Your Users Now Expect AI, Here's What That Means for Your Product Strategy

January 2026

The New Baseline

Two years ago, "smart defaults" meant pre-filling a form field based on previous entries. Today, users expect your onboarding flow to understand their intent from a single sentence. They expect dashboards that surface insights before they think to ask. They expect search that understands context, not just keywords.

This isn't about adding a chatbot to your homepage. It's about a fundamental shift in what "intuitive design" means.

We're seeing this play out in our audits every week. Products that seemed perfectly usable two years ago now feel laborious. The cognitive load that users once accepted as normal now registers as friction and friction kills conversion.

Three Patterns We're Seeing in High-Converting Products

1. Progressive Disclosure, Reimagined

The old model: hide complexity behind clicks and menus.

The new model: surface exactly what the user needs, when they need it, without being asked.

High-performing products in 2026 don't just hide features they dynamically reveal them based on user behavior, stated goals, and contextual signals. This requires rethinking information architecture from the ground up.

One example: a project management tool we audited used to show every user the same 12-field project creation form. After redesign, new users see three fields name, goal, and deadline while the interface intelligently suggests additional fields based on project type. Power users still access everything, but the entry point adapts. Their completion rate jumped 41%.

2. The Death of the Empty State

Empty states used to be an afterthought a placeholder message telling users to "add their first item." Now they're conversion killers.

The best products we're auditing treat empty states as onboarding opportunities. Instead of blank screens, users see intelligent suggestions, sample data they can manipulate, or guided paths based on their stated use case.

One B2B client increased their trial-to-paid conversion by 28% simply by replacing empty states with AI-generated starter templates relevant to each user's industry.

3. Conversational Doesn't Mean Chatbot

The most effective AI-enhanced interfaces often don't look like AI at all. They're forms that reduce fields dynamically. Search bars that understand natural language. Settings pages that ask "What are you trying to accomplish?" instead of presenting a wall of toggles.

The products winning right now are those that embedded intelligence into existing interaction patterns rather than bolting on a separate "AI feature."

What This Doesn't Mean

This isn't about chasing trends or adding AI for its own sake. Plenty of products are shipping half-baked "AI features" that create more confusion than value.

The point is simpler: the baseline for what feels intuitive has shifted. Users have been trained by their best software experiences to expect less friction, smarter defaults, and interfaces that adapt. Meeting those expectations doesn't always require machine learning—sometimes it just means designing as if you had it.

What This Means for Your Product

If you're a SaaS founder or product leader, here's the uncomfortable truth: the bar has moved, and it's not moving back.

Users who interact with AI-native tools daily whether that's their email client, their note-taking app, or their code editor are developing new muscle memory. They expect software to meet them halfway.

This doesn't mean you need to rebuild your product from scratch. But it does mean that your existing user flows, onboarding sequences, and core interactions need to be evaluated against these new expectations.

The questions to ask:

• Where are users providing information that your product should already know?
• What decisions are you asking users to make that could be intelligently defaulted?
• Where does your interface require users to learn your mental model instead of adapting to theirs?

The Audit Lens

When we conduct Revenue Impact Audits, we're evaluating products through this new lens. We're not just looking for usability issues—we're identifying moments where user expectations have outpaced your interface.

Often, the highest-impact recommendations aren't about adding AI capabilities. They're about removing friction that users no longer tolerate. Simplifying flows that feel dated. Rethinking defaults that once seemed reasonable but now feel like obstacles.

The revenue impact of these changes can be substantial. We recently identified $340K in recoverable ARR for a client simply by redesigning their trial onboarding to feel more anticipatory smarter defaults, better progressive disclosure, and an interface that adapts to user signals. No new AI infrastructure required, just design that meets AI-era expectations.

Moving Forward

The products that will win the next few years aren't necessarily those with the most sophisticated AI. They're the ones that understand how AI has changed user expectations and design accordingly.

If your conversion rates have plateaued or worse, started declining it might not be a marketing problem. It might be that your product is optimized for expectations that have already evolved.

The good news: closing that gap is often more achievable than you think. The changes that matter most are rarely the most complex—they're the ones that finally see your product the way your users do.

Northbound Product Lab helps growth-stage SaaS companies identify product and design changes that unlock revenue. Our audits pinpoint exactly where you're losing conversions and deliver actionable roadmaps with projected ROI. Apply for a review →