Every year, I pull out my metaphorical crystal ball to predict where the financial-services industry is heading. It’s never easy, change is relentless, disruption is constant, and the future rarely plays out as neatly as we’d like. Yet, after months of research, conversations with industry leaders, and analysis of the latest reports, certain themes are clear.  In 2026, financial-services firms face a defining moment. Economic headwinds, new regulation, and accelerating technological change are converging. For banks, insurers, wealth managers and capital-markets players alike, the priority is no longer if transformation is needed, but how fast and how well it can be executed.  Below are the key trends shaping the year ahead, and what they mean for the industry’s leaders.  Banking & Capital Markets: Fee income, AI acceleration and embedded finance  Interest income is stabilising, but growth in 2026 will come from new sources. According to Deloitte’s 2026 Banking & Capital Markets Outlook, “strong, diversified non-interest income should continue to be a key revenue driver for banks”. Fee-based growth, from advisory, wealth services, data monetisation and embedded-finance partnerships will separate leaders from laggards.  Capital-markets activity is also expected to rebound as borrowing costs ease, spurring new issuance and deal-making. AI continues to shape productivity and profitability; Financial News London estimates that AI adoption could add US $170 billion in global banking profits over the next five years.  Mobile banking penetration is near-universal, with 77% of U.S. households engaging monthly through banking apps, and 31% of new primary relationships now opened with challenger or fintech providers.  Key implications:  Embedded-finance models (banking-as-a-service, in-app payments, API lending) become mainstream. Data monetisation moves from concept to revenue stream. Cost-to-income ratios remain under scrutiny and, automation and workflow redesign are essential. Integration across advisory, trading and wealth functions enhances client value.  Insurance: Margin squeeze, model shifts and AI-driven underwriting  Insurers face persistent margin pressure. Global premium growth is slowing, particularly in property and casualty lines, as inflation and competition bite. Deloitte projects U.S. P&C combined ratios rising from 97.2% in 2024 to roughly 99% in 2026.  AI and automation are redefining operations. Fraud detection, underwriting, and claims automation are moving from pilot to production. Industry analysis suggests AI could help insurers combat up to US $160 billion in annual fraud losses.  Key implications:  Core-system modernisation is non-negotiable. AI-driven underwriting and claims are moving mainstream. Strong data foundations and governance are prerequisites for trustworthy AI. Regulation around climate, catastrophe and cyber risk demands agile analytics.  Wealth Management: Customisation, digital-first experience and advisor enablement  In wealth management, personalization is the new performance metric. The direct-indexing market is projected to grow from roughly US $400 billion in 2021 to US $730 billion by 2026.  Meanwhile, an estimated C$2 trillion in new Canadian household wealth is expected by 2026, reflecting similar trends in other developed markets. Millennial and Gen Z investors want mobile-first engagement and hybrid human-AI advice that’s transparent and proactive.  Key implications:  Hybrid advisory models, humans augmented by AI insights, will dominate. Digital engagement through personalized dashboards and mobile experiences becomes table stakes. Compliance, records management and operational agility define competitiveness. Wealth platforms evolve to blend investments, planning and digital advice.  Capital Markets & Asset Management: Alternatives, diversification and return pressure  Asset managers face a recalibrated return landscape. According to J.P. Morgan Asset Management’s 2026 Long-Term Capital Market Assumptions, expected returns in 2026 are ~10% for private equity and ~7% for core infrastructure.  Traditional equity-bond correlations are rising, prompting investors to diversify via private credit, infrastructure, and real assets. J.P. Morgan estimates that adding 30% alternatives to a 60/40 portfolio can improve risk-adjusted returns (Sharpe ratio) by about 25%.  Key implications:  Broader adoption of alternatives to offset return compression. Greater reliance on analytics and integrated platforms for multi-asset strategies. Heightened liquidity management as markets become more correlated. Convergence between wealth, asset management and capital markets accelerates.  Cross-industry mega-trends for 2026  Generative AI & automation:AI is moving from experimentation to execution, transforming client service, operations and risk. Success depends on high-quality data and governance. Digital-first customer experience: With 77% of U.S. households banking via mobile each month, expectations for seamless, hyper-personalised service are unprecedented. Embedded finance & ecosystem plays: Financial products are increasingly embedded in non-financial platforms: retail, travel, healthcare, opening new distribution frontiers. Regulation, risk & data governance: AI governance, climate disclosure, open-finance and cyber-resilience are top board priorities. Integrated compliance and automated reporting are must-haves. Talent & culture transformation: Firms must close the skills gap in digital, data and AI while fostering agile, collaborative cultures that can adapt to disruption.  What this means for financial-services executives  For industry leaders, 2026 is about disciplined acceleration. Incremental change won’t keep pace with market evolution.  Strategic focus: Pinpoint where your institution can lead, data monetisation, ecosystem partnerships, advisory platforms or operational efficiency and, commit resources accordingly. Execution excellence: Convert strategy into measurable impact; scale AI and automation responsibly. Customer-centric innovation: Redesign journeys around outcomes, not organizational silos. Operational resilience: Balance innovation with cost discipline and robust risk controls. Cultural agility: Equip teams to collaborate across functions and embrace continuous learning.  The firms that blend innovation with trust and technology with transparency, will define the next era of growth.  Final thoughts  The future of financial services won’t be written by the largest institutions, but by the boldest. 2026 marks a turning point: those that digitize deeply, act decisively and lead with purpose will redefine what resilience and growth look like.  Transformation is no longer just about technology, it’s about mindset. The winners of 2026 will be the ones who stop predicting the future and start living it.  Are you ready to innovate? Find out more about OpenText Financial Services products and solutions here. The post 5 forces reshaping financial services in 2026  appeared first on OpenText Blogs.

