Closing Panel Discussion: The Future of Content

 Panelists:

• Scott Abel
• Larry Swanson
• Rahel Anne Bilie 
• Dipo Ajose-Coker

What will be the next big thing in content production and delivery?

Dipo: Everyone excited about generative aspects of ChatGPT. Just starting to understand the generative isn't the only interesting part. 

Rahel: Intelligent content is content that is semantically categorized. Search engines will better be able to understand it. AI still favors structured content. 

Larry: We are the real intelligence behind AI. Guardedly optimistic. 

Scott: Precision matters. Many sad-ass excuses why companies are not precise with their content. Predict security issues. Will be a precision revolution. 

How will AI affect the ability of people to join our field?

Rahel: Every generation defines technology differently. The next generation of tech writers is going to be different, and they will "grow up" with that and will be more adaptable. 

Dipo: AI is just another tool. It'll be doing simple things. 

When you're hiring, what is your favorite interview question to see if they are a good fit?

Jack: I believe you can tell more about a candidate from the questions they ask. 

Dipo: Standard questions, depends on how they answer. 

Scott: Trying to find a good fit. Was asked once "What do you now want to do?"

When do you feel companies will start to look at ethical AI?

Rahel: We all know about accessibility. Going to be the same with AI. Now there is EU AI act. 

Larry: As a practitioner working with AI tools, be aware of what's happening upstream of where you are. 

What can we do across content ops to set us up for future success with AI?

Rahel: It's all interconnected. It takes a village to solve that kind of problem AI is just another tool in our toolkit. 

Scott: Need to be cheerleaders for the idea that everything needs to be standardized. Precision is what we need to work toward. If you can;'t tell me precisely what content you have, that means you don't have it. 

Larry: We can look to the data world for an example. There's this notion of FAIR (Findability, Accessibility, Interoperability, and Reuse) data. And there's the notion of trust, and AI does not have that trust. 

What are some other cautions that people should be aware of as they int3egrate AI into their business practices?

Scott: Need to figure out where is the value? 

Dipo: We know it consumes a lot of energy. We have also been creating and duplicating content, so why not use AI to reduce that? 

Rahel: Data sustainability is a big issue. 75% of data collected is never used, but energy for storage and search is huge.

Wednesday Closing Keynote

 Estimating for More Fun and Greater Profit

John Hedtke, Principal Consultant at Double Tall Consulting, said that knowing how to estimate is one of the most important skills we have as technical communicators. 

A good estimate contains a description of the project's deliverables, the time it will take to create the deliverables, the cost, and the confidence rate. 

A documentation plan states the goals, specifics the details, and describes the process. Most important to have a good outline. 

"Metric" is just a fancy way to measure something, and can be simple or complex. Nothing we do that we cannot measure in some way.

An estimate is a guess. A metric is a measure that is (hopefully) accurate. An estimate that uses metrics for its data may be a WAG or even a SWAG but it's still a guess. 

Personal metrics measure how long it takes you to do something. 

Rarely do we work on anything more than 15-020 minutes at a stretch. The act of logging keeps you honest. Logging also keeps you more honest. 

As you gather data, you can find out when you're most productive, when you're interrupted the most, times better for meetings/phones calls/administrivia, and how productive you are overall. 

If you're a contractor, 80-85% of your time is productive. Captives tend to work at 60-70%. Adding a day log tracks what you did and how you feel about it. 10-15% to your productivity can make a big difference. 

Day log tracks what you have done and how you feel about it. It is subjective. Identifies rhythms over the course of a project and helps predict cycles for future projects. 

Keep your day log private. Be complete, honest, clear. It's like a diary. 

Doc plan, time log, and day log gives you the infor to build your estimating spreadsheet.

Getting it WRONG: Lessons Learned from Building a Web Content Audit Tool

 Paulo Fernandes, Co-founder of Luscious Orange, said that they decided to build a content audit tool. Found that most tools focused on tech and metrics (automate it and add AI) and removed humans from the equation. Content audits exists in mashups of spreadsheets, tracking tools, and notes. 

Had to build a crawler first. Crawling is easy. Interpretation is hard. Computers don't have context to ignore what is not necessary to know about content. 

Most people don't know about sitemal.xml. CMSs generate it. Is a list of all files. A page on a website is not always the same as a page in a sitemap. A page does not need to be in your navigation to be visible to the whole world. 

A link and a file are not the same thing. Inventory is just a bunch of URLs. HTML has tags that can point to other resources. A file can exist on your server that is never linked. A link can exist on your site that points to nothing. 

Asset inventory is different from content inventory. 

Automation is great, but content audits are human work. AI can;t help with items that require deep context.

Audits generate tasks. Leverage all the great stuff that we already know about task management.

From Chaos to Clarity: How to Surface Strategic Insights from Content Inventories and Audits

 Vanessa Stuivenvolt Allen, Director of Content Strategy at Resolute Digital explained the different between a content inventory and an audit. Inventory is a living, breathing log of content on your site, and that includes metadatra. Key to a content inventory ias that it should be kept up-to-date. Amount of effort required depends on business needs. 

Basic content inventory can be just a spreadsheet. Should be collaborative. Can also use a database solutions. Can be helpful if there is more data you want to track. Can be your (C)CMS. 

