Let’s be honest—work isn’t slowing down. Expectations keep rising, inboxes keep filling, and disconnected tools keep piling on. You're asked to move faster, do more, and stay sharp… all while navigating a sea of tabs, systems, and files. 

There’s no shortage of AI tools promising help. But most fall short: too generic, too risky for company data, or simply unreliable.

If you’ve ever thought, "There’s got to be a better way,” you’re not alone.

That’s why we built OpenText™ MyAviator – a secure, enterprise-ready personal AI assistant that actually works for you.

Imagine your workday like this instead...

 With OpenText MyAviator, it’s simple. Upload your documents—reports, transcripts, spreadsheets, anything—and just ask.

“Help me prep for my customer meeting.”
 “Summarize this compliance doc.”
 “Draft a briefing from last week’s call.”

In seconds, you get relevant, actionable answers – minus the AI hallucinations or security risks. No prompt engineering.  No need to cross-check. Just clear results that help you move faster and smarter.

Need to translate a document or redact sensitive info? One click. Too much to read? Generate a podcast and listen on the go.

That means you get more done in less time without the noise, the risk, or the rework. That’s a smarter workday with OpenText MyAviator, designed to make your life easier and your outcomes stronger.

Meet OpenText MyAviator: A personal AI assistant that actually works for you

Most AI tools aren’t built for the enterprise. OpenText MyAviator is.

It’s secure by design, works out of the box with your OpenText products, and is available wherever you work—web or mobile. Just ask in everyday language and let it do the rest:

• Natural language search and summaries
   Ask questions like, “What were last quarter’s SLA violations?” or “Summarize these 10 case files.”
• Cross-format intelligence
   Analyze PDFs, transcripts, spreadsheets, even video files—in any format, in any language.
• Smart meeting support
   Upload transcripts and get instant action items, highlights, and follow-ups.
• Built-in tools that save time
   Redact PII, translate documents, generate FAQs, prep briefings, or convert dense reports into podcasts.

It’s powerful, personal, and built for everyday productivity.

Value you can see (and feel)

Most public AI tools come with more risk than reward. You end up rewriting, double-checking facts, worrying about data privacy, or just abandoning them altogether.

OpenText MyAviator is built differently to meet the needs of real work:

• Works with what you have: Supports all kinds of files and formats, from documents to videos and beyond
• Ready to go: Built-in tools to remove sensitive info, translate, summarize, and transcribe
• Secure and private: Backed by decades of OpenText experience in keeping enterprise data secure and compliant, the AI doesn’t “learn” from your data

 Real productivity for real work

OpenText MyAviator supports knowledge workers across functions:

• A financial analyst can turn spreadsheets into summaries in seconds.
• A customer support lead can auto-generate FAQs from case histories.
• A field engineer can convert equipment manuals into quick-reference guides.
• A legal team can summarize contracts, redact sensitive content, and prep case files—without switching tools.

From project managers to HR, from marketing to compliance, anyone buried in content can use MyAviator to cut through the noise and focus on what matters.

Why now?

The demand to do more in less time isn’t going away and not all AI tools are up to the task. If they can’t fit your workflow, protect your data, or deliver reliable results, they’re just adding noise.

OpenText MyAviator bridges the gap. It helps you:

• Make confident decisions by cutting through the clutter
• Save hours of manual work every week
• Stay compliant with built-in redaction and privacy controls
• Work smarter, wherever work happens

Let’s put AI to work for you

OpenText MyAviator isn’t just another AI add-on. It’s a personal, secure, always-on assistant to help you and your teams work faster, smarter, and more confidently.

Want to see it in action?

Sign up to get OpenText MyAviator access or meet us at OpenText World 2025 to connect with our AI experts and learn how we’re transforming enterprise work – one personal AI assistant at a time.

The post Meet your secure personal AI assistant appeared first on OpenText Blogs.

It’s a foregone conclusion that for an organization to become limitless it must embrace innovation. The two go hand in hand; it’s hard to imagine you can remove barriers and achieve your business goals without innovative technology paving the way.

Companies of all kinds are facing immense pressure to innovate and stay ahead of the competition. Many are looking to AI, not only because of its own capabilities but also as an enabler for other technologies to deliver advanced benefits.

AI as a force multiplier

By integrating AI with existing technologies, organizations can unlock new possibilities that were previously unattainable. For instance:

• Adding AI to traditional threat detection tools enhances them to automatically and continuously learn with rapid deployment—allowing new threat detection models to be in place within hours to protect from new and evolving threats.
• A secure AI assistant integrated with content management puts chat-based conversational search, content discovery, summarization, and translation right at employees’ fingertips to boost productivity.
• Integrating AI into DevOps optimizes the software delivery lifecycle by helping predict software delivery times, automating software testing, and simplifying QA and testing.
  

Agentic AI

AI innovation continues at a breakneck speed. Next up is agentic AI.

Agentic AI can perform tasks, make decisions, and solve problems without human intervention. One example of how agentic AI delivers benefits is with customer experience applications—by understanding and anticipating customer needs with a high degree of accuracy. For instance, an agentic AI system could analyze customer data and autonomously manage account issues, such as adjusting subscription plans or resolving billing disputes after assessing user preferences and historical interactions.

By reducing the reliance on human agents for routine tasks, agentic AI helps businesses enhance operational efficiency and significantly improve response times. And as these systems learn and evolve, their capabilities will only get better.

Partner to innovate

When it comes to innovation driving organizations forward, there’s no limit. The challenge is keeping up—how do you create a roadmap for innovation when technology moves so quickly? A trusted partner is the answer.

According to a recent survey conducted by Foundry,[i] respondents said the most important benefits they look for from a strategic partner to help them navigate AI and information management are to provide integration capabilities (52%), tools to classify and enrich information for AI (51%), and strong information security (48%).

At OpenText, we’ve spent more than 35 years creating information management solutions that build digital platforms for knowledge workers. Today, we’re helping our customers create the digital knowledge worker of the future, a future that isn’t just digital—it’s limitless.

[i] Foundry survey commissioned by OpenText, Information Management for an AI-Driven Future, June, 2025
 

Read more blogs from the Limitless series:

Limitless: What it means for modern customers in an AI-first world

Limitless: What it means for productivity

Limitless: What it means for integration

Limitless: What it means for peace of mind

The post Limitless: What it means for innovation appeared first on OpenText Blogs.

One of the biggest challenges holding organizations back from becoming limitless is a lack of security. While there’s no such thing as 100% secure, companies need confidence in their security posture and risk level to move forward and reach their goals.

