生、模組化的核心系統之上，支持區域擴張與長期競爭力。

參考

Monday, September 22, 2025
4 min read

Why It Is So Hard to Sell Core Banking Systems

Convincing a bank to replace its core banking system should be easy in theory. After all, the promise is huge: a safer, more reliable, and compliant engine that could save banks and their customers millions. Yet, in practice, it is one of the hardest sales in technology. The reasons go far beyond technology—they lie deep within the human, political, and regulatory fabric of the banking industry.

At the heart of the challenge are the people running the banks. Broadly speaking, there are two types. The first are stewards—or what I call “babysitters.” These bankers are conservative, risk-averse, and focused on not rocking the boat. They’re not sabotaging the bank’s future, but they aren’t championing innovation either. Their mindset is survival, not transformation. The second type are mavericks—rare individuals who look beyond their own tenure. They want to future-proof the bank, save customers money, and avoid being the “Kodak” or “Blockbuster” of finance. They embrace technology as the only way to adapt to a rapidly changing world. Unfortunately, the majority of decision-makers lean toward the steward side, while true mavericks are few and far between.

Replacing a core banking system is never a decision made by one person. Banks are regulated entities with complex governance structures, and every move requires the blessing—or at least the “non-objection”—of multiple stakeholders. Statutory board members are ultimately the only people who can sign off, but they face personal liability, which makes them cautious. CEOs may be the public face of the bank but are rarely the driving force. CFOs scrutinize the business case, sometimes comparing apples to oranges. COOs, responsible for both operations and IT, are often the most critical allies. Risk and compliance officers demand assurance that the new system meets regulations and often view change as more dangerous than sticking with the old. Security teams believe their standards are the best in the industry and impose their own requirements, while legal and procurement pore over contracts and policies, often dragging the process out. Ironically, the IT and operations staff who must actually use the new system often resist the most, fearing job loss or exposure of past failures.

Beyond the bank itself, external stakeholders shape the process as well. Regulators, auditors, consultants, and even investors all influence decision-making. Each has its own agenda, and alignment is rare. Regulators add yet another layer of complexity. Prudential authorities assess outsourcing risk, conduct regulators scrutinize consumer protections, privacy authorities demand adherence to strict data laws, and fiscal authorities determine tax implications that can reshape the business case. At best, regulators don’t object; rarely do they explicitly endorse. This culture of caution trickles down into every decision.

Selling a core banking system isn’t only about logic and cost savings—it’s about emotions and politics. Employees worry about their jobs. Executives worry about their reputations. Consultants may profit from legacy system failures and thus resist change. Sometimes resistance is downright irrational. One operations head once disliked automation because it replaced eighty people with eight. These personal and political dynamics often outweigh even the most compelling financial case.

For those selling core banking systems, the process feels less like sales and more like a long game of chess. Each stakeholder is a piece with unique powers, blind spots, and motivations. Winning requires patience, strategy, and the ability to anticipate moves many steps ahead. Brute force doesn’t work. Nor does a single champion. Deals are closed only when every piece is aligned—legal, regulatory, political, operational, and emotional.

The great irony is that the very systems banks cling to—patched, outdated, and inefficient—are the greatest risks to their survival. But change only happens when the right stakeholders see beyond the short-term and choose to embrace the future. Until more mavericks rise into leadership positions, the default stance of most banks will remain caution, not transformation.

Monday, September 22, 2025
in zh
1 min read

為什麼核心銀行系統這麼難賣

理論上，要說服一家銀行更換其核心銀行系統應該很容易。畢竟，承諾是巨大的：更安全、更可靠、更符合監管要求的引擎，可以為銀行和客戶節省數百萬美元。然而，在實際情況中，這卻是科技領域最難推銷的產品之一。原因早已超越了技術本身，而是深深根植於銀行業的人性、政治與監管結構之中。

挑戰的核心是經營銀行的人。大致而言，可以分為兩類。第一類是「看守者」——我稱他們為「保姆」。這些銀行家保守、害怕風險，專注於維持現狀，不想讓局面動盪。他們並沒有蓄意破壞銀行的未來，但也不會推動創新。他們的心態是「生存」，而不是「轉型」。第二類是「先行者」——少數能跳脫個人任期限制的領導者。他們希望讓銀行能長遠經營下去，為客戶節省資金，並避免成為金融界的「柯達」或「百視達」。他們擁抱科技，因為這是唯一能應對快速變化世界的方法。不幸的是，大多數決策者偏向前者，而真正的先行者少之又少。

