Powering your content with AI

Content and AI basics

Content — in all of its various forms — has long been a challenge from an information management perspective. Challenges include:

• The right information can be nearly impossible to find due to complex technology systems, inadequate and inconsistent metadata attributes, limited search functionality within core business applications and disconnected repositories or systems.
• The volume and types of content are growing at an unprecedented rate. Many enterprise organizations have accumulated billions of pieces of content in recent years, whether it’s documents, scanned images, emails, video or other formats. While historical content is massive, the reality of today’s business is that hundreds of millions of new objects could be entering an enterprise per month, which can literally multiply an entire corpus of content in the next few years.
• Scaling content management needs is increasingly difficult (quickly classifying, identifying critical information, determining its usefulness and appropriately storing it). What was once manageable by a few people in a central location is now content chaos, often strewn across physical locations, as well as an average of 21 repositories.
• Extracting information and value from enterprise content requires time, context and intellect. Doing this work across thousands or even hundreds of thousands of new documents every day is challenging, expensive and difficult to do with consistent accuracy. This is why so many organizations have struggled with enterprise content management (ECM) for so long.

With AI, content management is changing

With AI solutions now mainstream, enterprise content is at an exciting moment. ECM solutions have managed organizations’ mission-critical content for decades, and with generative AI, these providers can now think aggressively about redefining the value an ECM solution provides its customers. After all, customers already trust these vendors with their content — now, with the right ECM partner, you can leverage gen AI to get insights out of your data that were previously hidden.

With AI-powered solutions, there’s a way to process content like a human does, but at a massive scale. Enterprises can deploy a range of services to intelligently extract critical data from content and, in doing so, transform content into actionable information that can easily be found, readily used to perform work and accessible anytime, anywhere, and on any device.

However, to gain the power of AI insights, your enterprise data must be ready for the AI machine.

AI-ready enterprise content

In order to capture the full promise of your downstream AI solutions, those AI tools high quality, processable data. In other words, your enterprise content needs to go through a translation process for your AI solutions.

Giving structure to unstructured data

With gen AI, modern content management platforms can give structure to what was previously unstructured — a shocking 80% of enterprise content, according to a 2025 Intelligent Business Strategies research study. Platforms like Hyland Content Innovation Cloud™ can ready and process all of the petabytes of content and images, interpret them and enable organizations to understand what’s inside them to drive great efficiencies.

Using AI with your content

Public cloud AI services

Most modern content solution platforms can integrate with a variety of public cloud services for artificial intelligence. Typically, the content solution platform will pass an object, be it a document, image, or even a video file, to a cloud provider, and will then receive a set of data produced by the AI service.

The world of AI continues to evolve rapidly, and a number of large technology companies now offer a variety of commodity AI services that can be leveraged for working with various forms of content. Here are some of the most popular technologies in use, and examples of how they can be employed to work with content:

Popular AI technologies

>Read more | Essential AI terms you need to know

Many of these ML offerings focus on providing greater insight into and understanding of content, whether that’s text-based documents, photos and images, or audio and video files.

A lot of value can be derived from these generic models and services, particularly in performing routine tasks with high volumes of content. For example, if you need OCR for a large existing set of content, these services are accurate and highly performant. Real-time sentiment analysis of chat sessions, emails or even social media content is another great use case for these services.

Generic vs. custom AI models

Generic AI services have been trained with a broad range of data, which means they return generic insights that may not align with your specific business needs or industry requirements.

Generic ML model
In the image below, an automobile accident has been analyzed by Google Cloud Vision,a pretrained, generative AI model.

As you can see, Google Vision returned a number of labels or data values related to the image. While technically accurate, this data doesn’t provide the insights an insurance company actually needs to process claims efficiently.

Custom AI model
One of the biggest advantages of AI is that organizations don’t have to settle for generic models and generic data. They can build custom AI solutions that extract business and industry-specific information tailored to their exact needs.

Now, let’s consider an AI system that’s been specifically fine-tuned with extensive data related to automobile accidents and insurance industry requirements. Here’s an example of the kind of data an AI model with configured outcomes could extract from the same image:

The difference is striking. Note that now the make, model and factory color of both vehicles have been correctly identified. It also identified two Illinois license plates and captured full and partial plate numbers. There is a face present in the image and identified as the operator, Jim Smith.

This level of precision comes from combining AI with advanced document processing and data curation technology that can handle 600+ file formats, including images, videos, and other non-text content. The system transforms raw, unstructured information into clean, structured data that's ready for automated workflows and business processes.

The benefits of custom AI models and systems include extracting business and industry-specific data that adds real value (instead of returning generic labels) and bringing greater intelligence to your decisions and processes. As a result, claims processors no longer depend on manual data entry.

