The Search Landscape Has Changed Forever

If you've noticed your Google Ads performance shifting in recent months, you're not alone. Google's AI Overviews have fundamentally altered how users interact with search results—and consequently, how your ads perform. According to recent industry research from Semrush, AI Overviews now appear in more than 50% of all search results, with informational queries triggering these AI-generated summaries 99.2% of the time.

This seismic shift means that traditional search intent signals you've relied on for years are being reinterpreted through an AI lens. For PPC professionals managing campaigns across multiple accounts, this creates both challenges and opportunities. The queries that trigger your ads are changing, the intent behind those queries is being filtered through AI interpretation, and your negative keyword strategy needs to evolve accordingly.

This article breaks down exactly how AI Overviews are reshaping search intent, what this means for wasted ad spend, and how to adapt your negative keyword management to protect your budgets in this new reality.

What Are AI Overviews and Why Do They Matter for PPC?

AI Overviews are Google's AI-generated answer boxes that appear at the top of search results, providing users with synthesized information from multiple sources without requiring them to click through to websites. Unlike traditional featured snippets, these summaries are dynamically generated using Google's Gemini AI model and can span multiple paragraphs with citations to source content.

The scale of this change cannot be overstated. Data shows that AI Overviews now reach 1.5 billion monthly users across 200 countries, making it the largest generative AI deployment globally. For PPC advertisers, this matters because AI Overviews take up 42% of the screen on desktop and 48% on mobile, pushing your paid ads further down the page and fundamentally changing user behavior.

How AI Overviews Change User Clicking Behavior

The data on click-through rate impact is striking. Research from Search Engine Land shows that individual sites' CTR dropped 34.5% when an AI Overview appeared, with position one organic results falling from 7.3% CTR to just 2.6%. For PPC advertisers, this means fewer users are clicking ads for informational queries because they're getting their answers directly from the AI Overview.

Users are nearly 47% less likely to click a traditional search result when an AI Overview is present. This behavioral shift has profound implications for your negative keyword strategy because it changes which query types should be considered high-intent versus low-intent. Queries that previously indicated research behavior leading to conversion may now represent dead-end traffic that gets satisfied by the AI Overview without ever reaching your landing page.

How AI Overviews Are Redefining Search Intent Categories

Traditional PPC strategy has long relied on categorizing search queries into four intent buckets: informational, navigational, transactional, and commercial investigation. This framework helped advertisers determine which queries to bid on aggressively and which to exclude with negative keywords. AI Overviews are blurring these lines in ways that require a fundamental rethinking of intent classification.

Informational Intent: The Biggest Casualty

Informational queries have been hit hardest by AI Overviews. Research indicates that 99.2% of keywords triggering AI Overviews have informational intent, and approximately 84% of all AI Overviews appear for informational queries. For advertisers who've traditionally captured early-funnel traffic through educational content and lead magnets, this represents a significant challenge.

Consider a software company that previously bid on queries like "how to improve team productivity" or "what is project management software." These informational queries often led to content downloads and eventual conversions. Now, users get comprehensive answers directly in the AI Overview, complete with synthesized best practices and tool recommendations, without ever clicking your ad.

This shift requires recalibrating which informational keywords deserve budget allocation. Auditing search intent rather than just keywords becomes critical because the presence of an AI Overview changes the conversion probability for that query, even if the query itself hasn't changed.

Transactional Intent: Still Valuable, But Changing

While informational queries dominate AI Overview appearances, transactional intent is evolving. Data shows that AI Overviews for transactional keywords have gradually risen to 12.54% as of September 2025. This means Google is becoming more confident in using AI to influence purchase decisions, not just answer questions.

The challenge for PPC managers is that transactional queries now exist on a spectrum. Some transactional queries trigger AI Overviews with product comparisons and recommendations, while others still display traditional ad-heavy results pages. Understanding which of your target transactional keywords trigger AI Overviews—and how that affects conversion rates—is essential for optimizing negative keyword strategy.

