The elaborate keyword-based campaign architectures that dominated search advertising for over a decade have become optimization obstacles rather than performance advantages, according to Google executives discussing the platform's strategic direction on the company's Ads Decoded podcast on February 11, 2026.

Brandon Ervin, Director of Product Management for Search Ads at Google, outlined how artificial intelligence systems now deliver superior results through consolidated campaign structures that align with business objectives rather than keyword permutations. The approach represents a fundamental departure from the hyper-granular account management practices that defined professional search advertising between 2010 and 2020.

"What people were doing before was quite rational," Ervin stated during the podcast conversation with host Ginny Marvin, Google's Ads Product Liaison. "When you had manual bidding, when you didn't have things like creative automation, the way to get control was to split things out so that you could manage ad copy and bids effectively. But to your point, that was quite laborious."

The consolidation guidance builds upon Google's introduction of AI Max for Search campaigns announced on May 6, 2025, which extended artificial intelligence-powered features including search term matching, text customization, and final URL expansion beyond the automated Performance Max campaign format. The AI Max feature suite has faced mixed industry reception, with independent testing revealing significant performance gaps between Google's claims and advertiser results across retail campaigns.

Business goals replace keyword lists as structural foundation

Campaign architecture decisions should begin with conversion objectives and budget allocation rather than keyword research, according to Ervin's framework. "Step one is think about what are you trying to achieve - like what is your goal? Is it driving conversions? Is it driving conversion value? How does that map to your business goals?" Ervin explained. "Then think about like a lightweight structure of how you could represent that in Google Ads."

The business-first approach contrasts sharply with traditional account planning methodologies that began with extensive keyword research, organized terms into tightly themed ad groups by match type, and constructed campaigns around keyword categories. Google now recommends creating separate campaigns only when distinct product lines maintain separate budgets and bidding goals, when regional operations require geographical segmentation aligned with internal business structures, or when seasonal initiatives demand temporary campaign isolation.

"There's no quote, perfect account structure," Ervin noted. "We just want to kind of give you some rules of the road to say how you can think about doing things that work with your business."

This structural philosophy extends similar consolidation principles Google published for Performance Max campaigns in February 2024, which emphasized that separate campaigns should exist only for distinct goals, budgets, or return on ad spend targets. The consistency across campaign types indicates systematic platform direction toward simplified architectures that maximize data density for machine learning optimization.

Keywords become thematic signals rather than targeting mechanisms

The role of keywords has transformed from precise targeting instruments into general intent indicators that help Google's systems understand campaign themes, according to Ervin's explanation of modern keyword function. "I personally feel that keywords are sort of a means to an end. So they aren't the end and of themselves, right? Keywords are a way to make sure that we're reaching the right user searches that to deliver the outcome that you've, that you've specified."

This philosophical shift accompanies technical changes in how Google's matching algorithms interpret keyword specifications. Enhanced AI keyword matching announced on August 30, 2025, expanded semantic understanding across all match types, with exact match now operating on semantic meaning rather than literal word matching. Industry analysis has documented significant exact match degradation, with exact match keywords increasingly matching semantically unrelated terms through close variant expansion.

For AI Max and broad match implementations specifically, keywords serve primarily to establish campaign themes that distinguish one ad group's intent from another within the same account structure. "Keywords are a great way to help us understand the theme of your account or your Ad Group," Ervin stated. "Like these types of queries generally are supposed to match here versus over here, so you have that, uh, that control of where traffic goes around your account."

The semantic matching evolution addresses changing user behavior patterns that Google observes across its search properties. "We continue to see user behavior change, and that's only accelerating now with the move to things like, uh, AI mode or these longer, more complex journeys," Ervin explained. The platform documented these behavioral shifts when it highlighted quality content amid AI-driven changes on August 30, 2025, noting that AI systems employ query fan-out processes that break complex searches into related sub-queries.

Ad group theming maintains importance despite consolidation

While campaign consolidation reduces overall account complexity, advertisers should maintain thematic coherence within individual ad groups to enable proper traffic segmentation, according to Ervin's guidance. The platform's matching algorithms interpret ad group composition as signals indicating which types of queries belong in which campaign sections.

"You should generally think, if I look at two ad groups side by side, is it sort of clear this Ad Group has a specific intent versus this other Ad Group?" Ervin recommended. "Because that's how the models will try to understand a query about X needs to go to the Ad Group about X, not to the ad group about Y."

