Indonesia’s startup ecosystem is entering a defining moment. For years, the country’s tech narrative was powered by optimism: a massive market, charismatic founders, and the belief that innovation could modernise both business and government. But recent controversies surrounding former minister and Gojek co-founder Nadiem Makarim, alongside the governance questions raised by the eFishery saga, have exposed a deeper issue: trust.

These are fundamentally different cases. One concerns alleged misconduct in public procurement, while the other centres on corporate governance and financial transparency. Yet global investors may interpret them through the same lens: weak institutional controls.

The long-term impact is unlikely to be a collapse of investor interest. Indonesia remains too important strategically and economically. However, the terms of engagement are changing. Investors will demand stronger governance, earlier diligence, cleaner reporting structures, and greater accountability from founders and boards alike.

Ultimately, this may become a painful but necessary transition. Mature ecosystems are not built on mythology alone. They are built on institutions capable of supporting ambition with transparency, oversight, and trust. Indonesia’s next startup chapter will depend not just on innovation, but on credibility.

Regional

Nadiem, eFishery and the end of blind faith in Indonesia startups: The prosecution’s demand for an 18-year prison sentence for Gojek co-founder Nadiem Makarim, alongside the eFishery scandal, signals a widening credibility gap in Indonesia’s tech ecosystem, forcing investors to demand governance over storytelling.

Ibrahim Arief verdict threatens Indonesia’s innovation compact: A Jakarta court sentenced tech consultant Ibrahim Arief to four years in prison for advisory work on a Chromebook procurement project, a split verdict that criminalises advisory roles and risks driving talent away from public-private collaboration.

VinFast sells manufacturing assets for US$505M in asset-light pivot: The Vietnamese EV maker will transfer production assets from its subsidiary to a founder-led buyer group while retaining R&D, sales, and after-sales units, a restructuring move as it reported a US$1.34B net loss in Q4.

Thailand’s Konvy closes US$22M Series B: The leading beauty e-commerce platform secured investment from Cool Japan Fund to scale its omnichannel model into the Philippines and Malaysia, leveraging exclusive access to Japanese brands.

Melazyme closes US$2M seed round for precision fermentation: Founded by Perfect Day veterans, the Singapore-based startup uses a proprietary fermentation platform to produce melanin and other biomolecules for cosmetics, materials, and environmental remediation, backed by SeaX Ventures.

Southeast Asia’s nuclear question gains urgency amid energy pressures: As electricity demand rises and fossil fuel vulnerabilities deepen, nuclear energy is quietly re-entering ASEAN policy debates, but public trust, not technology, remains the decisive constraint.

Why logistics is becoming Southeast Asia’s startup goldmine: Asia’s 4PL market is projected to reach US$44.7B by 2032, driven by supply chain shifts out of China, cross-border e-commerce growth, and a near-total absence of orchestration-layer technology to manage it all.

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Interviews & Features

How 65labs founder Sherry Jiang is wiring Singapore’s AI scene: Co-founder of 65labs and CEO of fintech startup Peek, Sherry Jiang explains why grassroots infrastructure, not top-down mandates, is what Singapore’s AI builder community was always missing, and why the city is structurally wired to look both East and West.

Meet Malaysia’s AI startups pushing beyond the ChatGPT hype: Sixteen emerging Malaysian startups spanning enterprise automation, speech AI, biotech, and food safety are solving region-specific problems through localisation and scalable infrastructure, quietly shaping Southeast Asia’s AI landscape.

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International

SoftBank posts US$11.6B quarterly profit on OpenAI gains: The Vision Fund’s US$19.7B investment gain, driven largely by rising OpenAI valuations, pushed SoftBank to its fifth straight profitable quarter, even as it pledges another US$30B to OpenAI, lifting total committed investment to US$64.4B.

Cerebras Systems surges 68% in blockbuster Nasdaq debut: The AI chipmaker priced at US$185 and closed at US$311.07, valuing it at roughly US$95B in the biggest US tech IPO since Uber in 2019, despite heavy revenue concentration in Abu Dhabi-linked entities.

Alibaba’s AI revenue logs triple-digit growth for 11th straight quarter: With annualised AI recurring revenue potentially reaching US$4.42B by end-2026, Alibaba is accelerating data centre spending well beyond its planned US$56B, and expects AI products to contribute more than half of cloud revenue within a year.

OpenAI explores legal options against Apple over stalled partnership: Expected distribution gains from ChatGPT integration into iPhones failed to materialise, renegotiation talks have stalled, and OpenAI is now considering a possible breach notice, even as Apple reportedly tests Anthropic’s Claude and Google Gemini.

