Advertise on podcast: Inside Tech Comm with Zohra Mutabanna

Come behind the scenes with us before we take the LavaCon stage. What happens when two technical writers receive access to new AI tools, permission to experiment, and enough curiosity to keep asking, “What else could this do?” My guest, Elizabeth “Liz” Favre, is my colleague, experimentation partner, and LavaCon co-presenter. Over the past year, we have tested ideas, built prototypes, reconsidered our assumptions, and learned that the most revealing moments do not always arrive when an experiment works as expected. In this conversation, Liz and I retrace the road that led to our LavaCon presentation. We talk about how our collaboration began, what drew us into hands-on AI work, and why our early experiments changed the questions we were asking. This is not a polished success story or a preview that gives away the presentation. It is the origin story: the uncertainty, discoveries, disagreements, and unexpected turns that shaped what we now want to explore with the LavaCon audience. Listen for Why we stepped into AI experimentation before we felt fully prepared.How working together changed what each of us noticed.The tension between what AI can do and what a team actually needs.What happens when an experiment begins expanding beyond its original purpose.Why building something can expose questions that planning alone cannot.How experimentation affected our confidence as technical writers.The ideas we are carrying into our LavaCon case study.What we are still trying to understand.The episode brings you into the conversations that shaped our thinking. At LavaCon, we will take those experiences further through The Human and AI Handshake for Content Generation at Blackbaud. We will examine the experiments more closely, share what they forced us to reconsider, and invite attendees to apply the emerging ideas to their own content and AI work. If your team is experimenting with AI but still deciding what deserves to be built, this session is for you. Monday, October 26, 2026 1:15–2:00 p.m. Season 8 of Inside Tech Comm is sponsored by LavaCon. Use discount code ITC26 to save $200 on registration for LavaCon 2026. Show Credits - Intro and outro music - Az - Audio engineer - RJ Basilio Interested in being on the show? Let's connect.

Technical communication has moved from manual printing presses to AI agents, but the hardest parts of the work have not disappeared. Someone still has to think critically, ask the right questions, exercise judgment, understand the product, and recognize what users actually need. Ramesh Aiyyangar, founder of Tech Write Pro and the Institute for Technical Communicators of India, joins me to reflect on nearly four decades in communication and technical communication. His career has spanned journalism, editing, advertising, public relations, technical writing, leadership, training, mentoring, and community building—giving him a long view of what changes with technology and what endures. Ramesh challenges the idea that the future of the profession is simply a race to collect more tools or certifications. AI can take on repetitive, mechanical work, but it does not eliminate the need for discernment, structure, research, creativity, product depth, or a genuine understanding of the user. As he puts it, technical communicators are communicators first. We also examine the new titles appearing across the field—from content strategist to context engineer. Ramesh sees them less as evidence of an entirely new profession than as renewed attention to capabilities that strong technical communicators have always needed. The opportunity is not merely to adopt a new title, but to go deeper into the product, treat documentation as a product in its own right, and take greater ownership of the customer experience. That kind of growth requires more than individual effort. Our conversation turns to optimism, unlearning, self-reflection, honest mentoring, global networks, and the communities where practitioners can share experiments and learn from one another. Ramesh’s closing message is especially direct: our work should not be expected to speak for itself. Technical communicators must speak about the work, advocate for the profession, and give back to the communities that help it thrive. In this episode, we discuss: - What four decades of change reveal about technology and human judgment - Why AI makes critical thinking and discernment more important, not less - Looking beyond tools and certifications when planning a career - Treating documentation as a product rather than a set of deliverables - Going deeper into the product, engineering, and customer experience - Why asking better questions remains a defining technical communication skill - Unlearning old habits and moving beyond rigid career paths - Shifting from hyper-specialization toward a more flexible, connected practice - What new titles reveal about the profession’s longstanding core skills - Using self-reflection to make more deliberate career choices - What honest, individualized mentoring should provide - Why global communities, cultural awareness, and social learning matter - Speaking about our work and advocating for the profession Season 8 of Inside Tech Comm is sponsored by LavaCon. Use discount code ITC26 to save $200 on registration for LavaCon 2026. Show Credits - Intro and outro music - Az - Audio engineer - RJ Basilio Interested in being on the show? Let's connect.

