<h2>How Organizations Can Innovate with AI While Meeting Compliance, Security, and Trust Requirements</h2>AI is rapidly transforming industries worldwide. From automating clinical documentation in healthcare to accelerating risk assessment in financial services and improving quality control in manufacturing, AI is becoming a critical business capability.However, organizations operating in regulated industries face a unique challenge: how to harness the power of AI while maintaining strict security, compliance, privacy, and governance standards.For sectors such as healthcare, banking, insurance, life sciences, telecommunications, and government services, AI adoption cannot come at the expense of regulatory obligations. Security must be built into the foundation of every AI application, not added as an afterthought.This is why organizations are increasingly shifting their focus from simply deploying AI models to building secure, enterprise-grade AI solutions that are designed for compliance from day one.<h2>AI Introduces New Security Challenges</h2>Unlike traditional software, AI systems often process large volumes of sensitive data and generate dynamic outputs. This creates new risks, including data leakage, unauthorized access, model manipulation, and compliance violations.For example, healthcare AI applications may handle patient records, while financial AI systems analyze customer transactions and personal information. Any security failure can lead to regulatory penalties, reputational damage, and loss of customer trust.This makes security a fundamental requirement rather than an afterthought.<h2>Strong Data Governance Comes First</h2>Secure AI starts with strong data governance. Organizations need clear policies for how data is collected, stored, accessed, and used throughout the AI lifecycle.In regulated environments, maintaining control over sensitive information is critical. Effective governance helps ensure compliance, improves transparency, and reduces the risk of data misuse.Many AI initiatives succeed or fail based on the quality and governance of their data rather than the sophistication of the model itself.<h2>Embedding Security Throughout the AI Lifecycle</h2>Security should be integrated into every stage of AI development, from design and training to deployment and ongoing operations.Modern MLOps and LLMOps practices help organizations automate security checks, monitor model behavior, and maintain compliance without slowing innovation. By adopting a security-by-design approach, teams can identify risks early and build more resilient AI solutions.<h2>Transparency Builds Trust</h2>In regulated industries, organizations often need to explain how AI-driven decisions are made. Whether supporting clinical recommendations or fraud detection, AI systems must be transparent and auditable.Monitoring, logging, and governance mechanisms help organizations maintain visibility into model behavior while meeting regulatory expectations. Transparency not only supports compliance but also increases confidence among users, customers, and regulators.<h2>The Future of Enterprise AI</h2>As AI adoption grows, security and governance are becoming key differentiators. Organizations are moving beyond isolated AI experiments and investing in enterprise-grade platforms that combine data protection, access controls, model governance, and continuous monitoring.At the same time, responsible AI principles such as fairness, accountability, and explainability are becoming essential components of AI strategies.<h2>Conclusion</h2>Building AI applications for regulated industries requires far more than selecting the right model or technology stack. Success depends on creating secure, compliant, and governable systems that protect sensitive data, meet regulatory requirements, and maintain stakeholder trust.By adopting security-by-design principles, implementing robust governance frameworks, and integrating secure MLOps practices, organizations can confidently deploy AI solutions that drive business value without compromising compliance or security.As AI continues to reshape industries, the organizations that prioritize trust, security, and responsible innovation will be the ones that unlock its full potential.

Building Secure AI Applications for Regulated Industries

building-secure-ai-applications-for-regulated-industries

Pharma 5.0: How ALTEN accelerates digital transformation to cut time to market?The pharmaceutical industry faces a critical challenge: bringing life-saving treatments to market faster without compromising quality. Digital transformation powered by AI is reshaping how pharma companies operate, from clinical trials to quality control.&nbsp; &nbsp; For François Portier, Digital Transformation and Industrial Excellence Expert at ALTEN, the shift is clear: “Today, AI isn’t just an option; it’s the key to unlocking efficiency and innovation in pharma.”But how can AI bridge the gap between data overload and actionable insights and what role does ALTEN play in this transition?The pharma industry’s digital dilemmaPharma’s traditional processes reliant on manual documentation, siloed data, and human monitoring are no longer sustainable. 75% of pharma companies are small enterprises, many still using pen-and-paper systems. Meanwhile, larger firms struggle to integrate AI into existing workflows.Key pain points:Time-consuming compliance: Certification and deviation report often start from scratch, delaying critical decisions.Inefficient quality control: Manual inspections (e.g., visual checks for bottle defects) are slow and prone to error.Data underutilisation: Historically, 70% of pharma data went unused until AI made it actionable."Five years ago, we focused on automation, today, we’re deploying customised and flexible processes with AI solutions", notes François Portier.ALTEN’s approach: AI as a Business acceleratorALTEN collaborates with partners and customers to deploy AI where it matters most, reducing time to market while ensuring compliance and quality.Generative AI is reshaping how pharmaceutical engineers handle documentation. By auto-filling templates with historical data, it cuts report drafting time from two hours to just 20 minutes, while automating compliance checks and flagging regulatory gaps instantly.&nbsp;"AI doesn't replace expertise, it enhances it", notes François Portier: engineers stay focused on high-value decisions while AI handles the rest.On the production floor, computer vision detects defects in real time with greater accuracy than manual inspection. Predictive analytics then simulates clinical trial parameters to reduce failure risks before testing begins.&nbsp;“With only 14% of trials reaching success, AI helps prioritise the most promising paths," François Portier emphasises.ALTEN tailors its approach to each organisation's level of digital maturity. Smaller firms begin with data digitisation using no-code tools, while larger enterprises deploy agentic AI to unify operational and IT data. There is no single model: the right solution depends on where a company stands today and where it needs to go.The human-AI partnership: skills for the futureAI’s rise doesn’t diminish human roles, it redefines them. ALTEN emphasises upskilling teams to:Combine hard skills (e.g., IoT/robotics programming) with industry knowledge.Develop soft skills like problem-solving and project ownership. "The future engineer is strategic, not just technical", François Portier asserts."For pharma, the message is clear: AI isn’t a threat, it’s the tool to deliver treatments faster, more safely, and more effectively, with greater personalisation for patients."&nbsp;About our expertFrançois PortierDigital Transformation &amp; Industrial Excellence Expert at ALTEN FranceFrançois Portier drives AI-powered innovation in pharma, helping companies reduce time to market while ensuring quality and compliance. With expertise in generative AI, computer vision, and predictive analytics, he tailors solutions to each organisation’s digital maturity, from low-code tools for small firms to agentic AI for large enterprises.A firm believer in human-AI collaboration, François focuses on upskilling teams to blend technical skills with strategic thinking.Connect with François Portier: <a href="https://fr.linkedin.com/in/francoisportier/fr">LinkedIn Profile</a>&nbsp;