Security concerns have been topping IT leaders’ lists of troubles for years, and that’s not changing any time soon. In a recent survey conducted by Foundry,[i] security and compliance risk topped the list of information management challenges (44% among respondents). And 37% named enhancing security as a top-three IT priority for the next 12 months.

AI and security

This is a main reason why, along with boosting productivity, improving security is one of the most anticipated benefits of AI.
AI-driven security’s time has come, as threat detection grows more challenging every day. While traditional security approaches do a good job of catching existing threats, such as known malware, the creativity and sophistication of today’s threats require a more proactive approach. Whether it’s external bad actors attempting to infiltrate your network, internal bad actors who are already doing damage, or well-meaning users who accidentally expose a weakness, the human element grows more challenging to defend and requires companies to up the ante.

AI is well suited for such tasks. With AI you can more readily catch those who misuse permissions to infiltrate your internal systems and cause harm or detect anomalous behavior that could be a sign of an impending threat. And as global business dictates that data must travel across different geographies with varying regulations and standards for data protection, AI can also give companies an edge. It can analyze patterns across diverse and global data flows, providing real-time security information that transcends boundaries.

The security of AI

While AI is helping make organizations more secure, it’s also making them more vulnerable in new ways.

For instance, attackers can use AI tools to automate cyberattacks, making them faster and more effective. These AI-driven attacks can analyze network defenses in real-time, quickly identifying vulnerabilities that can be exploited. AI can be used to create deepfakes that deceive individuals or even systems into providing sensitive information. Phishing attacks can be enhanced by AI, generating highly personalized and convincing messages that are much harder for users to identify as scams.

So how do you reap the benefits that AI can deliver to your security strategy while protecting your organization from threats? Information management solutions and practices can help by implementing robust access controls, data classification, enhanced threat detection, and compliance management. When you know your information is well managed, governed, and protected, you can use AI with confidence. That’s peace of mind.

[i] Foundry survey commissioned by OpenText, Information Management for an AI-Driven Future, June, 2025

Read more blogs from the Limitless series:

Limitless: What it means for modern customers in an AI-first world

Limitless: What it means for productivity

Limitless: What it means for innovation

Limitless: What it means for integration

The post Limitless: What it means for peace of mind appeared first on OpenText Blogs.

The word “limitless” isn’t just a tagline—it’s a promise. A promise that technology, when thoughtfully applied, can remove the barriers that once held businesses back. For customers, “limitless” means more than speed or scale. It means possibility. It means potential. It means power—reimagined.

At its core, being limitless is about empowering people to do more than they ever thought possible. It’s about giving every employee a digital teammate: a digital knowledge worker that automates tasks and amplifies human capability. These AI-powered assistants don’t replace people—they elevate them. They summarize complex documents in seconds, organize meeting notes, surface insights from oceans of data, and even anticipate what’s needed next. They are always on, always learning, and always ready to help.

This is the new way to work. And it’s already here.

Free up teams with AI

Customers who embrace this shift are seeing real results. They’re using agentic AI to automate workflows, offload repetitive tasks, and accelerate decision-making. They’re freeing up their teams to focus on what matters most: creativity, strategy, and innovation. In doing so, they’re not just working faster—they’re working smarter.

But limitless isn’t just about what AI can do. It’s about what customers can become when they trust their information is secure, governed, and accessible. That’s why information management is the foundation of the limitless digital workforce. When data is centralized, connected, and protected, AI can be applied with confidence. Insights flow freely. Decisions get sharper. And transformation becomes inevitable.

The numbers back it up. According to recent research[i], 77% of organizations are already using AI in some form—and those with mature implementations are seeing measurable gains in customer experience, IT performance, and productivity. These early adopters aren’t just ahead of the curve—they’re redefining it.

Create the digital knowledge worker

So what does “limitless” mean for your business?

It means reimagining your workforce—not as a collection of roles, but as a dynamic ecosystem of human and digital collaborators. It means giving every department—from sales and support to HR and finance—the tools to move faster, think bigger, and deliver more. It means turning information into action, and action into advantage.

It means your company, your potential—limitless.

At OpenText, we’ve spent over 35 years building the digital platform for knowledge workers. Today, we’re helping our customers create the digital knowledge worker of the future. Because we believe technology should always elevate human potential. And we believe the future of work isn’t just digital—it’s limitless.

[i] Foundry MarketPlace survey commissioned by OpenText, Information Management for an AI-Driven Future, June, 2025

Read more blogs from the Limitless series:

Limitless: What it means for productivity

Limitless: What it means for innovation

Limitless: What it means for integration

Limitless: What it means for peace of mind

The post Limitless: What it means for modern customers in an AI-first world appeared first on OpenText Blogs.

In the business world, becoming limitless means empowering people to do more than they ever thought possible. It means elevating human potential and finding new ways to work that are more creative, strategic, and rewarding.

To become limitless, you must first improve productivity, so more work gets done in less time. With the extra time, employees have the freedom to think big thoughts that eventually break down barriers and move your business forward.

But that doesn’t mean working harder, it means working smarter.

Start with AI

AI is the driving force behind being limitless, and greater productivity is its most sought-after advantage. According to a recent survey by Foundry Research, improved productivity was the benefit of AI most often cited by respondents.[i]  And that benefit grows over time; 69% of AI users among the survey’s respondents said they could strongly attribute productivity gains to modern information and automation technologies, including AI. That number jumps to 78% among mature AI users.

AI significantly boosts productivity by helping companies operate more efficiently and make smarter decisions. By analyzing vast amounts of data in real time, AI provides insights to help identify areas for improvement. For instance, AI algorithms can optimize supply chains by predicting demand, allowing companies to maintain the right inventory levels and reduce waste. AI tools can streamline processes by automating workflows while minimizing errors and accelerating response times. This increased efficiency reduces costs and frees you to focus resources on growth and innovation.

Empower employees

As productivity increases with AI adoption, workers gain back valuable time that can be redirected toward more meaningful and impactful activities. Instead of being bogged down by repetitive tasks, employees can engage in creative problem-solving, brainstorming sessions, and innovation processes that drive the business forward.

For example, employees can focus on building stronger relationships with clients, enhancing collaboration among team members, and pursuing professional development opportunities. And so, greater productivity brings other benefits to organizations—helping improve job satisfaction and morale and fostering an environment of growth and innovation.

Empowered employees are more motivated and invested in their work, which contributes to a more dynamic and competitive organization. This empowerment encourages creativity and collaboration, as employees have the time and mental space to think critically and engage with their colleagues.

When workers are engaged in their tasks and feel valued for their contributions, they are more likely to invest in the success of the company and foster a culture of continuous improvement and innovation.

[i] Foundry MarketPulse survey commissioned by OpenText, Information Management for an AI-Driven Future, June, 2025.