Content inventories save time, are the basis for future audits, and has info to pull into reporting. 

If you don't know what's there, you're not managing it. 

Content audit is a qualitative/quantitative evaluation against a set of defined criteria. Allows measure content against business goals. It is a point-in-time artifact and ins not maintained. 

Audit tools are typically spreadsheets. Whiteboarding tools are also useful. 

Audits insights, gaps, and opportunities fo optimization.

Steps to create a content inventory.

Use technology. And teammates. Crawling tools, exports, and collaboration. 

Set up a site crawl. So much you can get, will extract a lot of data you need for an inventory. Also set up custom extractions, such as authors, publish and modify dates, and comment counts. 

Then you clean the export. You get a ton of information, not all of it needed. (Don't lose your original crawl file.) Clean out pages hat aren't required for content inventory, such as CSS, JavaScrpt, and redirect pages. Object it to capture content. 

Build the inventory. Non-crawl columns that could be needed include original and last-modified date, refresh date, template, author or content source, technical home. 

Some might need to be filled in manually. This is where teammates come in. But also let the technology help where possible. Use text to columns. Can help with URLs to understand where content lives. 

Why maintain an inventory? Know thy content. Avoid redundancy. 

Tools and software are not the solver bullet. It's aligning people and processes. 

Document a usable and scalable process for maintenance. Important to document who owns the fields. Make this part of your content team's onboarding. Educate them why inventory maintenance is so important. Delegate inventory owners and check in regularly. 

For content audits, why not audit everything? You likely don't have the time. Determine which content you care about to get the insights you need. Identify audit criteria and stick to it. Can always go back and audit later. Resist the urge to fix things along the way. 

High value actions audits can help identify inconsistencies, uncover patterns for highest conversions, and uncover areas of opportunity.

Tuesday Afternoon Keynote

 Phantoms of Content Strategy: The People Who Help and Hurt Content Projects and What You Can Do About It

 Michael Haggerty-Villa, Director of Content Strategy at Teradata started by telling a "true" and hopeful story, saying that one of the most effective content strategist he ever worked with wasn't a content strategist. It was a product manager. So much of content decisions come from product people. 

It is just "content strategy," no matter who is doing it. You don't have to be a content strategist to do content strategy.  

Stop talking about content strategy and just do it. Develop and use standards, systems, and processes that boost content. Guide others to ask the right question. Prepare to lose and be disappointed--and keep going. Celebrate the wins, big and small.

The DIY Revolution: Crafting a Content Experience to Drive Revenue and Save Support Costs

 Alisa Conboy, Director of CX at DocuSign, told us about how she would get so energized at past LavaCons and go back to the office ready to change the world--only to get hit by reality. 

Our north star, idealized goals, were personalized use assistance, in-product intelligence, automated content delivery, and analytic insights. 

Reality: Zero budget, limited skillset, and no leadership sponsorship. 

So we all suffer. Customers struggle, team morale suffers, and the company pays the price. Customers judge perceived value based on content. 

Our options are to wait, or to not wait and adopt a DIY attitude. Look into our toolset and figure out what to do. 

Projects selected included home page update, new "landing" page, content debt reduction, an enhanced feedback tool, and Google optimizations. 

$12M support cost savings, not from case deflection, but from just when support could point to existing content and close the ticket. 

The numbers got executive attention. It built credibility and trust, unlocked cross-team collaboration, and got invited to the "table."

Learned that adaptability is king, less is more when pitching an idea, build credibility from small wins before asking for more, and partnerships add power. 

And it unlocked our future.

Taxonomies in the Age of AI: Are They Still Relevant?

 Rebecca Schneider, Executive Director of AvenueCX claimed that taxonomies are still relevant. And even moreso today. 

Taxonomies: A system for organizing content according to shared characteristics, or a way of describing things. A taxonomy describes and organizes stuff. 

Organizations have large repositories that can be used to create LLMs. AI can leverage content to understand relationships between terms. 

Unstructured content can create confusing output. Content is not always high quality. And even if metadata is applied, it can vary in quality and availability. 

Well, when no money for taxonomy development, can still use AI. Your mileage may vary--can require training and retraining (and retraining), which can cost more money. 

AI is well positioned to look at relationships between terms across multiple types of content and data. In essence, AI creates a taxonomy as it processes content. But not entirely satisfied with results. 

A study found that LLMs were good with widely known domains, such as shopping, but do not perform well in specialized taxonomies, such as computer science. 

What goes into creating a taxonomy? Start with a traditional approach: business goals and objectives, what are current pain points, what content needs description, who is the audience and what is their content needs.

Use AI in the best of both worlds. For example, use AI to identify patterns and relationships. Can be used as a jumping off point, but needs review. Feed results back into the system for further training. 

Ontologies are a formal naming and definition of types, properties, and interrelationships of entities in a particular domain. Take taxonomies and their associated facets and describing relationships, you can enrich your data, which can further inform your AI engine.

An ontology can be used for a retrieval-augmented generation (RAG) solution. (the RAG space is a fast-moving target.) 

What is success? A well-tested, vetted taxonomy/ontology that is implemented. Structured content that is business ready. Clean data. Active governance for data and content. Contributions from SMEs because they are the ones who can judge the accuracy of responses.