The pharmaceutical industry faces a critical challenge: bringing life-saving treatments to market faster without compromising quality. Digital transformation powered by AI is reshaping how pharma companies operate, from clinical trials to quality control. 
 
For François Portier, Digital Transformation and Industrial Excellence Expert at ALTEN, the shift is clear: "Today, AI isn’t just an option; it’s the key to unlocking efficiency and innovation in pharma."

Pharma 5.0: How digital transformation cuts time to market

The pharmaceutical industry faces a critical challenge: bringing life-saving treatments to market faster without compromising quality..

How ALTEN accelerates digital transformation to cut time to market?

how-alten-accelerates-digital-transformation-to-cut-time-to-market

In 2026, AI governance is no longer optional, it has become a strategic business priority. According to recent industry research, 54% of IT leaders now consider AI governance as a critical enterprise risk, up significantly from 29% just two years ago (Kiteworks, 2026). In Vietnam, the introduction of the Law on Artificial Intelligence, effective from March 1, 2026, marks a major milestone in the country's digital transformation journey. The new regulatory framework requires organizations to move beyond AI experimentation and adopt a more structured approach aligned with the principles of Sovereign AI, enabling both regulatory compliance and new sources of competitive advantage.1. Overview of Sovereign AI and AI GovernanceAI Governance refers to the framework of policies, processes, standards, and controls designed to ensure that AI systems are developed and deployed responsibly, transparently, ethically, and in alignment with business objectives. Unlike traditional IT governance, which primarily focuses on managing static infrastructure and systems, AI governance addresses the dynamic nature of AI models that continuously learn, evolve, and influence business decisions over time.Sovereign AI describes an organization's or nation's ability to maintain control over its AI ecosystem, including data, computing infrastructure, and AI models, rather than relying entirely on foreign platforms and services. The objective is to strengthen digital sovereignty, ensure that critical data remains under local control, and enable strategic decision-making within national or organizational boundaries.2. The global AI regulatory landscape in 2026The year 2026 represents a significant turning point in the global enforcement of AI regulations.The EU AI actThe European Union's AI Act, the world's first comprehensive AI regulatory framework, will begin enforcing key obligations for General-Purpose AI (GPAI) models from August 2nd, 2026. The regulation introduces stringent requirements around transparency, accountability, and risk management, creating what many experts describe as the "Brussels effect," where global organizations voluntarily align with EU standards regardless of location.The rise of sovereignty initiativesCountries such as the United States and South Korea have introduced major executive orders and legislative initiatives throughout late 2025 and early 2026 to strengthen control over national AI capabilities. These efforts focus on protecting domestic data through Sovereign Cloud architectures while enhancing national competitiveness in AI development and deployment.Governance as a strategic obligationAI governance is no longer viewed solely as a compliance exercise. Increasingly, organizations recognize it as a core operational strategy that helps build trust with customers, partners, investors, and regulators.3. AI regulation in VietnamThe Law on Artificial Intelligence (Law No. 134/2025/QH15), effective from March 1, 2026, establishes a risk-based regulatory framework that aligns with international standards while addressing the country's unique regulatory and economic context.Three-tier risk classification framework<ul><li>High-risk AI systems: AI systems that may cause significant harm to human life, health, legal rights, public interests, national interests, or national security.</li><li>Medium-risk AI systems: AI systems capable of influencing, misleading, or manipulating users who may not realize they are interacting with an AI system or consuming AI-generated content.</li><li>Low-risk AI systems: AI systems that do not fall under the high-risk or medium-risk categories.</li></ul>Post-market supervision approachOrganizations are not required to obtain prior approval for low-risk AI applications. However, they must conduct internal safety assessments, maintain relevant documentation, and be prepared to demonstrate compliance when requested by regulators.Compliance timelineFor AI systems already in operation before the law takes effect:<ul><li>Healthcare, education, and financial services AI systems must achieve compliance within 18 months from the effective date.</li><li>AI systems in all other sectors must achieve compliance within 12 months from the effective date.</li></ul>4. Opportunities and challenges for businessesThe introduction of the Law on Artificial Intelligence in Vietnam is expected to reshape the country's AI ecosystem by creating both new growth opportunities and additional compliance responsibilities for businesses.From an opportunity perspective, the new regulatory framework provides stronger institutional support for AI innovation and commercialization. By formally recognizing AI models, algorithms, and data assets as legitimate business assets, the law creates new pathways for investment, fundraising, strategic partnerships, and intellectual property capitalization. At the same time, organizations engaged in AI development may benefit from enhanced government incentives, including tax preferences, technology funding programs, and access to national digital infrastructure.The government's broader commitment to AI development is also reflected in initiatives such as the National AI Development Fund, open data platforms, and shared computing resources. These initiatives have the potential to lower entry barriers for startups and SMEs while accelerating innovation across industries. In addition, regulatory sandbox mechanisms will allow organizations to experiment with emerging AI applications within controlled environments, reducing regulatory uncertainty and encouraging responsible innovation.However, these opportunities come with new challenges. As AI governance requirements become more strict, organizations will need to invest in risk management frameworks, monitoring systems, documentation processes, and governance capabilities to ensure compliance. For many businesses, the operational and financial costs associated with these requirements may be significant.Furthermore, Vietnam continues to face structural challenges related to AI readiness, including limited access to high-performance computing resources, fragmented data ecosystems, and a shortage of experienced AI and data professionals. Addressing these gaps will be critical for organizations seeking to fully capitalize on the opportunities created by the country's evolving AI landscape.5. A roadmap for building AI governanceTo prepare for the 2026 regulatory landscape, organizations should consider the following phased approach.Phase 1: Establish the foundationCreate a dedicated AI Governance function involving executive leadership, legal teams, risk management, and IT departments. Conduct a comprehensive inventory of all AI applications and classify them according to risk levels.Phase 2: Build governance processesDevelop acceptable-use policies, define risk thresholds, establish escalation procedures, and implement emergency controls to address AI-related incidents.Phase 3: Monitor and optimizeDeploy tools to monitor model performance, bias, drift, and compliance. Integrate AI governance into the broader Enterprise Risk Management (ERM) framework to ensure ongoing oversight and continuous improvement.6. The evolving role of technology companiesAs AI regulations become increasingly embedded in business operations, the role of technology companies is expanding beyond the development and deployment of AI solutions. In the past, competitive advantage was often defined by an organization's ability to integrate AI into products, automate processes, or enhance customer experiences. Today, success increasingly depends on the ability to deliver AI systems that are not only effective but also transparent, secure, compliant, and accountable throughout their lifecycle.This shift places new responsibilities on technology providers. Organizations that develop, integrate, or operate AI systems for clients must now consider governance requirements from the earliest stages of design through deployment, monitoring, and ongoing operation. As a result, AI governance is becoming a foundational component of enterprise architecture rather than a compliance layer applied after implementation.In this environment, capabilities such as cloud infrastructure, data engineering, cybersecurity, and enterprise governance become critical enablers of responsible AI adoption. The ability to connect these capabilities into a cohesive operating model may ultimately become as important as AI model development itself.From VMO's perspective, this transformation reflects a broader trend already emerging across global markets. As organizations accelerate AI adoption, projects increasingly require an integrated approach that combines AI, data, cloud, security, and operational governance. This aligns closely with VMO's multidisciplinary capabilities and experience delivering complex digital transformation initiatives for international clients.Looking ahead, the rise of Sovereign AI is expected to generate growing demand for AI infrastructure, data platforms, governance frameworks, integration services, and consulting expertise. Beyond regulatory compliance, this creates an opportunity for technology companies such as VMO to support organizations in building scalable, trustworthy, and business-driven AI ecosystems while strengthening Vietnam's position within the global AI value chain.ConclusionSovereign AI and AI Governance are more than regulatory requirements, they are becoming strategic differentiators in the next phase of digital transformation. Organizations that proactively align with the Law on Artificial Intelligence while leveraging the incentives and opportunities created by the new regulatory framework will be better positioned to build trust, improve operational resilience, and create sustainable competitive advantages.As AI adoption continues to accelerate, investing in governance today may be one of the most important steps organizations can take to ensure long-term success in the digital economy of tomorrow.