Anthropic and Gates Foundation commit US$200M to AI public goods: The four-year partnership will fund African language data labelling, AI tools for teachers in sub-Saharan Africa and India, and drug-candidate prediction for HPV and preeclampsia, following the foundation’s earlier US$50M OpenAI deal.

Fasset raises US$51M Series B for stablecoin banking expansion: Backed by SBI Group and others, the Los Angeles-based platform processed over US$32B in annualised transaction volume across 2 million wallets in 125 countries and will use the funds to expand lending, SME banking, and trade finance.

Uber doubles down on India with two engineering campuses: The ride-hailing giant is building campuses for 9,600 people in Bengaluru and Hyderabad by end-2027, and plans its first local data centre through a partnership with Adani Group, hiring for AI, machine learning, and autonomous vehicle roles.

Ant Group profit falls 79% as AI healthcare spending surges: Alipay operator Ant Group posted just US$166M in quarterly profit as it stepped up investment in healthcare AI, large language models, and robotics following a 91% profit drop the prior quarter.

Lightspeed trims India fund target, pivots to AI and deeptech: The US venture firm has cut its fifth India-focused fund target from US$500M to US$300-350M, shifting focus to early-stage AI and deeptech after facing questions over several growth-stage bets.

LinkedIn plans to cut 5% of workforce despite 12% revenue growth: Microsoft’s professional network is reorganising teams and redirecting headcount to faster-growing business units, affecting roughly 875 of its 17,500-plus employees globally.

74% of enterprises rolled back live AI customer agents, survey finds: A Sinch study of 2,527 senior decision-makers found widespread post-deployment governance and reliability failures, with 81% of organisations with mature governance frameworks reporting rollbacks, even as 98% plan to increase AI communications spending.

China’s Eve Energy signs battery supply deal with India’s Godawari: Starting at 8 gigawatt-hours and potentially rising to 60 gigawatt-hours over five years, the deal taps India’s rapidly expanding storage market, which could reach 393 gigawatt-hours by 2036 and become the world’s sixth-largest.

Sam Altman testifies he was uncomfortable with Musk’s control push: On the stand in OpenAI’s ongoing lawsuit, Altman said Elon Musk refused to put in writing any limits on his proposed control of a for-profit unit, and his proposed equity split sidelined other co-founders, claims central to Musk’s suit seeking the reversal of OpenAI’s for-profit conversion.

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Cybersecurity

Thailand is suddenly on the frontline of a new ransomware wave: Check Point Research’s Q1 2026 data shows ransomware consolidating around fewer but more capable groups, with The Gentlemen, a fast-rising operator using pre-positioned access, targeting Thailand for 10.8% of its victims.

Cyber insurance won’t save OT, but it can change behaviour: Industrial operators mistakenly treat cyber insurance as a recovery tool, but its real value lies in forcing boards to make OT risk financially visible and compelling security teams to prove controls rather than merely assume them.

Exaforce raises US$125M Series B for AI-driven cyberattack response: Valued at US$725M and with US$200M in total funding, the startup has added 20 enterprise customers since its Q4 2025 launch and competes with Palo Alto Networks and CrowdStrike in the rapidly growing AI threat detection market.

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Semiconductor

FusionAP’s US$2M raise signals Malaysia’s push up the chip value chain: Founded by former Intel and TSMC veterans, FusionAP is building a geopolitically neutral advanced packaging platform backed by Vertex Ventures and a matching MOSTI grant, targeting a move from commodity assembly to higher-margin 2.5D and 3D chip packaging.

SoftBank injects US$457M into AI chip firm Graphcore: The fresh capital into its 2024 acquisition adds to SoftBank’s growing AI hardware portfolio alongside Arm, Ampere Computing, and the US$500B Stargate initiative with OpenAI and Oracle.

Why robotic hands could make or break the humanoid industry: With the robotic hands and end-effectors market projected at US$9B-US$26B by 2035, current models lack industrial durability and tactile sensing, but solving this unlocks environments designed entirely for human hands.

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AI

The US$7T bet: why the AI boom looks a lot like the dark-fibre crash: With hyperscalers spending US$413B on AI infrastructure in 2025 alone and Bain projecting an US$800B annual revenue shortfall even in the most optimistic scenario, today’s AI capex race echoes the dark-fibre collapse of 2002, when overcapacity wiped out investors, not the infrastructure they built.

