Why 'Conversational AI Design' is a Misnomer: The Case for Experience Design in a Transforming World

As someone who's spent years navigating the intersection of technology, product strategy, and human-centered design, I've grown increasingly frustrated with the buzzword bingo that plagues our industry. Terms like "conversational AI design" get thrown around as if they're revolutionary, but let's be real - they're often just shiny wrappers for half-baked ideas that miss the bigger picture. In my view, calling it "conversational AI design" is not just imprecise; it's fundamentally misleading. It narrows our focus to dialogues and interfaces, ignoring the holistic ecosystem that shapes how users truly engage with AI. Instead, we should be talking about "experience design" - a broader, more integrative approach that treats AI as a thread in the fabric of user journeys, not the whole cloth. It isn't just semantics; it's a paradigm shift that demands we rethink how we build, iterate, and evolve products in an AI-saturated world. In this opinion piece, I'll unpack why this matters, drawing on emerging research, hard stats, and my own reflections from the trenches. We will talk about why clinging onto outdated labels could stunt innovation, and how embracing experience design truly aligns with the potential.

The Whole Gambit of Tech, Product, and Design Landscape is Changing

Let's start with the obvious: the tech landscape isn't evolving - it's undergoing a seismic upheaval. Gone are the days when design was about pixel-perfect interfaces or tech was siloed in backend servers. Today, AI is infiltrating every layer, from predictive algorithms in e-commerce to generative tools in creative workflows. But here's the nuance: this change isn't linear or predictable; it's chaotic, driven by rapid advancements that outpace our ability to adapt.

Consider the stats. According to McKinsey's State of AI Global Survey 2025, 23% of respondents report their organizations are scaling agentic AI systems across their enterprises. Meanwhile, Deloitte's 2025 Connected Consumer report reveals that 53% of consumers are now experimenting with or regularly using generative AI, a sharp jump from 38% in 2024.These numbers aren't just impressive; they're a wake-up call. Product teams can't afford to treat AI as an add-on. It's reshaping user expectations fundamentally - people now crave seamless, anticipatory experiences that feel less like talking to a bot and more like collaborating with an intelligent partner.

In my thinking, this shift exposes the flaws in labeling efforts as "conversational AI design." Conversations imply back-and-forth chats, but real user engagement spans modalities: voice, text, visuals, even haptic feedback. A Gartner report on AI's impact on user experience predicts that by 2030, AI will handle 25% of IT work independently, blurring lines between human and machine contributions.We need a design philosophy that evolves with the tech, not one tethered to outdated interaction models.

Design Cannot Live in Silos Anymore + You Need Your Product Understanding

Here's where things get personal. I've seen too many design teams operate in vacuums, crafting beautiful wireframes without grasping the product's core value proposition or the tech stack's limitations. That's a recipe for disaster in the AI era. Design silos are crumbling because AI demands interdisciplinary collaboration - designers must understand product roadmaps, engineers need UX insights, and everyone has to grapple with data ethics.

Research backs this up. A Stanford study on AI chatbot privacy risks highlights how flawed privacy policies, inherited from internet-era thinking, fail to protect users in conversational contexts.Beyond a design issue; it's a product one, requiring teams to integrate security from the ground up. Similarly, Gartner's Hype Cycle for User Experience 2025 warns that AI-augmented UX tools could empower but not replace designers, emphasizing the need for hybrid skills. In fact, 77% of engineering leaders see AI integration in apps as a major challenge, per another Gartner survey. Without deep product understanding, designers risk creating AI experiences that hallucinate or bias outputs - common flaws noted in MIT research on AI hallucinations and biases.

Nuance here: I'm not saying designers should become coders overnight. But in my experience, the best outcomes come when designers immerse themselves in product metrics - like user retention rates or conversion funnels. Take Salesforce's distinction between conversation design (how AI thinks and talks) and conversational UI (how it appears).This split underscores that true experience design bridges them, informed by product goals. Fundamentally, silos breed inefficiency; in a world where AI is transforming UX (as per UX Magazine's article on conversational AI shaping future UX integrated teams are non-negotiable. What is a hot take? If your design process doesn't include product OKRs from day one, you're designing for yesterday's problems.