In 2026, AI governance is no longer optional, it has become a strategic business priority. According to recent industry research, 54% of IT leaders now consider AI governance as a critical enterprise risk, up significantly from 29% just two years ago (Kiteworks, 2026). In Vietnam, the introduction of the Law on Artificial Intelligence, effective from March 1, 2026, marks a major milestone in the country's digital transformation journey. The new regulatory framework requires organizations to move beyond AI experimentation and adopt a more structured approach aligned with the principles of Sovereign AI, enabling both regulatory compliance and new sources of competitive advantage.

AI Governance: Preparing Vietnamese Businesses

In 2026, AI governance is no longer optional, it has become a strategic business priority. According to recent industry research..

ai-governance-preparing-vietnamese-businesses

Hanoi, Vietnam - June 2026VMO, an ALTEN Group company, today announced a leadership transition that marks an important milestone in its next phase of growth and development. The transition comes as VMO continues to expand its global footprint, strengthen international collaboration, and accelerate its long-term growth strategy.Effective June 15, 2026, Ms. Nguyen Khanh Diep (Jamie Nguyen) will assume the role of Chief Executive Officer (CEO) of VMO. Following the transition, Mr. Hoang Tuan Hai will take on the role of Chairman of the Founding Board and Member of the Board of Directors, continuing to contribute to the company’s long-term strategic direction and governance.Over nearly 14 years of growth, VMO has continuously evolved to meet changing market demands, expand its global presence, and strengthen its position as a trusted technology partner. As part of the ALTEN Group, VMO is entering a new chapter focused on scaling its capabilities, strengthening international collaboration, and creating greater value for customers worldwide. This leadership transition reflects the company’s commitment to building a sustainable organization while preparing for its next stage of development.Having played a key role in VMO’s growth journey, Jamie Nguyen brings extensive leadership experience, deep operational expertise, and a strong understanding of the company’s culture, people, and customers. As CEO, she will lead VMO’s business operations and strategic execution, driving the organization forward in an increasingly dynamic and competitive market.<figure class="image"><img src="https://vmo-ho-web-global.s3.ap-southeast-1.amazonaws.com/vmogroup-global/uploads/1781515570948-DSC00557.jpg"></figure>Biography of Jamie NguyenJamie Nguyen joined VMO in April 2019 and has held a series of leadership positions across the organization, including Head of Sales, Sales Director, CEO of VMO Global, and Vice CEO of VMO Holdings, prior to her appointment as Chief Executive Officer of VMO.She graduated from the Specialized Informatics Program at Hanoi National University of Education High School for Gifted Students, earned a Bachelor's degree from the Banking Academy of Vietnam, and later obtained an MBA from Ritsumeikan Asia Pacific University, Japan.Throughout her tenure at VMO, Jamie has contributed to the company's business growth, international expansion, and organizational development, helping strengthen VMO's position as a trusted technology partner in global markets.Entering this new chapter, Mr. Hoang Tuan Hai will remain actively engaged in supporting Jamie Nguyen and the leadership team on strategic initiatives that will drive VMO’s long-term growth.Mr. Hoang Tuan Hai stated:&nbsp;“Jamie Nguyen is the right leader to guide VMO through this next chapter. Throughout her journey with the company, she has played a critical role in many important growth milestones, demonstrating strong leadership, business acumen, and a deep understanding of our people, customers, and culture. Her proven track record, commitment to excellence, and ability to build and develop high-performing teams have earned the trust and respect of colleagues across the organization. I am confident that under her leadership, VMO will continue to grow stronger, adapt successfully to the opportunities and challenges ahead, and reach new levels of achievement.”Ms. Jamie Nguyen stated:&nbsp;“I deeply appreciate what generations of VMOers have built over the years, and I am genuinely excited about the opportunities that lie ahead. I look forward to working together with all of you and ALTEN Group to continue building a stronger VMO, creating greater value for our customers, developing new capabilities, and making VMO a place that we are always proud to be part of.”This leadership transition reflects VMO’s ongoing commitment to strengthening its leadership foundation, fostering innovation, and creating sustainable value for customers, employees, and partners. Backed by the global expertise and scale of the ALTEN Group, VMO remains focused on accelerating growth, delivering excellence to its customers, and expanding its impact across international markets.

VMO, an ALTEN Group company, today announced a leadership transition that marks an important milestone in its next phase of growth and development. The transition comes as VMO continues to expand its global footprint, strengthen international collaboration, and accelerate its long-term growth strategy.

VMO ANNOUNCES CEO TRANSITION AND LEADERSHIP EVOLUTION

VMO, an ALTEN Group company, today announced a leadership transition that marks an important milestone in its next phase of growth and development.

