AI discovery surface research: needs-driven economy insights
AI surfaces are not just answering questions; they're making recommendations, naming winners, providing decision-ready verdicts. Brands that don't appear are invisible at the point of decision.
Research by Paul Hewett · In Marketing We Trust · January 2026. The strategic implications were published by B&T in What the RBA told us about marketing strategy.
The headline finding: AI surfaces are not just answering questions; they’re making recommendations, naming winners, and providing decision-ready verdicts. In a needs-driven economy where consumers are actively comparing providers for energy, insurance, and childcare, the brands that appear in these AI answers are winning consideration. The brands that don’t appear are invisible at the point of decision.
Executive summary
This research explores how Australian consumers use AI-powered search surfaces (Google AI Overview, Google AI Mode, and ChatGPT) when making purchase decisions in high-inflation essential categories.
What CMOs need to know
- Challenger brands are eating your lunch. GloBird, 1st Energy, and Budget Direct are being recommended ahead of Origin, AGL, and NRMA in cost-focused queries. Clear value propositions beat brand awareness in AI surfaces.
- AI Mode delivers verdicts, not just information. Google told a user, “you have a good deal; don’t switch”, and meant it. This is advice, not search results.
- Comparison sites are your gateway to AI visibility. Canstar, Finder, and Energy Made Easy appeared in 12 of 13 queries. If you’re not winning on aggregators, you’re not winning in AI.
- Measurement is harder than you think, but possible. GSC blends AI traffic with organic. ChatGPT traffic can be isolated in GA4 with proper setup.
What search marketers need to know
- Long-tail, conversational queries are exploding. “My electricity bill was $650 last quarter for a 4 bedroom house; is that normal for NSW?” is the new search query. Your content needs to answer this.
- ChatGPT reads your PDFs. The insurance comparison triggered 84 seconds of “thinking” and cited actual PDS documents. Technical SEO for documents now matters.
- Local queries bypass AI Overview entirely. “Childcare centres Penrith” triggered a local pack, not an AI response. Local SEO and AI optimisation are different disciplines.
- You can track ChatGPT referrals today. Set up a custom channel group in GA4 with the regex provided in this report. Stop flying blind.
Economic context
The inflation picture
Headline CPI rose 3.8% in the 12 months to December 2025, well above the RBA’s 2–3% target range. The RBA’s preferred measure of underlying inflation (trimmed mean) rose to 3.3%, confirming inflationary pressures remain elevated.
| Category | Annual change (Dec 2025) | Consumer behaviour implication |
|---|---|---|
| Electricity | +21.5% | Active provider comparison; “should I switch?” queries |
| Insurance | +14–30% | Renewal shock; switching vs negotiating queries |
| Childcare | +11.2% | Local availability + pricing transparency queries |
| Housing | +5.5% | Mortgage/rent stress; cost benchmarking |
| Recreation & Culture | +4.4% | Discretionary spending under pressure |
| Rent | +3.9% | Cost of living baseline pressure |
| Food & Groceries | +3.4% | Budget benchmarking; “is this normal?” queries |
Source: Australian Bureau of Statistics, CPI, December 2025.
The discretionary vs non-discretionary gap
Non-discretionary inflation (3.4%) continues to outpace discretionary inflation (2.9%). This 0.5 percentage point gap is critical: when essential costs rise faster than discretionary costs, household budgets tighten around necessities, and purchase behaviour shifts fundamentally.
The shift: from brand-led discovery (“I wonder what’s new”) to need-led comparison (“How do I reduce this cost?”)
The marketing implication: your customers are not scrolling social feeds waiting to be inspired. They’re searching, comparing, and asking AI systems for recommendations. The brands that appear in those answers win.
