Designing Features for AI

AI is rapidly appearing in software interfaces. Search boxes are becoming prompts. Assistants are being added to dashboards. Users can ask questions in natural language instead of navigating menus.

These changes can make existing software easier to use. But adding a conversational interface does not, by itself, make a product AI-first.

AI-first design begins at a more fundamental level. It treats AI as an intended user of the software itself.

That means designing capabilities so an AI agent can understand them, invoke them, combine them, and control them reliably.

AI Is a New User of Software

Software has traditionally been designed for two broad audiences.

AI introduces a third kind of user.

An AI agent does not use software in the same way as either a business user or a developer. It can interpret a person’s intent, work with structured capabilities, and coordinate multiple actions on the user’s behalf. It can also ask questions when the original request is incomplete.

Product teams must now ask a new question.

A conversational interface can help users find and operate existing features. But if the underlying product was designed entirely around human interaction, the AI may still be constrained by the same assumptions as the original interface.

It may be able to navigate the software more conveniently, but it cannot necessarily access capabilities that were never exposed in a form it can reliably use.

Designing features for AI goes deeper into the platform. It means treating the agent as a first-class user when the capability itself is being conceived and built.

Designing for an Agent Changes the Rules

Human interfaces impose practical constraints on product design.

Every capability must be translated into something a person can see, understand, and operate. As the number of possible choices grows, the interface becomes harder to navigate.

Product teams respond by simplifying the capability, dividing it into steps, restricting it to power users, or leaving it out of the business-user experience.

This is the trade-off behind the Five-Click Ceiling. Software can be powerful, or it can be broadly accessible, but conventional interfaces make it difficult to maximize both.

A function might have dozens or even hundreds of possible parameters. Presenting all of them to a person would create an unusable interface.

An agent does not need to expose every parameter. It can interpret the desired outcome, identify the relevant options, and use the function on the person’s behalf.

This does not justify unnecessary complexity. Poorly defined software remains poorly defined software, whether it is used by a person or an AI. But it does remove an artificial constraint: every piece of analytical power no longer has to be represented by another visible control.

Designing features for AI therefore requires more than connecting a language model to the product. The underlying capabilities need to be structured so that the agent can reason about and operate them reliably.

That may include:

• Clear APIs that expose what the platform can do
• Formal expressions that provide precise instructions
• Metadata that explains the meaning of data and capabilities
• Modular functions that can be combined to accomplish a larger task
• Explicit permissions that define what the agent may access or change
• Predictable outputs that people and systems can inspect and verify

These elements were always valuable in software design. When AI becomes an intended user, they become central to the user experience, even though the business user may never see them directly.

Some capabilities are better suited to specialized agents.

A general-purpose assistant may have access to a wide range of capabilities, but it also has a large problem space to navigate. A specialized agent can be designed around a particular job. It can understand which functions are available, what information it needs, which assumptions are safe, and which questions it should ask before proceeding.

If the user’s request is incomplete, the agent can ask a relevant question:

• Do you want to compare calendar years or fiscal years?
• Should the difference be shown as a percentage or an absolute value?
• Do you want to compare every region with the median or only those currently below it?

This is more natural than a traditional wizard because the conversation can adapt to the user’s intent. The agent asks only for information that is missing or genuinely requires a decision.

The user does not have to understand every available option. The agent does. That allows the platform to retain its analytical power without transferring all of its complexity to the person using it.

AI-First, Not AI-Only

Treating AI as an intended user does not mean designing software exclusively for AI.

People still need direct ways to inspect information, operate familiar capabilities, and adjust results. A dashboard can communicate known metrics faster than a conversation. A visual control may be the simplest way to change a date or select a category. A business user may want to review the analysis and modify what the AI created.

The objective is not to replace these interactions. It is to avoid making them the only route into the platform’s capabilities.

They can operate the software directly when the interface provides an efficient path. When the desired outcome is difficult to construct manually, they can describe what they want and allow an agent to coordinate the work.

Human control remains essential.

AI can translate intent, assemble an analysis, and recommend an approach. But people provide the business context and judgment that determine whether the result is useful. They must be able to inspect what the AI has done, correct its assumptions, adjust its output, and approve consequential actions.

Governance must also remain part of the platform. An agent should not invent metric definitions, ignore permissions, or improvise access to data. It should work within established definitions and boundaries, just as a person using the software would.

In practice, the division of responsibility is clear:

• People provide intent, context, and judgment.
• AI interprets that intent and coordinates the work.
• Software supplies the capabilities, definitions, and controls required to produce a reliable result.

AI-first design makes AI part of the product itself, rather than a conversational layer added afterwards.

Once AI becomes an intended user, product teams are free to build capabilities that would previously have been too complex to expose.

The Qrvey Approach

Qrvey is focused on designing its platform so that analytical capabilities can be used effectively by both people and AI.

Comparative analytics provides a practical example. Common comparisons remain accessible through a visual interface. More complex periods and criteria can be represented through underlying expressions and reusable presets that an AI can create and manipulate without making the user configure every component.

The same capability can therefore support direct interaction, AI assistance, and more specialized analytical agents. Users retain the ability to inspect and adjust the resulting analysis.

The objective is AI-first, not AI-only: make the platform’s analytical power natural for AI to use while preserving human visibility, control, and judgment.