How EQ Bank connects AI learning, innovation, and governance
Explore EQ Bank’s practical framework for turning AI learning into responsible, measurable business impact. If you’re building an AI-ready workforce, employees may be starting with different levels of knowledge and confidence, but the organization still needs to find a shared path forward. In financial services, that challenge comes with an added requirement: Experimentation must coexist with oversight, security, risk management, and regulatory compliance. EQ Bank, a Canadian digital financial institution, addressed this challenge with an enterprise-wide model that connects four elements of AI workforce readiness: role-based learning, practical application, peer support, and responsible governance. You can use the same four-part framework to assess where your organization is strong and where employees may need more support. Build a common foundation for AI growth EQ Bank created its uniquely named AI Belt Programs to give employees clear, role-appropriate pathways for developing AI expertise: White Belt: AI fundamentals, responsible AI, and foundational Microsoft 365 Copilot skills. Blue Belt: AI agents, Copilot Studio, and solution development. Black Belt: Microsoft Foundry, AI operations, GitHub Copilot, and solutions designed to scale securely. Executive Belt: Strategic and responsible AI adoption for leaders. The programs provide multiple entry points and a path for continued growth. Today, 906 colleagues are building foundational AI literacy through White Belt, 242 technical specialists are advancing solution development through Blue Belt, 130 participants are developing advanced solutions through Black Belt, and 67 leaders are shaping AI strategy through Executive Belt. The lesson for other leaders is to meet employees at different levels of readiness while connecting everyone through a common learning framework. Turn AI learning into measurable business impact EQ Bank’s AI Centre of Enablement helps employees apply their skills through hackathons, immersive learning, agent-building opportunities, and an AI Innovation Hall of Fame that recognizes use cases developed through the Belt Programs. An example of AI innovation is HR Bestie, which gives employees and managers access to published HR information. Since launch, it has supported 16,214 conversation sessions and answered 4,433 questions, with 79.5% of sampled answers rated as good quality. By supporting routine inquiries autonomously, the agent can reduce reliance on manual support and help HR focus on more complex work. It also shows learners how trusted organizational knowledge can support everyday tasks. Another example is TestGenie, an implemented AI agent that demonstrates measurable productivity and capacity gains in software quality engineering. The agent turns user stories and acceptance criteria into review-ready test cases, while Quality Engineers retain responsibility for review and approval. With AI assistance, the number of user stories processed per day increased from approximately 8 per Quality Engineer to as many as 40, contributing to an estimated 80% to 90% reduction in manual test-authoring effort. The example shows how Quality Engineering teams can combine AI assistance with human oversight and accountability. Sustain adoption through community and recognition Applying new skills is important, but sustaining adoption also requires opportunities for employees to share their work and learn from their peers. EQ Bank supports that exchange through innovation events, including a pitch-style showcase where colleagues present AI solutions to a panel of leaders. Recognition encourages employees to share what they have built while giving colleagues concrete examples of responsible AI application. For example, EQ Rovo Agent, helps employees find and summarize information stored across EQ Bank’s project-management and knowledge systems. The agent respects employees’ existing access permissions and requires their confirmation before making changes, demonstrating how practical AI innovation can incorporate human oversight. EQ Bank’s AI Champions community helps extend that learning across the organization. Champions receive early exposure to emerging technologies, participate in specialized enablement, share use cases, and help successful practices spread across teams. Govern experimentation responsibly As employees created more agents and solutions, EQ Bank established an AI Governance Board that tracks every use case from submission through impact assessment to completion. Each request moves through an AI Impact Assessment lifecycle, creating a clear, auditable trail. AML Watchtower, an AI-assisted transaction-monitoring solution, shows how EQ Bank’s approach can support speed and accountability. Developed by an AI Champion, the solution helps teams translate anti-money-laundering risk scenarios into explainable, governed detection logic. The agent owner estimates that AML Watchtower reduced development time by approximately 20% to 30% and documentation effort by 70% to 80%, accelerating delivery while improving consistency and readiness for review and approval. The project demonstrates how governance capabilities can develop alongside technical solution-building skills. For regulated organizations, the takeaway is to design enablement and governance together. Clear ownership, centralized visibility, and an established review process can support experimentation while keeping solutions aligned with risk, security, and compliance standards. Partnering with Microsoft to accelerate practical AI readiness Microsoft support helped EQ Bank adapt learning to different roles and levels of technical readiness. That support included specialized training, technical workshops, GitHub Copilot enablement, and hands-on sessions focused on practical workflows and agent development. In less