vmo-announces-ceo-transition-and-leadership-evolution

FROM READING TEXT TO UNDERSTANDING THE WORLD: THE RISE OF MULTIMODAL AIIn the era of digital transformation, humans consume information through multiple channels concurrently speech, text, images, videos, and sensor data. As a result, traditional AI systems designed to process only a single type of data are gradually losing their competitive edge. By 2026, Multimodal AI is no longer an experimental concept, it has become a foundational technology for modern enterprises, enabling machines to perceive and understand the world in a far more comprehensive way. The Multimodal AI market is experiencing rapid growth and is projected to reach a value of USD 10.89 billion by 2030 (Emerline, 2026).1. Definition of Multimodal AIMultimodal AI refers to artificial intelligence systems designed to process and integrate multiple forms of data simultaneously, including text, images, audio, video, and sensor inputs. Its operating mechanism mirrors how humans perceive the world and consists of three key stages:<ul><li>Encoding: Specialized models analyze each type of data independently (e.g., computer vision for images, natural language processing for text…).</li><li>Fusion: This is the most critical stage, where information from different modalities is combined into a shared representation, allowing AI to understand relationships across data sources.</li><li>Generation/Inference:&nbsp;The AI generates outputs in various formats based on the integrated information (e.g., providing a text-based response to an uploaded image).</li></ul>2. Key benefits of Multimodal AIThe shift from single-modal AI to multimodal AI offers significant advantages, bringing machine intelligence closer to human-level perception.Comprehensive context understandingBy simultaneously processing text, images, audio, and sensor data, AI gains a richer and more holistic understanding of situations and tasks. This enables systems to interpret subtle non-verbal signals and contextual information that would otherwise be missed.Higher accuracy and resilienceMultimodal systems can achieve over 40% higher accuracy in complex tasks compared to traditional AI approaches (Stanford's AI Index Report, 2024). They are also more resilient to incomplete or noisy data. For example, if an image is blurry, the AI can rely on accompanying text or audio to maintain accurate performance. Faster decision-makingOrganizations adopting multimodal AI solutions report up to 40% faster decision-making cycles (Vyzer Solutions, 2026). By synthesizing insights from multiple data sources, businesses can detect market changes earlier, respond more effectively, and achieve stronger returns on AI investments.3. Leading Multimodal AI models in 2026By 2026, the market will no longer be dominated by a single model. Instead, it is shaped by specialized systems designed for different use cases:&nbsp;<ul><li>GPT-4o &amp; GPT-5 (OpenAI):&nbsp;GPT-4o ("omni") processes text, images, and audio in real time with extremely low latency, enabling natural voice-based interactions. GPT-5 introduces a layered architecture designed to balance speed and expert-level reasoning capabilities.</li><li>Gemini 3.5 Pro &amp; Flash (Google):&nbsp;Gemini stands out for its native multimodal architecture and massive context window of up to one million tokens, allowing users to analyze lengthy videos or large document collections within a single query.</li><li>Claude 4.5 Sonnet (Anthropic):&nbsp;Built with a strong focus on safety, Claude is optimized for autonomous software engineering tasks and is capable of resolving more than 70% of real-world technical issues.</li><li>Llama 4 Scout (Meta):&nbsp;An open-source model featuring a context window of up to 10 million tokens, enabling organizations to deploy and operate AI on private infrastructure while maintaining full control over their data.</li><li>Moonshot Kimi K2:&nbsp;A Mixture-of-Experts (MoE) model with one trillion parameters, designed for customer service automation and highly natural conversational experiences.</li></ul>3. Global use cases of Multimodal AICustomer Support &amp; SalesTechnical support teams can analyze screenshots alongside customer descriptions to diagnose issues much faster and more accurately. Sarah SDR by Ruh AI is an example of an autonomous sales agent capable of analyzing a prospect's website (images), social media presence (text), and communication history to create personalized outreach strategies without step-by-step human intervention.HealthcareMultimodal AI systems have been shown to improve diagnostic accuracy by 6% to 33% when assisting radiologists and medical professionals compared to single-modal approaches (Nature Medicine, 2025). IBM Watson Health combines Electronic Health Records, medical imaging data such as X-rays and MRIs, and clinical notes to predict treatment outcomes with enhanced accuracy.Retail &amp; E-CommerceConsumers can simply take a photo of a product they like