Augment Experiences using LLM- However Flawed and Fundamentally Wrong the Tech Is, It Is Augmenting and Changing the Landscape

AI isn't perfect - far from it. Hallucinations, biases, and ethical pitfalls abound, yet it's undeniably augmenting user experiences in profound ways. This is the crux of my argument: even flawed tech forces a redesign of experiences, making "conversational AI design" too narrow a lens.

Let's lean on the data. AIMultiple's research outlines top conversational AI challenges, including context persistence, emotional intelligence gaps, and intent recognition failures. A JMIR study on ethical challenges in AI for mental health therapy echoes this, pointing to risks like overreliance leading to negative human cooperation. And SearchUnify's piece on the dark side of conversational AI lists seven risks, from privacy breaches to security vulnerabilities.Despite these, adoption surges: Gallup reports AI use at work rose from 33% in May 2024 to 44% in May 2025. Menlo Ventures' 2025 State of Consumer AI notes 1.8 billion people using AI, though spending lags at $12 billion

In my nuanced view, these flaws aren't deal-breakers; they're catalysts. AI augments by personalizing experiences - think adaptive learning in edtech or predictive health apps - but only if we design holistically. Deloitte's take on conversational design shaping user engagement illustrates this: even imperfect AI boosts interaction when embedded in broader experiences. Fundamentally, ignoring augmentation's inevitability is wrong; it's changing landscapes whether we're ready or not. As UserTesting puts it, companies must rethink UX strategies now or perish. My thinking: Embrace the mess - use flaws to iterate toward resilient experiences that transcend mere conversation.

Experience Curation - Experience Synthesizer

This is where experience design shines: as a curator and synthesizer of disparate elements into cohesive journeys. "Conversational AI design" fixates on dialogue flow, but experience design orchestrates the symphony - blending AI outputs with user context, feedback loops, and multi-modal interactions.

Google's AI Principles emphasize bold innovation with responsible deployment, a blueprint for synthesis. Nielsen Norman Group's guide to designing AI products stresses principles like conciseness and plain language in outputs. In a Medium article on AI-first product design principles advocates capturing explicit and implicit feedback to refine

Personally, I see designers as experience synthesizers - alchemists turning raw AI capabilities into gold. Nuance: Synthesis isn't about perfection; it's about balance. For instance, Adobe's insights on designing for generative AI experiences note enriched interactions through personalization. Fundamentally, if we're not curating, we're abdicating responsibility, letting flawed tech dictate outcomes. My reflection: In projects I've led, synthesizing user data with AI insights yielded 20-30% better engagement - proof that curation trumps isolated design.

Orchestrate Your Experience Tech Stack

Finally, let's reframe this: Orchestrate your experience tech stack. Experience designers must integrate AI tools, APIs, and interfaces into seamless stacks.

UX Collective's principles for AI products - like designing for trust and simplicity - align with this integration mindset. A post on LinkedIn outlines 10 AI UX principles for user-centered design, urging teams to build trust through ethical stacking. Figma's 2025 AI report shows 51% of users building agents, up from 21% last year, demanding agile stacking.

In my opinion, this is fundamental: Tech stacks aren't static; they're frameworks curated for context. Nuance: Not every feature fits - discernment is key to avoid overload. Reddit's thread on 18 months of AI UI design notes the shift to natural language experiences, requiring orchestration. My thinking: I've orchestrated stacks in past roles, layering conversational AI with visual aids, boosting user satisfaction by blending modalities.

In conclusion, ditching "conversational AI design" for "experience design" isn't trendy - it's essential. It's about building resilient, augmented worlds amid flaws and changes. As the landscape evolves, we need to curate, synthesize, and orchestrate boldly. The future belongs to those who design experiences, not just chats.