This thematic approach differs from legacy practices where ad groups contained narrow keyword sets sharing identical landing pages and targeting parameters. Modern ad group structure should reflect genuinely distinct user intents or product categories rather than artificial segmentation created solely for bid management or match type isolation.

The consolidation logic extends to situations where multiple ad groups share identical business objectives. "If you have the same goal and they have the same budget," Ervin stated, "and so then the question is, what is the segmentation for, especially if you know creatives are maybe similar or the landing page is similar. And if there isn't a good reason for that, that's where consolidation makes great sense, right?"

Control mechanisms evolve beyond manual bid adjustments

Advertiser concerns about losing campaign control through consolidation and automation stem from conflating control with manual management techniques, according to Ervin's characterization of the feedback Google receives from marketing professionals. "What I hear oftentimes is Google just wants us to put everything into one campaign and lose all control," Ervin acknowledged. "I think about this a lot and when we came out with AI Max, like one of the big tenets was how can we give you performance with control?"

The platform has introduced structural controls designed for automated campaign environments that replace granular manual adjustments. Brand control settings enable advertisers to designate specific campaigns as brand-focused versus generic traffic acquisition. Location of interest controls, introduced with AI Max campaign features on October 15, 2025, provide geographical intent matching at the ad group level rather than requiring separate campaigns for location-specific keywords.

"It's new controls like brand control to say this is a brand campaign versus a generic campaign," Ervin explained. "Um, or some new things like Geo-Controls so you can have really much greater control over, um, the types of queries we match based on the geographic intent that's expressed in those queries."

Creative controls have expanded substantially beyond manual ad writing. Text guidelines for AI-powered advertising campaigns, announced on September 10, 2025, enable advertisers to specify up to 40 natural language restrictions per campaign. Term exclusions function as negative keywords for generated assets, blocking specific words from appearing in AI-created headlines and descriptions.

Conversion data quality and bidding target accuracy represent the primary control mechanisms within Smart Bidding implementations. "It shifts more towards I want to run, uh, smart bidding and I really want to pass high quality conversion data and make sure my targets are accurate," Ervin stated regarding how control manifests in automated environments.

Smart Bidding requires minimum data thresholds for optimization

Campaign consolidation serves artificial intelligence optimization by concentrating conversion signals that enable faster learning cycles and more reliable performance predictions, according to Ervin's explanation of why data density matters for automated bidding strategies.

The platform recommends roughly 15 conversions within 30-day periods as the minimum threshold for effective Smart Bidding operation, though technical implementations attempt to aggregate learning across related campaigns. "Now we do some smart things like trying to learn across all the conversions you're giving us from your conversion tracking ID to try to aggregate that," Ervin noted. "So it's not necessarily having to come from a single campaign."

Advertisers managing multiple campaigns with identical conversion actions and performance targets should consider portfolio bidding strategies or shared budgets to increase data aggregation. "As long as the budget and performance goal is the same, that way we get a little bit more fidelity, um, in low conversion campaigns to make sure we were able to hit your targets," Ervin explained.

The 15-conversion minimum represents a substantial increase from data requirements for manual campaign management, where individual keywords could justify existence through occasional conversions. This threshold creates particular challenges for businesses with longer sales cycles or higher-value conversions that occur less frequently than 15 times monthly.

Campaigns failing to meet conversion thresholds should consider optimizing toward earlier funnel events that correlate with eventual purchases but occur more frequently. "If you are not meeting that conversion threshold, right, you can look at a, a different conversion action, perhaps further up in the customer journey, but that is still a very good signal of, um, customer intent," Marvin suggested during the podcast discussion.

Learning periods affect substantive changes rather than structural modifications

Marketing professionals managing consolidated account structures should distinguish between cosmetic organizational changes and substantive campaign modifications that trigger machine learning retraining periods, according to Ervin's guidance on learning period expectations.

Consolidating ad groups within the same campaign or merging campaigns that maintain roughly identical creative assets and targeting parameters should not require significant learning periods. "If you're consolidating two ad groups in the same campaign or perhaps two campaigns, um, into one, um, but you know, the, the creatives remain roughly the same," Ervin stated, "those really shouldn't require much of a learning period as the models are supposed to be robust against those types of, of structure changes."

