Before you invest in AI, assess your AI-readiness
The promise and potential of AI is something every organization wants to align with, but one big part of its implementation is often overlooked: AI-readiness. Before AI can achieve what you expect it to, your enterprise content must be AI-enabled. Here’s the AI-readiness framework to consider.
Summary
- AI-readiness transforms businesses, yet most organizations aren’t AI-enabled: To capitalize on the promise of AI solutions, your enterprise data, infrastructure and workforce must be AI-ready.
- Quality data is key: High-quality, curated and AI-ready content ensures effective AI outputs, enabling organizations to utilize structured and unstructured data for insightful decision-making.
- AI framework for success: Build AI-readiness with strong infrastructure, quality content, governance, ethical practices and upskilled teams for secure, responsible and impactful AI implementation.
Your content’s AI-readiness is the key to unlocking the full potential of AI. It involves aligning organizational data, infrastructure and your workforce to effectively leverage AI technologies. By focusing on five key pillars — infrastructure, AI-ready content, governance, ethics and skills — you can ensure your AI investments achieve maximum impact.
AI-readiness empowers organizations to:
- Enhance decision-making: Leverage data-driven insights for strategic advantages.
- Optimize operations: Streamline processes and reduce costs through automation.
- Elevate customer experiences: Deliver personalized services and innovative solutions.
- Drive innovation: Foster a culture of innovation and explore new business opportunities.
- Effectively implement AI at scale: AI-ready data results in improved AI-powered searches, AI processing and AI agent performance.
What does it mean to be AI-ready? Your enterprise content must be configured so that high-quality, relevant and secure data can feed into AI systems, enabling it to produce all the benefits it promised.
Why does data need to be AI-ready?
AI can be powerful, but only if it has the right fuel.
Quality data doesn’t automatically exist in your enterprise content — it has to be readied. Enterprise content management providers are uniquely positioned to help customers transform their data for the endeavor.
Unlocking the insights from your enterprise content will drive operational and analytical outcomes that open up incredible opportunities, and doing so is a critical need for capitalizing on AI. There’s a misconception that everything in a repository and database can be thrown into an AI engine, and it’s going to learn everything. Unfortunately, that’s not how it works.
First, trying that isn’t economically feasible; second, AI models need to be trained with the right data.
The garbage-in, garbage-out concept — essentially throwing everything into an AI solution — means you’re not going to see the accurate, enhanced AI outputs you’re looking for.
Making data AI-ready
To capitalize on the power of AI, an enterprise’s data has to be ready for the machine. There’s a data translation that needs to take place; content that was created for human consumption needs to be processed for a computer.
“Consider a document full of text and images,” Tiago Cardoso, AI product manager at Hyland, said. “When retrieving content for an LLM, we need to understand its meaning and context and select only the relevant aspects.”
Additionally, enterprises need to select the right content to feed the machine. Starting a new model is a big lift, requiring the right data to train, test and fine-tune the system. It’s a science, and you want to avoid overfitting or underfitting the model. With too much of the wrong information — sometimes called annecdata — generative AI (gen AI) outputs start declining.
Once the right content is ready for machines, organizations can start implementing the impactful services AI offers.
Unstructured data enters the chat
Content, in both structured and unstructured formats, holds the important data an enterprise collects. However, research suggests less than 10% of unstructured data is being extrapolated to be used in business processes or decision-making, despite 80% of data sources being unstructured.
Imagine freeing all that inaccessible, unused data using gen AI. With unstructured data predicted to grow, the business implications of being able to fully capitalize on the data you already own are astounding.
Once AI can access and activate those data sources, organizations can draw insights at scale and benefit from the semantic relationships that AI can connect.
For example, once your data is AI-ready, AI agents have the necessary context to make enhanced decisions and take action. Additionally, more intelligent search results are made possible with AI’s ability to mine and interpret data not just from traditional structured sources, but also from the trickier unstructured documents. Without depending on narrowly defined metadata, an organization can get a fuller picture of the relationships between previously unconnected data points, making it possible to find things based on relationships rather than specific search criteria.
AI-readiness framework
We’ve developed a framework for its evaluation using five pillars:
1. Infrastructure
Infrastructure speaks to technical readiness. To leverage AI and do it securely, organizations need a robust, comprehensive infrastructure to manage data, as well as the right tools to do the work. The databases where information is stored need to be secure, compliant and scalable — ready for business booms or declines.
In the right infrastructure (a federated, cloud-native ECM platform) you can deploy modular services that transform enterprise content into machine-usable formats. This means extracting key data, enriching it with metadata, and storing it in a structured way that AI systems can access and interpret. With content processed and indexed this way, AI systems can establish real-time connections, retrieve contextually relevant information, and operate on content that’s secure, scalable and audit-ready.
2. AI-ready content
Having AI-ready content is another technical hurdle that needs to be cleared. Content must be curated and enriched for high-quality AI output.
The first step is to curate your data using proven tools. This process involves extracting, normalizing and structuring content, and it ensures it formats into clean and consistent data for your AI applications.
Next, you must tackle data normalization and structuring. In this stage, your unstructured text needs to be converted into standardized formats. This makes it ready for the machine learning (ML) models, analytics and automation workflows that deliver so much AI-powered value.
Finally, new metadata needs to be generated, so your system can improve information searchability and AI model accuracy.
This is what it looks like in reality: Imagine a major retail company needed to automate metadata generation and enhance product catalog insights. By transforming its enterprise content into AI-ready assets, that content can more seamlessly drive innovation and operational efficiency. Through automated metadata tagging, product details can be accurately extracted and structured across diverse formats, while contextual enrichment identifies key attributes like brands, specifications and categories. These improvements would enable precise data classification, enhanced natural language search accuracy and bolster the performance of recommendation engines.