What happens when there is no single version of a product—and therefore no single version of its documentation? In this episode, Fabrice Lacroix from Fluid Topics joins us to explore how made-to-order and highly configurable products are changing the way technical content must be structured, managed, and delivered. We look at why traditional document-based approaches begin to break down as product variants multiply, and why granular, structured content, metadata, and product configuration data become critical. Fabrice explains how documentation can be assembled dynamically around the exact product a technician, support agent, or customer is working with. This conversation also builds on recent Season 8 episodes about AI, context, and the content architecture beneath intelligent systems. Here, we take that discussion one step further: what happens when AI also needs to understand the exact product configuration before it can give the right answer? We discuss: Why bespoke products create a fundamentally different documentation challengeMoving from document-oriented content to granular, structured contentMapping content to product components, configurations, and bills of materialsUsing metadata to dynamically assemble relevant documentationWhy static PDFs struggle to keep pace with changing productsHow product context can reduce the risk of AI using the wrong informationWhy AI increases the need for explicit, structured, well-governed knowledgeWhy technical writers and content teams may become more strategically important—not less—as AI adoption growsSeason 8 of Inside Tech Comm is sponsored by LavaCon. Use discount code ITC26 to save $200 on registration for LavaCon 2026. Show Credits Intro and outro music - AzAudio engineer - RJ BasilioInterested in being on the show? Let's connect.

AI can retrieve and generate answers, but the quality of those answers still depends on the information beneath the model. If content is poorly structured, weakly governed, or divided into the wrong units, a more capable LLM does not automatically solve the problem. Patrick Bosek, co-founder and CEO of Heretto, joins me to examine how structured content shapes AI context. We begin by defining structured content and comparing the different approaches, from DITA and XML to headless content management systems. Patrick explains why the right structure depends on scale, interoperability, reuse, and the ways an organization needs to deliver information. This conversation builds on episodes Documentation Is Becoming the Interface and The Work AI Cannot See. If documentation is becoming a surface through which people interact with products—and AI cannot infer context it has never received—then content architecture becomes part of the AI system itself. This episode moves one layer deeper to examine that foundation. Patrick walks through chunking, retrieval-augmented generation (RAG), vector search, metadata, and knowledge graphs without treating AI as a replacement for established information practices. Metadata still provides deterministic controls. Traditional search may remain the better choice in some situations. Governance and provenance still matter when content comes from product, engineering, support, and documentation teams. We also consider the practical side. A lone writer can begin with a small style guide, consistent Markdown, useful metadata, and an AI agent that reviews content against established rules. At enterprise scale, however, tools alone are not enough. Information modeling, information architecture, governance, and deliberate implementation have to come first. In this episode, we discuss: What structured content means beyond a single format or toolWhen DITA becomes useful—and when it may be more than a team needsWhy clean content boundaries improve chunking and retrievalHow RAG systems retrieve information outside the language modelWhy metadata remains valuable in both deterministic and AI-driven systemsWhen traditional search can compete with or outperform vector searchHow metadata can connect structured content with knowledge graphsEstablishing authority, provenance, and a source of truth across teamsPractical governance steps for lone writers working in Markdown and GitHubWhy implementing a CCMS also requires information modeling and architectureWhy a well-structured website remains important when public AI systems answer customer questionsThe difference between buying tools and building the information foundation those tools requireSeason 8 of Inside Tech Comm is sponsored by LavaCon. Use discount code ITC26 to save $200 on registration for LavaCon 2026. Show Credits Intro and outro music - AzAudio engineer - RJ Basilio Interested in being on the show? Let's connect.