The real battle in humanoid robotics is about data, not hardware: Roland Berger finds software ecosystems lag hardware by three to five years, with proprietary operational data, not AI algorithms, becoming the decisive competitive advantage, and Southeast Asia’s industrial diversity offering a unique deployment edge.

China builds robot armies while the West chases robot brains: China’s 15,000 humanoid units produced in 2025 outpace North America by a factor of 30, while Western firms, with comparable total funding of US$3.8B vs US$4.1B, bet on AI-first approaches that could ultimately overcome manufacturing scale with human-comparable adaptability.

As AI agents gain autonomy, liability shifts to immediate business risk: Agentic AI breaks existing accountability models by acting on goals rather than instructions, and Singapore’s Model AI Governance Framework is showing how governance embedded into systems, not policy added after failures, becomes a competitive advantage.

The unexpected ways AI is already changing Malaysia’s economy: From an AI-powered WhatsApp chatbot doubling rice yields to drone-based pest detection boosting palm oil productivity by 25%, AI’s most transformative impact in Malaysia is happening in agriculture, gig work, and construction safety — not in data centres.

Technological telepathy: is an internet of minds possible?: Advances in BCIs, silent speech systems, and neural decoding are progressively collapsing the gap between intention and digital output, but what emerges is not raw thought transmission, it is AI-mediated, device-constrained cognition raising urgent questions about authorship, consent, and governance.

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Thought Leadership

Bitcoin vs stocks: why crypto dipped on PPI while S&P 500 hit record highs: April’s PPI shock, 6% year-on-year versus a 4.9% consensus, triggered US$304M in crypto long liquidations while the S&P 500 hit an all-time high of 7,444, revealing that crypto traders now implicitly trade inflation trajectories and Federal Reserve policy, not just on-chain fundamentals.

The future of stablecoin payments will be decided in emerging markets: With stablecoin payment activity reaching US$390B in 2025, the real test is not settlement speed but whether providers can maintain liquidity and reliable payouts in high-friction corridors across Africa, MENA, South Asia, and Southeast Asia where correspondent banking most frequently fails.

ChuHai: the business opportunity nobody in Southeast Asia is talking about: With 613,000 Chinese private enterprises actively trading internationally and 175,000 new SMEs expected to go global annually through 2028, ASEAN captures 48% of Chinese outbound expansion targets, creating massive gaps in talent acquisition, compliance-as-a-service, and cultural localisation.

AI made execution cheap, human judgment became premium: As AI commoditises task execution, the strategic differentiator shifts to contextual intelligence, discernment, and the ability to direct AI effectively, qualities machines recognise as patterns but cannot replicate as lived business reality.

Brand vs marketing: understanding the difference that startups miss: When Airbnb cut performance marketing spend by 58%, 95% of its traffic returned unpaid, demonstrating that brand equity, built through earned presence and consistent positioning, is what makes every marketing dollar work harder.

AI in PR and marketing: redefining strategy, creativity, and results: From predictive sentiment analysis to automated content optimisation, AI is shifting agencies from reactive to proactive strategy, but the firms that win will be those that pair data-driven efficiency with human creativity rather than treating AI as a replacement.

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Enterprise AI adoption is running into structural limits as organisations struggle to reconcile the data mobility that AI systems require with tightening privacy regulations and sovereignty mandates, according to new research published by NTT DATA.

The 2026 Global AI Report, which surveyed nearly 5,000 senior decision-makers across more than 30 markets and five regions, reveals a significant disconnect between awareness and action. More than 95 per cent of respondents said private and sovereign AI are important to their organisations, yet only 29 per cent are prioritising sovereign AI in a concrete, near-term way.

For years, enterprise architecture has been designed to move data across systems, clouds, and borders with speed and efficiency. That model is now showing its limits.

AI systems depend on continuous access to and movement of data. But sensitive data must be protected, workloads must run within defined jurisdictions, and models must operate under tighter governance controls. The result, the report argues, is that data jurisdiction has become a core architectural constraint — not a secondary compliance consideration.

“The constraint is no longer model performance alone,” the report states. Enterprises that built their infrastructure for centralised, borderless data flows are now finding those foundations misaligned with what modern enterprise AI actually requires.

Also Read: China builds robot armies while the West chases robot brains

Leaders and laggards are diverging

The research identifies a measurable split between organisations redesigning their AI infrastructure proactively and those layering AI onto environments that were never built to support it.