Here's a familiar scene.  Another year, another wave of manufacturing trend predictions. AI agents that will revolutionize operations. Digital twins that mirror reality. Supply chain platforms promising real-time visibility. Sustainability dashboards track every carbon molecule.  All of it matters. All of its real. But there's something else happening beneath the surface that deserves attention.  Manufacturing's biggest opportunity in 2026 isn't just the technology everyone's buying. It's the information foundations most are overlooking.  Let me explain why this matters.  Trend #1: Information-native AI (your AI needs better content, not better algorithms)  The AI momentum is undeniable. ServiceNow is targeting $1 billion in AI revenue. SAP just announced 130+ AI capabilities. IDC predicts 80% of enterprises will have generative AI in production by 2026.  The technology is ready. The question is: is your content?  Your AI is only as intelligent as the information it can find and understand. And in most manufacturing environments, that information is spread across quality reports, CAD files, supplier certificates, production logs, maintenance records, work instructions, quality procedures, historical customer data, and systems that weren't designed to work together.  Consider what you're asking AI to do. You want it to understand context, to know that lot number "12345" on a Certificate of Analysis is different from purchase order "12345" or work order "12345." In regulated industries, you need it to provide complete audit trails showing which documents informed which recommendations.  Here's the reality that often gets overlooked: 80% of AI implementation effort goes to data preparation, not model deployment. Not training algorithms. Not fine-tuning models. Finding the information and making it usable.  While many organizations focus on deploying smarter algorithms, the ones pulling ahead are building smarter information foundations. They're creating what I call "information-native AI"—systems that don't just process content but understand manufacturing relationships. They connect quality certificates to production runs to supplier batches to customer orders. This is what creating a true digital thread across the product lifecycle actually means.  The opportunity? AI platforms are becoming increasingly similar. Information architecture is where differentiation lives.  Trend #2: Autonomous documentation (supply chain speed requires documentation speed)  Reshoring is reshaping manufacturing in real time.  74% of manufacturers are reshoring or nearshoring operations. Microsoft is moving 80% of its server components outside China by 2026. Companies are diversifying suppliers and building more resilient networks.  But here's what often gets missed: supply chains can only move as fast as supply chain documentation.  Consider what reshoring means operationally. New suppliers need onboarding: typically, 50-100 documents per supplier. Manual processes? That's often 6-12 months per supplier. A company reshoring to bring in 15 new suppliers is looking at 7-15 years of sequential onboarding delays before achieving full network diversity. Cross-border operations require 15-20 documents per border crossing. One documentation error can cost $10,000+ in delayed shipments.  And reshoring to Mexico or Vietnam sometimes increases documentation complexity compared to established China operations, not decreases it.  Then there's a certificate challenge. Certificates of Analysis. Certificates of Conformance. Sustainability certifications. Conflict minerals declarations. Different customers require different formats for the same information. You cannot accept shipments without them. You can't release products without validating them.  As most suppliers put it, "Moving assembly lines is straightforward. Moving component supply chains within a short timeframe is the real challenge."  Meanwhile, SAP is launching Supply Chain Orchestration in H1 2026. Oracle's pushing MultiCloud visibility. These sophisticated platforms promise real-time analytics and AI-powered optimization.  But here's the gap: over half, if not more, of supply chain information lives in unstructured documents that many of these systems struggle to process.  The manufacturers achieving true supply chain agility in 2026 are treating documentation as infrastructure, not overhead. They're not viewing this as a compliance function but as a supply chain infrastructure layer. They're automating supplier onboarding. They're building intelligent certificate management. They're making documentation move as fast as their products.  Trend #3: Proof-of-sustainability (from claims to evidence)  Sustainability has moved from nice-to-have too essential. Companies track carbon footprints, report emissions, and embrace circular economy principles.  But 2026 brings an important evolution.  Sustainability is shifting from calculation to documentation. From "we're sustainable" to "here's the proof."  The EU Digital Product Passport regulation takes effect in 2026 for batteries and expands to other product categories. It requires documented proof of sustainability claims with a complete chain of custody. Not estimates. Not projections. Verifiable evidence.  In B2B contexts, buyers increasingly require carbon documentation before awarding contracts. If you can't provide verifiable sustainability data as quickly as your competitors, you're at a disadvantage before discussions even begin about quality or price.  Then there's Scope 3 emissions tracking across thousands of suppliers, each with different reporting capabilities. Some have sophisticated systems. Some use spreadsheets. Some are just starting their sustainability journey.  The challenge? You can't aggregate what you can't verify. And you can't verify what isn't documented.  The circular economy adds another layer. Remanufacturing requires component-level traceability: material composition, repair history, remaining useful life. Many manufacturers struggle to provide this documentation for recent products, let alone products returning after years in service.  The companies succeeding in 2026 aren't just the ones with the best sustainability analytics. They're building what I call "proof infrastructure” systems that capture, verify, and distribute sustainability documentation across their supply chains.  They're making transparency measurable: "This component has a documented 40% lower carbon footprint than alternatives."  The connection point  Notice the pattern?  These three trends share something fundamental. They're information management opportunities that look like technology challenges.  While organizations invest in platforms and tools, leaders are also investing in information foundations. They're asking practical questions: Where does this content live? How do we capture it at the source? How do we connect it across systems? How do we make it findable, trustworthy, and traceable?  Not the most exciting questions at an industry conference, perhaps. But they're the difference between AI that delivers value and AI that looks good in demos. Between supply chains that respond in days and supply chains that take weeks to produce a certificate. Between sustainability claims you can prove and sustainability claims that crumble under scrutiny.  Manufacturing's 2026 leaders won't necessarily have the flashiest AI or the biggest technology budget.  They'll have the most intelligent information architecture.  So before deploying your next AI agent or investing in another supply chain platform, consider one question: Can these tools actually find and understand the information they need to deliver value?  If that question gives you pause, you've identified your real opportunity. The cost of waiting? Companies that don't establish information foundations in 2026 will spend 2027-2028 retrofitting systems, manage data chaos, and watch their AI investments under perform while competitors with better information architecture pull steadily ahead.  The post 3 manufacturing trends for 2026 that nobody’s talking about  appeared first on OpenText Blogs.

