How Sydney Product Designers Actually Scope AI Projects (With Real Numbers)
Dec 22, 2025
Most product designers in Sydney freeze up when a client asks: "Should we be using AI for this?"
I've heard this question in six different project kickoffs this quarter. And I get it—AI project scoping feels different from regular UX work. The pricing is unclear, the timelines are guesswork, and half the time you're not even sure if AI is the right answer.
Here's what I've learned from scoping AI projects in Sydney's market: the gap isn't technical knowledge. It's knowing how to have the scoping conversation in the first place.
What AI Project Scoping Actually Looks Like
Scoping an AI project starts with a question most designers skip: "What problem are we actually solving?"
Not "can we add AI?" but "what's broken right now?"
When I worked on designing fintech dashboards, the client's first instinct was to add AI-powered insights. But after talking through their actual user complaints, we realized the dashboard just needed better hierarchy and clearer data visualization. No AI required.
That's the first scoping decision: AI vs. better UX.
If AI genuinely solves the problem, then you're looking at two project phases: discovery (2-4 weeks) and build (4-8 weeks). Those Sydney market rates reflect what I'm seeing across fintech, insurance, and e-commerce projects.
The Discovery Phase: What You're Actually Scoping
Discovery for AI projects isn't like regular product discovery. You're not just researching user needs—you're validating whether AI can meet those needs.
Here's what goes into that 2-4 week scope:
User research (but not the kind you're used to). You need to understand not just what users want, but how they'll react to AI suggestions. Will they trust them? Override them? Ignore them completely? This is where understanding when AI user research makes sense becomes critical.
Data audit (yes, even as the designer). You don't need to be technical, but you need to know: does the client have enough data to train or customize anything? Is it accessible? This single conversation can save you from scoping a project that's impossible to build.
MVP definition (the hardest part). What's the absolute minimum AI feature that proves value? Most clients want to scope "AI-powered everything." Your job is to scope "AI-powered one thing that we can ship and measure."
This phase typically requires 15-20 hours of research, client workshops, and documentation. That's not padding—that's what it takes to scope AI work responsibly.
When to Recommend AI (And When to Push Back)
Here's the decision tree I use:
Recommend AI when:
The problem involves prediction, personalization, or pattern recognition
The client has enough data to support it (or budget to acquire it)
Users will see immediate value from AI suggestions
You can define clear success metrics (accuracy rate, user adoption, time saved)
Push back when:
Better UX design would solve the same problem
The client doesn't have data infrastructure in place
Success criteria are vague ("make it smarter")
Timeline or budget can't support proper testing and iteration
Freelance UX designers in Sydney need to be comfortable pushing back. You're not being hired to just say yes to AI—you're being hired to recommend what actually works.
If discovery confirms AI makes sense, the build phase in Sydney typically ranges $25K-$50K depending on complexity.
Lower end:
Single AI feature (like autocomplete or basic recommendations)
Existing design system or simple UI
Standard accuracy requirements
4-6 week timeline
Upper end:
Multiple AI touchpoints or complex logic
Custom UI design and interaction patterns
High accuracy requirements (fintech, medical, legal)
6-8 week timeline with testing cycles
Those ranges include design, but assume development is handled separately. If you're scoping design-only, expect 3-5 weeks of work.
The Conversation Script That Actually Works
When a Sydney client asks "should we use AI?", here's how I structure the conversation:
"Let's start with what's not working right now. Walk me through the specific user problem."
(Listen. Take notes. Don't mention AI yet.)
"Okay, so users are spending too much time filtering search results. Before we talk about AI, have we tested better filters or smarter defaults?"
(This is the better-UX-first check.)
"If we did add AI-powered search, what does success look like? What metric would prove this was worth building?"
(This forces concrete success criteria.)
"Here's what scoping this would look like: 2-3 weeks of discovery to validate AI makes sense and define the MVP. Then 4-6 weeks to design and test it. Does that align with what you were thinking?"
(Clear timeline, clear budget, clear phases.)
Most clients appreciate the structure. The ones who just want "AI because AI" usually reveal themselves here—and you can decide if that's a project you want.
What Most Designers Get Wrong About AI Scoping
The biggest mistake? Scoping AI projects like regular product work.
You can't just multiply your hourly rate by estimated hours. AI projects need buffer for:
Testing and iteration (AI outputs are unpredictable)
User trust calibration (getting the confidence levels right)
Edge case handling (what happens when AI gets it wrong?)
Documentation (future you—or the next designer—will need it)
I now add 20-30% to my initial timeline estimates for AI projects. That's not pessimism—that's pattern recognition from six projects where "one more test cycle" was always needed.
The other mistake? Not involving development early enough. Even in discovery, you need a technical reality check. Is this buildable in the timeline? With the client's data? On their budget?
As a UX designer in Sydney, you don't need to answer those questions yourself—but you need to know when to ask them.
What This Means For Your Next Client Call
Next time someone asks "should we use AI?", you don't have to freeze.
You have a scoping framework: discovery first, build second. You have a decision tree: AI when it solves specific problems, better UX when it doesn't.
And you have a conversation structure that positions you as a strategic consultant, not just an order-taker.
If you're navigating these conversations and want someone who's been through this before, let's talk about your project.
