Knowledge Intelligence
Knowledge Intelligence (KI) is a framework that integrates institutional knowledge, business context, and expertise to enhance AI capabilities through effective knowledge and data management practices.
More on Knowledge Intelligence and Enterprise AI
Artificial Intelligence (AI) solutions have become a major component of most organization’s strategy and goals. Agentic and assistive AI applications can serve as a competitive advantage for implementing automated decision making and processes for greater productivity and intelligence. However, organizations are experiencing continued struggles building viable AI solutions because they haven’t articulated the immediate business applications in their planning, didn’t adequately understand and prepare for the quality of their content, data and overall knowledge assets required for reliability and trustworthiness, failed to include the necessary semantic modeling, or didn’t plan for the appropriate business logic and relationships AI needs to understand organizational context.
We coined the term Knowledge Intelligence (KI) to express our unique approach to AI, which includes the necessary design and asset foundations, addressing the major stumbling blocks leading to failure for most organization’s present AI initiatives. KI expresses a complete view of knowledge, information, data intelligence, and domain expertise that organizations need through the entire process of planning, design, implementation, and iterative enhancement of enterprise AI. KI not only connects data sources but also unifies intelligence and expertise across data science and analytics teams, bringing the necessary human experience and expertise into the model to avoid AI hallucinations and failures.
EK’s KI initiatives are anchored in proven information and knowledge management principles, and leverage standards and semantic building blocks (such as metadata/ontology design and knowledge graphs) to prepare your data and build a rich network of relationships between your organization’s key assets. This connective layer empowers a myriad of use cases, including intelligent agentic assistants, smart semantic search for multimodal and text content, recommendation engines, automated content assembly and personalization, and more. Knowledge Intelligence is the key for creating explainable AI and harnessing your organization’s knowledge to personalize organizational processes in the ways you always strived for but were never able to achieve.
The Problems It Solves
Applying Knowledge Intelligence frameworks to your initiatives maximizes the value of both your AI efforts as well as your collective knowledge. It integrates your organization’s expertise, information, and data assets in a way that makes them actionable and contextualized. This solves a number of the “big AI” implementation challenges many organizations face when trying to deploy scalable solutions, including:
- Getting the most out of the vast amount of data collected in your organization: fragmented data and content leading to inaccurate insights, prohibiting effective decision-making,
- Overcome AI Disillusionment: disparate content management across platforms is difficult and leads to higher maintenance and licensing fees that drain financial resources and AI success,
- Knowledge Retention, Faster Time to Proficiency and Upskilling: Too much time spent on redundant and outdated content diverts staff from strategic initiatives, and confusing content ecosystems result in poor user engagement, and
- Knowledge, Data, Content Quality: Lost business meaning and knowledge coupled with insufficient data diversity is limiting the organizations’ abilities to leverage AI and analytics for innovative and ethical initiatives.
EK’s Knowledge Intelligence framework helps organizations solve these issues with proven techniques including expert knowledge capture and transfer, AI-augmented tacit knowledge extraction that connects structured, semi-structured and unstructured data, and business context enablement through transforming data and knowledge assets into machine readable formats. Bringing to bear this approach, KI enabled point solutions might include:
- Advancing your enterprise AI-RAG search capabilities to understand your business language and return relevant, actionable answers (not a running list of links or documents),
- Augmenting your information governance capabilities with AI automation and knowledge extraction to handle the high volume of data and content creation or ingestion, and
- Creating a flexible data model with AI applications that is based on standards and powers diverse analytics and solutions of the future.
- Building tacit knowledge capture into your AI workflows, ensuring the expertise of the organization is reflected in your solutions.
Business Outcomes
Knowledge Intelligence (KI) will connect your information assets with context and business intelligence, creating a semantic ecosystem that is reliable, intuitive, and most importantly, actionable and secure for consuming AI applications and organizations that use them. Our KI framework will help organizations overcome AI disillusionment.
With an iterative approach, we will help your organization achieve AI maturity on your timeframe and level of effort, growing from quick wins and pilots to an organization-wide framework that addresses your biggest information challenges.
Though each KI initiative is unique, some of the most common outcomes we’ve helped our clients achieve are:
- Significant Returns on AI Investments: Increase revenue and enhanced competitive edge by aligning institutional knowledge with data to make connections between siloed content, allowing organizations to make better use of the vast amount of information and data they have.
- Faster Time to Proficiency and Upskilling: Deliver faster employee onboarding and proficiency by providing the ability to learn at the point of need and increasing access to expertise from across the enterprise.
- Reducing Operation Cost and Increased Efficiency: Augment information governance initiatives with automation to increase the speed of creation, ingestion, and curation of content, creating opportunities to minimize human error and freeing up staff’s time to focus on higher value activities.
- Rapid Innovation: Drive innovation by facilitating connections and collaboration among team members through the ability to access information across the enterprise and find experts, resulting in increased exposure to new ideas, driving innovation, creativity, and company performance.