AI can summarize meetings, monitor repositories, categorize support tickets, and generate instructions. But automation does not eliminate the need to understand what customers are trying to do—or what happens when a product enters the real world. Vinay Payapilli joins me to examine the assumptions beneath the rush to automate technical communication. Drawing on 26 years in the profession, he explains how mapping documentation’s critical path changed the problem his team was trying to solve. The largest delays were not in writing. They were in recovering context from scattered conversations and waiting for knowledgeable people to review the result. That distinction leads us to a larger question: What must be understood before any part of the work can be handed to AI? A system can describe the steps encoded in software. It cannot observe a cashier memorizing a sequence of keystrokes because reading the interface takes too long. It cannot know that customers are misusing a delete button because the product gives them no reason to stop. And it cannot decide, without human context, whether the customer needs another article or a better product. Our conversation moves between documentation practice and the wider uncertainty surrounding AI: The difference between generating instructions and helping customers make decisionsHow AI can surface patterns that manual analysis missesWhy customer workarounds may never appear in support dataThe role of technical writers in questioning product decisionsWhy rapid prototypes distort leadership’s expectations of deliveryWhat drives the demand for an organizational “AI story”How practitioners can respond to disruption without pretending to predict the futureVinay’s message is neither resistance nor blind optimism. Use AI. Experiment with it. But go deep enough into the product, the domain, and the customer’s reality to recognize what the technology cannot know on its own. Season 8 of Inside Tech Comm is sponsored by LavaCon. Use discount code ITC26 to save $200 on registration for LavaCon 2026. Show Credits Intro and outro music - AzAudio engineer - RJ BasilioInterested in being on the show? Let's connect.

Documentation is not just support material anymore. It is quietly becoming a product surface, a competitive advantage, and in many cases the raw fuel that AI systems use to help customers get work done. I’m joined by Niko Berry, a former technical writer who now works in product management at Contentful. We start with his move from technical writing into product management and the skills that carried over: technical storytelling, interviewing, and the ability to translate complexity across audiences. But the conversation quickly moves into a bigger question: What happens to documentation when AI becomes another way users interact with a product? We unpack headless CMS, structured content, APIs, and MCP in practical terms. Niko explains why separating content from presentation matters when that content may be consumed not only by websites and apps, but also by LLMs that interpret it for users or take action on their behalf. That shift makes context, structure, and governance increasingly important. If AI becomes another interface to your product, the quality of the experience still depends on the quality of the information underneath it. We also get into the tradeoffs: hallucinations, lower-quality AI-generated content, brand trust, data privacy, and a concern that gets less attention than job replacement—the fragmentation that comes from being expected to do more simply because AI makes us faster. This conversation is ultimately about more than using AI to write documentation. It’s about what documentation becomes when AI starts consuming it. Season 8 of Inside Tech Comm is sponsored by LavaCon. Use discount code ITC26 to save $200 on registration for LavaCon 2026. Show Credits Intro and outro music - AzAudio engineer - RJ BasilioInterested in being on the show? Let's connect.

Technical writers do work that can influence products, customers, and business outcomes. Yet we can still find ourselves described as the people who make things look good or “do the documentation.” In this episode, I talk with Nibu Thomas about changing that narrative. Nibu’s own career has moved through several S-curves—from manufacturing and quality-focused work to documentation leadership, marketing, and go-to-market support. We use that journey to explore what happens when you stop defining your career by the deliverables you produce and start thinking about the value you create. A big part of that shift is storytelling. How do you tell the story of your career? How do you make your value visible on a resume when a hiring manager may be scanning hundreds of them? And how do you build a professional network before you actually need something from it? We also talk about what AI changes for technical writers. As tools become better at generating and updating documentation, Nibu argues that writers need to move closer to customers and business outcomes—and think beyond producing more content. Some highlights from our conversation: Finding the common thread across career transitionsMoving beyond tools and deliverables when describing your workUsing storytelling to communicate your professional valueBuilding relationships outside your immediate professional circleApproaching networking with kindness rather than reciprocityMaking your resume stand out when hiring managers have little timeConnecting technical communication work to revenue and cost savingsMoving closer to customers and product outcomesRethinking the technical writer’s value as AI takes on more content workTreating yourself as the product you need to positionIf you've ever struggled to explain what you bring to the table beyond “I write documentation,” this conversation offers another way to think about your career. Season 8 of Inside Tech Comm is sponsored by LavaCon. Use discount code ITC26 to save $200 on registration for LavaCon 2026. Show Credits Intro and outro music - AzAudio engineer - RJ BasilioInterested in being on the show? Let's connect.