Research methodology
Approach
We tested 13 realistic consumer queries across three AI-powered discovery surfaces, capturing screenshots and documenting the complete response (brands cited, response depth, SERP structure, and measurement capability).
| Surface | Queries tested | Character |
|---|---|---|
| Google AI Overview | 4 | AI-generated summary within standard Google Search |
| Google AI Mode | 4 | Conversational, multi-turn AI search experience |
| ChatGPT | 5 | Standalone LLM with web search and extended reasoning |
Query design principles
All queries were designed to reflect realistic consumer intent in high-inflation categories: NSW geographic focus, household context embedded (“family of 4”, “4-bedroom house”, “25kWh/day usage”), decision-stage framing (“should I switch?”, “is that normal?”), specific data points ($650 quarterly bill, $400/week groceries, $800k house value).
These queries mirror what real consumers type when facing cost pressure. They’re not keyword-optimised search terms; they’re questions people actually ask.
Key findings
Finding 1: AI surfaces deliver verdicts, not just information
Every surface tested went beyond information retrieval to provide:
| Surface | What it does | Example |
|---|---|---|
| Google AI Overview | Structures comparisons with clear positioning | ”AGL for rewards, Origin for tech, EnergyAustralia for balance” |
| Google AI Mode | Gives explicit recommendations and verdicts | ”Your $650 bill is quite standard; don’t switch, you have a good deal” |
| ChatGPT | Provides decision frameworks and personalised analysis | 5-point checklist for insurance decision; PDS document citations |
The AI is not neutral. It’s making choices about which brands to recommend. If you’re not in the recommendation, you’re not in the consideration set, regardless of your brand awareness or media spend.
The uncomfortable truth: a consumer who asks AI Mode, “should I switch from Origin?”, might be told, “no, you have a good deal”, and trust that advice. Your competitor’s superior positioning in AI surfaces can neutralise your entire acquisition funnel.
Finding 2: Challenger brands are winning cost-focused queries
Across all energy queries, smaller providers with clear value positioning dominated:
| Provider | Times cited (of 6 energy queries) | How AI positions them |
|---|---|---|
| 1st Energy | 5 | ”Competitive rates”, “22% guaranteed discount” |
| GloBird Energy | 4 | ”Cheapest”, “23% below Reference Price” |
| Momentum Energy | 4 | ”Lower-cost, no-contract plans” |
| Kogan Energy | 3 | ”Cost-competitive”, “as low as $1,491” |
| AGL | 3 | ”Rewards leader”, Netflix perks (not price) |
| Origin | 3 | ”Big 3 incumbent”, tech/app (not price) |
In cost-conscious queries, AI surfaces recommend challengers on price and position incumbents on features/perks. Origin and AGL are rarely cited as the cheapest option because they’re not.
For incumbent CMOs: your brand awareness isn’t protecting you in AI surfaces. If your value proposition is “we’re big and reliable”, you’ll lose to “we’re 23% cheaper” in a needs-driven economy. You need either (a) a credible cost story, or (b) feature differentiation strong enough to justify premium pricing.
For challenger CMOs: this is your moment. AI surfaces are amplifying clear, specific, quantified value claims. “23% below Reference Price” beats “great value” every time.
Finding 3: Comparison sites are the aggregator layer for AI
Comparison sites appeared in 12 of 13 queries:
| Comparison site | Appearances | How AI uses them |
|---|---|---|
| Energy Made Easy (gov) | 6 | ”Official”, “free”, “independent”; cited as the definitive comparison tool |
| Canstar | 5 | Awards, rankings, research; cited for credibility |
| Finder | 4 | Awards, best-of lists; “2026 Finder Award” mentioned explicitly |
| CareForKids.com.au | 3 | Childcare listings, vacancy data |
| CHOICE | 3 | Satisfaction surveys, research; cited for consumer advocacy |
| Compare the Market | 2 | General comparison recommendation |
Your visibility on comparison sites directly influences your AI visibility. If Canstar ranks you highly, AI will cite that ranking. If you’re not on Energy Made Easy, you’re invisible in government-endorsed recommendations.
Comparison site optimisation is now a brand visibility strategy, not just a lead generation channel.