更換核心銀行系統從來不是某個人單獨能做出的決定。銀行是受嚴格監管的機構，治理結構複雜，每一步都需要多方利害關係人的同意——或者至少「不反對」。最終能簽字批准的只有法定董事會成員，但他們必須承擔個人責任，因此非常謹慎。CEO 也許是銀行的公開代言人，但很少是推動這類交易的人。CFO 會檢視商業案例，有時卻出現「橘子比蘋果」的錯誤比較。COO 負責營運與 IT，通常是最關鍵的支持者。風險與合規主管需要確保新系統符合規範，並且往往認為變革比維持現狀更危險。資訊安全團隊認為自己的標準最好，會提出額外要求。法律與採購部門則反覆檢查合約與政策，拖慢進度。諷刺的是，真正要使用新系統的 IT 與營運人員，卻往往最強烈反對，因為他們擔心失去工作，或害怕舊錯誤被揭露。

除了銀行內部，外部利害關係人同樣影響決策。監管機構、審計師、顧問，甚至投資人都會左右結果。每個角色都有自己的議程，要達成一致非常困難。監管機構更是增加了層層複雜性。審慎監管機關會評估外包風險，行為監管機構會檢查銀行是否保障消費者利益，隱私機構要求嚴格遵守個資法規，財稅機構則制定稅務規則，直接影響成本結構。監管者通常最多只會「不反對」，很少明確支持。這種謹慎文化滲透到銀行的每一個決策中。

銷售核心銀行系統不僅僅是邏輯與成本的問題，更是情感與政治的角力。員工擔心失業，主管擔心名聲受損，顧問有時靠舊系統出錯來賺錢，因此對改變沒有興趣。有時反對甚至完全不合邏輯——某位營運主管就因為自動化「太有效」而反對，理由是它讓八十人的工作縮減到八人。這些個人與政治因素往往比再強而有力的財務理由還要來得重要。

對於那些試圖推銷核心銀行系統的人來說，這個過程更像是一場長期的西洋棋對局，而不是單純的銷售。每個利害關係人都是棋盤上的棋子，各自擁有不同的力量、盲點與動機。要贏得比賽，需要耐心、策略，並能預測數步之後的局勢。蠻力行不通，單一支持者也不夠。只有當法律、監管、政治、營運與情感等各方面全部達成一致時，交易才可能完成。

最大的諷刺是，銀行緊抓不放的舊系統——那些補丁累累、過時低效的東西——才是它們生存的最大風險。但真正的改變只有在關鍵決策者能超越短期顧慮，選擇擁抱未來時才會發生。在更多先行者走上領導位置之前，大多數銀行的默認姿態將依然是謹慎，而非轉型。

Sunday, September 21, 2025
4 min read

Why Most Businesses Don’t Qualify for Venture Capital Funding

Recently I had dinner with a friend, and she mentioned that she wants to start her own business. I shared my experience that there are really two types of startups: one is the SME, or small and medium enterprise, which focuses on building a profitable business; the other is the venture capital-backed startup, which is a completely different game. Many people don’t realize how stark the difference is, and this misunderstanding often leads entrepreneurs to chase the wrong kind of funding.

Venture capital is a highly specialized business. Great companies can still be terrible VC investments. The first big misunderstanding is about profitability. Entrepreneurs naturally want to build profitable companies. Venture capitalists, however, are not interested in profits, at least not in the short term. Profits usually mean slowing down growth, and growth is the only thing that matters to investors. They prefer companies that reinvest or even overspend every dollar to capture markets as quickly as possible. If a company ever finds itself generating more cash than it can reinvest effectively, investors will usually push for a sale, an IPO, or even a leadership change to keep the money flowing back into growth.

Margins are another critical issue. Reasonable margins might look appealing to most business owners, but venture capitalists want outrageous margins. Software that can be copied endlessly, drugs that can be sold globally, and technologies that scale without much additional cost are far more attractive than services. Businesses that require adding more people or more assets for every increment of growth simply don’t fit the VC model. Platforms or products that scale exponentially with minimal cost are what investors are after.