and instantly discover similar items online. The system combines visual information with browsing behavior and personal preferences to generate highly relevant recommendations. Retailers are leveraging web activity, purchase history, and product images to improve recommendation quality, boost conversion rates, and strengthen customer loyalty.Transportation &amp; LogisticsMultimodal AI is transforming supply chain operations by simultaneously analyzing real-time GPS data, warehouse sensors, weather conditions, and demand forecasts to optimize routing decisions and minimize delays. Toyota provides a compelling example through its AI-powered digital vehicle manuals, which combine text, images, and real-world vehicle context to deliver more intuitive user support.Financial Services &amp; BankingBanks have reduced document processing times from contracts and signatures to official forms, from hours to just minutes, while improving accuracy from 92% to 98% (Deloitte's Banking and Capital Markets Report, 2025). By analyzing transaction history, customer behavior, and device signals simultaneously, AI can identify anomalies and fraudulent activities that traditional systems often overlook. JPMorgan's DocLLM combines textual information, metadata, and contextual signals from complex financial documents to enhance risk assessment and decision-making accuracy.4. Opportunities for VietnamVietnam is rapidly emerging as one of Southeast Asia's most promising AI development hubs, supported by several strategic advantages.Talent and Cost competitivenessVietnam's AI engineering workforce offers competitive expertise at approximately 87% lower cost than Silicon Valley. The country aims to develop a talent pool of 15,000 AI specialists by 2035. (VietNam Net, 2026)National AI strategyThe Vietnamese government has approved a National AI Strategy through 2030, with the goal of positioning Vietnam among the top four AI nations in ASEAN and the top 50 globally.Real-world Multimodal AI applications in Vietnam<ul><li>TPBank has introduced VoicePay, enabling customers to transfer money through voice commands.</li><li>VinBrain provides AI-powered diagnostic solutions that support medical professionals.</li><li>VinFast is investing in AI-enabled robotics to optimize manufacturing and assembly operations.</li></ul>Growing Interest from Global Technology LeadersGlobal technology companies such as NVIDIA and Google are actively partnering with Vietnamese firms to develop AI talent and accelerate AI adoption across industries.5. VMO's competitive advantages in the Multimodal AI era&nbsp;As Multimodal AI moves from experimentation to large-scale enterprise adoption, competitive advantage no longer belongs solely to organizations with access to advanced AI models. Instead, it belongs to those that can integrate AI into complex business environments, connect fragmented data sources, and transform technological capabilities into measurable business outcomes.With strong expertise across AI, Data, Cloud, IoT, and enterprise software development, VMO is well-positioned to capitalize on the rise of Multimodal AI. These capabilities are particularly valuable in multimodal environments, where success depends not only on model performance but also on scalable infrastructure, data integration, domain knowledge, and effective execution.Over the years, VMO has delivered AI-driven and digital transformation solutions for organizations across global markets. Supported by a global delivery model, a talented engineering workforce, and extensive experience serving international clients, VMO is well positioned to help enterprises accelerate Multimodal AI adoption and unlock fully business value from this transformation.6. ConclusionMultimodal AI is more than just another technological advancement, it represents a fundamental shift in how organizations process and extract value from data. By combining vision, language, audio, and sensor intelligence, AI is evolving into the eyes, ears, and brain of modern enterprises. By 2026, AI has moved well beyond the experimentation phase and is becoming deeply embedded in mission-critical business operations. Organizations that invest early in robust data infrastructure and develop teams capable of leveraging multimodal AI will be best positioned to maintain a competitive advantage throughout the next decade.

In the era of digital transformation, humans consume information through multiple channels concurrently speech, text, images, videos, and sensor data. As a result, traditional AI systems designed to process only a single type of data are gradually losing their competitive edge. By 2026, Multimodal AI is no longer an experimental concept, it has become a foundational technology for modern enterprises, enabling machines to perceive and understand the world in a far more comprehensive way. The Multimodal AI market is experiencing rapid growth and is projected to reach a value of USD 10.89 billion by 2030 (Emerline, 2026).

