In early 2026, Google’s Vice President of Ads and Commerce, Vidhya Srinivasan, published her third annual letter to advertisers, mapping out how the company plans to evolve advertising, commerce, and artificial intelligence together. This isn’t a narrow feature update; it’s a broader vision for agentic commerce, where search, shopping, and AI converge in ways that will reshape how brands connect with consumers.
The letter confirms real deployments that began late last year and previews expansions advertisers should prepare for throughout 2026. Those include the adoption of the Universal Commerce Protocol (UCP), new sponsored formats within AI Mode, tools that match creators with brands, and AI-powered creative enhancements in Google Ads.
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Below, we break down the major elements of this strategy, why they matter to marketers and retailers, and how they slot into the broader move toward an AI-driven commercial ecosystem.
Universal Commerce Protocol: Standardizing AI-Driven Shopping
One of the most important developments outlined is the expansion of the Universal Commerce Protocol (UCP). When first introduced, UCP was described as an open standard co-developed with partners like Shopify and designed to enable conversation-based shopping within Google’s AI tools. In her letter, Srinivasan confirmed that UCP now powers direct purchases from retailers such as Etsy and Wayfair directly within Search’s AI Mode and the Gemini app.
UCP functions as a connective layer between AI interfaces, merchants, payment systems, and fulfillment partners. It’s designed not just for discovery but for full transactional flow—from browsing to checkout—without requiring users to leave the Google environment. This mirrors similar architectures being embraced across the industry, where conversational AI becomes the primary interface for purchase decisions.
For brands and advertisers, UCP represents a fundamental shift: it blurs the line between visibility (being found) and conversion (closing the sale). Retailers that adopt UCP early may capture sales at moments of intent that were previously lost to external checkout processes.
AI Mode Advertising: Native Sponsored Experiences
Google tests new advertisement formats through AI Mode, which operates with UCP and Gemini-based conversational search capabilities. The new advertisement formats display results to users through their search activities instead of showing them traditional search ads, which appear above or next to links.
These ads are clearly labeled as “sponsored” to maintain transparency, but they’re placed contextually alongside organic recommendations generated by AI. For advertisers, this means:
- Visibility at earlier consideration stages, when users are asking open-ended questions rather than typed product searches.
- Opportunity to connect with users inside the assistant experience, not just on SERPs.
- Reduced dependence on traditional keyword targeting, since AI Mode queries often involve deeper, conversational intent.
In short, ads in AI Mode are less like classic search placements and more like contextual recommendations woven into answers. For example, if a user asks about the best laptops for graphic design, the response may include a “sponsored” section highlighting retailers who sell suitable models. This expands where paid visibility can surface beyond the ten blue links or shopping carousels advertisers have relied on for years.
Direct Offers: Personalized Deals Within AI
Another expansion mentioned in Srinivasan’s letter is Direct Offers, a Google Ads pilot first previewed at NRF 2026. The pilot allows brands to deliver tailored deals directly to users within AI Mode. Initially focused on straightforward price discounts, Google plans to broaden this to include:
- Loyalty incentives
- Bundled offers
- Non-price value propositions
These deals aim to meet users in the moment of intent, presenting incentives precisely when someone is considering a purchase. This moves advertising closer to commerce by effectively answering the question “Why should I buy now?” directly within the AI interface.
The new system enables marketers to develop innovative campaigns that track customer behavior through their search patterns, their current shopping readiness, and their active conversations. The system establishes a new method for measuring return on investment, which shifts focus from tracking clicks to evaluating user engagement and tracking offer redemption.
Creator-Brand Matching and YouTube Commerce
Srinivasan’s letter also touches on evolving creator commerce strategies. Citing research that positions YouTube creators as trusted influencers, Google is expanding tools that help brands identify and partner with creators based on content and audience alignment.
Rather than an afterthought, this capability reflects a broader trend wherein influence and commerce converge:
- Users often seek recommendations from trusted voices before making purchase decisions.
- AI tools can analyze video content and audience behavior at scale, identifying relevant partnerships that align with shopper intent.
