TL;DR: I break down Google’s AIAX for search—what it is, why it works, where it can fail, and the exact test and activation checklist I’d use to capture the conversion uplifts reported in early tests.

I watched Google Ads’ walkthrough of AIAX (AI-powered Search features) and I want to translate that into a practical playbook you can apply today. AIAX extends keyword targeting, customizes ad text with generative models, and dynamically selects the most relevant landing page. The results Google shared look promising: average advertisers see ~14% more conversions at similar CPA, and brands moving from exact/phrase-only targeting can see as much as a ~30% uplift. L’Oreal’s test even showed a 67% higher CTR on AIAX-driven keywords, 30% lower cost per conversion, and doubled conversion rates.

Table of Contents

• What was discussed and tested
• What went right (why AIAX is worth testing)
• What worried me (what went wrong or risk areas)
• What I would do differently (my recommended approach)
• Activation checklist — the tactical steps I use
• Controls, transparency, and reporting you must use
• How I’d combine AIAX with other channels
• Key takeaways and strategic checklist
• FAQs

What was discussed and tested

Google presented AIAX as a suite for modern search behavior: longer, conversational queries, images and voice, and users expecting faster, personalized answers. The core features:

• Search term matching / expanded targeting: Matches beyond your explicit keywords using intent signals from pages and assets (think broader DSA-like reach).
• Text customization: Uses headlines/descriptions plus landing page content and generative AI to craft hyper-relevant ad text per query.
• Final URL expansion: Dynamically selects the most relevant landing page for a given query, not necessarily the static final URL.

What went right (why AIAX is worth testing)

• Higher conversion volume without sacrificing CPA in Google’s tests (median +14% conversions).
• Big uplift for accounts relying on restrictive match types—nearly +30% in some cases.
• Practical controls: negative keywords, URL exclusions, asset pinning and reporting (asset, landing page, search terms).
• Clear reporting hooks: you can see “AIAX expanded searches” and “AIAX landing page matches” in the keywords table and search terms report.

What worried me (what went wrong or risk areas)

• Relevance risk if your landing pages or assets are thin—AI customization depends on high-quality page content. “Here’s where the campaign lost momentum for me…” if the site doesn’t match user intent.
• Brand safety and messaging drift—automated headlines and alternate landing pages can create awkward copy or send users to suboptimal pages if not controlled.
• Budget misalignment—AIAX looks for more volume. If campaigns are budget-capped, you’ll starve the algorithm and get no upside.
• Learning period noise—expect 1–2 weeks of learning; short tests or seasonal windows can put false signals into results.

What I would do differently (my recommended approach)

If I were running this, I would structure a careful AB experiment with explicit measurement and guardrails. Key changes I’d implement:

• Pick 1–2 stable, non-seasonal campaigns (generic rather than brand where possible) with enough historical conversions (aim ≥30 conversions per ad group/month).
• Validate and improve landing page content first—aim for clear H1/H2, CTAs, product detail, and schema where relevant. AIAX leans on page signals; feed it quality.
• Enable both text customization and final URL expansion together for full effect—only turn off final URL expansion if you need strict landing page control (and then use pinning).
• Set a pre/post testing window: 4 weeks baseline, enable AIAX, allow 1–2 weeks learning, then 4 weeks test. Compare CPA, CVR, CTR, conversion volume and ROAS after the learning period.
• Use negative keyword lists and URL exclusions proactively. Monitor the search terms report weekly and add negatives fast.
• Layer in measurement: enhanced conversions + data-driven attribution so the bid strategy has accurate signal.

Activation checklist — the tactical steps I use

1. Audit measurement: confirm conversion tags, enhanced conversions, and data-driven attribution are active.
2. Choose campaigns: pick low-season, non-budget-constrained campaigns with calm traffic patterns.
3. Landing page prep: add clear intent-matching content and at least a handful of high-performing CTAs.
4. Ad assets: provide varied headlines and descriptions, pin critical brand/legal lines if necessary.
5. Turn on AIAX in campaign settings and opt into both text customization and final URL expansion (unless you have a strong reason not to).
6. Monitor reports: search terms (filter by source = AIAX), asset reports, and landing page reports. Add negatives and URL exclusions as needed.
7. Post-test analysis: exclude the initial 1–2 week learning window, then compare the subsequent 4 weeks to baseline metrics.

Controls, transparency, and reporting you must use

Don’t treat AIAX as a black box. Google provides:

• Keywords table metrics for AIAX expanded searches and landing page matches.
• Search terms report with filters for source (AIAX vs. your keywords).
• Asset reports that show headlines and descriptions generated by text customization.
• Landing page reports to see which pages the system chooses and exclude underperformers.

How I’d combine AIAX with other channels

AI improves efficiency but rarely replaces creative activation. I’d:

• Layer in creator or influencer campaigns for high-consideration categories. “If I were running this, I would’ve layered in creator partnerships upfront.”
• Use social prospecting to generate intent signals you can capture with AIAX in search.
• Retarget AIAX users with highly tailored display or performance-max audiences to convert interest into purchases.

Key takeaways and strategic checklist

1. AIAX works best when your measurement, landing pages, and budgets are ready.
2. Run a disciplined pre/post test with a learning exclusion window to avoid misreading early data.
3. Use negative keywords and URL exclusions aggressively to protect brand and relevance.
4. Use asset reports to surface high-performing headlines and then add them to your base campaigns.
5. Remember: attention doesn’t always mean action—monitor conversion quality, not just clicks.

FAQs

How much lift can I expect from AIAX?

Google reported average advertisers saw about a 14% increase in conversions at comparable CPA; advertisers using mostly exact/phrase match saw close to 30% uplift. Results vary by vertical, landing page quality, and budget alignment.

Can I control where AIAX sends traffic on my site?

Yes. You can exclude specific URLs from final URL expansion and manage URL inclusions/exclusions. Use the landing page report to identify and block pages that underperform or conflict with brand messaging.

Should I enable text customization and final URL expansion together?

My recommendation is yes—opt into both for maximal personalization. If you need strict copy control, use asset pinning and consider disabling final URL expansion only as needed.

What reporting should I watch to evaluate AIAX?

Watch the keywords tab (AIAX expanded searches and landing page matches), search terms report filtered by source, asset reports (AI-generated headlines/descriptions), and landing page reports.

Is AIAX safe for brand campaigns?

You can use AIAX for brand campaigns, but many advertisers prefer starting with generic/non-brand campaigns because brand budgets and messaging are often tightly controlled. If you test brand campaigns, use strict exclusions and pin essential brand assets.

Final note:

I view AIAX as a force-multiplier if you do the prep work: quality landing pages, clear measurement, sensible budgets, and a tight testing plan. “Attention doesn’t always mean action—and this test proved that clearly.” Use the controls, iterate fast, and feed the model high-quality data—then let it find the long-tail intent your current keyword list misses.