‍The challenge of unifying knowledge for AI

Most companies are exploring ways in which GenAI and specifically LLMs can help improve product adoption and customer enablement using their existing knowledge assets. But, there’s a problem. Product knowledge is usually scattered, written in various formats and silos, and often includes sensitive content which, if not handled correctly, poses security and compliance risks. Scattered knowledge has been recognized as one of the most critical challenges facing companies looking to integrate AI into their day-to-day operations and critical missions*.

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* https://validio.io/blog/data-debt-everything-you-must-know-as-a-data-leader 

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Building a knowledge-infused AI application in-house seems easy: take the knowledge, index it in a vector store, pick an LLM and use a framework to build your AI application. 

However, in reality, it’s a mess:

• Developers don’t even know where to start looking for content. 
• Even once they find it, it's stored in different repositories, formats, and schemas. They now need to build multiple custom integration and transformation pipelines and do it in production quality and monitoring. Every new knowledge source found will require another pipeline.
• The knowledge might contain sensitive information: PII, PHI, and PCI*, and has to be inspected and scrubbed closely. 
• As if that’s not bad enough, developers need to then find a way to enforce access control, to ensure users aren’t getting back answers based on content they shouldn’t be seeing.

The implications can be disastrous:  

• By allowing developers to access your knowledge sources directly, your company is not able to achieve a reliable, central, and reusable governance infrastructure for AI development. Any new application being developed will result in reinventing the wheel and having duplicate infrastructure that attempts to do similar things.

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* - PII (Personal Identifiable Information): any data that can be used to identify a specific individual. This can include direct identifiers, such as name, Social Security number, and email address, etc.

- PHI (Protected Health Information): any information in a medical record or other health-related information that can be used to identify an individual and that was created, used, or disclosed in the course of providing a healthcare service, such as diagnosis or treatment. 

- PCI (Payment Card Information): details associated with credit and debit card transactions and cardholder data.

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• Writing integrations to complex knowledge sources and developing sensitive security code is not something your developers are familiar with, and they can make costly mistakes, resulting in material risk for data leakage, compliance violations, and brand damage.
• And last, having developers spend hundreds of hours on building tedious integrations and content pipelines is expensive.

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‍Knowledge (un)optimized for AI

But the problem doesn’t stop with developers wasting time, effort, and resources. The main issue is that scattered and siloed knowledge is very likely to be inconsistent, marked by diverse formats, schemas, taxonomies, and sensitive data - and thus completely not optimized for AI. Simply put - siloed knowledge means partial and inaccurate responses to your customers' queries. Your AI really has the potential to become a super-expert on your knowledge base, but with lacking, missing, and versioned data, it’s not going to happen. There’s just not enough data for your AI to do its job, and that means end users will suffer.

‍Partial knowledge= Partial AI.

Let’s summarize the main reasons why  AI needs to be grounded in unified knowledge to create the ultimate user experience:

‍How a Content Delivery Platform (CDP) unifies knowledge for AI

Companies that were able to successfully implement AI projects in Service and Support have one thing (at least) in common: they prioritized infrastructure -  getting the knowledge unified in a single platform. 

A critical layer that serves as the safeguard of knowledge, ensuring the confidentiality and integrity of data that fuels AI algorithms. Without this critical shield, the very foundation of any AI system is susceptible to compromise, leading to privacy breaches and undermining trust. By consolidating and securing enterprise knowledge from any source and format, a trusted CDP provides the content orchestration and governance platform that abstracts knowledge integration complexities and provides the AI developer with a single schema and point of integration.

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Once you have a single source of truth for the full breadth and depth of your knowledge, you can establish a robust governance infrastructure, future-proofing AI projects and ensuring a unified and efficient development process. You can safeguard sensitive information such as PII, PHI, PCI, and more, minimizing the risk of data exposure and maintaining compliance standards.

Adding a CDP to your tech stack also means your company will not need to make any changes in how knowledge is authored, by whom, and when. The ability to ingest any content type from any source and structure it in a unified scheme with consistent taxonomy is guaranteed and future-proofed for any AI applications you will implement. 

Thinking about adding a Content Delivery Platform (CDP) to your tech setup? Start by pinpointing where your current system might be falling short and how a CDP could really help. Looking for detailed, actionable insights? Join the upcoming webinar, where we will guide you through the essentials - from achieving seamless integration to enhancing search capabilities - equipping you with the knowledge to choose the ideal CDP for unmatched documentation success.