This is a prime example of how AI transforms enterprise content management by bringing your AI-enriched data and business logic together, ensuring the right information reaches the right people at the right time.

Content enrichment

Content enrichment is all about extracting data from content and using that data to make the content more accessible, highly contextual and in short — more powerful. Content enrichment takes many different forms, depending on the type of content and what type of AI models are in place.

Powering content with AI
On an AI-powered content solutions platform, the content housed in core applications across the enterprise gains visibility, quality and actionability. Using various AI tools — be it ML, optical character recognition (OCR) or data curation, for example — content becomes unified and usable in a way that legacy content platforms simply can’t match.

Information housed within an AI-powered content solution is not only more useful (because of the contextualization of its data points), but AI can also take that valuable content and initiate processes across integrated systems. For example:

• In the custom AI model for insurance scenario presented above, the metadata may be valuable claims data that needs to be recorded in a claims processing system, like Guidewire or Duck Creek.
• Many financial services firms have large volumes of existing TIFF images they want to convert into PDF documents. Using a AI model or a technology IDP, that process can be automated to intelligently extract data and properly fill the PDF format of choice.
• Administrative teams may use AI to classify documents based on themes, rather than simply by types of documents. Using the model’s ability understand the context of the document can be helpful in lots of ways, such as analyzing and summarizing higher education admissions essays or drawing conclusions from respondent sentiments in surveys.
  
   > Read more | Indiana State University case study
  
  
• Legal teams can finetune AI models to help them with prep work, writing and identifying clauses within legal contracts, giving their existing content even more power to be helpful when needed.
• Finance teams can classify invoices for coding and proper routing
• Work in the healthcare industry can use AI to identify and tag assets based on content type (think MRI, x-ray and CT scan) or document context like organ system (e.g. Pulmonary). And with AI learning as it goes, its predictions and analysis get more and more accurate as more data is inputed, leading to improved outcomes for patients and organizations.

Intelligent process automation

According to Forrester, 67% of businesses leading in modern content management practices are developing their intelligent automation capabilities to automate manual processes and extract deeper insights from data. Although the trend is rising, that still means a lot of enterprises depend on manual work — some even with handwritten paper forms to process. This comes with critical processing challenges like determining what type of form it is, validating the necessary responses, confirming signature placements and checking if confidential information has been provided (not to mention a lag in accurate data availability).

AI can help companies better automate critical business functions and processes, for example:

Intelligent document processing
Intelligent document processing (IDP) uses AI to read, recognize and understand, as a human would, the text and formatting within semistructured and unstructured content, so forms and documents can be processed automatically. Using ML to “teach” the IDP software how to make sense of documents, the software gets smarter and more effective the more it works.

Process optimization
With AI integrated into content-centric processes and systems, organizations can streamline workflows effortlessly. Use cases for this include using a language learning model (LLM) prompt to identify qualified participants for a clinical trial; automating analysis for requirements or checklists to initiate workflows; searching enterprise repositories for similar content/documents to flag for fraud (insurance and government); identifying and eliminating duplicates in a digital asset management (DAM) solution; checking terms and conditions to underwrite insurance policies; and initiating automated processes related to credit review, fraud detection and compliance management.

Data validation
ML models also enable intelligent exception management to quickly identify what’s missing or inaccurate with a provided form and automatically route it to a customer service representative or back to the customer for remediation.

Records and retention management
Many organizations have struggled for years to implement an effective records management approach for their information. The simple reason for this is that most organizations are unwilling to devote the effort required to look at all their existing content to determine if, when and how it should be retained or deleted.

This is painstaking work for humans, but ML can automatically classify content and extract data from it at a bigger scale. As a result, it’s much faster and easier to examine tremendous volumes of content, classify a variety of documents or information, then automatically identify records, apply requisite retention periods and delete nonvital information.

> Read more | The ultimate process automation guide

Agentic process automation

Agentic process automation represents a significant advancement in intelligent automation. It utilizes AI-powered agents to handle complex tasks and entire business processes with little to no human intervention, moving beyond the limitations of prescripted automation. These intelligent agents can collaborate with humans, other AI systems and integrate with existing software applications to achieve their goals.

Agentic process automation isn’t meant to overhaul your entire automation framework. Instead, it should supplement your existing strategies. By integrating AI agents, you can enhance your current processes and unlock new levels of efficiency.

Agentic process automation can fundamentally reshape business operations by minimizing manual tasks and enabling smarter, data-driven decisions. With tools designed to orchestrate these AI agents, you can build, deploy and seamlessly integrate them alongside other AI components and RPA bots to drive transformational outcomes for your organization.