For example, a query like "best CRM software for small business" might trigger an AI Overview with feature comparisons and pricing information. Users who click ads after viewing this overview have different intent than those who saw ads without the AI Overview—they're more educated and further along in their decision process, making them potentially higher quality leads despite lower volume.

The Blurred Line Between Research and Purchase Intent

Commercial investigation queries—those where users are researching before buying—are experiencing the most complex changes. These queries fall into a gray area where AI Overviews provide enough information to either advance users toward purchase or satisfy their curiosity without further action.

This is where differentiating between browsing and buying searches becomes more nuanced. A query like "is [your product] worth it" might previously have indicated strong commercial investigation intent. With AI Overviews, that same query might now be answered with a pros/cons summary that prevents the click entirely.

Your negative keyword strategy needs to account for this ambiguity by identifying commercial investigation queries that no longer convert at acceptable rates due to AI Overview interference. This requires analyzing performance data at the query level and correlating declining performance with AI Overview presence.

How AI Interprets Intent Differently Than Traditional Algorithms

Understanding how Google's AI actually interprets search intent is crucial for adapting your negative keyword strategy. Traditional search algorithms relied heavily on keyword matching and historical click patterns. AI sees search terms differently from humans, using contextual understanding, semantic relationships, and predictive analysis to determine what users really want.

Contextual Understanding Over Keyword Matching

Google's Gemini AI model analyzes the full context of a search query, including related concepts, user history, and broader search patterns, to determine intent. This means two queries with identical keywords can be interpreted as having different intent based on contextual signals you can't directly observe.

For instance, the query "negative keyword tools" might be interpreted as informational (researching what tools exist), navigational (looking for a specific tool), or transactional (ready to purchase) depending on the user's search history, geographic location, and time of day. AI Overviews appear or don't appear based on this contextual interpretation, which in turn affects whether your ad gets clicked.

This contextual interpretation makes traditional broad match negative keywords less predictable. A negative keyword that successfully blocked irrelevant traffic last quarter might now be blocking valuable traffic if AI's interpretation of that term has shifted. Regular review and adjustment of negative keyword lists is now a necessity, not a best practice.

Semantic Relationships and Query Expansion

AI doesn't just match keywords—it understands semantic relationships between concepts. This means users are getting AI Overviews for queries that are conceptually related to your keywords, even if they don't contain exact match terms. This expansion of what Google considers relevant creates new opportunities for irrelevant impressions and wasted spend.

Consider a B2B software company targeting "enterprise resource planning software." AI might now show your ads for queries like "how to integrate business systems" or "connecting finance and operations tools" because it understands these as semantically related to ERP solutions. While this query expansion can uncover valuable traffic, it also dramatically increases the pool of potentially irrelevant queries you need to manage with negative keywords.

According to Google's official AI-powered Search ads documentation, the platform uses AI to reach more relevant searches by understanding user intent beyond keyword matching. This is beneficial when the AI correctly interprets intent, but it also means you need broader, more sophisticated negative keyword coverage to catch irrelevant expansions.

Predictive Intent Signals

Perhaps most significantly, AI uses predictive analysis to anticipate what users want next. If AI detects that users searching for a particular query tend to follow up with specific types of questions, it may proactively provide that information in the AI Overview, changing the entire user journey.

This predictive capability means that query intent is no longer static—it's dynamically interpreted based on aggregate user behavior patterns. A query that sent high-intent traffic to your site three months ago might now trigger an AI Overview that preemptively answers follow-up questions, reducing click motivation.

Your negative keyword strategy must become similarly predictive, using performance trend analysis to identify queries where AI Overview interference is degrading performance before wasted spend accumulates significantly.

The Real Cost of Search Intent Misalignment in the AI Overview Era

The economic impact of failing to adapt your negative keyword strategy to AI Overviews is substantial. Data-driven analysis of search intent misalignment shows that the average advertiser wastes 15-30% of their Google Ads budget on irrelevant clicks—a figure that's increasing as AI Overviews change which queries generate clicks.

