Beyond the Horizon: A Journey Through the Evolution of AI Interaction

- 9 minutes read

A brief step back

Over the last decade I’ve seen design evolve from being a discipline stuck on its own to one that truly supports business goals. When I started my career, I was the only designer on projects and had to build my own methodologies from scratch. That taught me the value of simplifying complex ideas, using numbers to back decisions, and building reusable systems so that your team isn’t reinventing the wheel every time. Those same lessons apply to the current hype around AI.

A few years ago people thought design systems would solve everything; today it’s conversational AI. The first wave of Large Language Model (LLM) products wraps raw model responses in a chat window and calls it innovation. It’s impressive because it removes friction: you speak or type, and the machine tries to understand your intent. It reminds me of the early days of command‑line computing: there’s power under the hood but the interface is only usable by those who know what to type. Bill Gates himself calls generative AI “the most important advance in technology since the graphical user interface”, and Jakob Nielsen argues that we’re witnessing the first new user‑interface paradigm in 60 years. It’s exciting, but it’s also incomplete.

Why chat isn’t the answer

Chatbots are a great demo and an easy starting point. They make AI accessible, but they also reveal how much work we have ahead:

  • A blank canvas is intimidating. Most tools give you a single input box and expect you to know what to ask. Casey Newton observed that users face a blank‑page problem. The Shape of AI pattern library suggests using wayfinders, nudges and templates to help people discover what the AI can do.

  • People still struggle to articulate their needs. Nielsen points out that half the population may not write clearly enough to get good results from current chatbots. That’s similar to the old days when only query specialists could search medical databases. Do we really want to create another class of prompt engineers?

  • Chat hides context and state. Useful information quickly disappears in a long conversation. That breaks basic usability principles such as visibility of system status and discoverability. When Artium tested a chat‑first prototype for an innovation platform, users fixated on the conversation and missed other information on the screen.

  • Not everything can be expressed in words. Sometimes you need to sketch, gesture or show an example. Designers like Maximillian Piras remind us that AI interfaces should be multimodal and support interactions that words cannot describe.

These issues show that a chat box alone isn’t the future. It’s the DOS of AI: a proof of concept that will quickly be replaced by richer patterns.

Where interaction design is heading

As I build and lead design teams, I look for patterns that help us work smarter. The same approach helps make sense of AI trends. Here are the themes I believe will shape the next decade of interaction design:

Multimodal conversations

Talking to your device is convenient, but it’s not the only way. Voice dictation unlocks improvised thinking and gives LLMs richer context, while typing allows precision. Gestures and images matter too. Optasy notes that people now switch effortlessly between speaking, typing, gesturing and facial cues, and AI adapts to their preferences. For a designer this means planning for fluid input and output instead of single‑mode interfaces.

Context‑aware and dynamic interfaces

One of my favourite design lessons is to build systems, not screens. In the AI era that means letting the interface adapt itself based on what the user is doing. The Artium case study replaced chat with dynamic blocks, visual elements that appear and populate automatically based on AI analysis. They also introduced governor patterns, which let users verify AI‑generated content before accepting it, and milestone markers to visualize progress. These patterns transform the AI from a black box into a transparent partner.

Google already hints at this future: search results show weather widgets, movie showtimes or charts depending on your query. The interface changes to suit the task. As designers we should look for similar opportunities in our products.

Ambient intelligence

The most powerful technology often disappears into the background. Optasy describes homes that adjust lighting and music without explicit commands. On social media, the Grok button offers one‑click context when a post is confusing. These examples show how AI can support us without demanding our attention. When you build a product, ask yourself: can we anticipate what the user needs next and gently provide it?

Personalisation and emotion

Modern AI systems study subtle patterns and emotions. They rearrange shopping experiences based on browsing behaviour and detect frustration or confusion through facial expressions and vocal tone. That level of sensitivity can make interactions feel more human if we respect privacy and give users control over how their data is used.