than six months beginning in March 2026, broader AI Centre of Enablement programming supported 1,192 participants across 49 events, including nine customized sessions delivered with Microsoft for 161 participants across executive and business functions. Conclusion and next steps for you and your team EQ Bank’s experience offers a practical framework for connecting AI learning with application, community, and governance. Its integrated approach is already supporting faster solution development, greater employee support capacity, and more efficient delivery of governed AI solutions. For business, HR, learning, and IT leaders, four principles stand out: Build the foundation: Create role-appropriate pathways for employees at different levels of readiness. Connect learning to application: Give employees opportunities to address real business challenges and measure results. Scale through community: Use champions, recognition, peer learning, and employee examples to expand adoption. Govern experimentation responsibly: Establish ownership, visibility, review processes, and safeguards as solutions scale. Use these principles to identify where your organization is strong and where employees need more support. Then connect those needs to relevant learning experiences and opportunities for practical application. Ready to start learning? Explore Microsoft AI Skills Navigator for AI-powered learning experiences and credentials designed to build employee expertise and accelerate organizational transformation.How SCBX built AI literacy at scale and made AI part of daily work
Read how SCBX used a structured skilling plan with Trainocate to strengthen AI literacy, support role-based adoption, and help teams reduce time spent on repetitive work. Founded in 2021, SCB X Public Company Limited (SCBX) aimed to modernize its Siam Commercial Bank into a regional fintech conglomerate. By early 2026, SCBX had solidified its role as the core of one of Thailand’s most influential financial groups, leading a high-profile virtual bank consortium, scaling data and technology units, and embedding AI across every business line. But SCBX leadership recognized that meaningful change required an AI-fluent workforce. The organization needed a practical way to help thousands of employees truly adopt AI—not as a side project, but as a meaningful part of their everyday work. What needed to change Two issues stood out: AI literacy and aptitude varied across teams; and employees were spending too much time on repetitive work. The organization needed AI understanding at enterprise scale, along with role-based practices. And because SCBX operates in a highly regulated environment, the training needed to stay within strict governance and control requirements. SCBX needed to do all of this with thousands of employees. This challenge was as much about people as about technology. The approach: Phased skilling built for outcomes SCBX partnered with Trainocate, a global leader in IT training for more than 30 years. Together they crafted a phased, outcome-oriented roadmap designed to drive AI adoption—not just awareness. Microsoft provided vital support through its Organizational skilling program, enabling SCBX to commit to enterprise-wide scale without sacrificing depth, pace, or quality. Phase 1: Shared foundations The first phase focused on building shared language and confidence around AI, and it included: AI for Business Leaders, helping executives and managers lead confidently in an AI-driven world. The AI Tech Podcast series, focusing on building fluency in generative AI and Microsoft 365 Copilot. Prompt Engineering in Practice, sharing practical AI skills and experience. Phase 2: Role-based application The plan then focused on applying these skills to real-world work, offering: AI Bootcamps, an onramp to working with generative AI, including Copilot and Microsoft Power Platform. AI for Business Enablement, role-relevant training for nontechnical users in sales, operations, finance, legal, and marketing. Copilot Prompt Training for Business Users, covering Copilot fluency across roles and functions. Microsoft Power Platform Training, helping low-code and no-code developers streamline routine work with Power Apps, Power Automate, and Microsoft Copilot Studio, reducing dependency on IT. Advanced Technical Training tracks, with upskilling for developers, data pros, and security specialists. Role-based adoption at enterprise scale SCBX chose Trainocate Thailand because it could execute at scale while ensuring consistent quality, align training tightly to Microsoft technologies, and embed security and responsible AI practices suited to regulated financial services. Segmenting engagement-driven learning by audience meant training translated into true role-based adoption. Tying every program to business key performance indicators (KPIs) helped validate the investment. Positive outcomes and measurable impact By focusing on adoption and measurable learning outcomes, SCBX saw results throughout the company, including: 15,170+ total participants across the organization. 10,000+ completions of AI literacy and technical pathways. 700+ advanced learners completing deep technical tracks. 4.5/5 satisfaction rating average among participants, signaling genuine engagement. Productivity impact: Teams reduced time spent on repetitive work, like drafting emails, creating reports, summarizing information, and running routine workflows. This has freed capacity for higher-value thinking and decisions. The result is learning that translates into fluency and adoption—AI is improving how SCBX teams work, not just what they know. For leaders facing similar challenges, SCBX’s experience is a useful reminder that organizations build AI readiness with skill-building and real-world use. A clear, phased roadmap and the right partner helped SCBX grow AI skills, strengthen role-based applications, and build momentum that continues as employees across all roles apply their new abilities to improve the way work gets done. Your learners who want to build their AI skills can get started with AI Skills Navigator, our agentic learning space, bringing together AI-powered skilling experiences and credentials that help individuals build career skills and organizations worldwide accelerate their business.Real-World Success Stories with PostgreSQL on Azure