Three Categories of New Wasted Spend

AI Overviews have created three distinct categories of new wasted spend that traditional negative keyword strategies may miss:

1. Informational Queries That No Longer Convert: Users who click your ads after viewing an AI Overview for informational queries are often just seeking additional details, not conversion-ready prospects. They've already received a comprehensive answer and are clicking out of curiosity rather than intent. These clicks cost the same but convert at dramatically lower rates, effectively wasting budget on low-probability traffic.

2. Semantically Expanded Queries With Weak Intent: As AI expands query matching based on semantic relationships, you're getting impressions and clicks from queries that are topically related but intent-misaligned. A query might be about a similar problem that your product doesn't actually solve, but AI's semantic understanding connects it to your keywords. Without aggressive negative keyword coverage of these semantic variations, spend bleeds into irrelevant territory.

3. Research Queries Satisfied by AI Overview Citations: When your competitors are cited in AI Overviews, users may click your ad to compare but have already been influenced by the AI's framing of the topic. These clicks have lower conversion probability because the AI Overview has already positioned competitor solutions as authoritative answers. You're paying for comparison shopping that's biased against you from the start.

Quantifying the Impact on Your Accounts

To understand the economic impact on your specific accounts, you need to identify which queries trigger AI Overviews and correlate that with performance metrics. While Google doesn't directly report AI Overview presence in Google Ads, you can infer it through several methods:

First, analyze queries with sudden CTR drops of 30% or more in recent months. According to industry data, this magnitude of decline often correlates with AI Overview introduction for that query. Export your search query reports for the past six months and identify queries where CTR declined sharply despite stable impression volume and ad position.

Second, examine queries with increased CPC but declining conversion rates. When AI Overviews appear, competition for the limited ad space increases as advertisers fight to maintain visibility. This drives up costs while simultaneously reducing conversion probability as users get answers from the AI Overview. Queries showing this pattern are prime candidates for negative keyword addition or bid reduction.

Third, segment your search query report by intent category (informational, transactional, commercial investigation) and compare conversion rates over time. If informational query conversion rates have declined by 40-50% in recent months while transactional query performance remains stable, you're likely experiencing AI Overview impact and need to add informational-focused negative keywords to protect budget.

For a typical mid-sized B2B advertiser spending $50,000 monthly on Google Ads, if 20% of spend is going to informational queries that now trigger AI Overviews, and those queries convert at half the previous rate, you're effectively wasting $5,000 monthly on low-probability traffic. Over a year, that's $60,000 in wasted spend that could be recovered through AI-aware negative keyword management.

Building an AI-Aware Negative Keyword Strategy

Adapting your negative keyword strategy for the AI Overview era requires both technical changes to your keyword management process and strategic shifts in how you think about intent filtering. Here's a comprehensive framework for building an AI-aware approach.

Step 1: Intent-Based Query Segmentation

Start by segmenting your search query data into intent categories with AI Overview impact assessment. This goes beyond traditional intent classification to include AI interference probability.

Export all search queries from the past 90 days with impressions, clicks, conversions, and cost data. Categorize each query into one of four intent types: Pure Informational (how-to, what is, guide, tutorial), Commercial Investigation (best, top, review, comparison), Transactional (buy, pricing, demo, trial), and Navigational (brand names, product names). For each category, calculate average CTR and conversion rate.

Compare these metrics to historical benchmarks. If Pure Informational queries show CTR declines of 30%+ and conversion rate declines of 40%+, these queries are likely AI Overview-affected and should be candidates for negative keyword addition. Commercial Investigation queries showing moderate declines may benefit from bid adjustments rather than full exclusion.

This intent-based segmentation allows you to apply different negative keyword strategies by category rather than using a one-size-fits-all approach. Informational queries might require aggressive negative keyword application, while transactional queries need more selective filtering.