Read more blogs from the Limitless series:

Limitless: What it means for modern customers in an AI-first world

Limitless: What it means for innovation

Limitless: What it means for integration

Limitless: What it means for peace of mind

The post Limitless: What it means for productivity appeared first on OpenText Blogs.

Limitless organizations know that taking advantage of innovative technologies can help them remove barriers and drive their business forward. These technologies open up possibilities by providing the tools to make smarter decisions, take quick action, and unleash human potential.

One common obstacle that companies face on their way to becoming limitless is integrating their data. AI can’t live up to its promises if the data it’s being applied to is spread over disperse systems, in different formats, or is out of date, not secure, or incomplete. Keeping important corporate information in unconnected data silos contributes to the problem.

It’s time for a data cloud

One way to bring all that information together is with a data cloud. This unified, centralized platform manages and connects data from various sources within an organization, making it easier to access, query, and transform information into actionable insights. A data cloud aims to eliminate data silos and fragmentation, providing a single source of truth across different platforms and departments. Unlike traditional clouds that can encompass computing power, development environments, and application services, a data cloud specifically focuses on the management, storage, and analysis of data.

The use of data clouds is on the rise. According to a recent survey conducted by Enterprise Strategy Group, 63% of respondents said they are using data clouds today. And the majority of respondents say data clouds will help them solve their most pressing problems, including compliance and governance (76%), high operational costs (74%), data complexity and fragmentation (72%), and data quality issues (68%).[i]

Data clouds allow businesses to store, manage, and access vast amounts of data from anywhere, providing a centralized hub for information. Companies can ensure their data is organized and readily available. And, being a cloud, the solution can scale as you grow your data capacity without significant infrastructure investments.

Data clouds and AI

Perhaps most significant today is the role data clouds play in successful AI strategies to help companies get the most out of their data. By unifying data from various sources, data clouds help AI see the big picture to better understand complex scenarios, improve accuracy, and deliver personalized experiences. 

A unified data cloud helps AI understand the context and nuances of each task, leading to more accurate predictions and decisions. It also provides the infrastructure for efficient AI model training, validation, and testing. And a data cloud can provide agentic AI with dynamic, live information, enabling it to optimize workflows, respond to customer needs, and identify risks in real time.

When you’re confident that your data is integrated, secure, and accessible, the sky’s the limit. Information management solutions from OpenText can help you get there.

[i] Enterprise Strategy Group survey commissioned by OpenText, Investigating the Imperative to Reimagine Information Management, May 2025

Read more blogs from the Limitless series:

Limitless: What it means for the modern customers in an AI-first world

Limitless: What it means for productivity

Limitless: What it means for innovation

Limitless: What it means for peace of mind

The post Limitless: What it means for integration appeared first on OpenText Blogs.

OpenText™ Knowledge Discovery (IDOL) provides a data analytics platform for enterprises who need to extract maximum value from all their text, audio, video, and image data, from any repository in any file format. Providing data extraction, data enrichment, precise search and knowledge discovery, Knowledge Discovery helps organizations discover valuable information they did not know they had, whilst also identifying compliance risk associated with document contents such as personally identifiable information (PII) using entity grammars. 

With an unparalleled history in artificial intelligence and machine learning, Knowledge Discovery provides a unique set of optimized models to fit any application, accelerating time to value. Connecting and gathering information from diverse locations with over 160 connector types, including Public and Private Cloud and On-Premise, Salesforce, Microsoft® 365, Google Workspace and OpenText Content Solutions and can access and retrieve information and knowledge from over 2000 file types.  

May 2025: What’s new in OpenText Knowledge Discovery CE 25.2

This OpenText Knowledge Discovery (IDOL) CE 25.2 release includes various functional and performance improvements, new connectors, file format support, and many other additions. 

New connectors

• Improved OpenText Information Archive connector – An updated bidirectional connector for the Information Archive repository allows it to archive data as well as search and understand previously archived data.

• New Guidewire connector – A searching connector enables the ingestion of contract and policy content into the OpenText Knowledge Discovery index.

Named Entity Recognition - Landmarks exposed to pre-process

• Defined improvements in Named Entity Recognition – Added elements for Turkish Address, more city names in NZ Addressand the NZ Social Welfare number. 

• Defined improvements in PCI - Added extra delimiters for card details, in names and medical details, as well as expanded landmarks for Telephone context matching. 

• PII Japanese names accuracy improved - Increased accuracy of Named Entity Recognition detecting Japanese names, and improved ability to correctly identify Japanese names within a document.  

Opentext File Content Extraction updates and other improvements

• File formats - New Design Web Format (DWF) filter support, metadata now available from password-protected iWork files and additional formats detected. 

• Performance improvements – A reduction in the iWork reader disk footprint as well as a reduction of the footprint of the common third-party libraries. 

• Easier access to HTML Export from Filter – Improved ability to select XMP metadata elements and subfile extraction arguments added to C++ API 

• OEM platform – OEM Easier fault diagnosis with library-wide logging, improved security of inter-process mechanisms and better HTML Export fidelity for DWG Docs.  

January 2025: What’s new in OpenText Knowledge Discovery CE 25.1  

Organizations are looking to access all their Enterprise Data; but in a world where 90% of existing corporate knowledge is in Unstructured formats, over 50% of organizations are not tapping into this aspect of their knowledge with any form of discovery. Customers can use OpenText Knowledge Discovery with new interfaces, including using Natural language questioning to reach their information securely across all users. 

This OpenText Knowledge Discovery (IDOL) CE 25.1 release includes various functional and performance improvements, new connectors, file format support, and many other additions. 

New APIs added 

PII named entity resolution REST API – created a published REST API to access OpenTex Named Entity Recognition for PII functionality. Simplifies the adoption of Knowledge Discovery Al technology by internal and external OEMs 

Rich-Media analysis REST API – created a published REST API to access all Rich Media analytics functionality. Simplifies the adoption of Knowledge Discovery Al technology by internal and external OEMs 

Ingest and connectors 

New OpenText DAM connector

• A highly functional connector to the OpenText DAM product. This allows OpenText Knowledge Discovery to ingest and enrich content from the DAM repository and keep the data synchronized between the two systems to provide DAM content filtering using the Search Abstractor API 

Improved security in OpenText Content Management (Extended ECM) Connector  

• Increased alignment of document security policies between OpenText Content Management and OpenText Knowledge Discovery

• OpenText Content Management has numerous document security models above and beyond document folder based, where possible these have now been mapped to OpenText Knowledge Discovery's understanding of access rights

OpenText Named Entity Recognition - Landmarks exposed to pre-process 

Defined grammar landmarks are now visible at the pre-process stage of OpenText Named Entity Recognition

• Pre-processing can significantly help in reducing latency and workload, landmarks can be used to help make selection decisions at this early stage. DLP OEM vendors will benefit from this.  