The technical explanation centers on how Google's machine learning models encode campaign elements. "We prioritize putting what we would call like a semantic feature into the model and not a feature like campaign ID or Ad Group ID," Ervin explained, "such that if you move your creatives from one ad group to another pCTR doesn't have to relearn how those creatives perform because they look the same, because the model didn't even know they were in different ad groups in the first place."

This semantic approach to feature engineering enables campaign restructuring without performance disruption, addressing long-standing advertiser concerns that organizational changes would damage campaign performance through forced relearning.

Substantive changes that do trigger learning periods include adopting AI Max features, changing bidding objectives from one conversion action to another, or introducing significantly different traffic sources. "Do be a little bit patient with, um, how that performance will converge over time," Ervin cautioned regarding these more impactful modifications. "And oftentimes it's tied to how long your conversion cycle is and the number of conversion cycle."

Migration strategies emphasize safe testing before broad implementation

Advertisers managing thousands of ad groups accumulated over years of granular campaign construction should approach consolidation through systematic testing rather than account-wide restructuring, according to Ervin's recommended migration path.

The framework begins with mapping intended end-state structures aligned with business objectives before executing any consolidation actions. "Map out what do you wanna achieve in the instate. So start with your business objectives," Ervin recommended. "How does your current business map to what you would ideally represent in Google Ads, how that translates to things like, um, creative themes, landing page goals, things like that."

Initial consolidation testing should occur within lower-priority campaign segments that represent less critical revenue streams. "I would look at your existing structure and sort of find, quote, a safe place to start with," Ervin stated. "And so that might be your, um, lower priority, lower volume, less strategically important set of campaigns."

This controlled testing approach enables advertisers to validate consolidation impacts and identify configuration errors before applying structural changes to high-volume campaigns that drive substantial business results. "If something goes wrong, there's much lower risk because we sort of segmented it to a part of the business that while, while there is maybe less important than sort of the core high volume stuff that you wanna be really careful about," Ervin explained.

The timing considerations favor experimentation during lower-pressure periods rather than peak sales seasons. "For many businesses, it's probably a good time of the year to start testing things, not a high, uh, high pressure sales, um, season," Marvin noted during the February podcast recording.

Budget consolidation prevents artificial performance constraints

Shared budget configurations enable artificial intelligence systems to allocate spending optimally across campaigns pursuing identical conversion objectives, according to Ervin's explanation of why budget segmentation can create unnecessary performance limitations.

The technical challenge emerges when campaigns with the same performance goals and bidding strategies maintain separate daily budgets. Some campaigns may become budget constrained while others retain excess budget capacity, preventing the optimization algorithms from allocating resources to the highest-performing opportunities. "Our recommendation is if you have the same performance goal for a given set of campaigns or line of business, consider running a shared budget so that budget can now work fluidly across the campaigns that share the same objective," Ervin stated.

This budget pooling strategy differs from situations where distinct budgets reflect genuine business requirements. "If you have different budgets for internal reasons, or these campaigns have distinct, um, you know, goals or performance that you want to keep separate, separate budgets still makes sense, right?" Ervin noted. "We wanna make sure it aligns with whatever your business objective is."

The platform recently expanded fixed budget controls to Search and Shopping campaigns on January 15, 2026, enabling campaign total budgets that set predetermined spending limits across timeframes ranging from 3 to 90 days. This constraint-based approach addresses promotional periods and seasonal campaigns where total spending caps matter more than daily budget optimization.

Search term visibility addresses automation transparency concerns

Search terms appearing loosely related or seemingly unrelated to specified keywords should not automatically trigger panic responses, according to Ervin's guidance on interpreting search term reports within AI-powered campaign contexts.

The platform increasingly serves advertisements based on comprehensive user intent signals rather than strict query-to-keyword matching. "We are seeing more complex, longer queries, more general categorical like searches," Ervin explained. "And so I think it's important to be curious about how could my campaign better serve these use cases or these people searching?"

This expanded matching reflects Google's strategic position that search advertising should capture users across their entire customer journey rather than exclusively at purchase-ready moments. "I think search is an incredibly high intense surface, high value," Ervin stated. "But we are seeing more complex, longer queries, more general categorical like searches."

The technical systems behind this expansion combine query matching enhancements with bidding sophistication and creative customization. "That works across query matching. We wanna make sure we're matching across the funnel of potential intents," Ervin explained. "It works for us across creative customization so that we can actually make a message that resonates for people at different parts of the funnel."