The result? Reliable product data, improved search relevance and personalized recommendations that elevate the customer experience and support smarter, AI-driven business processes.
3. Governance
Governance overlaps both states of technical readiness and business readiness. Organizations have a great responsibility when it comes to governing AI. From monitoring data access and detecting malicious incursions to ensuring responsible AI practices throughout the organization, having strict standards enables organizations to implement AI safely and securely.
When incorporating AI into products and daily operations, organizations should develop clear guidelines for product teams and employees to mitigate AI-related risks in different aspects of the business.
An AI council can also help oversee the incorporation and implementation of AI, while ensuring guidelines reflect technological advancements and law changes.
Adhering to organizational security and compliance standards is essential. Given AI’s heavy reliance on data, having robust policies and the right technical tools in place provide a strong foundation for a secure AI implementation.
4. Ethics
You need to have an ethical foundation in place — it’s critical to deliver on responsible AI.
Ethical AI is a common point of concern among customers and in RFPs. Honesty, bias and explainability are all facets of this component of business readiness.
If an AI engine is going to make a decision or a recommendation, you need to be able to understand how it came to that conclusion and what benchmarks and evaluations are showing those conclusions as accurate. Being ready from an ethics standpoint means having guardrails in place.
- Beneficial to society, enriching us individually and collectively
- Transparent, so outcomes can be explained and decisions can be audited
- Secure and privacy-enhanced, so organizational and personal data is protected
- Built, used and deployed responsibly throughout the AI lifecycle
- Designed and deployed to monitor for and mitigate unintended consequences or unfair bias
AI-ready businesses can support quality AI outputs with ethical data, as well as monitor for things like bias. AI models also need to be able to defend against situations in which users might try to use disingenuous prompts to receive information they shouldn’t have access to.
The implications are very real for many industries, notably financial services, insurance and higher ed. From historical redlining practices in lending to fraudulent insurance claims and student evaluations, the stakes are high, and the data that feeds an AI model must be protected against bias and tainted data.
5. Skills
With AI capabilities popping up across new and familiar technologies in every industry, you can’t fully realize AI ambitions without the right people to take them to the finish line. The competition for AI skills talent is fierce and has created a talent gap ranging from engineering and data scientists to business users who need leverageable AI know-how.
Organizations are eager to bring on highly trained faces, but AI experts point to upskilling and adopting user-friendly interfaces as alternative routes. With proper upskilling, everyone in an organization should be leveled up from a knowledge perspective on AI; with intuitive interfaces likes point-and-click, low-code tools, everyday business users can leverage AI.
Snapshot: Components of the AI-ready enterprise
Once an enterprise achieves AI-readiness, the exciting work of building an AI-powered workplace launches. New processes — new possibilities, even — are on the table. At a high level, employees and customers should benefit from greater efficiency and visibility, including:
- Quick delivery of accurate business information
- Accelerated, quality decision-making
- Ability to outsource time-consuming intelligent work to AI
- Support for and amplification of the workforce
- More rewarding customer experiences
Comprehensive intelligent search
A more intelligent search is one of the things people most want out of AI. In fact, 67% of IT leaders Forrester surveyed said having a solution that can surface, govern and derive intelligence from content would significantly impact their innovation goals. Users want to be able to do their search with a natural language prompt, in a conversational way. To ask for some information and get the right answer back, even if the data is in multiple places. Users want AI to provide quick, contextual recommendations, guidance or even actionable insight to work from.
AI-powered platforms organize content in a more human-like way by going beyond the narrow data labels and filters of legacy systems. Additionally, gen AI can jump in to take the search to the next level by providing insight and answers.
Smarter automation
Modern content management solutions have native automation capabilities that can unlock structured and unstructured data to create relationships and drive new business processes.
Using AI agents behind the scenes helps move processes forward. And it’s not just pure automation of processes. AI can amplify and augment the people who work in those processes to help them be faster and more effective. AI models can even understand how a process works and recommend process flow changes based on what it’s learned.
With gen AI, we can now give structure to what was previously unstructured. We can read — literally read and process — all of the petabytes of content and images, interpret them, and enable organizations to understand what’s inside them and drive greater automation.
— Jitesh S. Ghai, CEO, Hyland
Intelligence beyond metadata
With AI-ready content, the entire information life cycle gets an upgrade. The relationships built between data points and understood by AI creates opportunities for enhanced content management, processes, search and governance. For example, AI enriches your workstream via:
- Content management: A higher volume and deeper value of information can be organized, recognized, extracted and activated from unstructured content, leading to more comprehensive content management and better decision-making.
- Processes: More complex processes can be automated with AI’s ability to make sense of connected and relevant data from unstructured content, in addition to its ability to use human-like intelligence to propel workflows.
AI in different roles
Leveraging AI effectively across an enterprise takes time, education and innovation. Many organizations ramp up their AI use as they gain confidence, competence and creativity. Let’s take a look at three scenarios of AI execution:
Level 1: AI supporting humans
Consider an existing process in which an employee looks at everything and makes the decision. AI can step in to support by providing answers to questions about content. Now, instead of an employee reading thousands of documents, AI can summarize the content and provide the worker the information they need to make a quick, well-informed decision.
Level 2: AI automates processes under human review
In this scenario, the process is set up for the AI model to do the leg work and present its findings to a highly skilled employee for review. For example, a process may have five decisions that can be automated by AI. As the AI model works through those decisions, it can go back for review, but it eventually works through the process. The model’s result goes to the highly trained employee for review. This augmentation of intelligent work and skilled review drives efficiency and improves the quality of work humans spend time on.
Level 3: 100% AI-driven
When an AI model reaches a 99% accuracy rate, it’s considered fully operational. Of course, the need for governance and quality assurance is still there, but at this stage, the AI is a fully automated part of the team.