PII Thailand names accuracy improved

• Increased accuracy of OpenText Named Entity Recognition detecting Thai names - the new grammar has an improved ability to correctly identify Thai names within a document  

Deployment and other improvements 

Automatic scaling OpenText Knowledge Discovery platform for Kubernetes

• Published Helm charts to deploy OpenText Knowledge Discovery with the ability to automatically scale resource - deployment size flexibility along with simplified expansion as and when required 

Vault integration with OpenText Knowledge Discovery components

• OpenText Knowledge Discovery microservices can securely store passwords and keys in the Vault repository (or equivalent)  
• Customers will benefit from cloud deployment best practice of secure central storage of passwords and keys etc. 

December 2024: What’s new in OpenText Knowledge Discovery CE 24.4 

Organizations are looking to access all their enterprise data; but in a world where 90% of existing corporate knowledge is in unstructured formats, over 50% of organizations are not tapping into this aspect of their knowledge with any form of discovery. Customers can use Knowledge Discovery with new interfaces, including using natural language questioning to reach their information securely across all users. 

This OpenText Knowledge Discovery (IDOL) 24.4 release includes various functional and performance improvements, new connectors, file format support, and many other additions. 

New API added 

Search Abstractor Rest API – created to support conversational question and answering with better context for AI generated responses. Conversation server, working with FAQ answers through Answer bank, Fact bank and now LLM’s remembers the context of questions within the conversations service in Knowledge Discovery. Adding to the great governance provided by curated answers. 

Filtering support in the Search Abstractor adds parametric fields for pre-filtering of documents. The search results can provide microscopic view of either single or multiple documents through criteria for fine grain filtering. The searching can now utilise prior knowledge to allow focus on RAG document retrieval, by using parametric fields to build a subset of entities for RAG. 

Working across text documents and images simultaneously in the search abstractor. Searching can now take criteria of both text and images as search criteria, and the results will also include similar images making Multi Modal search available. 

Ingest & connectors 

• Salesforce – Updating the connector to stay up to date with the changes in Salesforce. 

• Additional Pre filtering for focused selection added to OpenText Core Content Management & OpenText Content Management.

• IBM FileNet P8 Each document in FileNet can have multiple binary files which we now interrogate. 

Text analytics 

• Analytics: LLM based image matching - Image based transformers can be used to generate vectors for the Knowledge Discovery (IDOL) Index. Customers can now search for similar images based on not just the textual description but also the actual image content.   

Media Server 

• The Media Server as a NiFi Processor is now available with full functionality. This allow NiFi workflows and the ability to scale and manage the Media Server app though Nifi clustering. 

• Demo application with abilities to process and analyze images and other functionalities is now available in the Media Server for ease of use. 

File content extraction

• Filtering of Source Code comments 

• Support added for Metadata output for .mht files & QuickTime (.mov) files. 

• Extended format detection, with support for 93 additional file formats. 

• In HTML Export the pdf2sr reader to extract images from pages in a PDF file, you can now configure the size of the images to produce. 

Deployment & other improvements 

• HELM charts for Kubernetes provide a comprehensive set of options to assist complex deployments. With the 24.4 release we bring further enhancements to our HELM charts. 

• Named Entity Recognition SDK: You can load multiple grammars and then dynamically turn off what it is not needed for a particular document, allowing the flexibility of swapping grammars with less degradation of performance in comparison to the loading unloading cycle of grammars. The new thing is a reducing of the overall impact of this process.  

July 2024: What’s new in OpenText Knowledge Discovery (IDOL) CE 24.3

OpenText Knowledge Discovery CE 24.3 is a significant release for the third quarter of CY24. There is an increasing need for companies to reach a greater variety of data, which adds to the complexity of their search types. We allow all customers to achieve a suitable response in their enterprise-wide search across all levels of an organization.

Now part of Content Services, the combination of best of breed products will help organizations who are looking to access all their corporate knowledge through enterprise search. Bringing additional abilities to derive deeper insights, reach actionable conclusions quicker, and gaining a new level of investigative analytics, across content, teams, and projects. Document level security is also enhanced, with extra features to report and export securely and track data delivery end to end.

The Knowledge Discovery CE 24.3 release includes various functional and performance improvements, new connectors, file format support, and many other additions.

The main improvements in version CE 24.3 are listed below:

Ingest 

• Microsoft Teams, Zoom, Cisco Webex and Google Chat connectors – New connectors allow customers to collect collaborative video meetings for retrospective analysis and/or content retrieval as part of aviator search.

Text Analytics 

• Search abstractor – Customers will have better context provided by the RAG to pass on to the GenAI, therefore experiencing a higher likelihood of a correct answer being produced.

• Specific document retrieval – Customer can ask the system to find and return a specific document that matches their unique specification.

Rich Media Analytics 

• OCR improvement – Improved OCR for better support of scrolling text. Customers will experience a higher accuracy of extracted text from video imagery. 

• Speaker ID improvement – Customers will experience a higher accuracy of Speaker ID from video or audio files. 

KeyView 

• Added Audio and Video to the Export SDK.

• Added 30 new formats to File detection. 

• Metadata API Improved to limit duplicated data.

• New .NET API.

• Added support for Python 3.12.

Solutions 

• Knowledge Discovery Discover – Discover provides investigative analytics and advanced UI for searching and analyzing relations between objects, with project and team collaboration along with full oversight on the analytics process.

• Knowledge Discovery for Microsoft Exchange – Knowledge Discovery can be added as a BOT in MS Exchange allowing customers to directly access the functionality via simple natural language prompts emailed to a BOT email address in MS Exchange. 

Deployment & Licensing changes 

All components are now published to the Public Docker Hub repository to allow easier installation, maintenance and upgrades.

• Eduction Python EDK – Customers who prefer to program using the popular Python language can now use it to control our Eduction engine, simplifying their integration experience. 

• Eduction for Windows ARM – OEM users of Eduction EDK can now deploy on Windows ARM systems. 

• Additional license feature
  • From CE 23.2, we are introducing an additional license feature – a “version.key” 
  • The file is available in the SLD portal for customers with an active support contract and is required for all Knowledge Discovery installations running CE 23.2.
  • New key will be issued with every future Knowledge Discovery release. 

April 2024: What’s new in OpenText Knowledge Discovery (IDOL) CE 24.2

Organizations are looking for conversational access to enterprise data; in a world where 90% of existing corporate knowledge is in unstructured data, over 50% of organizations are not tapping into this aspect of their knowledge with any form of discovery. Customers can use the new interfaces with Q&A using natural language questioning to deliver their information with data securely to all users.