Another mismatch is return expectations. Doubling an investor’s money sounds impressive, but in venture capital it is not nearly enough. Because of the way funds are structured—with 10-year horizons, management fees, and the need to offset inevitable failures—VCs need to aim for tenfold returns just to stay competitive with the stock market. A steady business that can deliver consistent but limited gains will never satisfy those requirements.

Even if the economics made sense, the personal side often doesn’t align. Venture capitalists look for certain founder profiles: entrepreneurs they’ve already backed successfully, people with high-profile achievements in related industries, graduates of elite schools, or extremely charismatic personalities. Many solid business operators simply don’t fit the mold. Bias also plays a role, as women and minorities have historically been funded at lower rates. The truth is, who you are matters almost as much as what you’re building.

Fundraising itself is also a massive hurdle. For most first-time founders, raising a VC round takes six to twelve months of full-time effort, often ending with nothing. Meanwhile, the business they were supposed to be running suffers from lack of attention. For someone trying to manage operations day-to-day, that trade-off is impractical.

And then there’s the issue of control. Running your own business comes with freedom, but raising venture capital usually means adding investors to the board, and those investors have the power to replace you. Many founders don’t fully realize that taking VC money also means taking on a new boss.

So should you raise venture capital? Maybe. If you’re building software, biotech, or another kind of business that can deliver the explosive growth and outsized returns investors demand, venture capital can accelerate everything. It provides not only money, but also connections, credibility, and confidence. But for most businesses, there are better options. Revenue-based financing, crowdfunding, or simply reinvesting profits are often better aligned with the goals of the company. At the end of the day, venture capital isn’t about building good businesses. It’s about building VC-scale businesses. And that’s a very different thing.

Being in Singapore, I see a unique dynamic. Despite the government providing a lot of funding schemes and initiatives to cultivate startups, the local culture is not particularly entrepreneurial in the VC sense. At best, it leans toward SME-style ventures—focused on steady profits and sustainability, rather than aiming to grow fast, burn money, and get rich quickly through an IPO.

Sunday, September 21, 2025
in zh
1 min read

為什麼大多數企業不適合尋求創投資金

最近我和一位朋友共進晚餐，她提到自己想要創業。我分享了自己的經驗，其實創業有兩種類型：一種是中小企業，專注於建立一個能夠獲利的生意；另一種則是透過創投（Venture Capital, VC）融資的創業，這是一個完全不同的遊戲。許多人沒有意識到這兩者之間的巨大差異，因此常常誤走方向，追逐不適合自己的資金來源。

創投是一個高度專業化的行業。即使是很好的公司，也可能是很糟糕的創投投資標的。第一個常見的誤解是關於「獲利」。企業家自然會希望建立一個有利潤的公司，但創投短期內對獲利並不感興趣。獲利往往意味著放慢增長，而投資人只在乎成長。他們希望公司把每一分錢都再投入，甚至超支，以便盡快擴張市場。如果一家公司現金流超過了它能有效再投資的能力，投資人通常會推動出售、IPO，甚至換掉管理層，以確保資金繼續被用來加速成長。

毛利率也是關鍵問題之一。對於一般企業主來說，合理的毛利率已經很不錯，但對創投而言，他們追求的是極高的毛利率。軟體可以無限複製、藥品能夠全球銷售、科技產品能在成本不大幅增加的情況下擴張，這些才是創投眼中的理想標的。而需要增加更多人手或資產才能增加收入的生意，通常都不符合創投的模式。能以極低成本快速擴張的產品或平台，才是他們所追逐的方向。

投資回報的期待也是一大差距。把投資人的資金翻倍，聽起來很厲害，但在創投的世界裡，遠遠不夠。由於基金結構的關係——十年的週期、高額的管理費，以及必須抵消必然失敗的投資案——創投需要追求十倍的回報，才能保持與股市相當的競爭力。那些能帶來穩定但有限回報的企業，根本無法滿足這樣的要求。

