Shaping the Future of Knowledge Management: Collaborative Innovation in Intelligent Content, Taxonomies, and AI

 Alvin Reyes, of RWS, Harald Statlbauer, of NINEFEB, Mark Gross, of Data Conversion Laboratory, and Lance Cummings, of UNC Wilmington, were part of a Component Content Alliance-sponsored panel discussion to wrap up the conference. 

"If you have one dollar to invest in knowledge management, put one cent into information management and 99 cents into human interaction." - Larry Prusak

Preconditions for AI-driven solutions include organizational readiness, standardized metadata, and appreciation and application of structured knowledge and structured content.  

What are the key starting points for orgs to unlock the potential of their knowledge assets with taxonomies, intelligent content, and AI? 

RAG and AI are ways of getting info out at a faster and better rate than in the last, but basics have not changed all that much. People have come back to thinking that content has to be good content and has to be structured content. GenAI is output. Combining unstructured and structured methods to get advantage of structure. People are writing structured content, throwing away the structure publishing to PDF, and then using the PDF to feed LLMs. 

What roles and skills are essential to AI-driven knowledge management?

Work across teams, but writers will be at the table. Bridging gaps between technical documentation and learning and training.  Working across teams will require diplomacy skills. 

How can orgs create smarter workflows by aligning AI, taxonomies, and content strategies?

Start with terminology. Out of that, creating a taxonomy.  Workflows are driven by end results. Work backwards to support that. Content is messy, and most of the content you're going to be using is not content you have control over. Have to figure out how to add structure. This is where taxonomies are important. 

What's the relationship between structured content, structured documentation, structured knowledge, and AI?

In any complex system, both structured and unstructured information. Knowledge is mirrored in different types of media. Reclassification of content helps make it accessible. RAG is the approach for your content, not a random LLM. Context needs to know where the content is coming from. 

 

The Journey to "Docs as Code" and Back: Lessons Learned from Three Documentation Migrations

 Mike McGinnis, of Tridium, shared Tridium's journey of multiple docs migrations. The size of the team and the type of the people are factors in how you do migrations and whether to do migrations. 

Environment is agile, mostly software, some hardware, publishing to PDF, in-product help, and a document portal. 

The first migration was from FrameMaker to Windchill (Arbortext). Had too many files, DITA looked promising, wanted to avoid vendor lock-in. The migration took 2 years, Arbortext required separate training and tooling. 

Migration #2 was Windchill to Git, because the Windchill environment was being lost. Had short notice, and saw docs as code as an option. Had experience with Git and knew could set up a repo. Export not as easy as thought, had to export one book map at a time, and files were put in one folder. Had to refactor all references, mostly done with scripting. Writers needed to install and learn Git and Sourcetree. 

Docs as code led to merge conflicts and confusion because of no training, and it was easy to break things. We also had no dedicated support, which was a problem. 

Migration #3 was docs as code to CCMS. Publishing effort was high, legacy tools were expiring, writers were unhappy, the vision was unclear. Redefining the requirements need to include "easy." Easy system architecture, admin controls, operational controls, authoring, publishing, and reviewing. 

Lead people, not just tech. Create shared vision, and empower others. Adapt and reflect.

Structure Through Delivery: The New Gateway to DITA Adoption

 Frank Miller, of Ryffine, started by talking about DITA adoption over the years. Tried many things to get people to adopt structure and good content strategy. But there is resistance to change. Projects take too long and stall before showing value. Silos prevent cohesive adoption. Orgs struggle to justify costs, especially when benefits are years away. 

Today is a perfect time, with the knowledge and experience we have, and all the tools we have. This includes the AI revolution, the evolution of content delivery platforms, and market demand, when customers now require intelligent, contextual content delivery. Structured content is fast becoming a business imperative. 

So why now? We have years of implementation wisdom. Modern CDPs enable adoption. AI drives structured content demand. And there are now proven pathways to success. 

In the traditional approach, there was heavy structure, in the front end doing all the IA work, resource drain, high upfront costs in time and training, timeline risk, a long time to demonstrable ROI, significant resistance across teams, and benefits only after implementation. 

New approach starts with delivery, in modern CDPs, and show immediate value, have a gradual evolution, replacing a "big bang" with progressive structure, with early wins and flexible timing, all of which lowers risk. Give leaders a shiny object that is not only shiny, but valuable. That buys time, support, and budget. 

Content value can only be truly measured at delivery. We need feedback fast. 

Modern platforms support multiple formats seamlessly and connect with existing tools and workflows. We can use CDPs as aggregation points for our content. 