Organizations rarely leap into cloud migrations or AI-powered systems overnight. They progress in deliberate stages, establishing a reliable data foundation, optimizing for performance, and then accelerating innovation. Across healthcare, financial services, and AI startups, companies are navigating this journey on Azure Database for PostgreSQL: a fully managed, enterprise-ready PostgreSQL environment with 58% lower total cost of ownership (TCO) compared to on-premises deployments. This post walks through real customer stories that span the full arc, from lift-and-shift migration to production-grade AI agent development, illustrating how Azure Database for PostgreSQL supports scalability, performance, security, and AI-readiness at every stage. Migrating with Confidence: Apollo Hospitals & August AI Apollo Hospitals operates a network of more than 74 hospitals and needed to move beyond a legacy on-premises Oracle system that had become difficult to manage and couldn't keep pace with growing data volumes. IT teams were spending their time on maintenance rather than innovation. Apollo migrated its core hospital information system backend to Azure Database for PostgreSQL. Working with partner Quadrant Technologies, the team lifted and shifted critical applications while using Azure DevOps to orchestrate CI/CD pipelines and Azure Application Insights for telemetry and observability. The results: 99.95% availability across hospital systems Database transactions executing within 5 seconds 40% reduction in deployment times via modern CI/CD pipelines Decreased operational overhead, freeing IT staff for higher-value work With a stable, scalable PostgreSQL backend in place, Apollo is now exploring real-time analytics and AI-enabled tools like Microsoft 365 Copilot to advance patient care. "We saw Azure Database for PostgreSQL as the right foundation for the future. It's open, cost-effective, and capable of supporting the hospital information system we built in-house." — Shankar Krishna A., General Manager of IT, Apollo Hospitals Apollo's experience is not unique. August AI, a healthcare-tech startup offering an AI-driven medical companion, migrated its entire stack to Azure—with Azure Database for PostgreSQL storing mission-critical patient data while meeting strict compliance requirements such as HIPAA. The result: scaling from roughly 500,000 users to 3.5 million+ users worldwide, with zero downtime during the cutover, completed in just three months. As Founder and CEO Anuruddh Mishra noted: "We receive a log of queries that are not performing optimally, and within a couple of minutes we can optimize that query with PostgreSQL on Azure and move on". Modernizing at Scale: Nasdaq Migration is often the first step. Nasdaq demonstrates what becomes possible when organizations modernize their architecture on a scalable data foundation. To improve its Nasdaq Boardvantage platform—used by corporate boards to collaborate on governance documents—Nasdaq re-architected on Azure. The team containerized services with Azure Kubernetes Service (AKS) and adopted Azure Database for PostgreSQL alongside Azure Database for MySQL as persistent data stores for governance workloads. This architecture provided the flexibility, performance, and security required for a multitenant platform handling sensitive board materials. With the data layer in place, Nasdaq integrated Microsoft Foundry and Azure OpenAI to deliver AI-powered summarization and workflow automation. The measurable outcomes: 60% reduction in reading time through AI-powered document summarization 25% decrease in administrative preparation time across board workflows Up to 97% accuracy in AI-generated summaries and meeting minutes A reusable AI framework established for future extensibility "Both Azure Database for PostgreSQL and Azure Database for MySQL gave us the right balance of performance, security, and control. The governance workloads we handle are unique, so we needed something that could meet those isolation and encryption requirements." — Scott Ellison, Vice President of Technology, Nasdaq Building Intelligent Applications: SubgenAI and OpenAI Azure Database for PostgreSQL now supports native vector search via pgvector, high-performance DiskANN indexing, semantic operators and AI model management, and integrated graph capabilities for relationship reasoning—making it a production-ready foundation for intelligent applications. SubgenAI, a European generative AI company, built its flagship platform Serenity Star on Azure Database for PostgreSQL and Microsoft Foundry to transform AI agent development from a code-heavy, fragmented process into a streamlined, no-code experience. A core technical requirement: the platform's retrieval-augmented generation (RAG) system needs efficient vector search against embedded content while maintaining enterprise-grade reliability. After evaluating several database options, SubgenAI chose Azure Database for PostgreSQL with pgvector for its accurate and scalable vector similarity search. Serenity Star customers can now: Launch AI agents in as little as 15 minutes Cut coding and development time by 50% Resolve most AI agent queries in under 60 seconds [ "With Microsoft and Azure Database for PostgreSQL we have total control and an environment that is truly dynamic and can adapt to the evolution we're looking for." — Julia Schröder Langhaeuser, VP of Product Serenity