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Summary – Facing the growing complexity of legacy web dashboards that weigh down navigation and learning curves, the “interface-first” model has reached its limits and clashes with exploding AI expectations. LLMs take the lead with an intention-first design: invisible prompts, conversational memory, and chat/GUI hybridization adapt UX in real time, boosting productivity (–60% time) and support (–40% tickets). The redesign must rely on fine modeling of business intentions, modular conversation flows, user-in-the-loop guardrails, and a scalable microservices architecture.
Solution: shift to an AI-native UX by aligning generative AI, business logic, and progressive design.
Legacy software interfaces inherited from the web—made up of menus, dashboards, and complex trees—struggle to meet users’ current expectations. Thanks to the rise of Large Language Models (LLMs), a new “intention-first” paradigm is emerging, where AI becomes the interface and anticipates needs without forcing rigid navigation.
For CIOs, CTOs, and heads of digital transformation, this shift requires rethinking UX from the ground up to unlock AI’s full potential. This article explores why tomorrow’s AI products won’t resemble today’s applications, the strategic stakes of this transition, and best practices for designing truly AI-native experiences.
The End of the Traditional Interface
Dashboards and multiple menus are the result of logic inherited from the web. This “interface-first” approach creates complexity and frustration rather than fluidity.
A Web Legacy Breeding Complexity
Back when websites were limited to static pages, trees and menus were the only way to structure information. Dashboards became standard to consolidate metrics, but their proliferation has weighed down navigation. Dashboards
Every new feature adds another tab, button, or sub-section, forcing users to memorize multiple paths. This cognitive overload distracts from the business objective.
As a result, the learning curve lengthens and the risk of errors grows. Even minor updates become a challenge for product and support teams, limiting delivered value.
AI as the Main Interface
Prompts and contextual suggestions are gradually replacing buttons. AI becomes the interface, adapting UX in real time.
Prompts and Contextual Suggestions
The first “AI-enhanced” products simply added “Generate” or “Suggest” buttons to a classic UX. Today, the approach goes further: AI automatically offers options based on business context, without manual action.
For example, in a writing tool, AI anticipates the next sentence or refines style in real time, with no menu clicks. The prompt becomes invisible and seamlessly integrated.
This conversational design reduces cognitive effort and accelerates decision-making. The user retains control while benefiting from proactive assistance.
Conversational Memory and Chat/GUI Hybridization
Contextual memory enables AI to maintain the conversation flow, remember preferences, and deliver coherent interactions. It becomes an essential asset for complex workflows.
Hybridizing chat and GUI combines the best of both worlds: the flexibility of a text interface and the clarity of targeted graphical components. Users can switch at any time between free text input and structured results display. To learn more about creating a voice assistant.
This hybrid approach meets diverse needs: free exploration followed by synthetic visualization. UX builds dynamically according to intent, without locking users into a fixed tree.
Example: A Swiss Industrial SME
A Swiss industrial SME specializing in equipment manufacturing replaced its inventory management dashboard with an intent-entry module. Instead of navigating five screens to generate a report, managers now enter requests in natural language.
This simplification cut average report creation time by 60% and reduced related support tickets by 40%. The example demonstrates how a menu-free approach directly boosts team productivity.
It also confirms that shifting to an “intention-first” model can be implemented without a full back-end overhaul, thanks to an AI layer placed at the front end.
Why This Transition Is Strategic for Businesses
Embracing an AI-first UX answers an unprecedented acceleration in AI usage. It’s a key differentiator in a saturated market.
Accelerated AI Adoption and User Expectations
The maturity of LLMs and the democratization of APIs have exploded AI use cases in just a few months. Understanding the importance of API idempotence is crucial to ensuring interaction reliability.
Failing to meet these expectations leads to frustration and adoption of third-party solutions. Conversely, an AI-first interface fosters loyalty and positions a company as innovative.
In a market where speed of adoption makes the difference, anticipating these usages becomes a strategic priority to maintain a competitive edge.
Product Differentiation in a Crowded Market
In an environment where every vendor claims to be “AI-enhanced,” it’s vital to go beyond mere feature integration. True innovation lies in reworking UX around intelligence.
A conversational or contextual suggestion system becomes a unique value proposition, hard to replicate without expertise in prompt engineering, conversational design, and modular architecture.
Early adopters of this approach position themselves as leaders and capture attention from both end users and IT decision-makers.
Example: A Swiss Logistics Provider
A logistics services provider replaced its order-tracking portal with an integrated voice and text assistant linked to ERP and WMS systems. Operators make requests in everyday language, AI extracts relevant data, and replies instantly.
This project not only cut helpdesk tickets by 70% but also improved the accuracy of shared information. It illustrates how hiding complexity simplifies the experience and creates a competitive advantage.
It also shows that an AI-first approach can apply to demanding industrial contexts with heterogeneous systems and high security requirements.
Edana: strategic digital partner in Switzerland
We support companies and organizations in their digital transformation
How to Design a Truly AI-Native Experience
The key to AI-native UX lies in fine-grained user intent modeling and a modular architecture. Safeguards ensure trust and control.
Modeling User Intent
First, define business intents: what requests will users make most frequently? This analysis enables designing an optimized and relevant use case mapping.
A use case map should specify entities, constraints, and expected outcomes to guide the LLM and limit semantic or functional drift.
This initial phase requires close collaboration among business stakeholders, UX designers, and AI experts to capture intent diversity and calibrate responses.
Conversation-Driven Journeys
Instead of fixed workflows, create adaptive dialogues. Each AI response opens new branches based on the request and context, with dynamic suggestions to guide the user.
These conversation flows include validation checkpoints and feedback loops to ensure coherence and transparency of automated actions.
The result is a modular, evolvable experience that grows with user feedback and maturity.
Adding Safeguards (User-In-The-Loop)
To build trust, every AI action should be validated or adjusted by the user before execution. This “user-in-the-loop” system limits risks associated with LLM hallucinations.
You can offer writing suggestions, corrections, or operational decisions, while keeping the final control in human hands.
These validations also serve as opportunities to gather feedback and continuously improve the models.
Combining Generative AI, Business Logic, and Progressive UX
Generative AI provides the interaction surface, while business logic, implemented in microservices, ensures coherence and traceability of actions.
Progressive UX exposes features gradually as user proficiency grows: start with simple queries, then unveil advanced options based on usage.
This model promotes adoption and enriches the experience without creating discontinuities or surprises.
Designing a Modular, Scalable System
A microservices and serverless architecture makes it easy to add or modify AI modules while ensuring isolation and scalability. Each component can be updated independently.
Using open-source models and container orchestrators ensures both flexibility and cost control. You avoid vendor lock-in and maintain data ownership.
Such a design allows rapid integration of new use cases, performance optimization, and solution longevity.
Embrace an AI-Native UX to Gain Agility
Transforming from an “interface-first” to an “intention-first” model represents as much a cultural shift as a technological one. By making AI the main interface, companies simplify the experience, accelerate adoption, and stand out in an increasingly competitive market.
To succeed, you must precisely model intents, design conversational journeys, implement safeguards, and build a modular, scalable architecture. AI-native projects rely on a synergy of generative AI, business logic, and progressive design.
Our experts at Edana guide organizations through this transformation—from identifying use cases to deployment—focusing on open-source, scalable, and secure solutions. Discover our proven strategies for digital transformation.