Step 2: Semantic Negative Keyword Expansion

Because AI uses semantic understanding to expand query matching, your negative keywords need to cover semantic variations and related concepts, not just exact keyword matches.

Use tools like Google's Keyword Planner or third-party semantic analysis tools to identify conceptually related terms to your negative keywords. For example, if you sell premium software and want to exclude budget-conscious searchers, don't just add "cheap" as a negative—also add "budget," "affordable," "low-cost," "inexpensive," "discount," and "economical." AI's semantic understanding means queries with these variations will be treated similarly.

Similarly, if you want to exclude DIY searchers because you offer professional services, create a semantic cluster of DIY-related negatives: "DIY," "do it yourself," "tutorial," "how to make," "build your own," "free template," "free tool." This cluster approach catches the full range of AI-interpreted DIY intent.

Organize these semantic clusters into negative keyword lists in Google Ads, applying them at the campaign or account level as appropriate. This ensures that AI's semantic expansion works in your favor by excluding entire intent categories rather than playing whack-a-mole with individual irrelevant queries.

Step 3: Performance-Based Negative Keyword Automation

Manual negative keyword management cannot keep pace with the volume and velocity of AI-driven query expansion. You need automation that analyzes search query performance and automatically suggests or applies negative keywords based on data-driven criteria.

Traditional approaches to automation relied on simple rules like "add as negative if spend exceeds $X with zero conversions." This works for obvious waste but misses subtle intent misalignment where queries generate some conversions but at unprofitable rates.

AI-powered negative keyword management—like the approach used by platforms such as Negator.io—analyzes search queries in the context of your business profile, active keywords, and conversion data to determine relevance. Instead of just blocking zero-conversion queries, these systems identify queries that are topically related but intent-misaligned based on semantic analysis.

This contextual approach is essential in the AI Overview era because it catches the semantically expanded queries that human reviewers might miss. A query might seem relevant based on keyword matching but be fundamentally misaligned based on intent—AI-powered analysis can detect this misalignment and suggest appropriate negatives.

When implementing automated negative keyword management, ensure the system includes safeguards like protected keyword lists to prevent accidentally blocking valuable traffic. The goal is to automate the analysis and suggestion process while maintaining human oversight for final decisions, especially for high-spend accounts where mistakes are costly.

Step 4: Query Type-Specific Strategies

Different query types require different negative keyword strategies in the AI Overview era. Here's how to approach each:

Question Queries (Who, What, When, Where, Why, How): These overwhelmingly trigger AI Overviews and should be approached cautiously. According to research, long-form question queries combining seven or more words with informational intent have a 65.9% likelihood of triggering AI Overviews. Unless these queries have proven conversion history in your account, consider adding question-pattern negatives for broad match campaigns. Use exact match campaigns to selectively bid on proven question queries while excluding the broader category.

Comparison Queries (vs, versus, alternative, compared to): These trigger AI Overviews that often include feature comparisons and competitor citations. If your product isn't frequently cited in AI Overviews for these comparisons, the traffic quality will be poor as users have already been influenced by AI's framing. Monitor comparison query performance closely and add negatives for competitor comparisons where you're not winning citations.

Definition Queries (what is, define, meaning): Nearly always trigger AI Overviews with comprehensive definitions. Unless you're capturing this traffic for top-of-funnel brand awareness with very low CPC bids, exclude these query types entirely. The conversion path is too long and AI Overview satisfaction too high to justify the spend for most advertisers.

Tutorial Queries (tutorial, guide, step by step, how to): Similar to definition queries, these generate AI Overviews with step-by-step guidance. Exclude these unless you have content assets (like downloadable guides) specifically designed to convert tutorial seekers. Even then, expect low conversion rates and bid accordingly.

Step 5: Implement Protected Keyword Lists

While aggressive negative keyword application is necessary to combat AI Overview-driven waste, you must protect proven high-value queries from accidental exclusion. This is where protected keyword lists become essential.