即使在財務上勉強說得過去，個人條件也常常不對盤。創投偏好某些特定的創業者類型：曾經替他們賺過大錢的企業家、在相關產業有高知名度成功經歷的人、來自頂尖學府並已經創造出驚人成果的畢業生，或是極具魅力的 A 型人格。許多優秀的經營者根本不符合這些特質。而且，偏見也會影響結果，女性和少數族裔的融資成功率歷來都較低。現實是，你是誰，往往和你要做什麼一樣重要。

募資本身也是巨大的挑戰。對多數第一次創業的人來說，完成一輪融資通常需要六到十二個月的全職努力，最後還很可能一無所獲。與此同時，他們原本該經營的事業就被擱置了。對於需要每天親自打理運營的人來說，這樣的取捨幾乎不可能。

最後還有控制權的問題。自己經營生意最好的地方在於自由，但一旦引入創投，通常就要讓投資人進入董事會，而這些人擁有更換你的權力。許多創業者沒有充分意識到，拿了創投的錢，意味著你重新有了「老闆」。

所以，你該不該尋求創投？也許吧。如果你正在打造的是軟體、生物科技或其他能帶來爆炸性增長和巨大回報的業務，創投能加速一切。它不僅帶來資金，還提供人脈、聲譽與信心。但對於大部分的企業而言，其他方式可能更合適。基於營收的融資、群眾募資，或是單純地將利潤再投資，往往更符合公司的目標。歸根結底，創投不是用來打造「好公司」，而是用來打造「適合創投規模的公司」。這是完全不同的概念。

身在新加坡，我看到的是另一種現象。儘管政府提供了許多資金計劃和創新倡議來培養創業環境，但本地文化並不是特別創投導向。最多只能說比較偏向中小企業風格——追求穩定的獲利與可持續性，而不是快速成長、燒錢，然後透過 IPO 一夜致富。

Wednesday, September 17, 2025
4 min read

The Personal Qualities That Define Great Leaders

Leadership is never just about knowledge or strategy. It is about character, conviction, and the ability to inspire. Skills may help you solve problems, but it is your personal qualities that determine how far you can go, how resilient you will be, and how deeply you will influence those around you.

The greatest leaders are united by one thing: a relentless belief in their mission. They don’t just talk about goals; they embody them. Anchored in deeply held values, they push forward even when the odds are stacked against them. Every failure becomes fuel for growth, every success a stepping stone to the next horizon. Conviction, resilience, and courage are not optional traits. They are the foundation of enduring leadership.

Today’s leaders face a world of contradictions. They must drive vision from the top while empowering voices from the ground. They must serve both customers and employees. They must balance long-term investments with short-term pressures. They must dream big with divergent thinking while executing with laser-focused discipline. Great leaders are not paralyzed by these tensions; they embrace them. They act with courage, adapt quickly, and never stop learning. While no contradiction can be perfectly solved, dialogue, agility, and vision allow leaders to chart a path forward.

Look at Jeff Bezos guiding Amazon through the chaos of the dot-com crash. Many companies collapsed, but Bezos’s unwavering belief in Amazon’s mission to be Earth’s most customer-centric company carried the organization through near-death moments and laid the foundation for its global dominance. Consider Steve Jobs, who was once ousted from Apple but returned years later to rebuild the company into a symbol of innovation and creativity. His passion, his obsession with design, and his ability to inspire people with a vision of changing the world made Apple one of the most admired companies on earth. And beyond business, think of Lee Kuan Yew. With foresight and conviction, he transformed Singapore from a small, struggling nation into a thriving global hub. His clarity of purpose, pragmatism, and values-driven leadership carried a people through uncertainty and built a lasting legacy.

Another defining trait of great leaders is their relentless growth. Compare them today to who they were just a few years earlier, and the transformation is striking. Bezos evolved from a founder with an online bookstore idea to a global strategist reshaping industries. Jobs turned personal setbacks into fuel for creative reinvention, building Pixar and later revolutionizing consumer technology. Lee Kuan Yew continually adapted his policies to match the world’s shifts, learning and evolving while staying true to his vision of Singapore’s survival and prosperity. Great leaders never remain the same; they reinvent themselves, and through that reinvention, they reshape the organizations and societies they lead.