Star, SubgenAI At the extreme end of scale, OpenAI runs PostgreSQL on Azure to support production systems behind ChatGPT. As write scalability limits emerged on an initially unsharded single primary instance, OpenAI offloaded write-heavy operations to other systems and optimized read workloads using PgBouncer for connection pooling. The Azure Database for PostgreSQL team responded by developing the elastic clusters feature, enabling horizontal scaling through row-based and schema-based sharding. The team reduced connection latency from approximately 50 ms to under 5 ms, scaled reads horizontally with multiple replicas, and improved reliability by prioritizing critical requests—all achieved by a small team making systematic optimizations on open-source PostgreSQL. "After all the optimization we did, we are super happy with Postgres right now for our read-heavy workloads. It's really scalable and reliable." — Bohan Zhang, Member of the Technical Staff, OpenAI Meeting You Where You Are Beyond these stories, organizations like BMW Group (cloud-native applications at global scale), Ahold Delhaize (highly available retail applications), Mott MacDonald (an AI agent accelerating onboarding and spreading best practices across 220,000 employees), and Multitude (scaling responsibly in regulated environments) all run on Azure Database for PostgreSQL. The service offers 99.99% availability with automatic failover and SLA, independent compute and storage scaling, and intelligent performance recommendations, available across 60+ Azure regions. Developer tooling including the PostgreSQL extension for Visual Studio Code with GitHub Copilot further accelerates productivity. Whether you are planning your first migration or building production AI agents, these stories share a clear signal: Azure Database for PostgreSQL delivers a scalable, secure, AI-ready data foundation at every stage of growth. Explore full customer stories in depth in the eBook: Customer Success Stories with Azure Database for PostgreSQL.CloudThat: How the gamified CloudAttack app is transforming AI skilling
Learn how CloudAttack brings AI skilling to life with interactive modes, adaptive feedback, and community challenges that help learners stay engaged and prepare to earn Microsoft Credentials. AI skills are more essential every day across all industries, but some traditional training approaches don’t keep learners as engaged and motivated as they could be. Microsoft Training Services Partner CloudThat, an award-winning global leader in training and consulting, recognized this challenge and set out to reimagine how people build cloud and AI skills. The result is CloudAttack, a proprietary generative AI–powered gamified learning platform that’s making skill-building more interactive, enjoyable, and measurable. Finding the gaps and defining the goals CloudThat identified two key learner challenges: Low engagement. Some learners completed training but didn’t retain or apply their knowledge. A prompt engineering gap. Some struggled to craft effective prompts. CloudThat wanted to bring AI learning to life, while addressing a wide range of learning styles and all roles, including students, independent learners, professionals, corporate teams, and educators. The company also wanted to align with the Microsoft AI vision and to encourage Microsoft 365 Copilot adoption, overall AI readiness, and preparation for earning Microsoft Credentials. Approaching real-world AI skills with a gamified approach CloudThat’s answer to these challenges is CloudAttack, an app rooted in AI technology and gamification teaching methodology. Game‑like elements, including puzzles, levels, and leaderboards, transform complex topics into engaging experiences. As Marmik jain, a student at B.M.S. Institute of Technology and Management, Bengaluru, observes, “It was indeed a fun and interactive way to prepare for Microsoft Certifications and learn prompt engineering.” OpenAI‑based models evaluate learner inputs with high accuracy, adapting prompts and scenarios to create individualized learning paths. Skilling for every audience Corporate learning and development (L&D) teams use private leagues and leaderboards to drive engagement, while academic institutions rely on the app for interactive and competition-based learning. Learning through a game makes the experience engaging and effective... It’s a great way to upskill in cloud technologies and prompt engineering. Being part of communities like Cloud Attack, CloudThat, and Microsoft encourages continuous learning. Prompt engineering also helps improve our efficiency by enabling us to give precise commands and achieve better outcomes. Heemanshu Hemant Sute, student Rajarambapu Institute of Technology, Uran Ishwarpur, Maharashtra Working professionals can upskill without disrupting their jobs, using daily challenges and tournaments to strengthen prompts and get ready for credential exams or assessments. And independent learners get self‑paced, feedback‑driven practice, while educators and training partners use CloudAttack to make workshops and bootcamps more fun, interactive, and measurable. Teaching AI with AI: The Prompt-A-Thon One of the most impactful demonstrations of CloudAttack’s potential was the Prompt-A-Thon, a nationwide AI-skilling initiative sponsored by Microsoft. Over two months, this “AI teaching AI” approach brought together nearly 1,000 learners from universities, corporations, and startups, all competing to craft effective prompts and use AI tools, like Copilot, more confidently. In this immersive environment, participants received AI-generated feedback on every prompt they submitted, helping them understand where they could improve and seeing real results. As Professor Nanda MB from the Sri Krishna Institute of Technology (SKIT), Bengaluru, points out, "The prompt engineering learning session followed by [the] Prompt-A-Thon