A protected keyword list identifies specific queries or query patterns that have demonstrated conversion value and should never be blocked by negative keywords, even if they might match broader negative keyword patterns. This prevents overly aggressive automation from accidentally blocking your best traffic.

For example, if you sell project management software and "how to manage multiple projects" is a consistently converting query for you, add it to your protected list. This ensures that even if you add "how to" as a broad negative keyword to block tutorial seekers, this specific proven query remains eligible.

Build your protected keyword list by identifying all queries with at least three conversions in the past 90 days. Review this list monthly and add new proven queries as they emerge. When implementing negative keyword automation, configure the system to check against this protected list before applying any negatives.

This balanced approach—aggressive negative keyword application with protected keyword safeguards—allows you to adapt quickly to AI Overview changes while protecting proven performance.

Monitoring and Ongoing Optimization in the AI Overview Environment

AI Overviews are not static—Google continuously updates which queries trigger them, how they're formatted, and what information they contain. Your negative keyword strategy must include ongoing monitoring and optimization to adapt to these changes.

Key Metrics to Track

Establish a regular reporting cadence focused on metrics that reveal AI Overview impact:

CTR Trend Analysis: Track CTR by query category (informational, transactional, commercial investigation) on a weekly basis. Sudden CTR drops of 20%+ in any category indicate new AI Overview deployment and trigger a negative keyword review for affected queries.

Conversion Rate by Query Length: Research shows that longer queries (7+ words) are more likely to trigger AI Overviews. Monitor conversion rates segmented by query length. If long-tail query conversion rates decline while maintaining impression volume, AI Overviews are likely satisfying user intent before clicks occur.

Cost Per Conversion Inflation: Calculate cost per conversion by intent category. If informational query cost per conversion increases by 50%+ while transactional remains stable, you're experiencing AI Overview-driven waste. Reallocate budget by adding informational negatives and increasing transactional bids.

Impression Share vs. Click Share: Compare impression share to click share by campaign. If impression share remains high but click share declines, your ads are showing but users aren't clicking—often because AI Overviews are answering their questions. This indicates a need for either negative keyword application or pivot to different query types.

Quarterly Strategy Review

Every quarter, conduct a comprehensive review of your negative keyword strategy's effectiveness in the AI Overview environment:

Analyze the performance of queries you've added as negatives. Pull a list of all negative keywords added in the past quarter and estimate the spend they've prevented based on historical impression and average CPC data. Compare this to your overall cost savings and ROAS improvement to quantify negative keyword ROI.

Review high-spend queries for missed negative keyword opportunities. Sort your search query report by total spend and identify the top 100 spending queries. For each, assess whether performance justifies the spend or if negative keyword application would improve efficiency. This top-down approach catches high-impact waste that query-by-query review might miss.

Audit your protected keyword list for queries that no longer deserve protection. Market conditions change, AI Overview coverage expands, and previously high-converting queries may decline in value. Review all protected keywords and remove any that haven't generated conversions in the past 90 days or show deteriorating ROAS.

Analyze competitor citations in AI Overviews for your target queries. Use manual searches or tools like SEO platforms to identify which queries cite your competitors in AI Overviews. If you're being excluded from these citations, consider whether bidding on those queries is worthwhile given that users are being influenced by competitor content before seeing your ad.

Industry-Specific AI Overview Impact and Negative Keyword Strategies

AI Overview deployment and impact varies significantly by industry. Understanding your industry's specific exposure helps prioritize negative keyword strategy adjustments.

YMYL Industries (Finance, Healthcare, Legal)

Your Money or Your Life industries show mixed AI Overview patterns. Research indicates that 51.6% of YMYL health terms include an AI Overview, especially single-word medical searches. However, Google is more cautious with financial and legal AI Overviews due to accuracy concerns.

For YMYL advertisers, focus negative keyword efforts on educational and definitional queries where AI Overviews provide comprehensive answers that reduce click motivation. Queries like "what is term life insurance" or "symptoms of diabetes" almost certainly trigger AI Overviews and should be evaluated for negative keyword application unless they have proven conversion history.