Your knowledge matters, but knowledge alone is not enough. What defines you as a leader is your resilience when tested, your values when pressured, your ability to balance contradictions when pulled in opposite directions, and your hunger to keep growing. Leadership is not static; it is a living journey of courage, learning, and inspiration.

So when challenges come, as they always do, remember this: leadership is not just about what you know. It is about how you stand tall in adversity, how you inspire trust and hope, and how you choose to lead when the world is watching. Great leadership is born not from certainty, but from conviction and the courage to keep moving forward.

Wednesday, September 17, 2025
in zh
1 min read

定義偉大領袖的個人素質

領導力從來不僅僅是知識或策略，它真正關乎的是人格、信念，以及激勵他人的能力。技能或許能幫助你解決問題，但決定你能走多遠、能承受多少壓力、能影響多少人的，是你的個人素質。

最偉大的領袖有一個共同點：他們對使命的堅定信念。他們不只是談論目標，而是身體力行，將目標化為信念。憑藉深植內心的價值觀，他們即使在逆境中也能繼續前行。每一次失敗都成為成長的燃料，每一次成功都是邁向更高境界的踏板。信念、韌性與勇氣不是可有可無的特質，而是持久領導力的基石。

當今的世界充滿矛盾，領袖必須在拉扯之間找到平衡。他們既要推動自上而下的願景，又要賦能基層的聲音；既要服務顧客，又要關注員工；既要兼顧長期投資，又要面對短期壓力；既要用發散思維大膽創新，又要用專注思維精準執行。偉大的領袖不會被這些矛盾束縛，他們懂得擁抱矛盾，勇敢行動，快速調整，不斷學習。雖然矛盾無法徹底消解，但透過對話、靈活與遠見，他們能為未來找到前進的方向。

想想傑夫·貝佐斯（Jeff Bezos）如何帶領亞馬遜走過網路泡沫的崩潰。無數公司倒下，但貝佐斯對「成為全球最以顧客為中心的公司」的使命堅信不疑，使亞馬遜從瀕臨毀滅中走出，並奠定了全球霸主的基礎。再看看史蒂夫·賈伯斯（Steve Jobs），他曾被逐出蘋果，卻在多年後回歸，將公司重建為創新與設計的象徵。他的熱情、對設計的執著，以及以「改變世界」為核心的願景，使蘋果成為全球最受尊敬的企業之一。而在政治領域，新加坡的李光耀憑藉遠見與堅定，將一個資源有限、前途未卜的小國轉型為繁榮的全球樞紐。他清晰的使命感、務實的作風與價值驅動的領導力，不僅帶領人民度過不確定的時代，更奠定了持久的國家基業。

另一項定義偉大領袖的特質是持續成長。將他們現在與幾年前相比，你會驚訝於他們的蛻變。貝佐斯從一位創辦線上書店的創業者，成長為改變產業的全球戰略家。賈伯斯將個人挫折轉化為創意能量，打造出皮克斯，隨後更徹底革新了消費科技。李光耀則不斷根據世界局勢調整政策，在保持新加坡生存與繁榮願景的同時，不斷學習與進化。偉大的領袖從不原地踏步，他們在自我再造的過程中，也重塑了所帶領的組織與社會。

知識固然重要，但光靠知識不足以支撐你度過風暴。真正定義領袖的，是他在考驗中展現的韌性、在壓力下堅守的價值觀、在矛盾中找到平衡的能力，以及對成長的渴望。領導不是靜止的狀態，而是一場關於勇氣、學習與啟發的生命旅程。

所以，當挑戰來臨時——而挑戰永遠會來——請記住：領導不只是你知道什麼，而是你如何在逆境中挺身而出，如何激發信任與希望，以及你在眾人矚目之下選擇如何領導。偉大的領導不是誕生於確定，而是源於信念，以及不斷向前的勇氣。

Monday, September 15, 2025
5 min read

Learning to Build Agentic Apps with Azure AI Foundry

Building agentic applications with Azure AI Foundry can feel like stepping into a new world for a solution architect. The promise is huge, an entire ecosystem for creating, deploying, and managing AI agents at enterprise scale, but it requires rethinking how we design architectures, plan adoption, and integrate security and governance. Coming from a background of traditional solution design, I quickly realized that approaching this space with the right framework makes all the difference.