for our B-Tech [bachelor of technology] students was helpful. The gamification learning through CloudThat was very interesting, which made our students’ AI learning easy. [After] the contest, we saw 100% increase in use of AI [among] our students." Discovering real impacts The numbers speak volumes. As of this writing, CloudThat reports that more than 2,000 active CloudAttack users, over 10,000 downloads on the Play Store with a 4.4 rating, and a 5.0 rating on the App Store. CloudThat also reports a 100% increase over the last year in Microsoft AI and prompt engineering sessions at universities like Acharya Institutes, REVA University, Dayananda Sagar University, International School of Business & Research (ISBR), B.M.S. College of Engineering, and Shri Vaishnav Vidyapeeth Vishwavidyalaya. User testimonials highlight the transformative experience. Participants express new confidence in using Copilot daily, students feel invested in building fundamentals, and enterprise learners demonstrate stronger retention and meaningful engagement during training. CloudThat notes a 15% increase in Certification exam participation among enterprise learners. AI skilling for the new era CloudAttack isn’t just a learning app—it’s an immersive and personalized way of championing AI and cloud technologies. By blending generative AI technology with a gamified learning approach, CloudThat has created a dynamic learning experience that drives real results and that learners return to repeatedly and enthusiastically. It’s clear that the future of AI skilling is not only instructional but also interactive, competitive, and fun.Lenovo scales AI adoption worldwide with Copilot Chat training
Discover how Lenovo helped 30,000 employees build practical AI skills with scenario‑driven training to streamline daily tasks, strengthen communication, and achieve meaningful, measurable outcomes. Listed by Forbes as the world’s largest PC company, Lenovo is taking AI out of the abstract and delivering its power to people around the world through a full-stack portfolio of AI-enabled devices, infrastructure, and software. Lenovo’s vision of delivering smarter technology for all includes its own employees and processes. This goal informed its decision to deploy and adopt Microsoft 365 Copilot Chat and Microsoft Copilot Studio across the entire company. Empowering innovation and efficiency Lenovo aimed to establish enterprise leadership in AI, using it to enhance productivity and accelerate business processes across key functions, including research and development (R&D), marketing, and human resources (HR). The company did this by deploying Copilot Chat to its global workforce of over 75,000 employees. The initial onboarding target was more manageable but still ambitious: 30,000 employees in China—about 40% of its workforce. The primary challenge was not just the scale of deployment but also the need to ensure adoption. It was vital that employees could move beyond basic usage to effectively integrate these advanced AI tools into their specific, daily work scenarios to drive tangible business value. Lenovo recognized that success hinged on helping employees build confidence, understand real-world applications, and adopt an AI‑first mindset. Copilot Chat has fundamentally changed how we manage our daily workflow. What used to take hours now happens in minutes. It not only saves us time but also enhances the quality of our communication and strategic thinking. —Dan Zhao, Operations Management Professional, Lenovo China Laying the groundwork To achieve such massive upskilling at scale, Lenovo worked with Digital China Cloud Technology Limited, a leading digital transformation partner whose many services include helping enterprise-level clients establish future-oriented core capabilities. Together they created a training strategy to help employees move from casual interaction with AI tools to meaningful, scenario‑driven usage. The two-phased training combined targeted learning pathways, interactive workshops, and department-specific problem-solving. By embedding training into employees’ existing digital workflows, Lenovo helped to ensure minimal friction and maximum relevance. Phase one: Foundation building The first phase focused on delivering broad-based AI literacy across the targeted workforce so that every employee, regardless of technical background, could start implementing AI tools to streamline their work. Lenovo introduced an “Introduction to Prompt Engineering” course designed to teach employees how to interact effectively with AI systems. This core training provided: Essential AI concepts. Practical prompting skills. Confidence-building exercises. Foundational habits for high‑quality AI interaction. Phase two: Role‑based integration The second phase moved beyond generic training to deep, scenario-specific workshops for key departments. It focused on using Copilot Chat for complex querying and analysis and on working with Copilot Studio, a platform for designing and building custom agents to automate and execute specific business processes. “The key innovation,” Digital China Microsoft Certified Trainer (MCT) Yan Yan explains, “was our scenario-first and agent-centric learning methodology. Instead of teaching features, we focused on real-world business problems.” This mapped training directly to real needs rather than abstract hypothetical use cases. Phase two included: Customized agent development workshops tailored to unique departmental challenges, like automating HR inquiries and generating marketing content. Workflow‑embedded scenario training, designed around actual daily tasks, like meeting summaries, data analysis, strategic document drafting, and more. Measurable business impact Lenovo achieved significant results from its program, including: Accelerated core business processes. Teams across R&D, marketing, and HR saw faster