Service Industries (Restaurants, Travel, Entertainment)

Service industries experienced explosive AI Overview growth during Google's March 2025 core update, with entertainment queries up 528%, restaurant queries up 387%, and travel queries up 381%. Service industry queries also show high local AI Overview rates, reaching as high as 65% for certain query types.

Service industry advertisers should prioritize negative keywords for informational service queries like "what to do in [city]" or "best restaurants for [occasion]" unless these queries demonstrate conversion value. The high AI Overview rate means most users are getting their answers without clicking, making broad informational bidding inefficient.

E-commerce and Shopping

E-commerce and shopping queries show lower AI Overview growth rates, which is logical given their transactional nature. However, the 12.54% transactional query AI Overview rate as of September 2025 represents a significant increase from previous levels.

E-commerce advertisers should focus on comparison-based negative keywords for products where they're not competitive. If AI Overviews for "best [product category]" consistently cite competitors but not your products, adding these comparison queries as negatives prevents wasted spend on pre-influenced shoppers.

B2B and SaaS

B2B and SaaS typically target informational and commercial investigation queries heavily as part of content marketing and lead generation strategies. These query types are most impacted by AI Overviews, making B2B advertisers particularly vulnerable to AI Overview-driven waste.

B2B advertisers need aggressive informational negative keyword strategies combined with pivot toward bottom-funnel queries. Instead of bidding broadly on "what is [category]" queries, focus budget on "[your product] vs [competitor]," "[your product] pricing," and "[your product] demo" queries where AI Overviews are less prevalent and intent is clearer.

Future-Proofing Your Negative Keyword Strategy for Continued AI Evolution

AI Overviews represent just one phase of Google's AI evolution. Understanding where this technology is heading helps you build a negative keyword strategy that adapts to future changes, not just current conditions.

Preparing for Conversational and Voice Search

Google's AI Mode, announced in 2025, allows users to have multi-turn conversations with search results. This conversational interface changes search intent in fundamental ways that will require new negative keyword approaches.

In conversational search, users ask follow-up questions based on previous answers, creating query chains that traditional negative keyword logic doesn't account for. A user might start with an informational query, receive an AI answer, ask a clarification question, and then ask a transactional question—all within the same search session.

To prepare for this shift, consider implementing conversation-aware negative keyword strategies that account for query sequences rather than individual queries. If certain informational queries rarely lead to transactional follow-ups in your analytics data, they're candidates for negative keyword application even if the informational query itself occasionally converts.

AI-Generated Ad Creative and Dynamic Negatives

Google is testing AI-generated ad creatives that adapt in real-time to user context and AI Overview content. These dynamic ads will require equally dynamic negative keyword strategies.

Future negative keyword management will likely involve AI systems that monitor query performance in real-time and automatically adjust negative keyword application based on current AI Overview behavior. This moves beyond scheduled script execution to continuous optimization that responds to Google's AI changes as they happen.

Start preparing for this future by building API integrations and data pipelines that enable programmatic negative keyword management. The manual campaign-by-campaign approach to negative keywords won't scale when AI Overviews and ad formats are changing daily.

Intent Prediction Rather Than Intent Reaction

The future of negative keyword strategy is predictive rather than reactive. Instead of waiting for queries to generate waste and then adding them as negatives, AI-powered systems will predict which queries will underperform based on semantic similarity to known low-performers.

For example, if queries containing "free alternative" consistently waste budget with zero conversions, an AI system could automatically flag new queries containing semantically similar concepts like "no-cost option" or "budget solution" before they accumulate significant spend. This predictive approach prevents waste rather than cleaning it up after the fact.

Staying ahead of AI Overview impact requires adopting these predictive approaches. Traditional reactive negative keyword management—reviewing search query reports and manually adding negatives—is too slow when query volumes and AI interpretation are changing rapidly.