THE RISE OF MULTIMODAL AI: UNDERSTANDING THE WORLD

the-rise-of-multimodal-ai-understanding-the-world

<h2>Inside a Modern Healthcare Data Platform: Architecture Trends from the US &amp; EU Markets</h2>Healthcare organizations are generating more data than ever before from electronic health records and medical imaging systems to remote patient monitoring devices, claims platforms, and AI-powered clinical applications. Yet data volume is no longer the biggest challenge. The real challenge is turning fragmented healthcare data into a trusted, interoperable, and analytics-ready foundation.Across both the US and European markets, healthcare providers, payers, and digital health companies are investing heavily in modern healthcare data platforms that enable interoperability, AI adoption, regulatory compliance, and real-time decision-making. While regulations differ across regions, architectural patterns are increasingly converging toward cloud-native, API-first, and governance-driven approaches.<h2>Why Traditional Healthcare Data Architectures Are No Longer Enough</h2>Historically, healthcare data lived in isolated systems:<ul><li>Electronic Medical Records (EMRs)</li><li>Laboratory Information Systems (LIS)</li><li>Radiology and PACS platforms</li><li>Claims and billing systems</li><li>Patient engagement applications</li></ul>Today, healthcare organizations need platforms capable of:<ul><li>Integrating structured and unstructured clinical data</li><li>Supporting real-time interoperability</li><li>Enabling AI and machine learning workloads</li><li>Meeting stringent privacy and compliance requirements</li><li>Delivering analytics across operational, clinical, and financial domains</li></ul><h2>The Emerging Reference Architecture</h2><h3>1. Multi-Source Data Ingestion Layer</h3>The platform begins by collecting data from multiple healthcare systems and external sources:<ul><li>EHR and EMR platforms</li><li>Claims and payer systems</li><li>Laboratory systems</li><li>Medical devices and IoT sensors</li><li>Patient portals and mobile applications</li><li>Wearables and remote monitoring solutions</li></ul>Modern architectures increasingly use event-driven and API-based integrations instead of traditional batch ETL processes.FHIR APIs, HL7 messaging, and streaming technologies enable near real-time data movement while reducing integration complexity.<h3>2. Interoperability and Standardization Layer</h3>In the US, regulatory initiatives such as TEFCA and information-blocking requirements have accelerated FHIR adoption. Many healthcare organizations now expose patient data through standardized APIs.In Europe, the European Health Data Space (EHDS) is creating a common framework for health data exchange across member states, encouraging standardized data formats and cross-border interoperability.As a result, modern platforms typically include:<ul><li>HL7 v2 integration engines</li><li>FHIR repositories</li><li>Terminology services</li><li>Master Patient Index (MPI)</li><li>Data normalization pipelines</li></ul>FHIR is increasingly becoming the common language for healthcare APIs and application ecosystems.<h3>3. Lakehouse-Based Data Foundation</h3>One of the most significant architecture shifts is the move from traditional data warehouses toward healthcare lakehouse platforms.Instead of maintaining separate systems for analytics, machine learning, and reporting, organizations are consolidating workloads onto a unified lakehouse architecture. Benefits include:<ul><li>Lower data duplication</li><li>Unified governance</li><li>Support for structured and unstructured data</li><li>Real-time and batch processing</li><li>Better scalability for AI initiatives</li></ul>Healthcare organizations increasingly favor lakehouse approaches because they can support clinical analytics, operational reporting, and machine learning from a single governed platform.<h3>4. Governance, Privacy, and Compliance Layer</h3>Unlike many industries, healthcare architecture decisions are heavily influenced by regulatory requirements.US organizations must address:<ul><li>HIPAA compliance</li><li>Auditability</li><li>Data retention policies</li><li>Security controls</li></ul>European organizations face additional requirements, including:<ul><li>GDPR</li><li>EHDS governance frameworks</li><li>Cross-border data sharing requirements</li><li>Data sovereignty considerations</li></ul>As a result, governance is increasingly embedded directly into platform architecture rather than added later.<h3>5. AI and Advanced Analytics Layer</h3>Organizations are moving beyond retrospective reporting toward<ul><li>Clinical decision support</li><li>Risk prediction</li><li>Population health analytics</li><li>Revenue cycle optimization</li><li>Personalized patient engagement</li><li>Generative AI applications</li></ul>To support these use cases, modern platforms increasingly provide:<ul><li>Feature stores</li><li>Vector databases</li><li>MLOps pipelines</li><li>Real-time data processing</li><li>AI governance frameworks</li></ul>A key trend is ensuring AI workloads operate directly on governed healthcare data rather than creating separate AI data silos.<h2>Key Differences Between US and EU Architectures</h2>While architecture patterns are converging, regional priorities remain distinct.<figure class="table" style="float:left;width:99.4%;"><table><colgroup><col style="width:21%;"><col style="width:35.31%;"><col style="width:43.69%;"></colgroup><tbody><tr><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">Area</td><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">US Market</td><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">EU Market</td></tr><tr><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">Primary Driver</td><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">Interoperability and value-based care</td><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">Cross-border health data exchange</td></tr><tr><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">Standards Focus</td><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">FHIR, TEFCA, SMART on FHIR</td><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">FHIR, EHDS, EHRxF</td></tr><tr><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">Cloud Adoption</td><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">Aggressive cloud migration</td><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">Stronger emphasis on data sovereignty</td></tr><tr><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">Governance</td><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">HIPAA-driven</td><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">GDPR and EHDS-driven</td></tr><tr><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">AI Adoption</td><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">Rapid deployment of clinical AI</td><td style="border:0.75pt solid #000000;padding:5pt;vertical-align:top;">More governance-focused AI implementation</td></tr></tbody></table></figure> &nbsp;<h2>What Healthcare Organizations Are Prioritizing in 2026</h2>Based on ongoing investments across the US and Europe, five architecture trends stand out:<h3>1. FHIR as the Default Integration Layer</h3>FHIR is increasingly becoming the standard mechanism for healthcare data exchange and application integration.<h3>2. Lakehouse Architectures Replacing Traditional Warehouses</h3>Organizations want a single platform that supports analytics, AI, and operational reporting.<h3>3. Real-Time Data Processing</h3>Healthcare leaders are moving away from overnight batch processing toward event-driven architectures and streaming pipelines.<h3>4. Built-In Governance</h3>Compliance, security, and privacy are now architectural requirements rather than afterthoughts.<h3>5. AI-Ready Data Foundations</h3>Organizations are designing data platforms specifically to support future AI initiatives rather than retrofitting AI capabilities later.<h2>Final Thoughts</h2>The modern healthcare data platform is no longer just a repository for clinical information. It has become the digital foundation that enables interoperability, regulatory compliance, analytics, and AI innovation.Across both the US and European markets, leading healthcare organizations are converging on architectures built around FHIR interoperability, lakehouse data foundations, embedded governance, and AI-ready infrastructure. While regulations and implementation approaches may differ, the destination is increasingly the same: a unified, trusted, and intelligent healthcare data ecosystem.For healthcare organizations planning their next-generation digital strategy, the question is no longer whether to modernize their data platform but how quickly they can build the foundation needed for the future of connected and AI-driven healthcare.