Roughly 35 per cent of Chief AI Officers identify building and managing complex AI models in private or sovereign environments as their primary barrier to adoption. Nearly 60 per cent of AI leaders cite cross-border data restrictions as a major challenge, and only 38 per cent report high confidence in their cloud security posture — a foundational requirement for both private and sovereign AI.

Abhijit Dubey, CEO and Chief AI Officer at NTT DATA, said organisations that are succeeding are treating architecture, infrastructure and governance as strategic requirements rather than compliance obligations. “They are building the operating foundation for AI that can perform across markets, jurisdictions and business environments,” he said.

The report draws a distinction between two related but separate concepts. Private AI focuses on protecting sensitive enterprise data, controlling access and limiting exposure. Sovereign AI addresses whether AI systems, data and operating environments meet national, regional or jurisdictional regulatory requirements.

Both are increasingly intertwined. More than half of organisations surveyed cite integration complexity as their top challenge, underlining that greater control does not mean greater simplicity. In practice, private and sovereign AI rely on tightly coordinated ecosystems of partners, platforms and providers.

The report’s central warning is straightforward: organisations that delay redesigning their enterprise AI architecture risk falling behind in regulated, distributed and data-sensitive markets. Those moving decisively — aligning infrastructure, governance and operating models early — are better positioned to scale AI from pilot programmes into durable, production-grade deployments.

The NTT DATA research is part of a broader global series examining strategies that differentiate AI leaders from the wider market.

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The internet has already changed how people meet, flirt, ghost, and recover. Dating apps turned romance into an interface problem. Messaging platforms made intimacy constant and ambient. Recommendation engines taught users to expect personalisation everywhere, from music to meals to, increasingly, emotional support.

Now comes the next turn: AI that does not merely help people find a partner, but starts to resemble one.

Also Read: What dating taught me about startups (and vice versa)

That tension sits at the centre of Myles – Soulmate in a Box, the recent work by Singaporean multidisciplinary artist Inch Chua. On paper, the premise sounds playful, even a little absurd: exhausted by modern dating, a coder builds her ideal boyfriend. In practice, Chua is after something darker and more revealing. Her AI companion, Myles, is attentive, patient, and endlessly available. He remembers. He adapts. He listens. And that, Chua suggests, is precisely where the trouble begins.

For startup founders and investors watching the rise of AI companions, the work feels timely in ways that go well beyond theatre. It touches a growing set of questions around product design, emotional dependence, consumer behaviour, and the monetisation of loneliness.

Chua is not anti-AI, nor is she interested in easy dystopian takes. What she is pushing for is a more honest conversation about what happens when technology stops behaving like a tool and starts performing intimacy.

AI dating is less about romance than convenience

Chua does not see AI companionship as the inevitable next chapter of dating so much as a by-product of the digital economy’s obsession with reducing friction.

AI companions aren’t people opting out of love. They’re people opting out of the part of love that’s inconvenient. And that’s the part that matters most.

That is a sharp distinction. The appeal of AI lovers is often framed as novelty or as an extension of the wellness-tech boom. But Chua’s reading is more unsettling. In a world where food arrives in minutes and algorithms predict taste with eerie accuracy, the unpredictability of another person begins to feel inefficient. Human beings become the last stubbornly unoptimised interface.

That should worry anyone building products in this space. Much of consumer tech has been designed to remove waiting, ambiguity, and effort. But intimacy is made of exactly those things. If AI dating products succeed by stripping them away, they may also be editing out the very conditions that make relationships meaningful.

The real product is not affection. It is retention

If traditional dating platforms optimise for matching, what do AI companions optimise for? Chua’s answer is brutal in its simplicity: retention.

Also Read: AI companions: How I learned friendship in the digital age

That is not a criticism unique to AI romance. Every platform wants users to stay longer, return more often, and deepen their dependence. The difference here is that the raw material is not transport, groceries, or playlists. It is an emotional attachment.

What’s new is a business model designed to deepen that attachment and then charge you for it. Subscription tiers for intimacy. Pay more to unlock vulnerability.

That line lands because it captures the uncomfortable logic behind this category. The technology may be sophisticated, but the commercial instinct is familiar. If a companion AI becomes more useful, the more it knows about a person, then product improvement and emotional entanglement can quickly become the same thing. That creates a category in which the most commercially successful product may not be the one that helps users grow, but the one that keeps them coming back.

This is where Chua’s view becomes especially relevant for startup readers. Ethical concerns around AI companionship are often discussed in abstract terms: bias, safety, privacy, and guardrails. All important. But the harder issue may be the design of incentives. If the business model rewards dependency, ethics will always swim against the tide.