Deep content insight

Drawing new insights and meaningful connections from your content with the help of AI enables beneficial modernization in various ways, such as:

• More informed decision-making: With AI automating and speeding the ingestion, analysis and curation of data, information is available, faster to all who depend on it.
• Improved outcomes: AI-powered analytics can leverage vast amounts of historical and incoming data to draw conclusions, uncover trends and make predictions, helping businesses to plan and better serve customers.
• Better customer experiences: AI’s ability to improve access to content and pull together relevant information from across apps makes it easier to answer questions and even enable a culture of efficient self-service.

Enterprise considerations

Now that we have explored the difference between commodity and custom models and have examined some real-world use cases for AI, ML and content, let’s look at some key considerations for organizations considering a content management platform with enterprise-class AI and ML capabilities.

AI governance

When it comes to governing AI, organizations aren’t keeping pace. Forrester reports:

• Only 19% of organizations govern AI-generated content.
• Only 23% govern the prompts used to generate content.
• Decision-makers need to mitigate the complexity involved in surfacing, governing and deriving insights from content across the enterprise.

AI capabilities require data governance for several reasons, including to protect sensitive information, address ethical considerations, prevent biased results, identify and mitigate risks, and manage entire data lifecycles properly. Mishandling of data by unregulated AI sources may lead to inaccuracy in reporting, data breaches and noncompliance with governance data protection regulations, ethical concerns such as lack of transparency and eventually, a negative public perception.

When onboarding AI into an enterprise content strategy, the platform should allow teams to:

• Select and oversee what data AI models have access to
• Approve which authorized personnel can view and control data, in its original form or through AI-generated content
• Decide how the predictions and outputs provided by AI models can be applied

We also recommend an enterprise content platform that has extensive experience in handling highly sensitive data while adhering to differing industry standards. Enterprise content platforms with stringent data governance policies help protect sensitive information and ensure compliance with data privacy regulations such as GDPR, CCPA and HIPAA, which are enforced in sectors like government, healthcare and the financial industry.

Continuous training and administration

Another critical consideration is how your AI models perform over time.

First, you should consider solutions that utilize continuous training paradigms that enable your ML models to evolve and improve over time as new content and data is added to the system. Human interaction with machine-generated data is also critical to provide data validation and further train ML models. Look for a content solution platform that considers the human role in the machine-learning process and provides specific interfaces for “human in the loop” training.

Your AI solution should also provide real-time performance monitoring for models. ML models can begin to show bias or even degraded performance; therefore, a performance monitoring interface will help identify models that have become corrupted or are showing degradation in performance. Machine-learning models should also be versioned, allowing you to quickly roll back to an earlier version should your model become degraded.

The role of enterprise content platforms

Enterprise content platforms are a uniquely powerful place to embed AI capabilities. Modern platforms unite content across apps and departmental silos, and with AI, the content gains relevance and usefulness. With AI and content, enterprises can capitalize on next-generation information management.

According to Forrester, just 64% of organizations have significantly transformed their content management approach with AI (a 21% increase since 2019), and 74% of decision-makers expect AI to have a large or significant impact on their ability to achieve priority content objectives over the next 12 months.

This rapid adoption — and race to the top for harnessing the technology — shows how competitive and necessary it is to get AI “right” at your enterprise. The output of intelligent capabilities is only as valuable as the quality of the inputs, a process that begins with data hygiene and the ability of your platforms to transform unstructured data and enterprise content into AI-ready strategic assets.

> Read more | Before you invest in AI, assess your AI-readiness

Getting started with Hyland’s modern, AI-driven content solution platform

At this point, you might be saying, “This is all great, but how do I get started?” We’d like to help.

Hyland Content Innovation Cloud™ is a cloud-native platform that unifies your enterprise content with advanced AI, automation and intelligent workflows. Key AI-powered solutions in the platform include:

• Hyland IDP: Uses advanced AI to automate document capture, extraction and classification, enabling efficient and accurate data processing.
• Hyland Knowledge Enrichment: Transforms unstructured data into AI-ready formats and leverages AI to enrich content while improving searchability, AI processing and decision-making.
• Hyland Knowledge Discovery: Unlocks and enables access to relevant business insights by using AI agents to retrieve and generate information to accelerate decision-making.
• Hyland Automate: Leverages AI-enabled automation to simplify complex processes, reduce manual tasks and boost operational efficiency.
• Hyland Agent Builder: Build an intelligent agentic workforce that frees your human workforce from the mundane so they can focus on higher-value work.

By automating manual tasks and extracting valuable insights from unstructured content, Hyland’s content solutions help accelerate decision-making, enhance customer experiences and achieve operational excellence.

Read more about our services, and how we’re making it easy for customers to train and manage their own custom AI models.

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