I began with Microsoft’s Cloud Adoption Framework, which breaks down the journey into familiar stages. Defining the strategy helped me clarify why the business wanted to adopt agentic AI in the first place and what value we expected. Planning translated those motivations into actionable steps, and preparing the environment with Azure landing zones gave me confidence that the foundations were solid. Adoption meant actually building and deploying workloads, and the final piece, securing them, was a reminder that AI systems must follow the same rigorous governance standards as any enterprise platform.

The next learning curve was understanding AI landing zones. These act as the enterprise-scale foundation for AI adoption and can be deployed with or without a broader platform landing zone. With a platform landing zone, services like networking and identity are centralized, offering scalability and compliance. Without one, you can start faster, but consistency suffers. As I came to see it, landing zones are the equivalent of a data center for AI agents, and they form the baseline that everything else plugs into.

Once the infrastructure was clear, I had to choose how to actually build agents. Azure AI Foundry makes it possible to experiment in multiple ways: low code or no code tools for fast prototyping, and pro code environments with VS Code extensions, REST APIs, or Semantic Kernel SDKs for full customization. At first I leaned on low code tools to get hands-on experience, then gradually moved into pro code scenarios as integration needs and complexity grew. The key lesson was to start simple and deepen over time, carefully selecting models and balancing cost versus performance while deciding which tools the agent should integrate with, such as Azure AI Search, Bing grounding, or Logic Apps.

Another critical design decision was whether to rely on single agents or adopt multi-agent systems. Single agents are predictable and easier to debug, making them a good starting point. Multi-agent setups, however, shine in dynamic or decomposable workloads where specialized agents collaborate, such as combining HR, IT, and compliance agents for employee onboarding. Semantic Kernel provides the orchestration layer for this coordination, allowing workflows to scale as complexity grows. The approach that worked for me was to start with single agents and only move to multi-agent orchestration once the use cases demanded it.

One of the biggest mindset shifts was recognizing that observability and evaluation are not optional. Unlike traditional apps where metrics are straightforward, agents can feel like black boxes unless you design for visibility. Azure AI Foundry’s traceability features log tool calls and agent interactions, while its evaluation metrics check groundedness, fluency, and relevance. Combined with AI safety tooling, these capabilities help ensure outputs remain safe, reliable, and aligned with organizational goals. For me, it was the equivalent of application performance monitoring in conventional systems: without visibility, improvement is impossible.

Of course, none of this matters if the system isn’t secure. Foundry layers governance and security controls across the stack. Managed identities and login protect users, while prompt and content filters ensure responsible AI practices. Virtual networks, NSGs, and VPN gateways provide network security, and Defender for Cloud adds threat protection. Purview further enhances data governance and compliance. I realized that while agents may feel like futuristic AI entities, architecturally they must be treated as microservices that adhere to the same enterprise-grade security principles as any other system.

Looking back on my early steps with Azure AI Foundry, several lessons stand out. Choose your building approach based on the maturity of your use case, whether no code, low code, or pro code. Pick models and tools carefully, weighing cost against performance. Start small with single agents, and scale into multi-agent orchestration when the complexity justifies it. Bake in observability, evaluation, and responsible AI practices from day one. And finally, leverage AI landing zones for enterprise-ready deployments that bring security, scalability, and governance to the forefront.

For me as a solution architect, Azure AI Foundry has become more than just a platform for deploying language models. It is a bridge between experimentation and enterprise readiness, providing the frameworks, tools, and safeguards needed to build agentic applications responsibly. The journey can feel daunting at first, but with a structured approach and focus on architectural principles, agentic AI quickly becomes less of a mystery and more of the next natural step in modern system design.