Video feels like the “obvious” way to teach software until you’re the one stuck updating walkthroughs after every release. We sit down with Sagar Garuda, founder and CEO of Zenious, to talk about the real bottleneck behind product education videos: the editing, the reviews, the sync work, and the high cost of keeping content current when a SaaS product changes fast. We follow Sagar’s path from engineering to technical writing, then through customer education leadership at fast-growing startups, where onboarding speed and tribal knowledge become existential scaling problems. He explains how multiple teams describe the same product differently across the customer journey and why technical writers create the most value when they stop operating in silos and start orchestrating a single source of truth across help portals, knowledge bases, LMS content, and digital adoption platforms. Then we get concrete about how Zenious approaches the workflow: record your screen, speak naturally, and let transcript-based editing handle polishing, tightening, and syncing audio to what’s happening on screen. We also explore why structured documentation matters more in the AI era, how “knowledge layers” power contextual AI, and where human judgment still beats automation when deciding what belongs in customer-facing content. If you’re building a documentation strategy, scaling customer education, or trying to make video sustainable for your team, subscribe, share this with a content friend, and leave a review with your biggest challenge: what’s the one thing that keeps you from shipping more video? Season 8 of Inside Tech Comm is sponsored by LavaCon. Use discount code ITC26 to save $200 on registration for LavaCon 2026. Show Credits Intro and outro music - AzAudio engineer - RJ BasilioInterested in being on the show? Let's connect.

What if we stopped thinking about AI simply as a tool for doing our existing work faster—and started treating it as a new material for designing experiences? In this episode, I talk with Josh Clark and Veronica Kindred, co-authors of Sentient Design, about what happens when intelligence moves beyond the chatbot and becomes part of the interface itself. Josh and Veronika define sentient design as the practice of creating intelligent interfaces that are aware of context and intent and can adapt to users in the moment. We explore what that looks like in practice, from dynamically assembled dashboards to AI agents that behave more like collaborators within an interface. And for technical communicators, there’s an interesting implication: our ability to define context, instructions, rules, guardrails, and behavior could make us important contributors to these emerging experiences. Want to explore the ideas further? Inside Tech Comm listeners can get 20% off Sentient Design from Rosenfeld Media with code SENTIENT-TECHCOMM through October 31, 2026. Some highlights from our conversation: Moving from AI as a tool for production to a material for productWhat sentient design actually means—and what it doesn’t Combining probabilistic AI with deterministic systems How adaptive interfaces can respond to context without giving AI unlimited control Why technical communicators may have a new audience: the AI systems themselvesWhat Salesforce and Cisco are already experimenting with in adaptive interfaces Why AI could change collaboration across content, design, development, and product Making room for experimentation and “mad science” Using defensive design to build trust and guardrails into AI experiences Designing AI to express uncertainty instead of pretending to know everything Why critical thinking and AI literacy may become more—not less—important Season 8 of Inside Tech Comm is sponsored by LavaCon. Use discount code ITC26 to save $200 on registration for LavaCon 2026. Show Credits Intro and outro music - AzAudio engineer - RJ BasilioInterested in being on the show? Let's connect.