Healthcare organizations are generating more data than ever before from electronic health records and medical imaging systems to remote patient monitoring devices, claims platforms, and AI-powered clinical applications. Yet data volume is no longer the biggest challenge. The real challenge is turning fragmented healthcare data into a trusted, interoperable, and analytics-ready foundation.

Modern Healthcare Data Platforms: US & EU Architecture Trends

Inside a Modern Healthcare Data Platform: Architecture Trends from the US & EU Markets

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About the RoleWe are looking for a Senior DevOps Engineer with strong expertise in AWS, Kubernetes, and CI/CD to build and operate scalable cloud-native platforms. You will work closely with development teams to automate infrastructure, optimize deployment processes, and ensure system reliability, security, and performance.Responsibilities<ul><li>Design and manage scalable, secure, and highly available AWS infrastructure.</li><li>Build and maintain Kubernetes environments (EKS or self-managed).</li><li>Develop and optimize CI/CD pipelines for automated deployments.</li><li>Implement Infrastructure as Code using Terraform or CloudFormation.</li><li>Collaborate with engineering teams on cloud-native and microservices architectures.</li><li>Monitor system performance and ensure reliability through logging, metrics, and alerting.</li><li>Apply security best practices across infrastructure and deployment pipelines.</li><li>Participate in Agile development processes and technical discussions.</li><li>Create and maintain technical documentation.</li></ul>

DevOps Engineer (Tokyo, Japan)

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<ul><li style="text-align:justify;">Develop and maintain backend systems and APIs using Python Django.</li><li style="text-align:justify;">Build and support frontend applications using Angular.</li><li style="text-align:justify;">Design, develop, and optimize scalable, secure, and high-performance applications.</li><li style="text-align:justify;">Work with PostgreSQL for database design, integration, and optimization.</li><li style="text-align:justify;">Implement authentication and authorization solutions using OAuth2 and OIDC.</li><li style="text-align:justify;">Write clean, maintainable code and ensure software quality through testing and code reviews.</li><li style="text-align:justify;">Participate in system design discussions and contribute to technical solutions.</li><li style="text-align:justify;">Collaborate with cross-functional teams in an Agile environment.</li><li style="text-align:justify;">Support troubleshooting, performance improvements, and mentoring team members when needed.</li></ul>

Fullstack Developer (Python Django, Angular - English Fluent)

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Summary:&nbsp; The Data Scientist role focuses on building propensity models and sizing market opportunities for VMO project with banking clients. This hybrid position requires a blend of data science and engineering skills to create production-grade pipelines. The role is pivotal in driving sales strategy through data-driven outputs. Candidates should have a strong background in Python and SQL, along with proven experience in propensity modeling.<ol><li>Propensity Modeling &amp; Ranking: &nbsp; &nbsp; Predict CTR and engagement probability &nbsp; &nbsp; Use models like XGBoost / LightGBM &nbsp; &nbsp; Optimize ranking using NDCG, MRR, Precision@K</li><li>Feature Engineering: &nbsp; &nbsp; Build features from user behavior, interaction signals, and context &nbsp; &nbsp; Handle sparse and noisy data</li><li>Cold Start Handling: &nbsp; &nbsp; Design strategies for new users and new offers &nbsp; &nbsp; Implement hybrid and fallback approaches</li><li>Bias Handling: &nbsp; &nbsp; Mitigate popularity and exposure bias &nbsp; &nbsp; Implement diversity and re-ranking strategies</li><li>Model Evaluation: &nbsp; &nbsp; Define and track CTR, conversion, NDCG, AUC &nbsp; &nbsp; Continuously improve engagement metrics</li><li>Scalability &amp; Deployment: &nbsp; &nbsp; Build systems for millions of users &nbsp; &nbsp; Enable real-time or near real-time inference</li></ol>

AI Engineer ( Strong Python) - ENG GOOD

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Job Summary We are looking for 2 Senior Full-Stack Developers to join a cloud-native product team building enterprise-grade applications on Microsoft Azure. The role involves designing and implementing frontend interfaces, backend APIs, and microservices that integrate deeply with Azure cloud services. You will work across the full delivery lifecycle, from architecture decisions to CI/CD pipeline setup and production deployment.Key Responsibilities • &nbsp; &nbsp;Design and build responsive frontend applications using React or Angular, TypeScript, and chart libraries • &nbsp; &nbsp;Develop and maintain backend APIs and microservices using C# and .NET, following RESTful design principles • &nbsp; &nbsp;Implement background services, messaging queue patterns, and the Saga Pattern for distributed transaction management • &nbsp; &nbsp;Integrate applications with Azure services including Azure Front Door, API Management, Application Gateway, Azure Service Bus, Azure Redis, Azure Key Vault, and managed databases • &nbsp; &nbsp;Write and maintain CI/CD pipelines on Azure Pipelines, including build, test, scan, and deploy stages following DevSecOps practices • &nbsp; &nbsp;Apply security practices including SAST, dependency scanning, image scanning, secret management, and pipeline security gates • &nbsp; &nbsp;Containerize services using Docker with multi-stage builds; deploy and troubleshoot workloads on Azure Kubernetes Service (AKS) • &nbsp; &nbsp;Participate in Agile/Scrum ceremonies; understand and apply the 12 Cloud Native Factors in application design • &nbsp; &nbsp;Follow governance and security policies for Azure Resource Groups, service configurations, and user access control