Governments across the globe are entering 2026 with an all-too-familiar reality: high accountability, low authority, and mounting expectations from citizens. Public sector CIOs are asked to deliver world-class services in line with private-sector benchmarks while dealing with outdated systems, constrained budgets, and increasingly complex mandates. The question is no longer whether governments need to modernize, but how quickly - and strategically - they can make it happen. According to Deloitte’s Government Trends 2025, nine key forces are reshaping the public sector, from the rise of mission-driven efficiencies to reimagined funding models. These insights echo what we at OpenText see every day: the urgent need for governments to adopt smarter, more integrated approaches to modernization that balance compliance, cost control, and citizen experience. The mandates driving change US federal agencies and their global counterparts face a growing list of executive orders and memoranda that dictate standards around cybersecurity, customer experience, privacy, digital accessibility, and AI readiness. Each initiative typically requires its own budget, integration project, and oversight. The result? Lengthy to-do lists, often longer at year’s end than at the start. But this fragmented, one-project-at-a-time approach is no longer sustainable. Government leaders are beginning to embrace platform-based strategies—leveraging cloud-based information management to meet multiple mandates simultaneously. With the right foundation, agencies can strengthen cybersecurity, digitize citizen records, modernize applications, and create AI-ready data structures with far smoother integrations and lower cost. From compliance to citizen-centricity Executive Order 14058, Transforming Federal Customer Experience, has placed a spotlight on delivering personalized, responsive services. Citizens now expect real-time updates on license renewals, grant applications, or benefits disbursement, mirroring the digital journeys they experience in the private sector. Governments that adopt smarter information management strategies can deliver on this mandate. By unifying structured and unstructured data across systems, agencies can:  Provide personalized self-service portals with timely, tailored updates.  Push critical alerts (like emergency weather instructions) across channels.  Deploy AI-powered chatbots that resolve common queries instantly. The result is a total citizen experience where transparency, speed, and trust are built into the service fabric.  Securing the public mission  There are heightened cyber risks governments face, with malicious access to public systems potentially impacting entire nations. The zero trust security model, now in its fourth year of broad adoption, provides a proven framework. But security must extend beyond the perimeter: it requires embedding compliance, encryption, and access controls into every workflow. By aligning modernization with cybersecurity and privacy mandates, agencies can transform compliance into a force multiplier for efficiency and trust. The path forward: Smarter information, smarter governments Deloitte’s research shows that governments worldwide are experimenting with new funding models, ecosystem partnerships, and citizen-centric approaches to overcome systemic constraints. Agencies that treat information as a strategic asset - GenAI-ready, secure, and connected - are the ones best positioned to thrive. Instead of incremental fixes, governments should prioritize scalable, platform-based solutions that:  Integrate seamlessly with existing systems.  Empower AI at scale while maintaining compliance.  Deliver measurable outcomes across multiple mandates simultaneously. By doing so, governments can shift from being reactive caretakers of outdated systems to proactive shapers of the citizen experience, achieving transformation not in spite of constraints, but because of them. A final thought... 2026 is the year where constraints can become catalysts. Public sector leaders who embrace cloud-based information management and AI-ready strategies will not only meet compliance checklists but also redefine the relationship between government and citizens. Smarter information is the foundation and smarter governments are the outcome. Are you ready to take action? Is your agency ready to turn constraints into transformation? Explore how OpenText can help you modernize securely, scale AI responsibly, and deliver a total citizen experience. Learn more about OpenText Government Solutions. The post Building smarter governments in 2026 appeared first on OpenText Blogs.

SMBs need help exchanging supply chain documents seamlessly, complying with industry standards, and delivering them on time all while keeping costs under control. That’s where OpenText™ Business Network B2B Integration Essentials comes in. B2B Integration Essentials, designed specifically for SMB businesses, is a cloud-based solution that enables SMBs to digitize and automate business document exchange with their customers, suppliers, and logistics partners using cost-effective, scalable, and easy-to-use platform to meet electronic data interchange (EDI) requirements without investing in complex infrastructure or specialized IT resources. With B2B Integration Essentials you can: Exchange EDI supply chain documents with your Trading Partners hassle-free without the need of understanding EDI or any need for extra IT expertise Connect quickly to the OpenText Business Network (the world’s largest with over 1 million trading partners) and start exchanging documents with trading partners in days, not months. Automated transactions to replace manual processes with automated EDI workflows for purchase orders, invoices, shipping notices, and 85+ other supported EDI document types. Ensure compliance with industry standards and connectivity requirements of large enterprises and marketplaces. Gain visibility and track document status and transaction history through an intuitive web portal. Scale easily by adding new trading partners and transaction types as your business grows without major IT investments. What’s New in the Latest Releases? OpenText Business Network recently released several new features as part of its B2B Integration Essentials solution designed to make digital supply chains automation and collaboration more accessible to SMB organizations. Release 25.2 and Release 25.3 Highlights 3PL transaction support: organizations receive enhanced connectivity with third-party logistics providers with support for key 3PL transactions, improving visibility and coordination in shipping and fulfillment. FSMA standard support: By meeting FSMA standards, your B2B Integration Essentials empowers users with enhanced food traceability, faster compliance, and secure, automated data exchange—ensuring safety, efficiency, and trust across the supply chain. Tabbed UI browsing and workflows for improved user experience: users can now more easily manage multiple workflows simultaneously, reducing time spent switching between screens and boosting productivity. API for Advanced Shipping Notice (ASN) labels: organizations can now connect to an API to generate ASN labels, enabling faster, automated label creation and ensuring compliance with trading partner requirements. Trading Grid Online Single Sign-On: organizations can now streamline user access by enabling SSO through OpenText Trading Grid, improving security and simplifying user authentication across internal and external applications. Expanded trading partner kit support: organizations can now access a library of 500+ pre-configured trading partner kits, accelerating onboarding of trading partners, enabling faster compliance with major retailers and marketplaces, and reducing complexity setup for new partners. Ready to Simplify Your B2B Integration? For SMB organizations, B2B Integration Essentials empowers you to participate fully in global supply chains without the burden of managing complex integration technology. Learn more about how OpenText Business Network and B2B Integration Essentials can help your business thrive in today’s connected economy. The post What’s New: How B2B Integration Essentials simplifies global SMB supply chain connectivity appeared first on OpenText Blogs.