project cycles—from accelerated code development and quicker market analysis to more streamlined employee onboarding. Higher productivity and efficiency. Employees showed major gains in handling complex tasks, such as data synthesis and content creation, reducing time spent on routine work. A more engaged workforce. The program boosted employee confidence and helped cultivate a culture of innovation and AI-first thinking. Transformational impact on customer-facing teams. Sales teams experienced dramatic improvements in communication quality, speed, and strategic preparation, reducing timelines from hours to minutes. Realizing significant gains with a practical approach Lenovo’s AI adoption journey demonstrates what’s possible when an organization pairs visionary goals with a practical, scenario‑driven implementation strategy. By empowering employees with foundational skills and then guiding them through real‑world applications, Lenovo achieved widespread adoption, substantial efficiency gains, and a more empowered global workforce. This project not only accelerated Lenovo’s internal transformation but also positioned the company as a leader in enterprise‑wide AI enablement. Explore AI skilling opportunities for all your teams—technical and non-technical alike—on Microsoft Learn for Organizations. For more inspiration, read AI and human potential: Advancing skills, innovation, and outcomes.Akkodis and Microsoft share field‑tested tips for your AI skilling program
Generative AI technology is creating unprecedented opportunities to improve your business efficiency, fuel innovation, and gain a competitive edge. Given these benefits, equipping your workforce with the skills to effectively use AI-powered tools is a high priority. At the same time, the rapid evolution of AI technology, along with the potential impacts on your established processes, means that matching people with the right skills requires a deliberate approach. Akkodis and Microsoft partner to bring AI skilling to customers Across the Asia-Pacific region, global engineering and digital solutions company Akkodis helps organizations design, build, and operate technology driven solutions. Its Akkodis Academy integrates Microsoft technology and training, including industry-recognized Microsoft Credentials (role-based Microsoft Certifications and scenario-based Microsoft Applied Skills) that cover AI, into its customized learning and consulting programs. These programs help customers build learning cultures that keep up with the increasing pace of technological change. Building an AI skilling strategy that works Based on its extensive experience, Akkodis has found five strategies that can help you build (or sustain) a successful AI skilling program. 1. Start small, learn fast, iterate AI capabilities (and your business needs) evolve quickly. With iterative training cycles, you don’t need a perfect blueprint to get started. Instead, short, focused training sprints let you try ideas, collect feedback, and quickly improve. This lowers the intimidation factor for newcomers and helps build confidence and momentum with staff and stakeholders. 2. Tie skilling directly to your business goals The most successful skilling programs align training with technology and business goals. They also secure sponsorship from company leaders to help establish priorities and reinforce adoption efforts. Anchor training efforts to concrete business outcomes, like productivity, time‑to‑market, operational costs, or other real-world business metrics. 3. Establish a culture of continuous learning The traditional skilling model of “take a course, and you’re done” doesn’t work well for such rapidly evolving technology. Establishing an always‑on learning culture using webinars, tech talks, and collaborative community learning can move your teams from a “know-it-all” to a “learn-it-all” mindset, keeping your teams’ skills fresh and curiosity growing. 4. Combine technical skills with business and governance knowledge Organizations that realistically evaluate the business utility of AI tools tend to adopt this technology more effectively and efficiently. This means AI skills need to be complemented by business and operational knowledge. Alongside technical instruction, your teams should understand and apply your organization’s governance policies related to data privacy, ethical AI use, and other regulatory requirements. 5. Provide practical, applied learning Focus on real-world skills that show tangible application—not only what AI is but also how to effectively use it. Bootcamps, role‑based labs, and an emphasis on practical scenarios can help bridge the gap between theory and real-world use and can directly correlate with productivity gains and other key outcomes. Real-world AI skilling success stories Explore how AI skilling strategies have led to practical business gains: Commonwealth Bank of Australia invested heavily in AI skilling, equipping employees to effectively adopt AI tools. As a result, 84% of its 10,000 Microsoft 365 Copilot users report that they wouldn’t go back to working without it, and developers are adopting ~30% of GitHub Copilot code suggestions. Adecco Group’s AI skilling strategy has increased productivity for recruiters by 63%. Plus, the company’s AI-driven CV Maker generated 200,000 résumés, and 35,000 employees completed responsible AI training, driving better client interactions. Next steps An effective AI skilling program is about more than technology—it requires a workforce that can adapt and thrive as AI reshapes the business world around them. Successfully building AI fluency across your organization can accelerate your organization’s technology adoption, create improved business outcomes, and lead to tangible competitive advantages. Ready to grow your team’s AI skills? Read Create an AI Learning Culture.
Shopify Campaign Buy X Get Y fullfillment.