- Creator partnerships may increasingly support AI-driven commerce experiences by supplying contextual signals that help AI recommend products authentically.
This approach mirrors trends in affiliate and social commerce, where creators serve as trusted intermediaries that guide purchase decisions.
AI-Driven Creative Tools: Scaling Asset Production
Another thread woven throughout Srinivasan’s letter is the rapid adoption of AI creative assets within advertising workflows. According to internal data cited in the letter, Google saw a threefold increase in Gemini-generated creative assets in 2025, with Q4 alone producing nearly 70 million assets across tools like AI Max and Performance Max campaigns.
Tools such as:
- Veo 3 for video generation
- Nano Banana for rapid creative development
are now integrated directly into the Google Ads Asset Studio, allowing advertisers to automate parts of creative development. This trend is significant for a few reasons:
- It reduces production time and cost for asset creation.
- It enables rapid iteration and testing.
- It democratizes access to high-quality creative, even for smaller brands.
It also points to a future where creative production and campaign execution are increasingly AI-assisted, allowing advertisers to focus more on strategy and optimization rather than asset manufacturing.
AI Max and Performance Expansion
The letter also reinforces Google’s stance that AI-augmented tools like AI Max — a suite designed to drive performance through broader targeting and automatic optimization — are key to unlocking additional ad revenue streams and capturing queries that traditional campaigns may miss. Google claims AI Max has begun “unlocking billions of net-new searches” that advertisers had not previously reached.
This supports a broader narrative: AI doesn’t replace Search; it expands it. Queries are becoming longer and more exploratory, meaning that traditional keyword and match-type frameworks may have diminishing effectiveness if not paired with AI-capable campaign types.
Advertisers embracing these tools may find improved reach and responsiveness across diversified user intents, but with added complexity in how performance is measured and optimized.
What This Means for Marketers in Practice
Taken as a whole, Google’s 2026 direction represents a philosophical shift in advertising — from standalone placements to integrated, conversational, and action-oriented experiences. The key practical implications include:
- Expanded touchpoints: Ads will not just appear alongside search results but within AI-driven conversations when intent is most acute.
- Commerce deeper in the user journey: With UCP and in-AI checkout, users may complete purchases entirely within AI Mode, reducing friction and abandonment.
- New creative paradigms: AI-generated assets and offers demand marketers rethink content and personalization strategies.
- Cross-platform coherence: With tools like Direct Offers and creator matching, campaigns will likely need to span search, video, and AI-driven touchpoints cohesively.
Together, these trends signal a redefinition of paid media strategy — one where conversational context, commerce integration, and AI-assisted creativity shape decisions that historically relied only on keyword intent and separate landing page optimization.
The Broader Industry Context
Google’s push to expand commerce and ads within AI environments comes amid broader competitive pressures. Other AI platforms — including Microsoft’s Copilot and OpenAI’s experiments with in-conversation monetization — are exploring similar directions, albeit with varying approaches and timelines. Google’s advantage lies in its entrenched search and shopping ecosystems, allowing it to blend organic discovery, paid visibility, transaction capability, and AI assistance into a single user journey.
For brands and advertisers, this means adapting not just to new formats but to new user expectations. Users increasingly seek solutions that are:
- Immediate
- Personalized
- Transactionally ready
- Trusted by context
Whether it’s shopping for products, comparing services, or exploring complex queries, the lines between information and conversion are blurring — and Google’s ad strategy is built around capturing that convergence.
Anticipating Change Through 2026
While Srinivasan’s letter outlines foundational directions, many implementation details — specific launch dates, reporting mechanics, and campaign controls — are still forthcoming. Google Marketing Live and subsequent developer announcements will likely clarify operational specifics, but the broader themes are clear:
- AI-driven ad experiences are expanding beyond traditional SERP placements.
- Commerce protocols like UCP are bridging discovery and purchase paths.
- Creative and performance tools are increasingly AI-native.
- Advertisers must adjust strategies to operate in a conversational and transactional search environment.
The marketers who succeed in this changing market need to understand both AI Mode and agentic commerce because they need to know how these systems function and how users behave with them.