This OpenText IDOL 24.2 release includes various functional and performance improvements, new connectors, file format support, and many other additions.

New solution added: Teams client

• Customers can access functionality using simple natural language prompts directly via the Teams interface.

Ingest & connectors

• Drupal Connector – Updated to support the latest API changes and allow data extraction from old and new Drupal versions.  

• Google Workspace Connectors – Content extraction is now accessible from the major Google Workspace apps including Mail, Calendar and Chat using dedicated connectors.

• Web Connector 2FA – Updated to allow 2Factor Authentication. 

• Additional new connectors – Stack Exchange; Moodle and OpenText eDocs.

• IDOL Media Server – Can now run within NiFi, allowing processing of media streams & sources.

Text analytics

• Analytics: Search abstraction – IDOL can now automatically decide which index type to use to achieve the best response. The functionality abstracts the complexity of search types, democratizing Enterprise search, and allowing all users to achieve a suitable response, across all levels of an organization. 

• Multi-document summarization – Generative summary compiled from answers sourced from multi documents. The functionality provides richer answer to any question asked as it can be the combination of multiple parts sourced from different documents. 

• Dynamic clustering of vector results to identify grouping – Customers can use the grouping of results to select a variation in documents returned rather than returning very similar documents with no added information. 

• Analytics: LLM based image matching – Image based transformers can be used to generate vectors for the IDOL Index. Customers can now search for similar images based on not just the textual description but also the actual image content.   

Media Server

• The Media Server as a NiFi processor is now available with full functionality. This allow for NiFi workflows and the ability to scale and manage the Media Server app though Kubernetes. 

• Demo application with abilities to process and analyze images and other functionalities is now available in the Media Server for ease of use. 

KeyView

• Tabular data detection for One Note & pipe separated text.  

• OCR is now available on MacOS through the KeyView SDKs.

• Header & Footer are now configurable through Python APIs.

• Significant Performance improvements through Shared Memory Streaming.

• Additional Formats, Security & Compatibility improvements.

Deployment & other improvements

• HELM charts for Kubernetes provide a comprehensive set of options to assist complex deployments. CE 24.2 brings further enhancements to our HELM charts. 

• Eduction SDK Post-processing access to match context.

The post What’s new in OpenText Knowledge Discovery appeared first on OpenText Blogs.

Let’s start with the elephant in the room: predictive analytics has been overhyped and underutilized in IoT. Most organizations claim to be “data-driven,” yet struggle to operationalize even basic foresight. 

The problem isn’t a lack of models. It’s that predictive intelligence is being treated like a dashboard feature instead of a business enabler. 

When predictive analytics is siloed from your operational workflows, it becomes retrospective—ironic, isn’t it? To truly unlock its value, you must embed predictive intelligence in the orchestration layer, where decisions are made, not just visualized. 

Let’s explore 7 uncommon ways high-maturity organizations are using predictive analytics to fundamentally transform their IoT operations—not just report on them. 

Why it’s time to add a new brain to your IoT stack 

The next evolution of predictive analytics isn’t just mathematical — it’s conversational. OpenText Aviator IoT is now integrating a Large Language Model (LLM) into its orchestration layer, adding natural language understanding to your operational intelligence. 

This means operators, engineers, and supply chain leaders can ask complex questions in plain English — like “What’s the predicted failure risk for our pumps next week?” or “Show me anomalies in energy usage across our top 5 facilities” — and get real-time, contextual responses from the system. No queries to write, no dashboards to configure. 

By embedding LLM capabilities into Aviator IoT, OpenText is laying the groundwork for more intuitive human-AI interaction. Rather than simply visualizing predictive outcomes, users may soon be able to engage with them conversationally—potentially transforming how insights are accessed and applied across frontline operations. This paves the way for AI to become more explainable, usable, and operationally relevant at scale reducing the reliance on complex query languages.  

And in high-stakes industries like energy, manufacturing, and transportation, that means faster decisions, fewer errors, and smarter orchestration — without deep technical training. 

What are predictive analytics?  

Predictive analytics aren’t crystal balls. They provide clarity. Predictive analytics leverage historical data, statistical techniques, machine learning, and real-time inputs to anticipate what is expected to happen next. In today’s hyperconnected industrial ecosystems, where thousands of signals stream in from products, sensors, and machines, predictive analytics distills chaos into foresight. 

At its core, predictive analytics transform fragmented IoT data into forecastable outcomes—whether identifying a failing conveyor belt before it halts production or forecasting a demand spike for a critical pharma SKU. To operationalize predictive intelligence effectively, organizations must go beyond models and vendors. They must adopt platforms that connect insight with orchestration, enabling frontline execution at machine speed. That’s exactly where OpenText Aviator IoT delivers. 

7 Ways to leverage predictive intelligence for your IoT strategy 

The promise of predictive analytics in IoT is clear: fewer breakdowns, optimized operations, and smarter decisions. But while many organizations collect data and run models, few are turning those insights into real-world ROI.  

The difference? Execution. The following seven principles reveal what it takes to embed predictive intelligence into your operations, and unlock its full strategic value. 

1. Predictive maintenance isn’t about maintenance—It’s about asset strategy 

Yes, predictive analytics can help prevent downtime, but that’s just the starting point. The real unlock is using predictive insights to rethink your entire asset lifecycle strategy, and reshape how and when you deploy assets. 

Instead of reacting to failure, organizations are leveraging IoT-enabled models to simulate wear, stress, and fatigue long before equipment is deployed. This forward-looking approach allows teams to determine not just when to maintain, but where, how, and even if an asset should be deployed at all. 

By proactively identifying underperforming components or high-risk usage scenarios, enterprises can optimize asset placement, extend equipment life, and prioritize capital investment where it delivers the most value. The result isn’t just fewer breakdowns—it’s smarter capital planning, reduced total cost of ownership, and greater confidence in long-term infrastructure decisions. 

Key stat: Manufacturers using predictive analytics report up to 30% savings in maintenance costs (McKinsey). 

2. Anomaly detection should trigger autonomous action 

Most anomaly detection stops at alerts. But alerts without orchestration are just noise. The leaders are taking it further by tying anomalies directly into automated workflows. That means triggering repairs, routing alerts to service bots, or initiating self-healing routines.  

This approach transforms detection into resolution, reducing response times and minimizing downtime without requiring manual intervention. By embedding autonomous actions into everyday operations, organizations can move from reactive problem-solving to proactive system performance, so that issues are handled before users even notice. 

Think about it this way: If your system knows it’s breaking, why can’t it start fixing itself? 