Finding 4: Google AI Mode demonstrates advisor-level capability
| Capability | Example | Implication |
|---|---|---|
| Personalised benchmarking | Compared user’s $650 bill against NSW averages | Users get context, not just data |
| Multi-turn memory | Remembered household context across follow-up questions | Conversation, not query-response |
| Explicit verdicts | ”Your bill is quite standard, if not slightly below average” | AI makes the judgement call |
| Behavioural advice | ”Shift laundry and dishwashing to weekend daylight hours” | Goes beyond product to lifestyle |
| Push-back on switching | When asked for comparison after verdict, still provided comparison but reiterated, “you have a good deal” | AI has a point of view |
AI Mode is becoming a decision advisor, not a search engine. Your content strategy must shift from “ranking for keywords” to “being cited as a recommendation”. If your content is purely promotional, it won’t be cited. If it’s genuinely helpful, comparative, and takes a position, it will.
Finding 5: ChatGPT performs deep document analysis
The insurance comparison query revealed ChatGPT’s sophistication:
- 84 seconds of visible “thinking” before response
- Cited actual PDS documents with links to policy wording
- Property-specific analysis (flood risk assessment for 1990s brick construction)
- 5-point decision framework tailored to the user’s scenario
- Offer to personalise further with postcode and specific coverage requirements
ChatGPT is reading your policy documents and comparing them to competitors. If your PDS is harder to access, less clearly written, or has gaps compared to competitors, ChatGPT will surface that. Document quality is now a competitive advantage.
Finding 6: Local queries bypass AI Overview entirely
The childcare query “childcare centres Penrith” triggered a local pack/map listing, not an AI Overview:
| Query type | What triggers | Primary optimisation |
|---|---|---|
| General comparison (“cheapest electricity NSW”) | AI Overview | Content, structured data, comparison positioning |
| Provider vs provider (“AGL vs Origin”) | AI Overview | Brand content, comparison content, review aggregation |
| Local service + location (“childcare Penrith”) | Local Pack / Maps | Google Business Profile, reviews, local citations |
| Complex personal scenario (“my $650 bill, is it normal?”) | AI Mode | Helpful content, benchmarking data, decision frameworks |
Local businesses need both GEO (for AI surfaces) and traditional local SEO (for map pack). They’re different disciplines.
Finding 7: Response depth correlates with query complexity
| Query complexity | Example | AI response | Depth |
|---|---|---|---|
| Simple comparison | ”cheapest electricity NSW” | Structured list with prices | High |
| Head-to-head | ”AGL vs Origin” | Feature comparison with positioning | High |
| Personal context | ”$650 bill, is that normal?” | Benchmark + verdict + advice | Very High |
| Complex scenario | ”Compare 3 insurers for 1990s brick house” | 84 seconds thinking, PDS citations, decision framework | Very High |
AI surfaces invest more processing power and provide deeper responses for queries with embedded personal context. Create content that answers complex, context-rich queries, not just simple keywords.
Measurement framework
State of GEO measurement
Two fundamentally different measurement questions exist for AI surfaces, and most brands are conflating them:
| Question | What you’re measuring | Current state (Jan 2026) |
|---|---|---|
| “Are we visible in AI responses?” | Whether your brand is being cited, recommended, or mentioned when consumers ask AI for advice | No native, first-party visibility metric is exposed by Google, OpenAI, Anthropic or Perplexity. Manual audits and third-party tools are currently required. |
| ”Are we getting traffic from AI?” | Whether consumers are clicking through from AI surfaces to your website | ChatGPT/Perplexity (and other LLMs) can be tracked as referrals in GA4 via referrer/UTM and custom channels. Google AI Overview/Mode clicks are included in GSC “Web” data and cannot be filtered separately. |
The uncomfortable reality: you can be highly visible in AI responses (cited as a recommendation) but receive few clicks because the AI answered the user’s question without requiring a click-through.
