Mike attended Google I/O 2026 as a guest. This is his executive briefing on how an AI-native search ecosystem will reshape agency operations.

Every year after Google I/O and Marketing Live, the industry produces a wave of often alarmist headlines. This year, the commentary is warranted—but most of it misses the operational implications.  A week out from the events, let’s review.

Following the 2026 Google I/O and Marketing Live events, it’s clear that the discrete search sessions are evolving rapidly to a world where users can have a conversation to get to their answers and set up agents to follow up on those requests. 

Google is moving toward an AI-native experience: one where structured data, not creative or keyword bids, determines whether a brand participates in the answers Google generates for consumers. For brands and advertising agencies, surviving Google’s shift requires moving away from manual campaign updates and building an automated, protocol-level system of record that utilizes the latest best practices across ALL your campaigns.  This means you need to have strategies and systems in place to migrate your existing campaigns. Here’s what you need to know. 

H2: How Google’s shift to AI Overviews and information agents alters the search landscape

Google reported that AI Mode now has over 1 billion monthly users and AI Overviews top 2.5 billion monthly users. AI is the product now, not just a feature of the search experience. The discrete search session is over.

Google is deploying what it calls "information agents." These are persistent AI tools that run 24/7, monitoring the web and alerting users when conditions they've defined are met. A buyer no longer executes a search. Instead, they set a monitoring task, and the agent surfaces results when they match. 

I heard Google’s Head of Search, Liz Reid, say that the agent will "keep track of those changes and let you know when the conditions are met." 

Alongside this, generative UI replaces the list of links entirely. Search results are now dynamic interfaces built on the fly for each individual query. The new search box went live last week. Generative UI rolls out this summer!

Why structured data feeds are replacing keyword bids as Google’s primary ad visibility driver

Google's concurrent rollout of AI Max for Shopping and Business Agent for Leads fundamentally reconfigures the advertising landscape for large-scale and multi-location operations. Two key product announcements from the advertiser side of Google I/O 2026 have significant implications for large-scale and multilocation advertisers: 

• AI Max for Shopping reconfigures static Merchant Center feeds into dynamic, real-time ads that adapt to nuanced user intent. For franchise systems, home services brands, and multifamily operators, the feed is now the ad. 
• Business Agent for Leads introduces conversational ad modules in which an AI agent communicates with a prospect directly inside the ad format, capturing intent without a click-through. For businesses that run on lead generation—HVAC, dental, financial services, apartment leasing—this changes the conversion surface entirely.

Google also introduced Ask Advisor, a unified agent for cross-platform workflow management. This is a clear signal for where operational management is heading.

The throughline across both Google I/O and Marketing Live is straightforward: Google is building a closed loop. If you’re an advertiser, your data is either structured well enough to participate in it or it isn't.

What it looks like: three real-world scenarios showing how AI information agents process ad data

Automotive: Winning localized automotive search queries without relying on geo-targeted creative

An agency managing 80 rooftops in the Midwest that’s traditionally competed on geography and creative is entering an entirely new arena. The question is no longer whether they can keep winning with great creative, it's whether their feeds and data are structured well enough to compete at all.

With these changes at Google, a buyer doesn't need to search for "Chevy Silverado dealer near me" anymore. Instead, the buyer sets up an agent: find me a used Silverado 1500 under $42,000, under 30,000 miles, within 25 miles. Google's information agents run on the buyer's behalf around the clock, monitoring inventory and alerting them the moment a vehicle matching their exact criteria appears. 

Simultaneously, AI Max for Shopping transforms a dealer's Merchant Center feed from a static listing into a live, adaptive ad that reconfigures itself to match the nuance of each query. The dealers who win won't have the best creative or the highest bids. They'll have clean, descriptive inventory data that Google can actually read and activate. 

Restaurant franchise: Capturing zero-click conversational leads without local landing page reliance

Consider an agency supporting a 200-location fast casual brand. Under the old search model, a shopper clicked through to a local landing page. Under the new model, a buyer's information agent pulls directly from structured location feeds (hours, promotions, menu variations) in real time. 

With Google’s Business Agent for Leads, an AI agent can now answer a prospect's questions about a specific location's current offers directly inside the ad, without a click-through. 