TLDR: The Aviator Playground is where AI gets real. It’s a hands-on space to explore how intelligence can solve everyday challenges—from managing content to securing data and speeding up decisions. No buzzwords, just working AI you can see, test, and believe in. Check it out at OpenText World.  Picture this: You walk into the Music City Center in Nashville, and the energy hits you immediately. Screens glow, conversations buzz, and at the center of it all is something extraordinary—the Aviator Playground. It’s not a booth. It’s not a demo. It’s a living, breathing AI ecosystem designed to spark imagination and show you what’s possible when intelligence meets enterprise scale. This year at OpenText World, we’re not just talking AI, we’re inviting you to experience it. To play. To build. To believe. Why we built the Aviator Playground AI isn’t just a trend—it’s transformative. But transformation needs more than slides and soundbites. It needs proof, play, and people. That’s why we created a space where you can roll up your sleeves and see how Aviator works across every OpenText business unit. It’s hands-on, immersive, and tailored to real-world challenges. Explore the pods: Where AI meets real-world challenges The Aviator Playground is organized around a series of pods. Each pod is a chapter in the story of how businesses thrive with AI. Step inside and see what happens when intelligence transforms everyday work: Content AviatorPicture a world where compliance checks and sustainability goals aren’t a scramble. Content Aviator turns mountains of documents into clear, actionable insights—so planners can design smarter spaces without the guesswork. Experience AviatorImagine creating digital experiences that feel effortless to build and unforgettable to use. Experience Aviator blends creativity and intelligence to craft dynamic, personalized web journeys that connect instantly, scale seamlessly, and delight every visitor. Business Network AviatorWhat if you had your own supply chain advisor that could predict the future? Business Network Aviator spots disruptions before they happen, keeping goods moving and businesses resilient in a world that never slows down. Cybersecurity & AppSec AviatorPicture a digital fortress that learns every day. Cybersecurity Aviator doesn’t just react.  It anticipates and neutralizes threats before they can strike. Service Management AviatorImagine HR questions answered instantly, IT tickets resolved before they escalate. Our Service Management Aviator makes service feel effortless, so teams can focus on what matters most. DevOps AviatorThink of development cycles that move at the speed of ideas. DevOps Aviator automates the heavy lifting, so innovation never waits for a bottleneck. OpenText MyAviator: Your AI assistant that works like you do.   At the heart of the Playground is MyAviator: your personal productivity assistant for effortless work. For the project lead, it turns 50-page reports into five key points. For the manager, it delivers instant decision briefs with budgets, risks, and recommendations. For the knowledge worker, it connects threads across sources to surface what matters. For the IT service manager, it translates incidents and SLAs into clarity. And for the team lead, it transforms raw notes into polished slides in seconds. MyAviator helps you test out ideas, build smarter outcomes, and believe in productivity again. It’s not just an assistant. It’s how enterprise work starts to feel effortless. Then there’s Aviator Studio: the creative engine of the Playground. It’s where ideas turn into intelligent agents without a single line of code. Want to automate a workflow? Build a custom AI assistant? Test prompts that make your processes smarter? Aviator Studio gives you the tools to design, deploy, and refine AI solutions in minutes. More than a sandbox, Aviator Studio is your launchpad for customizing your own agentic AI digital workforce. Why It Matters Every business function (IT, customer support, sales, marketing, HR, etc..) faces its own challenges—content chaos, customer churn, supply chain shocks, security risks, operational inefficiencies. The Aviator Playground is a live demonstration of how AI can solve your problems today, not someday. It’s where strategy meets execution, and where you’ll leave with ideas you can act on immediately. Come play where AI gets real—visit the Aviator Playground at OpenText World Nashville and see intelligence in action or visit our virtual AI playground and come play, explore and learn more about our Aviator AI.  We would love to hear from you!  Send us an email at aviator@opentext.com and let us know what you liked most about the Aviator playground, which Aviator is your favorite and/or what was your favorite use case. The post Come Play Where AI Gets Real: Inside the Aviator Playground at OpenText World appeared first on OpenText Blogs.