Hi, I understand from an article that campaign articles Buy X Get Y (campaign items) in Shopify must currently be fulfilled manually. But I’m wondering if anyone has found a workaround for this limitation. Specifically: - Is it possible to adjust the Shopify Connector so that the fulfillment process can be automated? - Alternatively, can we fulfill the order in Business Central and then have that fulfillment successfully sync back to Shopify? Here’s what I’ve found so far: - There is a synchronization function in Business Central: Sync Shipments to Shopify. - The Sales Shipment Line (Table 111) in BC has a row number that matches with the Shopify Order Line (Table 30119). - However, the Shopify Fulfillment Order Line (Table 30144) does not have a row number that links back to the Shopify Order Line (30119). This creates a problem: If the order contains both “normal” items and campaign items, then the normal items are fulfilled as expected. But the campaign items are ignored during the fulfillment process, because of the Shopify Product IDs and Variant IDs are identical. Has anyone found a way to handle this? Maybe through a modification of the connector? Or another method to make sure campaign items also get fulfilled automatically when syncing shipments from BC to Shopify? Any tips, experiences, or suggestions would be greatly appreciated!UBS unlocks advanced AI techniques with PostgreSQL on Azure
This blog was authored by Jay Yang, Executive Director, and Orhun Oezbek, GenAI Architect, UBS RiskLab UBS Group AG is a multinational investment bank and world-leading asset manager that manages $5.7 trillion in assets across 15 different markets. We continue to evolve our tools to suit the needs of data scientists and to integrate the use of AI. Our UBS RiskLab data science platform helps over 1,200 UBS data scientists expedite development and deployment of their analytics and AI solutions, which support functions such as risk, compliance, and finance, as well as front-office divisions such as investment banking and wealth management. RiskLab and UBS GOTO (Group Operations and Technology Office) have a long-term AI strategy to provide a scalable and easy-to-use AI platform. This strategy aims to remove friction and pain points for users, such as developers and data scientists, by introducing DevOps automation, centralized governance and AI service simplification. These efforts have significantly democratized AI development for our business users. This blog walks through how we created two RiskLab products using Azure services. We also explain how we’re using Azure Database for PostgreSQL to power advanced Retrieval Augmented-Generation (RAG) techniques—such as new vector search algorithms, parameter tuning, hybrid search, semantic ranking, and a graphRAG approach—to further the work of our financial generative AI use cases. The RiskLab AI Common Ecosystem (AICE) provides fully governed and simplified generative AI platform services, including: Governed production data access for AI development Managed large language model (LLM) endpoints access control Tenanted RAG environments Enhanced document insight AI processing Streamlined AI agent standardization, development, registration, and deployment solutions End-to-end machine learning (ML) model continuous integration, training, deployment, and monitoring processes The AICE Vector Embedding Governance Application (VEGA) is a fully governed and multi-tenant vector store built on top of Azure Database for PostgreSQL that provides self-service vector store lifecycle management and advanced indexing and retrieval techniques for financial RAG use cases. A focus on best practices like AIOps and MLOps As generative AI gained traction in 2023, we noticed the need for a platform that simplified the process for our data scientists to build, test, and deploy generative AI applications. In this age of AI, the focus should be on data science best practices—GenAIOps and MLOps. Most of our data scientists aren’t fully trained on MLOps, GenAIOps, and setting up complex pipelines, so AICE was designed to provide automated, self-serve DevOps provisioning of the Azure resources they need, as well as simplified MLOps and AIOps pipelines libraries. This removes operational complexities from their workflows. The second reason for AICE was to make sure our data scientists were working in fully governed environments that comply with data privacy regulations from the multiple countries in which UBS operates. To meet that need, AICE provides a set of generative AI libraries that fully manages data governance and reduces complexity. Overall, AICE greatly simplifies the work for our data scientists. For instance, the platform provides managed Azure LLM endpoints, MLflow for generative AI evaluation, and AI agent deployment pipelines along with their corresponding Python libraries. Without going into the nitty gritty of setting up a new Azure subscription, managing MLFlow instances, and navigating Azure Kubernetes Service (AKS) deployments, data scientists can just write three lines of code to obtain a fully governed and secure generative AI ecosystem to manage their entire application lifecycle. And, as a governed, secure lab environment, they can also develop and prototype ML models and generative AI applications in the production tier. We found that providing production read-only datasets to build these models significantly expedites our AI development. In fact, the process for developing an ML model, building a pipeline for model training, and putting it into production has dropped from six months to just one month. Azure Database for PostgreSQL and pgvector: The best of both worlds for relational and vector databases Once AICE adoption ramped up, our next step was to develop a comprehensive, flexible vector store that would simplify vector store resource provisioning while supporting hundreds of RAG use cases and tenants across both lab and production environments. Essentially, we needed to create RAG as a Service (RaaS) so our data scientists could build custom AI solutions in a self-service manner. When we started building VEGA and this vector store, we anticipated that effective RAG would require a diverse range of search capabilities covering not only vector searches but also