3. Demand forecasting is obsolete without edge intelligence 

Using yesterday’s demand to plan tomorrow’s operations doesn’t cut it anymore. Forecasting needs to happen at the edge, where demand is shaped in real time by weather, market signals, or human behavior. Static models sitting in the cloud can’t keep up with dynamic environments.  

Edge intelligence allows organizations to process and act on data locally, without the latency of round trips to centralized systems. This means faster adjustments to inventory, pricing, and resource allocation based on what is happening right now. Not what happened last week. It’s how modern businesses stay agile in constantly shifting conditions. 

Use case: Smart retailers adjust supply chain inventory based on in-store footfall and ambient temperature, streamed via IoT. 

4. Asset optimization is the new sustainability strategy 

Everyone’s talking net zero. Few are connecting it to predictive analytics. Yet IoT can predict energy surges, idle time, and asset strain, allowing you to reduce emissions while optimizing performance. ESG meets ROI.  

Predictive insights help identify inefficiencies that traditional monitoring might miss, enabling smarter scheduling, right-sizing of energy loads, and timely asset adjustments. The result is a more sustainable operation that not only meets regulatory expectations but also drives measurable cost savings and long-term resilience. 

OpenText Insight: Unified intelligence and digital twins can model energy outcomes and adapt asset schedules in real time. 

5. Operational efficiency must be preemptive 

Don’t wait for a KPI to drop. Use predictive models to simulate bottlenecks before they cascade. Logistics, production, and facility management teams are now using digital twins powered by live IoT data to test "what-if" scenarios daily—not quarterly.  

This shift enables proactive adjustments to scheduling, routing, and resource allocation, reducing the likelihood of costly delays or disruptions. By modeling stress points before they occur, teams can make faster, smarter decisions that keep operations running smoothly. It means no surprises, and no scrambling. 

Why it matters: Operational resilience isn’t a nice-to-have. It’s now a board-level mandate. 

6. Predictive personalization is the frontline of retention 

You’re not just predicting churn. You’re predicting what makes people stay. Use interaction data from connected systems to forecast when to upsell, when to support, and when to innovate. If your product isn’t adapting to user behavior in real time, someone else’s will. 

Predictive personalization turns passive usage data into proactive customer engagement, creating moments of value before users even ask for them. It’s how connected brands move from one-time purchases to lasting loyalty, anticipating needs and delivering relevance at every touchpoint. 

Modern example: Smart appliances offering feature updates based on user behavior patterns. 

7. Energy forecasting is infrastructure resilience 

Power grids, manufacturing floors, and HVAC systems now face extreme volatility from climate disruptions and unpredictable consumption patterns. Predictive energy analytics allows organizations to simulate peak loads, optimize distribution, and preempt outages.  

It enables better planning for energy-intensive operations, supports sustainability targets, and reduces the risk of equipment failure due to overload. By anticipating demand and dynamically adjusting supply, businesses can maintain uptime, control costs, and build resilience into every layer of their infrastructure. 

Urgency: According to the IMF, technological fragmentation alone could shave 5% off GDP.  

Predictive analytics is not just a feature, it’s a foundation

Predictive analytics isn’t just a capability; it’s a strategic mindset shift. When integrated natively into your IoT orchestration layer, it transforms your entire operation into a self-correcting, insight-driven system. 

And that’s exactly what OpenText Aviator IoT delivers. Aviator IoT enables predictive maintenance to reduce downtime and optimize asset utilization 

️It’s not just about predicting failure. It’s about embedding foresight into every process. From asset orchestration and supply chain traceability to compliance and customer connection, Aviator IoT brings predictive insight to the edge, in real time. 

Ready to stop reporting and start orchestrating? 
Explore OpenText Aviator IoT 
Dive into our Track & Trace Solutions 

The post 7 Ways to operationalize predictive intelligence in IoT  appeared first on OpenText Blogs.

In today’s fast-moving, interconnected world, businesses are turning to digital supply chains to stay competitive, resilient, and customer-focused.  

By leveraging advanced technologies like AI, IoT, and blockchain, digital supply chains offer a smarter, faster, and more efficient way to manage operations—from procurement to delivery.  

In this blog, we explore eight powerful benefits of supply chain digitization and how it can transform your business for long-term success. 

What is the digital supply chain? 

Today’s most successful companies have achieved advanced levels of digital sophistication and maturity within their supply chains. Research from IDC shows a strong correlation between digital maturity and superior business performance, including higher revenue growth and profitability. 

Unlike traditional supply chains—which are often linear, fragmented, and reliant on outdated systems—digital supply chains operate in real time, adapt dynamically to changing conditions, and foster stronger collaboration. They are built around ecosystems of interconnected partners and suppliers, linking internal systems with external data sources to enable seamless information sharing, responsiveness, and end-to-end visibility. 

At the heart of a digital supply chain is a digital backbone—a foundational infrastructure that allows all transactions to occur digitally. This enables efficient, transparent collaboration across stakeholders, including suppliers, customers, logistics providers, and financial institutions. The digital backbone serves as a platform on which companies can deploy advanced technologies to pursue strategic goals and seize new opportunities. 

The real power of the digital supply chain lies in its ability to collect accurate, comprehensive, and timely data—and transform that data into actionable insights. These insights lead to better decisions, improved operational efficiency, reduced costs, and more resilient supply networks. 

Supply chain digitization vs. supply chain digitalization 

While often used interchangeably, supply chain digitization and supply chain digitalization represent two distinct stages in the evolution of modern supply chains. 

• Supply chain digitization is the process of converting physical documents, such as paper-based records, into digital formats. This foundational step improves data accessibility and enables more efficient handling of information. 

• Once data is digitized, companies can move to supply chain digitalization, which involves using digital tools and technologies to automate workflows, enhance visibility, and improve process efficiency. Digitalization is about transforming operations—not just making them digital, but also smarter, faster, and more integrated. 

Together, these steps lay the groundwork for building a fully connected and intelligent digital supply chain. 

Key benefits of the digital supply chain 

Digitalization of the supply chain enables better use of resources, optimized production, stronger supplier relationships, increased visibility, and a healthier bottom line. Let's dive into the key benefits of the digital supply chain.  

1. Reduced cost and improved revenue 

By virtually eliminating manual tasks, the digital supply chain dramatically reduces human error and input time while freeing staff for higher value activities. According to one study, the average annual cost for organizations to manually enter data into ERP and back-end systems alone was more than $1 million. 

At a strategic level, improving the speed, quality and accuracy of tasks, such as demand forecasting, inventory management and order fulfilment, directly drives revenue and profitability. 