Now, instead of having all content in a single repository, the dream, teams want their own repos, so having all the content in one CDP is a good compromise. 

You have to do the metadata/taxonomy to get the output right.

Content Conversion Hacks: Tips and Tricks for Success

 Adelheid Certik, of Mizuho OSI, recently made the transition from unstructured authoring to structured authoring. Here to talk about the content conversion. 

Prioritize the content to convert. Some immediate, some ongoing, some never. For example of the latter, older consent or content not a candidate for reuse. Also wanted to identify a subset of content for a pilot, one complex and relevant enough to develop a good content model.  

Do you want to outsource or do work in-house. This is a money vs. time equation. 

In pre-conversion, edit your content to align with your information model. Create styles to cover elements/attributes in your information model. Apply styles consistently and then break content into topics or chapters. 

For the actual conversion process, conversion tables are available as a plug-in in tools such as Oxygen and FrameMaker. These assign tags to styles. When going from XML to DITA, adjust the XML structure to align with the information model. If you have the in-house knowledge, can use XSLT.

After conversion, upload to CCMS, then create maps, add links to images, add conrefs, and create keys, keyrefs, and conkeyrefs. 

Do not skimp on editing your content to match your information model or identifying main areas of reuse. 

Bridging the Gap Between TechDoc and FieldService and Support

 Harald Stadlbauer, CEO of NINEFEB, noted that the session will deal with large, heavy equipment, and how to serve the service technician, often forgotten at a company, so they can service these types of products. 

iiRDS (intelligent information Request and Delivery Standard), an international standard for delivering technical content to service technicians. 

Gap between tech doc and field service, writing topics, usually published as PDFs. Service tech gets large PDF, too much info not tailored for specific situation. 

Out use case: intelligent content as a service. Content for the right context as a service when you need it. 

A service engineers wants context-specific information for problem resolution. 

iiRDS is an international standard that consists of a standardized vocabulary and relations between the vocabulary entries and classes.

The Docs Pipeline of the (Near) Future

 Manny Silva, of Skyflow, started by saying that he's going to talk about how the ways we create content is going to change. 

Today's pipeline consists of authoring, publishing, and collaboration tools. (C)CMS pipeline includes DITA tooling, WYSIWYG editors, PDF & HTML, and Sharepoint and Jira. Kind of looks like first get a request in Jira, author content in CMS, and then output. Docs-as-code gets a but more complicated. More tools, each one doing less. 

What about AI? Much digital ink has been spilled about how AI has upended everything. It's not going to replace writers. (But will change how we work.) Generative AI benefits from personal, targeted use. Not everything can or should be solved by AI. 

Challenges with current pipeline: content is created manually, time-consuming validation, and maintenance overhead. 

Three categories of new tools that will help automate routine tasks. 

Content generation. Tools that assist in creating documentation content. Rapidly produce first drafts, freeing us to focus on refinement. For creating novel technical content. 

But generation challenges include that there are a lot of generation tools already, with more coming out. 

Content testing, which embodies docs-as-tests as a strategy. Docs are testable assertions of product behavior. Validates information before publication. Not for validating syntax or style, which is what linters do, but that doesn't validate the actual content accuracy. 

Testing challenges include a learning curve (not traditional tech writer tools).

Content self-healing tools represent the cutting edge of documentation automation. They automatically identify and resolve common documentation issues with minimal intervention. 

Self-healing tools require a deep understanding of technical documentation content and why the structure is important. They don't understand IA and why we do the things we do. There are also security issues. How can you be sure that there will be a human in the loop?

Thyese advancements are not about replacing technical writers, but about empowering them. They allow tech writers to focus on high-value tasks and produce more accurate, trustworthy documentation.  You can maintain larger doc sets and adapt quickly to product changes. This elevates the role of tech comm.

Fighting Words: DITA and the Battle for Better Content

 Jake Campbell, of Scriptorium, assumed a basic (basic) knowledge of DITA to start his presentation, described a problem of a lot of content spread across several poor-quality PDFs with no bookmarks, no searchable text, and file size issues. 

Initial solution extracted data into spreadsheets. A newer approach was a modern PDF, but still issues. 

Core issues were irrelevant information, information accessibility, and resource usability. 

Solution was to collect into one PDF with navigation and clickable links. 

Used DITA-OT 3.6.1, oXygen, Antenna House Formatter, and Bitbucket. 

Process included selecting conversion target, build a content model, decide on a workflow, and then do the writing. 

Created a template with section elements, most with titles. Added Details and Tags sections. An otherprops added to the root element allows for filtering.