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Innovating with MongoDB | Customer Successes, April 2026
The energy across the tech industry is high, but it’s shifting. While the initial wave of excitement around generative AI was defined by what was possible in a sandbox, we are squarely in the era of execution. I spend more and more of my time talking to founders who are working to move past experimentation, and to build autonomous systems that handle high-stakes tasks without human intervention.
Security in the Age of AI
Everything we do at MongoDB starts with customers and we work backward from their needs. As a result, I frequently engage with customers, partners, and industry peers to proactively find new ways to collectively strengthen our defenses—ideally, long before any issues arise.
A Year of Momentum: Why MongoDB Is Winning by Collaborating with Google Cloud
Today, the race to deploy AI is at an inflection point—2026 is the year of the mandate, and making good on AI’s promise. Enterprises are no longer satisfied with chatbots; they are focused on building the next generation of AI applications: autonomous, agentic systems that can reason, act, and execute complex business logic.
LG Uplus Works With MongoDB to Expand AI Services and Modernize Architecture
LG Uplus, a key subsidiary of LG Corporation and a leader in mobile, internet, and AI transformation, today announced that it will work with MongoDB to expand the use of generative AI and accelerate its modernization strategy across the company.
Google Cloud Pub/Sub is Now Supported in Atlas Stream Processing
Atlas Stream Processing lets developers build real-time data pipelines directly within MongoDB, without stitching together separate streaming infrastructure. Today, we're extending that reach with native Google Cloud Pub/Sub sink support.
Design and Develop Apps on MongoDB Faster with Google ADC
The journey from an initial vision to a production-grade application is often stalled by the infrastructure tax, those grueling hours spent navigating complex configurations and rigid data schemas. In the modern cloud landscape, the integration of MongoDB Atlas with Google Cloud’s Application Design Center (ADC) is helping to eliminate these hurdles by offering a unified path for visualizing, designing, and deploying cloud-native applications at scale.
New Research Reveals Overcoming Legacy Tech Issues Key to AI Success
This guest post comes from IDC’s Dr. William Lee, Senior Research Director, Service Provider and Core Infrastructure Research. MongoDB commissioned IDC to explore the connection between legacy infrastructure, data challenges, and AI across Asia Pacific, and today we’re happy to share that work. For more, see the full MongoDB-sponsored IDC InfoBrief, Modernizing Legacy: Winning in the Age of AI, Doc #AP242555-IB, April 2026. AI ambition is everywhere across Asia/Pacific. But ambition alone does not determine success. Organizations are discovering that AI outcomes are directly tied to the quality, accessibility, and modernity of their underlying technology stack and associated data technology foundations. Organizations that have managed to stay abreast of technical and data management changes across the application and infrastructure stacks, by embedding modernization into their organizational DNA, are experiencing 3x more digital revenue growth than those that are bound up in technical and data debt. To better understand this connection, IDC surveyed 1,400 organizations across eight Asia/Pacific markets. The findings reveal that modernization is no longer a side initiative. It is the core of a sustainable AI strategy. The AI readiness divide: Leaders versus mainstream IDC’s latest Asia/Pacific Modernization Survey, sponsored by MongoDB, identifies two distinct groups: The Mainstream Cohort: organizations still burdened by technical debt, siloed data, and skills gaps The Leaders Cohort: organizations that have embedded modernization into their strategy and experience the business results to match This divide is not theoretical. It is measurable in business performance. Organizations in the Leaders Cohort generate nearly three times more digital revenue than their peers. The difference is not simply higher AI spending. Leaders modernize core infrastructure, align executive support with transformation goals, and invest in skills development alongside technology. They treat AI readiness as an enterprise capability—not a standalone initiative. APAC IDC Blog Image 1 media The rigidity trap: Technical debt and AI failure risk A significant portion of Asia/Pacific organizations remain constrained by legacy architectures. According to IDC’s research, 43% report that their existing architecture is a major obstacle, making it difficult to build new applications without extensive modernization. This rigidity creates what IDC refers to as data debt—siloed, redundant, outdated, and poor-quality data that undermines AI performance and increases operational cost, and is in addition to the growing levels of technical debt that are being accumulated by organizations due to the slow modernization of older applications. When AI systems are trained on fragmented or inconsistent data: Outcomes become unreliable Bias risks increase Operational costs rise Business trust erodes IDC predicts that CIOs who fail to launch data debt remediation initiatives will face 50% higher AI failure rates and rising costs by 2027. Yet one-third of all enterprises continue to rely on legacy relational databases. Many such databases have been implemented in support of a wide array of business applications where business leaders expect they can use AI. Yet the legacy RDBMS-type databases are not capable of delivering on the dynamic, rapidly evolving, high-volume real-time demands that AI requires. Organizations that are unable to move to AI-ready application stacks are being left behind by those that have already made the switch. The gap between AI investment and infrastructure readiness is widening. APAC IDC Blog Image 2 media Legacy drag: The real business impact The consequences of technical debt are already visible. 