Your 90-Day Implementation Roadmap

Transforming your negative keyword strategy to address AI Overview impact doesn't happen overnight. Here's a phased 90-day roadmap for implementation:

Days 1-30: Assessment and Foundation

Week 1 - Baseline Assessment: Export all search query data from the past 180 days. Segment queries by intent type and calculate baseline metrics for CTR, conversion rate, and cost per conversion by category. Identify queries with significant performance declines in recent months that correlate with AI Overview deployment.

Week 2 - Query Classification: Categorize your top 500 spending queries into intent types and AI Overview impact levels (high, medium, low). This classification forms the foundation for prioritized negative keyword application.

Week 3 - Quick Win Negatives: Apply negative keywords to the most obvious waste—pure informational queries with zero conversions and high spend. Focus on question patterns, definition queries, and tutorial searches that have no conversion history.

Week 4 - Protected Keyword List: Build your protected keyword list of proven converting queries. Document all queries with 3+ conversions in the past 90 days and configure safeguards to prevent their exclusion.

Days 31-60: Strategic Implementation

Week 5-6 - Semantic Negative Expansion: Develop semantic negative keyword clusters for your key exclusion categories (budget seekers, DIY searchers, competitor researchers, etc.). Build negative keyword lists in Google Ads and apply them across relevant campaigns.

Week 7 - Automation Setup: Implement automated negative keyword discovery using scripts, third-party tools, or AI-powered platforms. Configure thresholds, exclusion rules, and protected keyword integration.

Week 8 - Reporting Infrastructure: Build dashboards and reports that track the key metrics for AI Overview impact monitoring. Set up automated alerts for significant CTR drops or conversion rate declines by query category.

Days 61-90: Optimization and Scaling

Week 9-10 - Performance Analysis: Analyze the impact of your negative keyword additions from Days 1-60. Calculate spend saved, ROAS improvement, and identify any accidental blocking of valuable traffic. Refine your approach based on results.

Week 11 - Scale Across Accounts: For agencies managing multiple accounts, document your successful approach and scale implementation across all client accounts, customizing for industry-specific differences.

Week 12 - Ongoing Optimization Process: Establish the ongoing processes for quarterly reviews, weekly monitoring, and continuous negative keyword refinement. Train team members on the new approach and documentation.

Conclusion: Adapt or Accept Waste

Google's AI Overviews have fundamentally changed the relationship between search queries, user intent, and ad performance. The queries that worked yesterday may waste budget today. The intent signals you relied on for years are being reinterpreted through an AI lens that changes user behavior before they ever see your ad.

For PPC professionals managing campaigns in this new reality, the choice is clear: adapt your negative keyword strategy to account for AI Overview impact, or accept increasing waste as more queries trigger AI answers that reduce click motivation and conversion probability. The data shows that AI Overview presence can reduce CTR by 35% and cost per conversion can inflate by 50% or more for affected queries.

The strategies outlined in this article—intent-based segmentation, semantic negative keyword expansion, performance-based automation, protected keyword lists, and continuous monitoring—provide a comprehensive framework for adapting to this change. Implementation requires effort, but the alternative is watching an increasing percentage of your Google Ads budget flow to low-intent traffic that AI Overviews have already satisfied.

The advertisers who thrive in the AI Overview era will be those who move from reactive negative keyword management to predictive, automated approaches that leverage AI themselves. Just as Google is using AI to change search behavior, you must use AI-powered tools to analyze search intent and exclude misaligned traffic at scale.

The 90-day roadmap provides a practical starting point, but this is not a one-time project—it's an ongoing optimization discipline. AI Overviews will continue evolving, covering more query types and providing more comprehensive answers. Your negative keyword strategy must evolve in parallel, continuously adapting to protect your budgets from AI-driven waste while preserving access to high-intent traffic that still converts.

The search landscape has changed forever. Your negative keyword strategy must change with it. Start with assessment, implement systematically, and optimize continuously. The waste you prevent today compounds into significant savings over time—savings that can be redirected to high-performing queries, new campaign initiatives, or straight to your bottom line.