Fullstack (.Net+ReactJS/Angular) DU33 - ENG GOOD

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POSITION OVERVIEWAs a Senior Engineer, you will lead the design and development of high-performance, multi-core system software for LTE/5G baseband systems. You will play a key role in architecture decisions, performance optimization, and technical mentorship.

Baseband (PHY / UPC) Software Design Engineer (Senior/Experienced) - Telecom Industry

baseband-phy-upc-software-design-engineer-senior-experienced-telecom-industry

<ul><li>GenAI developers to design, prototype, and deploy AI-driven solutions that optimize internal business processes.&nbsp;</li><li>The team will collaborate on a shared project under the guidance of a closely involved Technical Leader and work closely with the Product Owner on daily execution and prioritization.</li><li>Sample project scopes includes: - HR Agent – Build an AI assistant to respond to HR-related inquiries and support internal staff.&nbsp; - Power Automate Integration – Develop automated reporting workflows and streamline operational processes.&nbsp; - Legal Agent – Create an AI system capable of reviewing customer contracts and performing structured analysis for the legal team. &nbsp;</li></ul>

AI Engineer (ENG) - AIS

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<ul style="list-style-type:disc;"><li>Designing and maintaining scalable integration pipelines using Azure Logic Apps, Azure Storage, API Management, Microsoft Fabric, and related Azure technologies</li><li>Implementing API-based, event-driven, and scheduled integration workflows</li><li>Ensuring integration solutions are secure, scalable, highly available, and compliant with standards such as GDPR</li><li>Collaborating with technical teams and business stakeholders to support data accessibility and integrity</li><li>Monitoring, troubleshooting, and continuously improving integration reliability and performance</li><li>Supporting CI/CD and infrastructure deployment using Azure DevOps and IaC tools such as ARM, Bicep, or Terraform Providing technical leadership and mentoring junior engineers</li><li>Documenting workflows and processes for both technical and business audiences &nbsp;</li></ul>

Data Engineer (ENG) - AIS

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<ul><li>Xây dựng, cải tiến và ban hành quy trình làm việc trong các phòng ban thuộc mảng Vận hành, Kinh doanh và 1 phần của mảng Sản xuất (đã có QA &amp; PMO phụ trách chính).</li><li>Phối hợp với các bên liên quan để tạo mới, cập nhật và duy trì toàn bộ tài liệu liên quan đến hệ thống QMS &amp; ISMS của Công ty.</li><li>Chịu trách nhiệm quản lý, duy trì và kiểm soát hệ thống tài liệu ISO của Công ty.</li><li>Chịu trách nhiệm chứng nhận, duy trì và cải tiến liên tục hệ thống quản lý chất lượng (ISO 9001) và an toàn thông tin (ISO 27001) của Công ty.</li><li>Tổ chức và hỗ trợ các phòng ban thực hiện các cuộc đánh giá nội bộ và bên ngoài cho QMS và ISMS</li><li>Hướng dẫn, giám sát các phòng ban khắc phục điểm không phù hợp sau các cuộc đánh giá.</li><li>Hỗ trợ khối Sản xuất, Kinh doanh review và trả lời các hạng mục/ survey liên quan tới chứng chỉ, tiêu chuẩn, quy trình/quy định của Công ty.</li><li>Thực hiện chương trình đánh giá rủi ro và quản trị rủi ro hàng năm</li></ul>

ISO Executive

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We are seeking a highly skilled Fullstack Developer with experience in both Backend and Frontend development, utilizing TypeScript, SQL, and NoSQL databases (e.g., MongoDB, DynamoDB). Candidates with a solid understanding of DevOps and Cloud platforms, along with proficiency in Datadog for observability and monitoring, are highly preferred.

Fullstack Engineer (ENG) -DU35

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1. Product Strategy &amp; Vision Define and evolve the omnichannel product vision for Health &amp; Beauty, aligning with business KPIs Lead cross-functional alignment with Marketing, Supply Chain, Retail Stores, Business Analyst, and E-Com teams. Conduct competitive benchmarking and identify white-space opportunities. 2. E-Commerce Platform Leadership Own digital roadmap for the e-commerce website and mobile app. Drive conversion optimization through: Unified customer loyalty Personalized product discovery Simplified stacked promotion Frictionless checkout) Manage integrations with ERP, OMS, Fulfillment, and payment gateways. 3. Omnichannel Experience Design Design seamless cross-channel journeys.&nbsp; Partner with store operations to deploy digital in-store experience. Enable unified customer profiles across touchpoints.\ 4. Data-Driven Optimization Partner with Analytics and Data Science to run A/B tests and personalization experiments Define and track North Star metrics: Customer Lifetime Value, Omnichannel Engagement Rate, Digital Revenue

Baseband (PHY / UPC) Software Design Engineer (Senior/Experienced) - Telecom Industry

Baseband (PHY / UPC) Software Design Engineer (Senior/Experienced) - Telecom Industry

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