The Challenge

A well-known financial institution faced challenges in providing frictionless, personalized service to customers due to widespread issues with knowledge base content. Due to outdated legacy systems, both agents and customers experienced difficulties finding and using content; similarly, internal content management teams struggled to maintain and update both agent-facing and customer-facing content.

Internal teams sought to establish a multichannel contact center but were unable to do so effectively. The nature of the content’s multi-authoring process, long-history, and complex documentation led to duplicative and occasionally conflicting content that was restricted to one channel. These challenges also posed a risk to regulatory compliance, exposing audit risks and barriers to servicing necessary legal verbiage or processes. Moreover, when the company tried to reuse agent-facing knowledge base content in LLM-powered chatbots to support self-service, it resulted in hallucinative responses. The inaccurate results produced risks in legal compliance, customer trust, and day-to-day operations. The organization sought content engineering assistance in future-proofing thousands of pieces of content to enable two key outcomes:

1. The ability to reuse content in multiple channels, and for some of those channels to be LLM-powered chatbots.
2. The ability to improve the efficiency of content operations by managing content in one, central repository, thereby reducing duplicate content.

The Solution

EK worked with the organization’s knowledge management, content authoring, and user experience teams to create a semantically meaningful content model. This helped to distribute knowledge base content to multiple internal and external audiences across multiple channels, including LLM-based chatbots. EK consultants collaborated with key stakeholders to identify the pain points of the current content, and filter down the most important business concepts relevant to the help content. The structured content model includes components such as tasks, procedures, and steps with precise metadata to support complex workflows with conditional branching and parallel steps. Each of these features was validated through rigorous joint sessions to ensure user buy-in. 

This robust structure not only facilitates the seamless integration of components across different customer service channels, but also prepares the organization’s content to be leveraged for AI-driven use cases such as a chatbot. By transitioning from a traditional content management approach which inconsistently applied structure and metadata, to a content model in which content structure and metadata are semantically meaningful, the organization could increase the value of help content. Additionally, EK delivered a scalable and repeatable framework for content operations and a solution expansion roadmap for implementation, including targeting content delivery based on specific customer scenarios and agent servicing groups. This strategic transformation aligns with digital modernization goals, reducing operational risks and laying a groundwork that supports future expansions into more personalized and automated customer service solutions.

The EK Difference

As the largest dedicated knowledge management firm in the world, EK was uniquely positioned to solve the financial institution’s content problems through a holistic, knowledge asset approach. At EK, we understand that to truly make your organization “AI-Ready,” your content strategy cannot be siloed from your overall data and knowledge management strategy. As stated in a recent EK blog, “Knowledge assets comprise all the information and expertise an organization can use to create value. This includes not only content and data, but also the expertise of employees, business processes, facilities, equipment, and products. This manner of thinking quickly breaks down artificial silos within organizations, getting you to consider your assets collectively, rather than by type.”

Our knowledge asset approach meant that EK did not begin the content engineering process with the content model. Instead, our analysis and design work began with a domain model to understand the structure of–and relationships between–knowledge assets. As EK modeled the organization’s domain, we sought to understand the important business concepts relevant to a sector of the entire enterprise, rather than simply understanding the structure of the content. This approach had two key outcomes: 

1. Established a semantic mindset as a foundation for content engineering. As we moved into the more traditional phases of the content engineering work (designing the content model and supporting content operations as they are to be implemented in a content management system), the team was mentally prepared to establish a semantically meaningful content structure, rather than a reflection of how they imagine content should be presented on the page. 
2. Created an organizational framework for AI Readiness. The domain model that was created as a pre-content modeling step is an impactful deliverable that can be applied to diverse knowledge assets across the organization. When knowledge assets are managed through a unified approach rather than establishing silos for content management and data management, the organization is accelerated towards true AI Readiness.

The Results

This engagement demonstrated the tangible impact of EK’s knowledge asset approach. By grounding the content engineering process in a domain model and a semantic understanding of business concepts, the financial institution unpacked the underlying content structure necessary to enable multichannel content delivery and operational efficiency. The resulting content model created a unified foundation for connecting content, data, and knowledge across the organization, while streamlining content management and reuse. This positions the institution to leverage AI technologies and enable dynamic, context-driven customer and agent experiences. With EK’s help, this organization was able to move towards a sustainable, scalable foundation for AI readiness and long-term content transformation.

Looking to improve your organization’s content reusability and personalization capabilities while being mindful of AI readiness and future-proofing of your content operations? Contact us today!