Associations sit on decades of trusted, vetted content. Historically, protecting that intellectual property meant a simple binary choice: keep it open to the public to drive web traffic, or put it behind a member login or paywall to drive revenue. But as artificial intelligence systems increasingly seek out high-quality, specialized information to train models and answer user queries, those traditional walls are proving porous.
The question for association leaders is no longer just whether AI will find your content, but under what terms you allow it to do so. Allowing AI companies to freely scrape your website is rapidly becoming an outdated approach, yet completely locking down your digital presence risks making your organization invisible to the next generation of search and discovery tools.
Dr. Jessica Miles, founder of the advisory firm the Informed Frontier and former VP of Strategy and Investments at Holtzbrink, categorizes AI content access into three distinct tiers. Understanding these tiers is critical for any association looking to develop a modern data strategy that balances discoverability with rigorous content control. Moving from the lowest tier to the highest represents the evolution from passive vulnerability to active, monetized control.
Before exploring the three tiers of access, it is necessary to address a common misconception among association executives: the belief that content is entirely safe from AI simply because it sits behind a paywall or a member portal.
While a login screen provides a significant layer of friction, it is not an impenetrable shield against AI crawlers. Miles points out that crawlers often access paywalled content through secondary channels. For scientific, technical, and medical publishers, content is sometimes acquired through illegitimate or pirated databases. Once a proprietary journal article or association report is uploaded to a public pirating site, it becomes publicly available data that AI bots will inevitably scrape.
Additionally, vulnerabilities exist within the authentication systems themselves. Many academic institutions and large organizations use legacy authentication methods, such as IP-based access, to allow large groups of users to view content seamlessly. These IP addresses can sometimes be proxied or faked by malevolent actors, granting unauthorized access to premium content.
Because paywalled content holds unique, premium value for an association, relying solely on a login screen is insufficient. Organizations must adopt a proactive data strategy that addresses how both open-access and premium content interact with AI systems.
The most basic, and currently most common, form of AI access is crawl-based access. This occurs when AI developers deploy web crawlers—often referred to as bots or spiders—to scrape publicly available content across the internet.
In this scenario, the AI system gains unmitigated access to whatever it can read on your public-facing web pages. The developers then use this scraped data either to train their foundational models or to power real-time retrieval methods, where a chatbot searches the live web to answer a user's prompt.
For associations, crawl-based access is the least desirable tier. It operates entirely as a "back door" mechanism. The organization receives no compensation for the use of its intellectual property, maintains absolutely no content control, and has zero transparency into how its proprietary frameworks, research, or standards are being utilized in the AI's outputs.
While some organizations have responded by using tools to block AI crawlers entirely, this creates a difficult balancing act. Blocking all bots protects the content but actively harms the association's discoverability. If a professional asks an AI assistant for the latest industry standards and the association has blocked all access, the AI will simply surface a competitor's information or, worse, hallucinate an incorrect answer. The goal is not to board up the house entirely, but to force AI developers to come through the front door.
The second tier of access is bulk ingest, which represents a significant step up in terms of content control and potential revenue.
Bulk ingest involves an information provider—such as an association or a publisher—voluntarily giving an AI company upfront access to a large corpus of content. The AI system ingests this data in a single-use, bulk manner to train its models or build specific applications. Because this access is granted proactively, it requires formal AI licensing agreements.
For associations, this tier turns a vulnerability into an asset. Instead of having content scraped for free, the organization licenses its data, creating a new revenue stream. Miles notes a distinct shift in how these licensing deals are structured. In the early days of generative AI, companies sought massive, broad-scope datasets for foundational model training—deals that were typically only accessible to the world's largest commercial publishers.
Today, the focus has shifted toward specialized, domain-specific knowledge. AI developers are building specialized applications—such as diagnostic assistants for medical professionals or regulatory tools for financial advisors—and they place a high premium on the exact type of niche, vetted content that associations produce. This shift means that even smaller associations with highly specialized content libraries are now in a position to negotiate bulk ingest licensing deals.
However, bulk ingest is not without its drawbacks. While an association gains revenue and a legal agreement governing the use of its data, it still lacks operational transparency. Once the content is ingested, the association is entirely dependent on the AI application provider to measure usage. The association has no direct line of sight into how often its content is surfaced, how it is weighted against other sources, or how accurately it is represented in the final outputs.
To achieve true transparency and ultimate content control, associations must look to the third tier: runtime access.
Instead of handing over a bulk corpus of data for an AI company to ingest and store, runtime access forces the AI tool to request information on an as-needed basis through an authenticated access method, typically an Application Programming Interface (API). When a user asks the AI a question, the AI system pings the association's API in real-time, retrieves the specific piece of necessary information, and uses it to generate a response.
Miles identifies runtime access as the gold standard for content holders. It offers all the benefits of licensing while solving the transparency problem inherent in bulk ingest. Because the AI must authenticate and request data through the association's API for every query, the association retains complete visibility into exactly what content is being accessed, how frequently, and by which applications.
This level of insight is invaluable. As traditional web metrics like page views and download counts become less reliable indicators of content engagement, API access logs provide a highly accurate, granular view of what the industry actually cares about. If an association sees a massive spike in API queries regarding a specific new regulatory standard, it can immediately pivot its educational programming and event agendas to address that demand.
Furthermore, runtime access allows associations to update their content dynamically. If a medical society updates a clinical guideline, they simply update their own database. The next time an AI tool queries the API, it automatically pulls the revised guideline, ensuring that professionals are always receiving the most accurate, up-to-date information. With bulk ingest, an AI model might continue surfacing outdated information until the next licensing refresh.
The challenge with runtime access is operational. Building and maintaining secure, authenticated API endpoints requires a higher level of technological maturity and ongoing expense than simply handing over a hard drive of PDF files. However, as the industry increasingly demands greater transparency and accuracy in AI outputs, investing in API access infrastructure is becoming a necessary step for associations that want to future-proof their digital assets.
Implementing a robust data strategy is further complicated for the many scientific and professional societies that do not manage their own publishing operations. These organizations often partner with large commercial publishers to produce and distribute their journals, books, and standards.
When these large publishers negotiate multi-million-dollar AI licensing deals, the content of their society partners is frequently included in the package. However, the societies themselves are rarely at the negotiating table. Miles advises that associations in this position must proactively seek transparency from their publishing partners.
Many existing publishing contracts were drafted five to ten years ago, long before generative AI licensing was a consideration. These legacy agreements may not explicitly cover who owns the copyright for AI training purposes, what specific AI activities the publisher is authorized to permit, or how licensing revenue should be shared.
Association leaders must initiate these conversations early. Organizations need to decide whether they are comfortable being opted into a commercial publisher's aggregate licensing deals, or if they wish to retain the right to advocate for themselves and negotiate their own AI access terms independently. Assuming that your organization's interests are automatically protected in a third-party negotiation is a strategic risk.
The transition from human-centric web publishing to AI-centric data access requires a fundamental shift in how associations view their content. It is no longer just a collection of articles and PDFs to be read; it is a structured dataset to be queried.
Moving beyond crawl-based scraping is essential for protecting the integrity and value of association intellectual property. While bulk ingest licensing offers a practical middle ground and a new revenue stream, the ultimate goal should be establishing runtime API access. By building the infrastructure to authenticate, monitor, and control how AI systems interact with their knowledge bases, associations can ensure they remain the definitive, trusted authorities in their respective industries—no matter what technology is asking the questions.