95% of organizations report project delays 90% have experienced failed modernization initiatives 89% acknowledge technical debt as a major modernization obstacle In addition, organizations cite weak security integration, limited engagement with the business users, and outdated workflows as compounding challenges. Modernization failures are rarely just technical. They are organizational and structural. What sets leaders apart The Leaders Cohort does not operate in a constraint-free environment. Instead, they respond differently. IDC defines Leaders as organizations—across both digital-native and traditional industries—that have broken free from legacy rigidity and embedded modernization into ongoing operations. Their distinguishing characteristics include: Continuous, multi-pronged approaches to addressing legacy systems Alignment between executive leadership, funding, and AI outcomes Investment in modernization as a long-term capability, not a one-off project Strong focus on AI and modern application development skills The result is not just better IT performance. Leaders grow digital revenue faster and are positioned to extract value from AI initiatives earlier and more consistently than their peers. Cloud-centric data management: A strategic enabler Modern data platforms are central to this shift. In IDC’s research, 38% of Asia/Pacific organizations identify cloud-centric data management platforms as their top modernization investment priority for 2026. The motivation is clear: support hybrid architectures and AI workloads without introducing additional complexity. While AI enablement is a universal requirement, Leaders distinguish themselves by prioritizing: Security and compliance Flexibility across structured and unstructured data Scalable architectures aligned to modern AI toolchains This capability is increasingly critical. Much of today’s AI-relevant data—including content, sensor outputs, and customer interactions—resides in unstructured formats that traditional architectures struggle to integrate effectively. Handling both structured and unstructured data seamlessly has become a competitive differentiator. Modernization as a continuous strategy IDC’s perspective is clear: modernization is no longer a technology refresh cycle. It is a strategic operating model. Successful organizations approach modernization across three dimensions: People Leaders invest deliberately in AI and modern application development skills. They recognize resistance to change as a strategic risk and actively manage it. Process They adopt cloud-native approaches rather than repeating short-term “lift-and-shift” migrations that simply relocate technical debt. They use structured prioritization frameworks to embed modernization into business-as-usual operations. Technology They modernize to data platforms that support scalability, diverse data types, rapid feature development, and alignment with contemporary AI ecosystems. The ROI equation: Risk of action versus risk of inaction Modernization is often perceived as expensive and risky. However, IDC’s analysis suggests that the risk of inaction is frequently underestimated, and this study affirms that those who invest effectively, and continuously, into their application modernization program are experiencing both better ROI and higher digital revenues! Organizations that modernize report: Significant reductions in reporting time Double-digit productivity improvements Meaningful cost savings Hundreds of thousands of dollars in quantified cost reductions While full application rewrites and database modernization demand greater upfront investment than lift-and-shift migrations, they can deliver up to three times the long-term benefit. For CIOs and business leaders, the decision should not be framed as modernization cost versus status quo stability. It is modernization investment versus escalating AI failure risk. The Path forward: Legacy systems are not permanent Overcoming legacy is often perceived to be as significant a risk as taking on new technologies. IDC notes that many CIOs across the region focus on risk avoidance as a priority. In contrast, business leaders are seeking innovative solutions that drive new business opportunities, and so CIOs must balance their risk aversion concerns with the business demands. IDC’s research shows that legacy migrations that are under-funded pose significantly higher risk, and deliver lower returns, than those that are sufficiently funded from the outset. Organizations that proactively address technical debt, modernize systems, and align leadership and funding around AI-enabled outcomes will increasingly separate themselves from the pack. Those that delay will face structural disadvantages: Growing technical debt Escalating modernization costs Underperforming AI systems Slower digital revenue growth IDC’s research shows that the next wave of AI advantage in Asia/Pacific will not be determined solely by model sophistication. It will be determined by architectural foundations. Ultimately, without modernization, there can be no sustainable AI strategy.
Improved Multitenancy Support in Vector Search: Introducing Flat Indexes
The future of AI is personal. The more accustomed to AI tools users are, the more they want their experience of working with them to be personalized and agentic. Whether it is an AI assistant recalling your past conversations, a legal tool reviewing a specific company's contracts, or a personal knowledge base searching through your private documents, these applications all rely on one core capability: providing "memory" specific to a single user or business.