We talk with Lindsey Rix about moving beyond AI anxiety and experimenting with what AI can actually make possible for technical writers. Drawing from her presentation, Content Is the Strategy, Lindsey shares how she moved from experimenting with custom agents and AI skills to working with developer-adjacent tools, source code, and MCP connections. She explains why she starts with the problem rather than the tool, how AI is helping her gather and interpret information more efficiently, and why technical writers’ language, research, critical-thinking, and problem-solving skills may become even more valuable. Some highlights from our conversation: Reframing AI from threat to tool through hands-on experimentationUsing content standards, patterns, and style guidance to improve AI resultsRecognizing how technical writing skills translate to prompt engineering and AI workflowsLearning new tools by building personal projects outside workCreating repeatable AI “skills” from real pain points and busyworkDrafting from pull requests and source code using connectors and MCPAsking for read-only access and demonstrating value through small experimentsBuilding resiliency by troubleshooting, iterating, and continuing to learnSeason 8 of Inside Tech Comm is sponsored by LavaCon. Use discount code ITC26 to save $200 on registration for LavaCon 2026. Show Credits Intro and outro music - AzAudio engineer - RJ Basilio Interested in being on the show? Let's connect.

We talk with Sharon Burton, editor of Women in Technical Communication, about how women’s stories reveal the real history of technical communication and why our work is bigger than “typing words.” We connect documentation to revenue, customer stickiness, and institutional knowledge while facing the disruption and cost realities of AI.  Episode highlights: Sharon’s path into TechCom and how the book comes together How the field flips from mostly men to mostly women over 50 years Why AI changes workflows but does not replace task analysis and judgment Tying documentation and user assistance to retention, adoption, and revenue Why being left off the “team” slide hurts and how writers validate each other Using conferences to learn the current language for proving value Shared patterns for women in tech across cultures and generations Documenting careers and decisions before retirements erase context The pay gap still showing up in today’s data Season 8 of Inside Tech Comm is sponsored by LavaCon. Use discount code ITC26 to save $200 on registration for LavaCon 2026. Show Credits Intro and outro music - AzAudio engineer - RJ Basilio Interested in being on the show? Let's connect.

We explore how to navigate bias, claim space, and thrive in systems not built for women and minorities. Kae Williams shares practical ways to build influence, handle microaggressions, and reach senior leaders with competence and calm. We touch upon these topics: mapping systemic patterns that reward proximity over performancetying work to business goals to earn visibilityone‑on‑one networking and cross‑team collaborationscripts to approach CFOs and VPs for inputworking with ego‑first leaders and assessing fitidentifying microaggressors and setting boundariesdocumenting severe incidents and staying professionalbuilding external support networks and mentorshipcreating psychological safety and inclusive teamsspeaking up with data, questions, and clarityFind Kae’s book Not Made For You on her website with links to Amazon and more. Follow Kae on LinkedIn, Substack, and Instagram. Guest Bio Kae Kronthaler-Williams is a marketing leader who knows how to turn big ideas into real growth. She’s led teams as CMO at companies like Ocelot and Adroit North America, working at the intersection of AI, supply chain, manufacturing, and data-driven strategy.  Kae is also a coach and speaker who cares deeply about people, purpose, and performance. She helps leaders build better cultures, strengthen their influence, and lead with confidence and clarity. She’s passionate about equity, growth — both professional and personal — and what it really takes to show up as your best self in work and life.  Show Credits Intro and outro music - AzAudio engineer - RJ Basilio Interested in being on the show? Let's connect.

We ask Julie Trostad how authors can protect their work in an AI-first world and where ethics, law, and new licensing models meet. From content fingerprinting to smarter metadata, we map practical steps, real risks, and hopeful paths for fair compensation. We start with the foundation: what intellectual property actually protects, how work-for-hire changes ownership, and why attribution still matters. From US lawsuits that punish pirated training data versus Europe’s stricter model for text and data mining rights, Julie explains how content fingerprinting and registries like ISCC plus AMLET give AI systems a machine-readable “do and don’t” list—ranging from research-only to fully licensed generative use—so rights can be recognized and respected across platforms. Guest Bio Julie Trelstad is a publishing strategist with 30+ years of experience helping authors and publishers navigate industry change. She currently leads U.S. Publishing at Amlet.ai, where she focuses on how creators can protect and license their work in the age of AI. Julie’s background spans traditional, digital, and rights-driven roles across major publishing segments, and she’s a voice on ethical AI use, content discovery, and digital rights for creative work.  Show Credits Intro and outro music - AzAudio engineer - RJ Basilio Interested in being on the show? Let's connect.