We’re living through a seismic shift in enterprise technology. The rise of generative AI, powered by large language models (LLMs) trained on 10–15 zettabytes of public data, has captured global attention. But here’s the real game-changer: behind enterprise firewalls lies a treasure trove of proprietary data, 100–150 zettabytes strong. When this data meets AI, the possibilities are transformative. AI isn’t a distant future. It’s here. And it’s not waiting for us to catch up. The misconception that AI will arrive as a single, all-knowing super-agent is misleading. The reality is far more nuanced and promising. The next generation of enterprise AI will be built on domain-specific agents. Think -- RCA agent, FalsePositive Agent, TicketAgent, TranslationAgent, ReleaseNotesAgent, ContractReview Agent, and more. These agents won’t just mimic human intelligence.  Instead, every human will have an army of agents, and those agents will need an orchestrator to keep them all coordinated. The orchestrator can take on the identity and permission settings of the human – e.g., what data it can access, what applications it is entitled to, and what decisions it can make. This is a game changer.  Have you ever had an experience of approving a workflow only to realize that you require a bunch of data from different sources to make a decision?  Well, now Agentic AI can be applied to securely automate that critical business process.  Or have you ever tried getting information about your paystub, raised a ticket, and waited for days to get an answer?  Agentic AI can be applied to cross functional collaboration with relevant insights and speed.  But here’s the catch: AI is only as good as the data it’s trained on. And hallucinations, those moments when AI confidently delivers false information, are real and potentially damaging. For example, a recent incident involving a government agency saw an AI-generated summary cite fictitious judges, leading to reputational harm and eroded trust. In another instance, a customer service agent incorrectly gave a customer a refund that was not within the company’s policy.  So, how do we avoid the risk of false confidence?   How do we go from agents that give 90% correct answers to 100% correct answers if the 10% risk is too high for a mission critical process?  How do we ensure that data accessed was only entitled to that agent? How do we avoid agent sprawl and prevent shadow AI in the future?   The answer to secure AI lies in trust and data.  Without data, there is no context. Without context, enterprise AI fails. Organizations must take control of their digital agents, define the knowledge they possess, the policies they follow, and the decisions they’re authorized to make.  Security cannot be an afterthought; it must be built in. For years we’ve designed governance, permissions, and lifecycle management for humans, now it is time to do the same for the agents.This is not just an IT challenge. It’s a C-level strategic imperative. This is where the OpenText AI Data Platform (AIDP) comes in.  OpenText is shaping our product roadmap to bring important capabilities to customers: Discover – harness the proprietary private data across functions (structured and unstructured) to feed AIConnect – your OpenText applications as data products to see information in new ways to drive cross-functional insightsAct – via an AI control plane designed for secure enterprise and customize agents to meet your specific needsGovern – with compliance aware services that enable you to redact and retain data based on your company policies    Monitor – model drift and hallucinations, track feedback loops, refine prompts and models, maintain audit trailsOur goal is to be the most open AI Data Platform through an architecture enabled by APIs and strategic partnerships that bring together the structured and unstructured data. We have been the custodians for 30 years of passive data sets from our customers, and now it is time to help them bring new life to old data.  While we don’t know exactly how quickly and what new innovations will come next with AI, we want to provide customers with the platform and capabilities to innovate faster.  Just like the original mobile app store didn’t know it would become the foundational enabler for future businesses like Uber, we are at the beginning of the next frontier of practical AI. To Get Started Today, OpenText is introducing OpenText Knowledge Discovery – a set of tools to help ingest structured and unstructured data and to automate metadata tagging to fuel AI.  This solution category includes: OpenText Capture and Intelligent Document Processing OpenText AI Content Management (Connectors, Knowledge Graphs, etc.)   OpenText Observability (Applications, Infrastructure, Assets, Networks)   OpenText Quality Management (Quality Logs, Code Sources, etc.) OpenText Metadata Service API   Also, OpenText is introducing OpenText Data Compliance – a suite of services and APIs that make governance proactive and persistent: OpenText Advisory Services (Get AI Ready Assessment) OpenText Retention Service API (Records Retention Policies) OpenText Risk Guard Service API (Risk and Compliance Parameters) OpenText Data Privacy and Protection (Voltage) OpenText Core Threat Detection and Response    Lastly, OpenText is introducing OpenText Aviator AI Services – a group of professional services experts who help customers from discovery to deployment on their AI journey: PLAN: AI Roadmap Definition AI Data Readiness Workshop LLM Discovery Workshop Business Workspace Workshop BUILD: AI Adoption Acceleration AI Data Discovery and Preparedness AI Business Value Definition  RUN: AI Usage Enablement Aviator Learning Services Aviator Studio Agent Accelerator Aviator Model Services These capabilities abstract the complexity of compliance and enforce it by design, not after the fact. But governance doesn’t stop at the enterprise’s edge. As AI becomes more embedded in global operations, questions around sovereign data and sovereign AI are rising. Where is your data stored? Where are your models deployed? What borders does your data cross? Whether your workloads reside on-prem, in the cloud, or in hybrid environments, OpenText gives you control and choice.  To support various deployment models, our Aviator AI capabilities are multi-cloud, multi-model, and multi-applications. With an upgrade to release 26.1, OpenText Aviator entry tier package will now be included in Core Content Management, Core Service Management, and Core Communications Management. Also with release 26.1, Aviator will be now available on-prem for key OpenText applications including Content, Content Management, Communications, Service Management, Software Delivery, and Application Security. Why does all this matter? Because the payoff is exponential. AI isn’t just about efficiency; it’s about new capabilities. From personalized customer experiences to proactive operations and new revenue streams, AI enables enterprises to respond faster to markets, risks, and opportunities. But speed without security is reckless. Innovation without governance is fragile. That’s why OpenText is building the tools, services, and platforms to help you design the future, secure it, and lead with intelligence. The future of enterprise AI won’t be dictated by vendors or regulators. It will be designed by your data, your workflows, and your values. So, buckle up. The journey to intelligent, secure enterprise transformation has begun. The post From Hype to Practical Enterprise AI appeared first on OpenText Blogs.