2. Increased supply chain visibility 

In a recent survey (Reuters Events, The state of European Supply Chains 2024) addressing supply chain visibility gaps was prioritized as a top area for investment, reported by 68 percent of the respondents.  

The problem is the vast number of disconnected legacy systems used to address each stage in the traditional supply chain process. This includes a lack of integration between information technology (IT) and operational technology (OT) systems. 

Digitizing the supply chain creates opportunities for breaking down these barriers and connecting disparate systems both internally and externally. As a result, data can pass quickly and securely across the entire supply chain, enabling near real-time visibility. Supply chain visibility makes it possible for staff to instantly see the current status of any activity, enabling them to make informed decisions. 

3. Improved decision-making  

For most businesses, decisions need to be made quickly, and agility is vital. Basing decisions on historical reports and spreadsheets is far from optimal. Research has shown that more than two thirds of supply chain managers still use Microsoft® Excel® as an inventory management tool. In the world of big data, this isn’t ideal.  

A digitized supply chain allows organizations to gather and analyze massive amounts of data with far less effort and in far less time. The ability to gain insight from real-time data generated anywhere in the supply chain offers significant benefits in every aspect of the business, from product development to sales and marketing to customer experience. 

4. Building supply chain resilience  

The COVID-19 pandemic revealed serious and systematic weaknesses in supply chains. Global supply chains have become extended and complex to take advantage of low-cost sourcing, lean inventories, and Just-in-Time manufacturing practices.  

When supply chains became disrupted, organizations found it difficult to maintain logistics routes or switch to alterative suppliers. IDC found that diversifying their sourcing strategies, along with improving supply chain visibility, were the most important focus areas for organizations to mitigate the impacts of disruption. 

Digital supply chains allow for deeper connections and improved collaboration between trading partners, as well as an ecosystem of suppliers, which makes alternative sourcing arrangements faster and more effective.  

Overall, digitalization helps build supply chain resilience and more sustainable supply chains. This enables increased mobility and accountability while driving proactive responses to emerging problems.  

5. Supply chain automation  

Automation improves overall supply chain performance by eliminating friction and choke points between functions. In addition, it creates new business opportunities and a better customer experience through enabling innovative self-service options.  

However, most organizations have yet to realize the full benefits of supply chain automation. As the use of AI expands and more complex processes are targeted for automation, the effectiveness of these efforts increasingly depends on robust process transformation, high-quality data, and strong digitization and digitalization capabilities to deliver desired outcomes. 

6. Driving collaboration and innovation  

The digital supply chain enables multi-enterprise collaboration by breaking down data silos within an organization and between external partners. It allows seamless and secure integration between the systems of the organization and its suppliers, customers, logistics, and financial institutions.  

Through digital enablement, data becomes actionable; workflows are streamlined; and critical information such as order milestones, inventory statuses, and payments can be shared securely and instantly.  

Organizations and their partners can quickly establish shared responsibilities and accountability and track them in real time. As organizations look towards their partners to improve their competitiveness in the market and drive increased collaboration in product design and development, a digital supply chain is the foundation of success. 

7. Enhancing sustainability  

With consumers increasingly favoring companies and products they perceive as sustainable, brands are seeking external partners with good sustainability records and may require their existing partners to address identified gaps. The digital supply chain has a major role to play in helping businesses reduce their impact on the environment through efficient management of stock and avoidance of waste.  

Digitalization enables improved inventory and materials management, ensures accurate tracking of goods in transit, and helps avoid materials shortages, all of which leads to more efficient use of resources. It also improves visibility into the supply chain, which helps organizations to better identify, and address risks related to sustainability and ethical business practices. 

8. Enabling technologies 

Organizations can increase revenue, save expenses, reduce risk, and boost customer experience by developing digital supply chain capabilities to improve operations and make smarter decisions faster. 

 There are several complementary digital technologies that enable this, including: 

• Multi-Enterprise Collaboration Networks 
  These cloud-based platforms connect all supply chain partners on a single network, enabling seamless collaboration, real-time data sharing, and greater agility across the ecosystem. 

• Internet of Things (IoT) 
  IoT devices provide real-time visibility into assets and shipments, linking the physical and digital worlds to improve tracking, operations, and decision-making. 

• AI and Machine Learning 
  AI/ML analyze large volumes of supply chain data to enhance forecasting, optimize operations, and automate decision-making for improved efficiency and cost savings. 

• Digital Twins 
  Digital twins create virtual models of physical products or processes using real-time data, allowing predictive simulations and better insights into operations and supply chain scenarios. 

• Supply Chain Command Centers 
  Supply chain command centers centralize and analyze supply chain data to enable proactive monitoring, issue resolution, and faster responses to disruptions and changing conditions. 

• Blockchain 
  Blockchain secures and tracks supply chain transactions in an immutable ledger, enhancing transparency, reducing fraud, and eliminating intermediaries. 

• Identity and Access Management (IAM) 
  IAM systems control and secure access to supply chain platforms and data, ensuring only authorized users and devices can interact with sensitive systems in real time. 

Digital supply chain transformation: How to get started 

Today, digitally transforming supply chains is crucial for most organizations. It is an increasingly important step to ensure long-term business success, drive efficiency, deliver sustainability, and, most importantly, create better customer experiences.  

However, this is no simple undertaking. It is unlikely that any business can implement a “rip and replace” strategy when digitizing their supply chain. Instead, an incremental, planned approach designed to produce tangible benefits is the best way to get started. 

 To take the first step toward a smarter, more connected supply chain, organizations need the right partner and platform. OpenText Digital Supply Chain Collaboration provides tools to digitize operations, enhance visibility, and drive seamless, secure collaboration across your entire ecosystem. Learn how OpenText can help you accelerate transformation and unlock the full value of your supply chain.  

The post 8 Benefits of leveraging the digital supply chain  appeared first on OpenText Blogs.

In 2025, Agentic AI has entered the vocabulary of nearly every CIO around the world. Many companies are still in the exploratory stages of Agentic AI, which means there’s no better time than now to embrace this next evolution of AI.

CIOs have been inundated with a mix of different technologies over the past decade. For example, in 2018, blockchain looked promising for the industry—but struggled to get off the ground as originally intended due to its complexity. Agentic AI and Gen AI are different propositions, but promise to add true value to a business operations.

The main reason for the success of Gen AI to date is that anyone can use the technology, both at work and at home. We’re all welcome to use it on our own PCs or mobile devices, enabling us to discover new insights. 

This ‘consumerization’ of Gen AI has been key to why it has spread so quickly around the world, inside companies of every size. But what happens when you combine process automation with AI capabilities? For supply chain and integration leaders, it means gaining intelligent EDI agents that proactively resolve issues, adapt to shifting demands, and free up their teams to focus on higher-value work—not manual troubleshooting.