But here’s the catch: an agent can’t give buyers accurate information if the location’s data is stale, or worse, non-existent. If 40 store locations have outdated hours or expired promotional data, they simply won’t be part of the conversation: no impression, no engagement, and no conversion. The manual data management that was merely inefficient before is now a direct revenue leak.

Health and fitness franchise: Deploying hundreds of multi-location promotions without manual bottlenecks

A fitness franchise with 300 locations is running a January membership promotion with a narrow window. Their ad agency needs the first two weeks of January to get campaigns live for all 300 locations, resulting in a lot of campaign build work and QA. 

Under the new search experience, though, Google's information agents will only surface results for locations whose promotional data is structured, accurate, and live. If the fitness company’s ad agency utilizes structured, automated campaign activation, it can have updated promotional information for all 300 locations in-market on day one. 

Losing half of a high-intent promotional window to manual campaign production is no longer an option. If you want to survive in an agentic ecosystem, you need automated infrastructure.

Why your current campaign infrastructure cannot support Google’s 2026 AI protocol layers

The agencies and in-house teams that will successfully navigate this radical transition within Google’s advertising ecosystem share one characteristic: they built a centralized system of record for their advertising data before they needed it. 

The problem, as identified in our 2026 AdOps Benchmark Report, is that routine executional tasks already consume 39.75 hours per strategist per month (roughly 25% of available working time). That’s before accounting for the data structuring demanded by this new search environment.

The direction signaled across both Google I/O events points toward a world of Agent-to-Agent (A2A) commerce. Soon, a consumer's AI agent will communicate directly with a brand's advertising infrastructure to discover, evaluate, and transact. For advertisers, that means they need to activate structured client data—inventory, locations, offers, audience parameters—simultaneously across every channel at a scale no human team can replicate manually. 

Open protocols like AdCP (Ad Context Protocol) are already being developed to enable ad buying, creative adaptation, and audience matching at machine speed through Model Context Protocol (MCP) and A2A frameworks. The implication is clear: advertising infrastructure needs to be both organized and machine-readable at a protocol level. The platforms positioned to operate in this environment are those built on a single, structured system of record. 

How to run your AdOps post-Google I/O: Three operational mandates for advertising leaders

To maintain search visibility under Google's new protocol layers, ad operations teams must prioritize feed enrichment, system centralization, and budget automation.

1. Evaluate, enrich, and structure your data feeds. Queries in AI Mode are now three times longer than traditional keyword searches. They’re conversational, specific, and use natural language. A feed built solely around SKUs and prices won't match what users are looking for. Descriptive attributes, accurate location data, and real-time pricing used to be a competitive advantage. Now, they’re required to even show up in AI Mode.
2. Centralize ad operations around a single system of record. Agencies that respond to ecosystem shifts in days (not months) run their data, campaigns, budgets, and reporting from one place. This structure is what makes feeds legible to emerging agentic protocols.
3. Automate budget management. Google's new Missed Opportunity Reporting will soon show your clients exactly how much revenue was lost due to pacing gaps. This means budget optimization is more critical than ever.

What agile ad agencies are doing right now to prepare for Google Ads changes

Agencies leveraging advanced data structures are securing immediate performance wins by deploying automated operational guardrails and proactive client intelligence tools. We’re already seeing this pay off for the agencies furthest ahead of this shift:

1. Define operational guardrails to automate quality control at scale. The best agencies use tools like Fluency’s Performance Bounds to define acceptable performance ranges for each client vertical upfront. When a campaign moves outside these boundaries, the agency is alerted to the shift before the client brings it up. Client relationships become more advisory and less reactive. For example, this means more time to discuss strategy during QBRs instead of justifying the work your team did.
2. Use proactive client intelligence to prescribe next steps before clients ask. These agencies use AI to prepare for client conversations instead of documenting them after the fact. Before a client meeting, the best agencies can ask their AI tools to identify which campaigns are trending toward underperformance and what to do about it. They can walk into the room with a point of view and recommendation for the client instead of a dashboard printout. This is the difference between telling clients what happened and telling clients what to do next.

The agencies and teams already operating this way didn't build for Google I/O. They built because the operational business case was already there. What these announcements did was make the timeline for everyone else considerably shorter.

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