more traditional document searches or even relational queries. Therefore, we needed a database that could pivot easily. We were looking for a really flexible relational database and decided on Azure Database for PostgreSQL. For a while, Azure Database for PostgreSQL has been our go-to database at RiskLab for our structured data use cases because it’s like the Swiss Army Knife of databases. It’s very compact and flexible, and we have all the tools we need in a single package. Azure Database for PostgreSQL offers excellent relational queries and JSONB document search. When used in conjunction with the pgvector extension for vector search, we created some very powerful hybrid search and hierarchical search RAG functionalities for our end users. The relational nature of Azure Database for PostgreSQL also allowed us to build a highly regulated authorization and authentication mechanism that makes it easy and secure for data scientists to share their embeddings. This involved meeting very stringent access control policies so that users’ access to vector stores is on a need-to-know basis. Integrations with the Azure Graph API help us manage those identities and ensure that the environment is fully secure. Using VEGA, data scientists can just click a button to add a user or group and provide access to all their embeddings/documents. It’s very easy, but it’s also governed and highly regulated. Speeding vector store initialization from days to seconds With VEGA, the time it takes to provision a vector store has dropped from days to less than 30 seconds. Instead of waiting days on a request for new instances of Azure Database for PostgreSQL, pgvector, and Azure AI Search, data scientists can now simply write five lines of code to stand up virtual, fully governed, and secure collections. And the same is true for agentic deployment frameworks. This speed is critical for lab work that involves fast iterations and experiments. And because we built on Azure Database for PostgreSQL, a single instance of VEGA can support thousands of vector stores. It’s cost-effective and seamlessly scales. Creating a hybrid search to analyze thousands of documents Since launching VEGA, one of the top hybrid search use cases has been Augmented Indexing Search (AIR Search), allowing data scientists to comb through financial documents and pinpoint the correct sections and text. This search uses LLMs as agents that first filter based on metadata stored in JSONB columns of the Azure Database for PostgreSQL, then apply vector similarity retrieval. Our thousands of well-structured financial documents are built with hierarchical headers that act as metadata, providing a filtering mechanism for agents and allowing them to retrieve sections in our documents to find precisely what they’re looking for. Because these agents are autonomous, they can decide on the best tools to use for the situation—either metadata filtering or vector similarity search. As a hybrid search, this approach also minimizes AI hallucinations because it gives the agents more context to work with. To enable this search, we used ChatGPT and Azure OpenAI. But because most of our financial documents are saved as PDFs, the challenge was retaining hierarchical information from headers that were lost when simply dumping in text from PDFs. We also had to determine how to make sure ChatGPT understood the meaning behind aspects like tables and figures. As a solution, we created PNG images of PDF pages and told ChatGPT to semantically chunk documents by titles and headers. And if it came across a table, we asked it to provide a YAML or JSON representation of it. We also asked ChatGPT to interpret figures to extract information, which is an important step because many of our documents contain financial graphs and charts. We’re now using Azure AI Document Intelligence for layout detection and section detection as the first step, which simplified our document ingestion pipelines significantly. Forecasting economic implications with PostgreSQL Graph Extension Since creating AICE and VEGA using Azure services, we’ve significantly enhanced our data science workflows. We’ve made it faster and easier to develop generative AI applications thanks to the speed and flexibility of Azure Database for PostgreSQL. Making advanced AI features accessible to our data scientists has accelerated innovation in RiskLab and ultimately allowed UBS to deliver exceptional value to our customers. Looking ahead, we plan to use the Apache AGE graph extension in Azure Database for PostgreSQL for macroeconomics knowledge retention capabilities. Specifically, we’re considering Azure tooling such as GraphRAG to equip UBS economist and portfolio managers with advanced RAG capabilities. This will allow them to retrieve more coherent RAG search results for use cases such as economics scenario generation and impact analysis, as well as investment forecasting and decision-making. For instance, a UBS business user will be able to ask an AI agent: if a country’s interest rate increases by a certain percentage, what are the implications to my client’s investment portfolio? The agent can perform a graph search to obtain all other connected economic entity nodes that might be affected by the interest rate entity node in the graph. We anticipate the AI-assisted graph knowledge will gain significant traction in the financial industry. Learn more For a deeper dive on how we created AICE and VEGA, check out this on-demand session from Ignite. We talk through our use of Azure Database for PostgreSQL and pgvector, plus we show a demo of our GraphRAG capabilities. About Azure Database for PostgreSQL Azure Database for PostgreSQL is a fully managed, scalable, and secure relational database service that supports open-source PostgreSQL. It enables organizations to build and manage mission-critical applications with high availability, built-in security, and automated maintenance.Viva Goals Retirement
Hi, Microsoft just announce at https://learn.microsoft.com/en-us/viva/goals/goals-retirement that Viva Goals ending in December 2025. How do you feel about this announcement? It was one of the top business use cases with significant business value in Viva Suite after Viva Topics deprecation.
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