Let’s dive into what agentic EDI is, and how it will transform supply chain orchestration.

What are EDI agents?

Electronic Data Interchange (EDI) transactions have been exchanged across global supply chains for decades, but only now can we start to explore opportunities to really ignite the potential of using the data within an EDI transaction to drive insights and optimize supply chain processes like never before.

Many new technologies have been introduced over the past 50 years since the very first EDI transaction was exchanged, and the technology has evolved with each new piece of technology that entered the market. Agentic EDI represents the next evolution of technology that will fundamentally change the EDI landscape.

In the ever-evolving world of supply chain and procurement, the pressure to move faster, smarter, and more efficiently has never been greater. Traditional EDI systems—while foundational—are increasingly seen as rigid, rule-based, and reactive.

Enter agentic EDI: a transformative fusion of Agentic AI and EDI that promises to revolutionize how organizations manage data, decisions, and disruptions.

Agentic EDI refers to the integration of autonomous AI agents into EDI systems, enabling them to not only exchange structured data but also interpret, act on, and optimize that data in real time. Unlike traditional EDI, which simply transmits purchase orders, invoices, and shipping notices between systems, agentic EDI systems will:

• Make decisions based on contextual data
• Adapt workflows dynamically
• Collaborate with other agents or humans
• Learn from outcomes to improve over time

This shift mirrors the broader trend of Agentic AI—AI systems that operate with a degree of autonomy, capable of initiating actions, making decisions, and collaborating across complex enterprise environments.

With EDI agents we have an opportunity to unlock the potential of the data that resides inside an EDI transaction. Here at OpenText we help companies exchange more than 31 billion transactions per year, if you consider that each transaction has 80 data fields, on average, that represents 2.5 trillion pieces of data.

Why agentic EDI matters to supply chain and procurement leaders

For executives, agentic EDI represents a leap from automation to autonomy. It’s not just about reducing manual effort—it’s about enabling resilient, intelligent, and proactive operations. In a world where supply chain disruptions, compliance risks, and cost pressures are constant, EDI agents offer a strategic edge.

This technology aligns with broader digital transformation goals, helping organizations move from siloed, reactive systems to connected, intelligent ecosystems. This evolution is critical for staying competitive in a global market where agility and foresight are paramount.

Five high-impact use cases of agentic EDI

1. Autonomous supplier onboarding and compliance

Traditional supplier onboarding is slow, manual, and error prone. Agentic EDI systems will autonomously:

• Validate supplier credentials against third-party databases
• Check compliance with ESG, regulatory, and contractual standards
• Initiate onboarding workflows and flag anomalies

This potentially reduces onboarding time from weeks to hours and ensures compliance from day one. It also improves supplier experience and accelerates time-to-value.

2. Dynamic purchase order optimization

In conventional EDI, purchase orders are static documents. With agentic EDI, AI agents will:

• Analyse real-time inventory, demand forecasts, and supplier performance
• Adjust quantities, delivery windows, or even suppliers dynamically
• Negotiate terms autonomously within predefined guardrails

This ensures optimal procurement decisions even in volatile markets. It also reduces stockouts, excess inventory, and procurement costs.

3. Proactive risk detection and mitigation

Agentic EDI systems will continuously monitor:

• Supplier delivery patterns
• External risk signals (e.g., geopolitical events, weather, financial health)
• Internal KPIs (e.g., late deliveries, quality issues)

When a risk is detected, the system can autonomously reroute orders, notify stakeholders, or trigger contingency plans—minimizing disruption without human intervention. This capability is vital for building resilient supply chains.

4. Intelligent invoice reconciliation and fraud detection

Invoice matching is a classic EDI function, but agentic EDI will take it further:

• AI agents can detect anomalies in pricing, quantities, or payment terms
• Cross-reference invoices with contracts, POs, and delivery receipts
• Flag or resolve discrepancies autonomously

This not only reduces fraud and errors but also accelerates payment cycles and improves supplier relationships. It also supports touchless invoicing, freeing up finance teams for strategic work.

5. Collaborative demand planning and forecasting

Agentic EDI systems will integrate with demand planning tools and external data sources (e.g., market trends, social signals) to:

• Predict demand shifts
• Collaborate with suppliers to adjust production schedules
• Automatically update procurement plans and inventory targets

This creates a more agile, responsive supply chain that can pivot in real time. It also supports just-in-time and just-in-case inventory strategies.

Additional use cases for EDI agents

As agentic EDI matures, new applications will emerge:

• Sustainability Tracking: Agents will monitor carbon emissions, ethical sourcing, and ESG compliance across the supply chain.
• Contract Lifecycle Management: AI will track contract terms, trigger renewals, and ensure compliance.
• Returns and Reverse Logistics: Agents will automate return authorizations, restocking, and credit issuance.
• Supplier Collaboration Portals: AI agents will act as digital assistants for suppliers, answering queries and guiding processes.
• Multi-Tier Visibility: Agentic EDI will extend beyond Tier 1 suppliers to monitor Tier 2 and Tier 3 risks and performance.

Implementation considerations for agentic EDI

While the potential is vast, implementing agentic EDI requires careful planning:

• Data Quality: AI agents are only as good as the data they consume. Clean, structured, and real-time data is essential.
• Change Management: Teams should be trained to work alongside autonomous systems and trust their outputs.
• Governance: Guardrails must be in place to ensure AI agents operate within ethical, legal, and strategic boundaries.
• Integration: Agentic EDI must seamlessly connect with ERP, TMS, WMS, and supplier systems.
• Security and Compliance: As agents access sensitive data, robust cybersecurity and compliance frameworks are critical.

The strategic payoff of agentic EDI

For supply chain and procurement executives, agentic EDI is more than a technology upgrade—it is a strategic enabler. It allows organizations to:

• Improve decision accuracy through real-time insights
• Enhance resilience by proactively managing risks
• Free up talent to focus on strategic initiatives
• Improve supplier collaboration and satisfaction

Those who embrace this shift early will not only streamline operations but also gain a competitive edge in an increasingly complex global landscape.

EDI agents are the natural evolution of supply chain automation—moving from static data exchange to intelligent, autonomous orchestration. For procurement and supply chain leaders, it offers a powerful way to drive efficiency, agility, and strategic value in a world where speed and intelligence are the new currency.

The journey to agentic EDI is not without challenges, but the rewards are substantial. Organizations that explore the potential of these technologies now will be better positioned to navigate uncertainty, capitalize on opportunities, and lead in the next era of digital supply chains.

Looking to reimagine your supply chain operations? Learn how OpenText Business Network can help.

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