Advancements in AI and Large Language Models (LLMs) have fundamentally transformed the creation and consumption of mission-critical content. While developing new content is now a matter of a few clicks, this content does not necessarily equal knowledge. The role of machines in these processes raises profound questions about the formats and methods that best serve both humans and machines.
Historically, mission-critical knowledge – the essential information underpinning societal governance, safety, and progress – has relied on human-centric formats like books and PDFs. However, these formats often fall short in the machine-driven environments that make up most offices and work contexts today.
Current challenges of knowledge management
Previous generations of technological advancement have sought to drive progress for content creators across the board. However, these systems often mirror outdated paper-based processes. This has led to persistent issues, such as:
- Obstacles in facilitating collaboration across teams and stakeholders
- Complexities in version control and localization
- Difficulty adapting to ever-changing rules, standards, and regulations
- Challenges in publishing for both human and machine audiences
- Inefficiencies in tracking content throughout its lifecycle
Efforts to address these issues must ultimately balance achieving organizational goals with the needs of subject matter experts (SMEs), compliance managers, and technical writers without whom, there is no content.
Artificial intelligence (AI): transforming mission-critical content
AI technologies, in particular generative AI, are revolutionizing knowledge management. Emerging thought leadership and research suggest that businesses lag behind individuals in AI adoption, despite its growing ubiquity in daily life. AI offers powerful tools for managing large volumes of content and its potential benefits to mission-critical content are numerous. The body of content that SMEs may be navigating can be huge and further complicated by issues such as broken links, out-of-date templates, and no single source of truth that allows organizations to pinpoint with certainty the master version at a given time.
AI’s benefits for the creators and consumers of mission-critical content are also numerous. For example, distilling vast amounts of content into concise or simplified versions. For businesses operating in heavily regulated industries, company policies and procedures can be buried in unnavigable systems prompting staff to turn to unverified sources (like search engines) for answers. Businesses can now leverage their own AI-enabled content as a set of assets, making it more usable and contextual for their team members.
However, challenges like bias and hallucinations make its application to mission-critical content particularly sensitive.
The role of machines in mission-critical knowledge processes raises profound questions about the formats and methods that best serve both humans and machines.
Building the foundation: knowledge graphs and RAG
Emerging solutions like knowledge graphs and Retrieval-Augmented Generation (RAG) offer promising approaches to mitigate AI’s challenges. Knowledge graphs create a structured, reliable foundation for AI models, while RAG dramatically improves precision and accuracy outputs by grounding Large Language Models (LLMs) in trusted data.
Technology companies specializing in solutions for mission-critical knowledge are already utilizing this technology to develop features that enable teams to navigate organization-wide content, improve content creation processes.
The ability to essentially ‘converse’ with a given organization’s content is only the beginning of what AI-driven technology can do to improve outcomes for creators and consumers of mission-critical content. In fields where accuracy and reliability are paramount, these tools can corroborate facts, provide deep contextual insight, handle updates and new data in real-time, and provide users with a trail back to the ground truth – the fundamental, verified reality or accurate data.
The proliferation of knowledge today means that anyone can be a creator of what may appear to be mission-critical content – or a very good copy. RAG LLMs and knowledge graphs are opportunities for the creators of mission-critical content to take control of the dialogue and offer certainty on the path ahead. However, in order to fully take advantage of the opportunity of AI, organizations must address their approach to content.
Shifting from documents to data
AI-ready today: laying the groundwork for tomorrow’s knowledge infrastructure
Time is required to address the technological shift needed to adjust to the AI reality. The sands of time are already running, however the steps required to get there are clear. Mission-critical content must be underpinned by technology solutions that allow it to be utilized by humans and machines – shifting from the world of documents to the world of data in a manner that doesn’t sacrifice the user experience for SMEs.
This is the foundation for the knowledge infrastructure of the future.