At OpenText World this week, we look ahead to the next chapter of innovation for all our business units. From content to cybersecurity, each business is delivering breakthrough capabilities that reflect our commitment to AI-first design, customer-centricity, and cloud modernization. Content Management: From Content in Context to AI in Context OpenText Core Content Management continues to set the standard as the leading SaaS AI content management platform, offering seamless out-of-the-box integrations with enterprise systems including SAP, Microsoft, Salesforce, and more. This deep connectivity empowers organizations to unify their content ecosystems and accelerate digital transformation. Coming with the release of OpenText (OT) 26.1, customers will be able to unlock the power of OpenText Content Aviator, our AI-driven capability suite designed to streamline and elevate key business processes. From claims processing and invoice management to talent acquisition and onboarding, these intelligent use cases will help teams work smarter, faster, and with greater precision. Looking ahead to release OT 26.2, we’ll be introducing OT MIGRATE - a robust set of cloud migration tools and professional services that combine automation with configuration intelligence. These solutions are purpose-built to simplify and accelerate the move from static repositories to the AI-enabled workflows in the cloud—reducing complexity, minimizing disruption, and enabling customers to fully capitalize on the agility and scalability of modern content platforms. Digital Experience: Conversations as the New Digital Storefront OpenText Experience is redefining customer engagement with a bold vision rooted in disruptive simplicity. With the upcoming launch of release OT 26.2, we will introduce a unified communications platform designed to help organizations embrace the transformative power of AI as the new UI. Without compromising the robust technology stacks that drive dynamic content and digital optimization, OpenText Experience bridges the gap between innovation and integration, enabling customers to harness AI while preserving the systems that support their operations. As customer expectations evolve, customer engagement is no longer about static interactions; it’s about real-time, personalized conversations. The innovations in the OpenText Experience roadmap are designed to help organizations move from promise to practice, turning AI-powered possibilities into measurable outcomes. Business Network: AI Driven Supply Chain OpenText is advancing a bold vision for an AI-optimized supply chain—transforming fragmented data into unified intelligence. The days of “more apps, less maps,” where commerce and trade data moved in isolated, point-to-point silos, are behind us. In today’s landscape of rising compliance and regulatory complexity, OpenText is building a smarter, more connected future. At the heart of this transformation is a powerful data orchestration layer within the OpenText Business Network, designed to activate AI agents that simplify the intricacies of global supply chains. These agents—focused on product delivery, supply forecasting, and quality management—are reshaping how businesses anticipate, respond to, and manage operational dynamics. By integrating these intelligent capabilities, we’re enabling organizations to shift from reactive firefighting to proactive decision-making. A prime example is the release of OpenText Trading Grid 25.4, which delivers network-wide visibility and anomaly detection. This empowers businesses to identify bottlenecks, predict demand fluctuations, and ensure quality compliance—before issues escalate. Observability & Service Management: Building a Ticketless Enterprise OpenText Service Management is evolving to meet the needs of modern organizations—now with purpose-built modules for IT, HR, and Customer Support. These enhancements bring workflow intelligence to life, leveraging AI to automate service delivery, accelerating resolution times, and simplifying both employee and customer experiences. In today’s complex digital environments, OpenText Observability is also essential to bring total asset visibility — you can’t manage what you can’t see. From applications to infrastructure to networks, visibility across systems is foundational to effective triaging and preventing issues and incidents. OpenText helps organizations shift from reactive support models to proactive, intelligent service ecosystems—where every interaction is optimized, and every outcome is elevated. At the heart are AI-powered operations, where intelligent agents are programmed to handle high-value, precision tasks. These agents deliver measurable efficiency gains—but only when guided by the right data, parameters, and guardrails. This ensures that automation is not only fast, but also accurate, secure, and aligned with business objectives. Cybersecurity: Secure Information Management OpenText Cybersecurity’s strategy centers on empowering AI to act as a force multiplier in cybersecurity. Intelligent agents, programmed with the right data, precise parameters, and governance guardrails, are key to automating SOC analyst roles and drive better threat detection and response. This ensures that security is not only robust, but also agile—ready to adapt to the dynamic nature of today’s digital environments. Our latest innovations in release 25.4 reflect a deep commitment to proactive, AI-driven cyber resilience. We’ve delivered advanced capabilities in data privacy, encryption, and application security, alongside the launch of Core Identity Management in the Cloud. Our Threat Detection and Response solutions now feature expanded integrations, offering real-time protection and compliance across hybrid environments. Cyber defense today requires a multi-layered approach—spanning data, applications, networks, and the edge. To stay ahead of evolving threats, organizations must protect identities, safeguard sensitive data, and defend against malicious actors both externally and internally. With these advancements, OpenText is helping organizations build resilient, intelligent security frameworks that protect what matters most—data, people, and trust. DevOps: AI-First DevOps for Regulated Industries OpenText’s DevOps is embracing an AI-first approach to software delivery—reimagining DevOps for the enterprise. At the heart of our strategy is a commitment to intelligent automation, secure development, and industry-specific innovation. Our platform now delivers tailored solutions for highly regulated industries, with a strong focus on Financial Services Institutions (FSI), Life Sciences, and Automotive. These solutions embed compliance directly into workflows, including e-signature capabilities, ensuring faster, safer deployments that meet stringent regulatory requirements. The OpenText DevOps roadmap is driven by a vision of autonomous testing and delivery, powered by specialized AI agents and a connected knowledge hub. These agents enable predictive insights, continuous improvement, and fit-for-purpose automation across the software lifecycle. Lastly, our fully integrated solution with OpenText and GitHub Copilot provides the industry leading solution for end-to-end functional testing, particularly as we bring in application security.  These strategic alliances enable ADM to be the #1 testing platform for enterprise applications including SAP, Oracle, Workday, Salesforce, and more. Analytics & Legal Tech: Intelligence that Works for You OpenText is advancing a new era of data insight and legal innovation with OpenText Intelligence Aviator and OpenText eDiscovery Aviator. Designed to put AI to work for data analysts and legal teams alike, these innovations transform how organizations uncover meaning and manage risk. Intelligence Aviator turns enterprise data into a strategic asset—using AI-driven analytics to reveal patterns, predict outcomes, and guide smarter decisions. eDiscovery Aviator applies the same intelligence to the legal domain, automating document review, case summarization, and investigations to save time while strengthening defensibility. Together, they represent the future of informed, intelligent action—where AI not only analyzes but truly understands. Across all BUs, our roadmap is more than a set of features—it’s a reflection of our purpose: to help customers unlock the power of information through AI, cloud, and secure platforms. We’re excited to bring these innovations to the market and look forward to partnering with you on this journey. Stay tuned for more updates as we light up the future—together. The post Practical Enterprise AI: A Unified Innovation Roadmap Across OpenText Businesses appeared first on OpenText Blogs.