The AI tools arrived, the dashboards lit up, and the ROI… didn’t. Monica Marquez of Flipwork unpacks what actually drives meaningful adoption—and what kills it. The short answer: people and process, not just platforms. The longer answer: psychological safety, shared playbooks, and a clear path from micro efficiencies to measurable impact. Monica walks us through the mindset shift from fearing replacement to becoming “people squared,” where AI handles the drudge work, and humans double down on judgment, taste, and lived expertise. From thawing the “frozen middle” of management to building agentic AI that connects steps across a workflow, we map a practical route to real ROI. Monica shares Flipwork’s 90‑day sprint, the Flip Factor readiness assessment, and “Flippy,” a non-judgmental change agent that helps individuals identify their "zone of genius" and teams redesign processes. Along the way, we spotlight case studies like IKEA’s upskilling strategy, and we explore how micro wins—clarifying requirements, synthesizing research, in-context coaching—compound into major gains. Let's move past the hype and make AI a partner that elevates your craft.  This conversation outlines the practices, mindsets, and tools required to achieve this goal. Guest Bio Monica Marquez is a workplace AI strategist, leadership, learning, and inclusion expert, and serial entrepreneur. She is the founder of FlipWork, Inc., the only enterprise-wide AI workforce transformation system that ensures Fortune 500 companies adapt quickly to disruptive change. FlipWork helps employees at every level adopt AI successfully by transforming how people see, think, and work so they can keep pace with rapid workplace change. She also publishes ¡Ay Ay Ay, AI!, a weekly newsletter that gives leaders five minutes of clear, actionable guidance on thriving in the age of AI. Monica brings more than 20 years of executive experience spanning talent, learning, and inclusion at Google, EY, Bank of America, and Goldman Sachs. At Goldman Sachs, she pioneered the Returnship and New Directions programs, setting the industry benchmark for helping experienced professionals reenter the workforce. At Google, she co-founded the Product Inclusion Council to shape more inclusive product design. She is also the co-founder of Beyond Barriers and has hosted more than 300 podcast episodes spotlighting transformative leadership journeys. Recognized among Entrepreneur Magazine’s 100 Women of Influence and ALPFA’s Most Powerful Latinas in 2024, Monica continues to champion equity, innovation, and leadership in every arena. Show Credits Intro and outro music - AzAudio engineer - RJ Basilio Interested in being on the show? Let's connect.

We kick off the new season by breaking down the “contractor mindset” for writing prompts — the key to getting AI to deliver to spec, not with unnecessary “flair.” Chuck Nealis joins us to explore how to write high-impact prompts through the lens of storytelling. We dig into the simple but powerful guardrails that help navigate AI’s context window. Because as conversations grow, crucial constraints tend to fade — and that’s when prompt drift begins. So what do you do when the whiteboard starts getting messy? In this episode, we unpack: Explicit prompts with clear purpose, audience, and formatContractor mindset over poetic creativityContext window limits and the 80% summary handoffLabeling drafts and starting fresh threadsTriangulated reviews across multiple AIsVerifying sources and rejecting fabricated citationsBuilding repeatable, reliable prompt blueprintsWhy consistency leads to accuracyTreat AI’s memory like a whiteboard, not a library — and start designing prompts that stick. Guest Bio Chuck Nealis is a seasoned instructional designer, Organizational Change Management lead, and AI prompt expert with over 20 years of experience helping organizations deploy practical AI‑powered learning solutions and training programs.  Chuck is also an author and thought leader on effective AI usage, focusing on precision‑engineered prompting and learning design.  Show Credits Intro and outro music - AzAudio engineer - RJ Basilio Interested in being on the show? Let's connect.