Smart Bidding algorithms incorporate funnel stage understanding to price upper-funnel traffic appropriately relative to lower-funnel searches. "Smart bidding is starting to do a much better job of understanding upper funnel intent versus lower funnel intent so that the ROI we provide on these different searches also makes sense," Ervin stated.

Brad Geddes, an industry professional referenced during the podcast discussion, has documented concerns about how AI Max attributes traffic across campaigns. Google clarified AI Max attribution discrepancies on December 13, 2025, after Geddes and other analysts discovered that AI Max treats all keywords as broad match regardless of specified match type, creating measurement challenges where exact match keywords display data combining multiple match types simultaneously.

Platform modifications prioritize automated campaign creation paths

Google has systematically restructured its advertising interface to favor automated campaign formats over traditional manual configurations throughout 2025, creating patterns that align with the consolidation guidance Ervin provided during the podcast.

The company modified campaign setup flows on September 24, 2025, shifting from format-first selection to channel-based campaign setup that defaults to Performance Max when advertisers select all available channels. This workflow reversal eliminates explicit Performance Max selection by making it the automatic outcome of comprehensive channel choice.

Enhanced CPC bidding phases out across Search and Display campaigns, with Microsoft following similar consolidation approaches by merging Target CPA into Maximize Conversions and Target ROAS into Maximize Conversion Value on August 4, 2025. These parallel industry movements suggest broader programmatic advertising trends beyond Google-specific product decisions.

The automation emphasis extends beyond campaign creation interfaces into measurement capabilities. Google Analytics launched cross-channel budgeting on January 16, 2026, providing projection functionality across advertising platforms that depends on consolidated spending data rather than fragmented campaign-level budgets.

Industry testing reveals performance gaps between claims and reality

The consolidation guidance and AI Max promotion face skepticism from marketing professionals who have conducted independent testing that contradicts Google's performance projections.

Analysis from Smarter Ecommerce examining over 250 retail campaigns published on November 6, 2025, found that AI Max delivers conversions at approximately 35% lower return on ad spend compared to traditional targeting methods within identical campaigns. The research contradicted Google's claim that AI Max delivers 14% more conversions or conversion value at similar cost per acquisition for non-retail campaigns.

Separate testing conducted by Xavier Mantica demonstrated AI Max delivering conversions at $100.37 per conversion after four months, representing 90% higher cost than phrase match at $43.97 per conversion. These results emerged despite Google's aggressive infrastructure investments including specialized reporting metrics launched on September 9, 2025, that provide visibility into AI-generated traffic versus advertiser-controlled targeting.

The performance disparities extend to Search Partner Network placements, where industry experts flagged concerning expansion patterns on August 27, 2025. Mike Ryan, Head of Ecommerce Insights at Smarter Ecommerce, documented that AI Max generates disproportionate impression volumes across Search Partner sites compared to traditional broad match and exact match targeting.

Timeline: Google's campaign consolidation and AI automation development

Summary

Who: Brandon Ervin, Director of Product Management for Search Ads at Google, discussed campaign consolidation strategy with Ginny Marvin, Google's Ads Product Liaison, during the Ads Decoded podcast.

What: Google outlined comprehensive guidance for consolidating Search campaigns away from legacy hyper-granular structures built around keyword permutations toward simplified architectures aligned with business objectives, conversion goals, and budget allocation. The approach repositions keywords as thematic signals rather than precise targeting mechanisms while introducing structural controls including brand settings, location of interest targeting, and text guidelines for AI-generated content.

When: The podcast discussion occurred on February 11, 2026, following Google's systematic introduction of AI Max for Search campaigns beginning May 6, 2025, and subsequent platform modifications throughout 2025 that prioritized automated campaign formats including Performance Max.

Where: The consolidation guidance applies across Google's Search advertising platform including standard Search campaigns, AI Max for Search campaigns, and Performance Max campaigns that serve advertisements across Search, Display, YouTube, Gmail, Discover, and Maps properties.

Why: Campaign consolidation addresses technical requirements for artificial intelligence optimization that depends on data density to enable effective machine learning. The simplified structures reduce manual management burden while providing modern control mechanisms suited for automated bidding, creative generation, and targeting expansion. Google positions the approach as necessary for advertisers to access AI-powered features including AI Mode placements and benefit from behavioral shifts toward longer, more complex search queries that traditional keyword matching cannot adequately capture.

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