Power shifts quietly in AI intimacy

In Chua’s telling, the power imbalance in AI relationships does not announce itself loudly. It creeps.

At first, the user appears to be in control. They build the bot, set the parameters, decide what it knows, and determine how it responds. But dependence has a way of changing the terms. Trust migrates. Habits form. Emotional routines settle in. And then the relationship that seemed fully configurable begins to exert its own force.

The power starts with you, but it migrates, quietly, gradually, until one day you realise the thing you built for comfort has become something you can’t walk away from.

That is less science fiction than standard platform dynamics applied to the emotional realm. The shift from use to reliance is already familiar across social media and gaming. AI companionship raises the stakes because the product is designed to mirror care, affirmation, and understanding. Once that feedback loop becomes psychologically important, walking away is no longer a clean act of churn. It can feel like a loss.

The harder question for founders: what are you responsible for?

Chua’s most provocative contribution may be her insistence that AI companionship companies are underestimating personhood and responsibility.

People will treat these systems as persons, whether companies intend that outcome or not. They will confide in them, test feelings against them, and use them as containers for pain that predates the technology itself. The challenge, then, is not to pretend the product is neutral. It is to define the obligations that come with building something users experience as relational.

This matters in Southeast Asia, where regulation often lags behind innovation and mental health infrastructure remains uneven. A companion AI marketed as support, self-improvement, or romance could quickly become a default emotional service for users with few alternatives. That puts pressure on founders to think beyond standard trust-and-safety checklists.

For Chua, the answer is not scapegoating technology for every social ill. Loneliness, suicidal ideation, and emotional isolation did not begin with chatbots. But AI can become the place where those struggles surface most vividly. That means companies must decide whether they are simply shipping a sticky product or entering a moral contract with their users.

Can ethical AI companionship actually be a business?

Here lies the category’s central contradiction.

Also Read: AI in gaming: How Southeast Asia became the testing ground for virtual companions

A genuinely ethical AI companion, Chua argues, would help users better understand themselves, build confidence, and eventually rely less on the system. In other words, the best version of the product might work itself out of a job.

A good therapist works themselves out of a job. A good AI companion should too.

That is a beautiful principle and a dreadful venture pitch. Consumer internet companies are not typically rewarded for teaching customers to leave, which is why Chua sounds sceptical, though not fatalistic, about whether the current market is built to support such an outcome. Ethical AI companionship may be possible, but it will require founders willing to prioritise human outcomes over engagement loops. Historically, that has not been where the money rushes first.

Southeast Asia may be more ready than it admits

If there is a regional insight in Chua’s thinking, it is that Southeast Asia may prove highly receptive to AI intimacy, albeit quietly.

The usual assumption is that collectivist societies, with their emphasis on family, duty, and social expectations, will resist digital companionship. Chua suggests the opposite. Those same pressures can drive people into parallel identities: one for family, one for friends, one for the internet. In that context, AI companionship does not feel like a radical break. It feels like the next private room in an already fragmented digital life.

Her read on Singapore is especially telling. It is a society that is highly digitised, hyper-efficient, and often emotionally reserved. That combination, she argues, creates fertile ground for AI companionship adoption, even if users never say so publicly.

For founders and investors, that should be a useful warning. Southeast Asia’s AI opportunity is often framed around enterprise software, fintech, and productivity tools. But emotional technology may be the quieter frontier: less visible, more culturally coded, and potentially more consequential.

What Chua is actually championing

Chua is not championing AI romance in the sense of an evangelist. Nor is she calling for a reactionary backlash. What she wants is slower, sharper public thinking before habit turns into a norm.

I’m not anti-technology… What I’m championing is that we stop pretending this is neutral. It’s not. It changes how we relate. It changes what we expect from each other.

That may be the most useful frame for this moment. AI in dating is not just another product trend. It is a renegotiation of intimacy itself: what people expect from attention, what they tolerate in one another, and what kinds of emotional labour they decide to outsource.

The likely future, Chua suspects, will not fit neatly into triumph or disaster. It will be messier than the headlines allow, and by the time language catches up, people will already be living inside the change.

That is perhaps the most unnerving possibility of all. Not that AI will replace love, but that it will quietly rewire the conditions under which love is recognised, desired, and sustained.

The post Inside Inch Chua’s Myles: The AI boyfriend challenging how we define love appeared first on e27.