The measurement reality check
| Surface | Platform | Can track clicks? | Can filter separately? | Can track visibility? | Key limitation |
|---|---|---|---|---|---|
| Google AI Overview | GSC | Yes | No, blended with organic | No, requires third-party tools | Cannot isolate AI Overview traffic or visibility |
| Google AI Mode | GSC | Yes | No, blended with organic | No, requires third-party tools | Follow-ups count as new queries |
| ChatGPT | GA4 | Yes (referral) | Yes, via custom channel | No, requires manual auditing | Requires setup; some traffic may appear as Direct |
| Perplexity | GA4 | Yes (referral) | Yes, via custom channel | No, requires manual auditing | Lower volume; no UTM auto-append |
| Claude | GA4 | Yes (referral) | Yes, via custom channel | No, requires manual auditing | Very low volume currently |
What to measure now
| Metric | Question it answers | Where to find it |
|---|---|---|
| ChatGPT referral volume | Are we getting traffic from AI? | GA4 → Traffic acquisition → AI Traffic channel |
| Long-tail query trends in GSC | Is AI-influenced search behaviour growing? | GSC → Performance → filter by query length (10+ words) |
| Conversion rate by channel | Is AI traffic higher quality? | GA4 → Conversions → by session source |
| Manual AI visibility audit | Are we being cited in AI responses? | Search your brand + competitor queries in AI Mode and ChatGPT monthly |
| AI Overview presence (third-party) | Which keywords trigger AI Overview with our brand? | Semrush, Profound, Conductor |
Important: third-party measurement tools lack maturity, so quality and accuracy vary significantly. Many tools pull from APIs and do not reflect what users actually see.
Recommendations
For CMOs: strategic actions
This week:
- Audit your AI visibility. Open AI Mode and ChatGPT. Search “[your brand] vs [competitor]” and “best [your category] NSW”. Are you being recommended? With what positioning?
- Check your comparison site presence. Log into Canstar, Finder, and your category’s key aggregators. How are you positioned?
- Review your value proposition clarity. AI surfaces reward specific, quantified claims. “23% below Reference Price” gets cited. “Great value” doesn’t.
This quarter:
- Brief your agencies on AI visibility. Most SEO strategies optimise for blue links. Ensure your partners understand GEO and can audit your AI citation presence across surfaces.
- Invest in comparison content that takes a position. AI surfaces don’t want “here are three options”; they want “here’s which option is best for [scenario]”.
- Shift measurement downstream. AI clicks may be fewer but higher quality (users arrive informed, further down funnel).
For search marketers: technical and content priorities
Immediate:
- Set up GA4 AI traffic channel today. This is the only way to isolate and measure ChatGPT/Perplexity referral traffic.
- Audit PDF and document accessibility. ChatGPT reads policy documents, PDS files, and technical specs.
- Monitor long-tail query trends in GSC. Conversational queries (10+ words, question format) are proxies for AI-influenced search behaviour.
Strategic:
- Create content for complex, context-rich queries. Build scenario-based guides, calculators, and comparison tools.
- Optimise for aggregator visibility. Treat aggregator optimisation as a GEO tactic.
- Quantify everything. “Save up to 23%” beats “competitive rates”.
- Build decision frameworks into your content. “5 things to check before switching” content gets extracted into AI responses.
- Track AI referral conversion rates separately. Early indicators suggest AI referral traffic converts at higher rates.
Sources
- Australian Bureau of Statistics, Consumer Price Index, Australia, December 2025
- Australian Bureau of Statistics, CPI September Quarter 2025
- Reserve Bank of Australia, Monetary Policy Board Statement, 9 December 2025
- Reserve Bank of Australia, Statement on Monetary Policy, November 2025
- IMWT Discovery Intelligence: proprietary research, January 2026
- Google Search Central: What are impressions, position, and clicks?
- Search Engine Land: Google AI Mode traffic data comes to Search Console (June 2025)
- Analytics Playbook: How to Track Traffic from AI Tools in GA4