Collaborative AI

The best AI tools don’t replace what we already know; they integrate into our workflows. I’ve seen this in the design system world: build a system to automate repetitive tasks so your team can focus on problem‑solving. In AI, developers using Cursor IDE can ask questions about their code and get answers inline. Writers using Anthropic’s Claude produce Artifacts, reusable assets generated through dialogue. These integrations turn AI into a colleague rather than a tool.

Trust and transparency

People won’t rely on AI if they don’t understand how it works. The Deepseek reasoning model shows its chain of thought, giving users confidence in its output. Perplexity.ai cites its sources so you can verify where information comes from. Governor patterns, trust indicators and citation features should become standard. Make it clear when AI is acting and allow users to review or undo its actions.

Hybrid interfaces and agent ecosystems

Nielsen argues that future systems will merge intent‑based actions with traditional GUIs. Chat isn’t going away; it will coexist with visual and voice interfaces. Meanwhile, we’re moving toward networks of specialised agents: domain experts, personal assistants, system coordinators and third‑party services. The challenge will be to share context between them and to design transitions that feel seamless.

Practical design guidelines

Based on these trends and my own experience building design systems, here are principles to apply when designing AI interfaces:

  • Collaborate with your AI. Don’t force users into a chat window. Use dynamic UI elements and let users approve AI‑generated content.

  • Build systems, not screens. Create atomic components and flexible design systems that can generate countless variations. Move quickly to working prototypes to validate with real users.

  • Guide, don’t overwhelm. Use wayfinders, nudges and templates to help users get started. Show them AI features when they’re most relevant.

  • Earn trust. Provide citations, reasoning and approval mechanisms so people feel in control. Be transparent about data usage and give users choices.

  • Blend the familiar with the new. Retain useful elements from existing paradigms, just as design systems reuse patterns, to reduce cognitive load.

  • Design for ethics and privacy. Embed fairness and transparency at the core, not as an afterthought.

Buzzvil: applying these ideas

Working on reward‑based advertising at Buzzvil gives me a real‑world laboratory for these trends. Our platform is built on the belief that ads can deliver value for people as well as brands. Rewarded ads on Buzzvil achieve an average click‑through rate of 39 %, four times the conversion rate of non‑rewarded ads, and retention of 82 % among top advertisers. Behind those numbers is a dynamic reward system that adapts incentives to each user’s behaviour and campaign goals. Our AI‑powered ad engine draws on billions of data points to map user interests and uses deep‑learning models to predict which ads will perform best, while cohort‑based targeting protects privacy.

The future isn’t just about better algorithms; it’s about exposing these capabilities through humane interfaces. Imagine the Benefit Hub or lock‑screen ads morphing in real time to suit the user’s context. Instead of a static banner, you could see an offer that expands into a mini‑wizard when you tap it or speak a command, with the reward adjusting based on your engagement. Multi‑agent orchestration could be baked into our campaigns: one agent analyses behavioural patterns, another generates personalised creative, a third optimises budgets and rewards, and a coordinator ensures that the experience feels coherent rather than fragmented. When combined with patterns like governor dialogues and milestone markers, users would not only get tailored offers but also understand why they are seeing them and remain in control of the exchange. Done right, these interactions would feel less like ads and more like a helpful assistant guiding you toward value.

Looking ahead

If chatbots are the DOS of AI, then the next decade is our chance to build the MacOS and iPhone moments for this technology. We’ll move from typing into a blank chat box to interacting with adaptive, multimodal systems that understand our context and collaborate with us. We’ll work alongside networks of smart agents that anticipate what we need and free us to focus on the problems that matter. And if we do our job right as designers, these interfaces will feel natural, transparent and empowering rather than intrusive.

Changing our approach from chat‑first to AI‑first will take time. But by applying human‑centred design principles, learning from past technological shifts and building on the patterns emerging today, we can create experiences that are as transformative as the shift from command line to GUI. As always, the goal is not to build for technology’s sake but to help people achieve more with less friction. That is what design has always been about.

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