Dipo Ajose-Coker is a Solutions Architect and Strategist at RWS, helping regulated organizations modernize content operations through structured content management and AI-ready governance. With nearly two decades of experience across structured authoring, DITA, and regulated workflows, he bridges business, regulatory, and technical teams to implement scalable content operating models. His work focuses on lifecycle integrity: reducing duplication, strengthening traceability, and building explainable evidence frameworks that hold up under audit and support safe automation. Dipo advises organizations on connecting content to the systems that define product intent and control, enabling faster change response and more consistent delivery across channels and markets. He is a frequent speaker at global industry conferences on structured content strategy, governance, and the practical realities of preparing enterprise knowledge for AI

Inclusive Content In The AI Era: Bias, Culture, And Who Gets Left Behind

Bias is inherent in our everyday lives. But when AI becomes part of the content toolchain, it can scale clarity, or it can scale confusion, exclusion, and risk. The difference is rarely the model. It is the choices we make about data, labels, objectives, review, and what we consider “good enough” when customers live in different cultures, languages, and contexts. This panel makes inclusive content practical for content leaders adopting AI. We will unpack the “bias supply chain” from authoring and training data through summarization, translation, and delivery. We will share examples of how cultural expectations differ, why that matters for customer experience, and what governance and review patterns reduce harm without slowing everything to a crawl. You will leave with a simple checklist you can apply in real workflows, plus a clear sense of where human review is non-negotiable. (Bias points to test, cross-cultural QA prompts, “red flag” outputs, mitigation options)

In this session, attendees will learn:

•  Spot the “bias supply chain” in content operations: authoring, training data, retrieval, summarization, translation, and delivery
• Run a lightweight “inclusive content risk assessment” that fits real deadlines
• Handle cross-cultural variation without creating 200 versions of everything (and without pretending one global tone fits all)
• Put human review where it reduces risk most, not where it feels comforting
• Set governance that supports teams instead of policing them

Build Your First AI Agent And Then Make it Agentic: Hands-On Workshop for Content Workers

AI agents are announced at every product launch, agentic AI dominates roadmaps, and you feel left behind. This hands-on workshop cuts through the confusion by building working examples together. You’ll create a simple AI agent for a content workflow task, build an agentic AI prototype using accessible tools, and see firsthand what each approach can and cannot do. We’ll define the technical distinctions that matter, evaluate your own content operations to determine which automation makes sense, and assess whether your organization is ready for autonomous intelligent systems without breaking your governance model or budget. You’ll leave with working prototypes you’ve built, a decision framework applied to your workflows, and a completed readiness assessment for your organization. Whether you’re pushing back on a half-baked vendor pitch or building the case to leadership, you’ll have artifacts and examples to make your argument.

In this workshop, attendees will learn:

• Build simple working examples of both AI agents and agentic AI systems using accessible tools during the workshop
• Distinguish between AI agents, agentic AI, and intelligent assistants through hands-on experimentation
• Apply a decision framework to their own content workflow scenarios and determine which automation approach fits
• Complete a readiness assessment for their organization that identifies gaps before deploying autonomous AI capabilities
• Map their existing content operations to identify high-value automation opportunities versus low-return complexity
• Understand the first steps in scaling what they’ve built using internal enterprise models or foundation models
• Create an incremental adoption roadmap that protects governance while enabling intelligent automation