Monday, September 15, 2025
in zh
2 min read

學習使用 Azure AI Foundry 建構 Agentic 應用程式

對一位解決方案架構師來說，使用 Azure AI Foundry 來建構 agentic 應用程式，就像踏入一個全新的世界。這是一個龐大的承諾——一整套可用於建立、部署與管理 AI 代理的生態系統，能在企業規模下運作，但同時也需要我們重新思考如何設計架構、規劃採用方式，以及整合安全與治理。來自傳統解決方案設計背景的我，很快就發現，用正確的框架來面對這個領域會帶來完全不同的效果。

我從微軟的雲端採用框架開始，它將這段旅程拆解為熟悉的階段。首先是定義策略，幫助我釐清企業為何要導入 agentic AI，以及我們期望獲得的價值。接著是規劃，將這些動機轉化為可執行的步驟；準備環境則是利用 Azure 登陸區，讓我對基礎建設有了信心。當進入採用階段時，代表真正開始建構與部署工作負載；最後的安全階段提醒我，AI 系統必須遵循與任何企業平台相同的嚴格治理標準。

接下來的學習曲線是理解 AI 登陸區。它們是 AI 採用在企業規模下的基礎，可以選擇有或沒有更廣泛的平台登陸區來部署。有了平台登陸區，像是網路與身分服務會集中化，能帶來可擴展性與合規性；沒有的話，雖然能更快開始，但一致性會受到影響。對我而言，登陸區就像是 AI 代理的資料中心，成為其他一切元件的基礎。

當基礎設施清楚之後，我需要決定實際如何建構代理。Azure AI Foundry 提供了多種嘗試方式：低程式碼或免程式碼工具可以快速原型設計，而專業程式碼環境則透過 VS Code 擴充套件、REST API 或 Semantic Kernel SDK 提供完全自訂的能力。一開始我依靠低程式碼工具來獲得實作經驗，隨著整合需求和複雜度提升，逐漸轉向專業程式碼的場景。最重要的教訓是從簡單開始，隨時間加深，並謹慎挑選模型、在成本與效能之間取得平衡，同時決定代理需要整合的工具，例如 Azure AI Search、Bing grounding 或 Logic Apps。

另一個關鍵的設計抉擇是要依賴單一代理，還是採用多代理系統。單一代理可預測且容易除錯，是很好的起點。然而在動態或可分解的工作負載中，多代理更能發揮優勢，不同專業的代理可以協作，例如在人員入職過程中結合 HR、IT 與合規代理。Semantic Kernel 提供了這種協作所需的協調層，讓工作流程能隨著複雜度增加而擴展。對我來說，最有效的方法是先從單一代理開始，只有在需求出現時才轉向多代理協調。

其中一個最大的思維轉變，是意識到可觀測性與評估並非選項，而是必須。不同於傳統應用程式的指標相對直觀，若沒有設計可視性，代理就像黑盒子。Azure AI Foundry 的追蹤功能能記錄工具呼叫與代理互動，其評估指標則檢查回應的依據性、流暢度與相關性。結合 AI 安全工具，這些能力能確保輸出安全、可靠，並符合組織目標。對我來說，這就像傳統系統的應用程式效能監控：缺乏可見性，就不可能改善。

當然，如果系統不安全，其他一切都沒有意義。Foundry 在整個堆疊中加入治理與安全控制。受控身分與登入保護使用者，提示與內容篩選確保負責任的 AI 實踐。虛擬網路、NSG 與 VPN 閘道提供網路安全，而 Defender for Cloud 則新增威脅防護。Purview 進一步強化資料治理與合規性。我意識到，雖然代理看似未來感十足的 AI 實體，但在架構上，它們必須被視為微服務，遵循任何其他企業系統相同的安全原則。

回顧我在 Azure AI Foundry 的早期探索，有幾個教訓特別清晰。根據使用案例的成熟度來選擇建構方式，無論是免程式碼、低程式碼，或專業程式碼。謹慎挑選模型與工具，仔細衡量成本與效能。從小規模的單一代理開始，當複雜度增加時再擴展到多代理協調。從第一天起就將可觀測性、評估與負責任 AI 實踐納入基礎。最後，善用 AI 登陸區來進行企業級部署，確保安全性、可擴展性與治理。

對我而言，Azure AI Foundry 已不僅僅是部署語言模型的平台，而是連結實驗與企業就緒之間的橋樑。它提供了必要的框架、工具與防護措施，讓我們能以負責任的方式建構 agentic 應用程式。這段旅程起初可能令人望而生畏，但只要採用有結構的方法並專注於架構原則，agentic AI 很快就不再神祕，而是邁向現代系統設計的下一個自然步驟。