If you're leading a legal department at a manufacturing company, you know the numbers all too well. The average manufacturing firm faces regulatory compliance costs, of $29,100 per employee annually, which are more than double what other industries pay. For small manufacturers, that figure jumps to a staggering $50,100 per employee. Add to that the rising tide of product liability litigation, with product liability cases climbing to 5,826 filings in 2022, and you've got a perfect storm of legal complexity that's draining resources and demanding attention at the C-suite level. However, here's what's changing: Manufacturing companies are no longer using AI solely to optimize production lines and improve quality control. Forward-thinking legal departments are leveraging the same technology to transform how they manage risk, regulatory, and internal investigations, respond to litigation, and drive strategic decisions. And the results? They're game changing. From production lines to legal timelines: The manufacturing legal challenge Let's be honest. The legal landscape for manufacturers has never been more complex. Your team is juggling product liability claims across global supply chains, navigating a patchwork of regulations spanning GDPR, CCPA, and industry-specific requirements protecting intellectual property in an era of rapid digital transformation, and managing cybersecurity incidents that require immediate legal response. On top of that, there's an information explosion. We're talking about emails, contracts, presentations, text files, quality reports, and production documents. The unstructured data that's critical when litigation hits, but overwhelming to review manually. And when that content is multilingual, the complexity multiplies exponentially. Meanwhile, you're being asked to do more with less. Legal budgets aren't growing at the same pace as these challenges, and outside counsel fees continue to climb. It's a pressure cooker situation that many of your peers are facing right now. How can AI improve manufacturing legal operations? This is where AI-powered legal technology fundamentally changes the equation. And we're not talking about some theoretical future state—this is happening today, delivering measurable results for manufacturers who've made the leap. The transformation extends beyond legal departments, too. Manufacturing companies are increasingly recognizing AI as a strategic imperative across operations. From optimizing supply chains and enhancing quality control to driving predictive maintenance, AI is reshaping how manufacturers compete. The legal department shouldn't be left behind in this evolution. In fact, it's uniquely positioned to demonstrate AI's value in managing risk and protecting the business. Speed to facts: How Bosch revolutionized legal decision-making When Bosch's legal team faced the reality of manual document review processes slowing their case strategy development, they knew something had to change. As one of the world's leading manufacturing and technology companies, Bosch couldn't afford to be reactive when it came to legal matters. They needed to be proactive, targeted, and fast. Partnering with OpenText and our GenAI experts, they explored ways in which AI could streamline investigations, reduce costs, and deliver data-driven insights.  This allowed the team to build on its use of OpenText™ eDiscovery, which has historically helped Bosch's team process millions of documents and thousands of terabytes of data. By using OpenText eDiscovery, this is work that would have taken months with traditional manual review. The platform's machine translation capabilities eliminated the need for human translators, a particularly critical feature for a global manufacturer dealing with multilingual communications across dozens of markets. But here's what really caught my attention: Franziska Fuchs, Bosch's Vice President of eDiscovery, shared insight into the power and flexibility of the platform. During product shortage situations, something every manufacturer has dealt with in recent years, they used the technology to track and analyze communications patterns, giving them unprecedented visibility into how issues were being communicated and managed across the organization. "We can see a significant return on investment," Fuchs explained in relation to GenAI usage. "I estimate cost savings in the millions." That's not marketing speak; that's peer reporting real results from a real implementation. This shift is fundamental: Bosch is transforming from reactive firefighting to proactive, forward-thinking for its investigations. Their legal team gained the ability to assess cases faster, develop a strategy earlier, transform legal workflows, and make data-driven decisions that are aligned with business objectives. Why is eDiscovery important for manufacturers? Let's talk ROI, because that's what matters when you're presenting technology investments to your CFO and CEO. Here's what manufacturing legal teams should expect from modern eDiscovery and legal AI platforms: and legal AI platforms: Faster case assessment and strategy development: What used to take weeks now takes days or even hours. Early case assessment becomes truly early, giving you the strategic advantage of time. Dramatic cost reduction: Outside counsel fees for document review can be slashed. We're talking about billable work that might have cost hundreds of thousands of dollars now being handled in-house for a fraction of that investment. Multilingual document review efficiency: For global manufacturers, this is huge. No more waiting for translators or paying premium rates for multilingual review. Technology handles it seamlessly. Enhanced risk management: Early visibility into case facts means better risk assessment, improved win-loss analysis, and more informed settlement decisions. You're not flying blind anymore. Better business alignment: When you can quickly analyze data and present insights to business leaders, you become a strategic partner rather than just a cost center. Your legal decisions are grounded in data, not gut feelings. Scalability without headcount: Here's the kicker: you can handle increasing volumes of legal work without proportionally increasing headcount. That's a powerful argument in any budget discussion. As noted in the Bosch success story: "Speed to facts is critical in the legal process and managing risk should be proactive, not reactive." And the numbers back up that urgency. Companies' legal spending has jumped nearly 30%, with average litigation costs for companies with $1 billion+ in revenue reaching $4.3 million in 2024, which is up from $3.9 million just a year earlier. In this environment, having technology that delivers both speed and accuracy isn't a luxury. It's a competitive necessity. Building legal technology capabilities that scale: Learning from Bosch's success So what can we learn from Bosch's approach? A few key principles emerge: Maintain in-house control: While outside counsel will always have a role, bringing core eDiscovery capabilities in-house gives you control, reduces costs, and builds institutional knowledge. It's your data, your cases, and your strategic decisions. Invest in AI literacy: Your team needs to understand not just how to use the tools, but how to think about AI-assisted legal work. Training and change management aren't afterthoughts; they're essential to success. Choose trusted technology partners: For high stakes matters, you need partners who understand both the technology and the legal landscape. This isn't about buying software; it's about building long-term capabilities. Integrate with business processes: The technology should fit into your existing workflows and systems, not force you to work around it. Think about how eDiscovery connects to your broader legal operations and business processes. Plan for change management: Be realistic, introducing AI-powered tools will change how your team works. Some people will embrace it immediately; others will need more support. Plan for that. The future of manufacturing legal operations: Necessity, not luxury Here's the bottom line: AI-powered legal technology has moved from "nice to have" to "must have" for manufacturing legal departments. The complexity isn't decreasing. The data volumes aren't shrinking. The pressure to do more with less isn't letting up. But the good news? The technology is ready. It's proven. Companies like Bosch are demonstrating that forward-thinking legal technology partnerships deliver measurable results. Not in the distant future, but right now. The question isn't whether to embrace AI in your legal operations. It's how quickly you can move to capture the advantages it offers. Ready to transform your legal operations? Explore these resources to learn more: Read the full Bosch case study Watch Bosch's legal transformation story (video) Find your eDiscovery solution for Manufacturing Discover how AI is driving efficiency across manufacturing operations  Understand how we are leveraging GenAI with OpenText eDiscovery Aviator The post How AI-powered legal technology is transforming manufacturing appeared first on OpenText Blogs.