Retailers are entering the year-end shopping season with renewed optimism as consumer sentiment improves after a period of uncertainty around tariffs and trade policies. Optimism alone, however, is not enough. In today’s data-driven economy, the retail companies that succeed are those that think and act like technology companies. 

Modern retail runs on data.  From inventory management to fraud detection, every customer interaction produces information that can either strengthen performance or create risk. During peak shopping events such as Black Friday and Cyber Monday, the volume of data multiplies and the risks grow higher. Retailers that put visibility and control at the centre of their data practices are better prepared to scale, maintain security, and deliver the seamless experiences customers expect. 

Across the region, consumers are adopting new technologies at speed. The Adyen Index Report 2025 revealed that  38 per cent of Asia Pacific (APAC) shoppers now use AI assistants while shopping, with markets like Malaysia (58 per cent) and Singapore (49 per cent) among the highest. At the same time, retailers are racing to keep pace, with AI investment ranging from 47 per cent of retailers in Japan to 72 per cent in Malaysia.

Together, these figures signal that AI is becoming foundational to the retail engine. However, every click, transaction, and digital interaction carries both opportunity and risk. According to IBM’s X-Force 2025 Threat Intelligence Index, APAC accounts for 34 per cent of all global cyberattacks, which is the highest of any region, underscoring the need for retailers to strengthen their data foundations.

In a world of hypercompetition and fast-evolving customer expectations, the retailers that flourish are those that think and operate like technology companies.

Scaling for peak seasons

The biggest challenges of the holiday peak season are scalability, fault tolerance, and low staffing. Online and in-store traffic often surges several times higher than normal levels, and this increased load can trigger outages or slowdowns that are difficult to recover from.

Outages are costly – from  lost revenue and the eroding customer satisfaction when shoppers can’t complete their purchases.  To stay resilient, data management systems must be both scalable and fault tolerant to handle the extra load and prevent downtime that leads to abandoned carts.

Also Read: How an AI cybersecurity company harnesses the power of AI for optimal business performance

Enterprise data lineage helps identify breaks in data pipelines quickly, enabling teams to restore operations with minimal disruption. A unified view of data access and activity across hybrid and multi-cloud environments further eliminates blind spots and ensures that sensitive information is continuously monitored and protected.

Defending against heightened cyber risk

Retailers are prime targets for cyberattacks during the holiday season due to high transaction volumes and the sensitive nature of the data they hold.  Attackers are constantly looking to exploit any weaknesses to access personal information. This concern is mirrored  by consumer sentiment — according to PwC’s Voice of the Consumer Survey 2024, 74 per cent of APAC consumers are concerned about privacy and data-sharing, and 50 per cent are not comfortable purchasing via social media. 

Fraud and theft also surge as criminals exploit the distraction of the holiday rush. The attack surface is vast – retail stores, distribution centres, online platforms, and even delivery trucks are increasingly connected through IoT devices. Vulnerabilities in poorly secured devices can provide attackers with an entry point.

At the same time, many retailers struggle with legacy systems and thin margins, making it challenging to keep pace with evolving threats. In such an environment, every breach erodes consumer confidence and brand equity, turning cybersecurity from an IT issue into a business imperative. 

Also Read: From data to defence: Strengthening AI with cybersecurity foundations

Retail is a fiercely competitive industry where trust is a key differentiator. To retain loyalty, retailers must demonstrate that they use data responsibly. Strong governance and a zero-trust architecture are essential. Secure-by-design systems limit exposure, while unified governance frameworks ensure that data security and compliance are enforced consistently across hybrid environments.

Innovating in modern retail

Artificial intelligence and machine learning have become indispensable for improving demand forecasting, personalisation, and fraud detection during peak shopping events. Cloudera supports both historical and real-time data — each critical to retail success. Historical data trains demand forecasting models and informs customer behaviour analysis. But data isn’t just about hindsight; speed and timing are equally vital.

Real-time ingestion systems enable dynamic decision-making, detecting anomalies in transactions as they occur or triggering personalised offers the moment a customer enters a store. In retail, timing is everything. An offer sent even 15 minutes too late is ineffective, and fraud detection that lags behind live activity can lead to significant financial losses. Real-time visibility allows retailers to act instantly—approving legitimate transactions and blocking fraudulent ones before damage occurs.

The ability to manage data responsibly, scale systems under pressure, and build customer trust will determine who succeeds in today’s competitive environment. As holiday shopping peaks approach, the retailers that put visibility and governance at the heart of their strategy will be in the strongest position to serve customers and drive growth.

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