From geopolitical disruptions to shifting customer expectations, businesses need more than just digital connectivity, they need intelligence to thrive in today’s volatile global economy. That’s where Artificial Intelligence (AI) steps in, transforming traditional supply chains into autonomous, insight driven ecosystems.  Powered by real-time analytics, intelligent automation, and connected IoT devices, today's most innovative businesses are rapidly embracing AI-driven technologies to proactively manage complexity, enhance transparency, and unlock unparalleled efficiencies.   Creating an ecosystem that enables smarter collaboration  Businesses that connect EDI and APIs with their ERPs, e-commerce platforms, and supply chain management systems gain a competitive advantage in agility, efficiency, and customer satisfaction, for example:   A retailer integrates EDI with its ERP and e-commerce platform to automatically adjust inventory levels and reorder stock based on real-time sales data, reducing out-of-stock and speeding up replenishment.  A manufacturer connects EDI and APIs with its supply chain management system to automate order processing and shipment tracking, eliminating manual data entry and reducing order cycle times.  A distributor uses EDI-to-API integration to provide real-time order status updates and accurate delivery estimates to customers, improving transparency, and trust across the supply chain.  This level of B2B integration enables real-time data collection across multiple internal and external systems that can then be optimized, or cleaned, for AI consumption. It also makes for more seamless operations, faster onboarding, and smoother trading partner relationships within their supply chains.   Accelerating automation through AI-powered traceability and IoT  EDI and B2B integration solutions are increasingly leveraging IoT devices and sensors, enabling real-time data collection from logistics, warehouses, and more, further supporting live inventory updates and supply chain optimization.   Many supply chain traceability solutions also now link AI with IoT sensor networks, providing a transparent view of shipments, assets, and production status across global networks.   With these measures in place, businesses can monitor goods in transit, detect anomalies, ensure regulatory compliance, and grow customer trust by optimizing their supply chain and customer experience.   This work also reduces manual effort, minimizing errors, and accelerating productivity; enables intelligent workflows that adapt to changing conditions, and improves data quality across a supply chain.   Gaining automation through supply chain visibility and predictive analytics   Visibility has long been the holy grail of supply chains. Yet many organizations still struggle with fragmented data, siloed systems, and delayed insights, keeping decision-making reactive instead of proactive.   Doing the necessary work up front – creating a connected, automated ecosystem that enables smarter supply chain collaboration – not only improves how businesses interact with suppliers, customers, logistic providers, and financial institutions, it also aids AI-readiness.   With AI layered on top of a strong, secure, and connected digital supply chain foundation, businesses can:  Gain real-time visibility into inventory levels, shipment status, and trading partner performance to improve responsiveness and decision-making.  Leverage actionable insights to forecast demand more accurately, automate inventory replenishment, and minimize costs from overstocking or stockouts.  Receive intelligent alerts that detect anomalies and recommend immediate actions, while predictive analytics anticipate disruptions and suggest workflow improvements across planning, scheduling, and delivery.  How OpenText helps build smarter, more connected AI-ready supply chains  AI in supply chains has moved far beyond simple automation. Modern solutions like OpenText Business Network Cloud integrate deep learning and predictive analytics to optimize end-to-end supply chain operations and collaboration, so that supply chains are not just connected but intelligent.   OpenText Business Network Cloud has embedded AI into its Trading Grid platform and linked B2B integration solutions, to provide an AI self-service advisor that assists with day-to-day operations, so users:   Get instant, accurate answers in plain language to use OpenText Trading Grid more efficiently as part of supply chain operations   Gain deeper supply chain insights linked to business data by finding and sharing contextual responses based on customer-specific B2B and EDI inquiries   Analyze simplified EDI payload and transaction information shared in business-ready language so everyone in an organization can understand it regardless of technical expertise   Achieve faster resolution times by offering clear, actionable guidance on complex error codes while also providing the ability to create a support ticket while in the app.  OpenText’s expertise ensures the path from visibility to autonomy is seamless, secure, and scalable, so every supply chain is not just surviving but thriving with AI.  The post From visibility to autonomy: AI’s role in optimizing supply chains  appeared first on OpenText Blogs.