From Google I/O to Growth Loops: How Agentic AI is Rewriting Marketing Stack

Google’s I/O 2025 made one thing crystal clear – AI is now deeply embedded into every layer of digital experience, be it search, ads, content creation, or content optimization. Up until now, agentic AI has been defined as an autonomous AI capable of acting independently to achieve business goals. Now, we are a step ahead with agentic AI shaping the entire marketing stack. This includes: 

• Audience building and segmentation
• Creative generation and testing (e.g., Gemini in Ads)
• Conversion optimization and attribution modeling
• Real-time budget reallocation based on signal feedback

From Campaign Setup to Signal Feedback Loops — Agentic AI is rewriting how the modern marketing engine runs. 

Agentic AI in Marketing – What, Why, and How?

The transition to automation is happening fast. Unlike traditional AI, agentic AI can act on its own, adapt to new data, and improve its performance. This self-learning attribute is one reason marketers are slowly relying on AI agents to make strategic decisions and go beyond automating specific ground-level tasks. 

Marketing teams need to keep up with audience behaviors, changing market trends, and growing competition. What better way than to deploy AI agents who can tirelessly catch up on the changing winds faster than the wind itself.  

Marketers are trusting agentic AI for its core benefits, like – 

1. Autonomous decision-making
2. Goal-oriented
3. Learns from past actions
4. Real-time adaptation
5. Scalable 
6. Analyzes customer data for better personalization
7. Analyzes marketing data to automate recommended actions

From Task Automation to Agentic Proactivity

AI agents can handle repetitive tasks like a pro. From data collection to sending marketing emails, it will do it relentlessly while marketers focus on scaling operations and improving marketing strategies. These agents can analyze customer data and understand how customers are interacting with the brand. They are at the core of the hyper-personalization of marketing messages that can foster better customer relationships. In addition, agentic AI models can track ongoing marketing campaigns with changes in trends, user behavior, and competitor strategies, and they can recommend possible strategies that align with business marketing and sales goals.

Until agentic AI happened, marketers did rely on automation to streamline repetitive tasks. Think of email sequences triggered by a sign-up, social media posts scheduled in advance, or simple data exports at set intervals. These systems operate on pre-defined rules: “If X happens, then do Y.”

However, these systems cannot adapt, learn, or initiate actions beyond their programmed parameters. They execute, but don’t think or strategize. 

Unlike rigid automation, agentic AI analyzes a vast amount of data, understands the nuances of a situation (context), and infers intent. For instance, instead of sending only a follow-up email, an agentic system can recognize the shift in market sentiment, analyze competitor activity, and proactively suggest a new campaign angle or budget allocation. 

This understanding of context enables AI to take initiative. It doesn’t wait for a human to give it specific instructions for every step. If its goal is to “maximize conversion rate,” it might autonomously test new ad creatives, adjust bidding strategies in real time based on fluctuating demand, or even identify and suggest improvements to landing page content—all without direct, step-by-step human intervention.

The key differentiator is the focus on a goal. Instead of simply automating the task of “sending emails,” an Agentic AI might be tasked with “optimizing customer lifetime value.” To achieve this, it will orchestrate multiple tasks, analyze feedback loops, learn from its actions, and continuously refine its approach to reach that overarching objective.

Marketers have made the shift to overseeing intelligent systems that can self-learn and scale with the business to accommodate marketing needs at every juncture of growth. 

Let’s discover how.

Campaign Management with AI – The Gemini + Ad Tool Synergy

Marketers know how labor-intensive campaign management is. But that is a thing of the past now. At Google I/O 2025, Google unveiled game-changing capabilities of Gemini AI integrated directly into core advertising platforms—Google Ads, search, and Performance Max. With this move, Google has brought a fundamental shift in how marketers interact and manage campaigns. 

Thanks to its multimodal capabilities, Agentic AI powered by Gemini can instantly generate a wide range of ad creatives. This includes creating compelling headings, engaging descriptions, and high-quality images or video snippets. It does everything in a way that is tailored to a specific product and target audience. 

Gemini now has new formats, such as Peak Points for YouTube ads, allowing seamless integration with precisely timed placements. Simultaneously, genAI is extending videos in all aspects for broader reach. — Google Marketing Live 2025. 

Here’s a quick look at how Gemini is reshaping campaign management for marketers –

When given a prompt, AI autonomously takes some of the critical steps –

1. Building the audience segments

AI goes beyond the basic demographics. It analyzes vast datasets to construct exact audience segments. It can identify “in-market” buyers based on their search behaviors, online interactions, and even broader trends related to sustainability and ethical consumption. 

2. Optimizing the campaign process

AI proactively outlines and implements an optimization strategy. From setting initial bids to allocating budgets across channels and suggesting budget adjustments to achieve campaign targets while the campaign is on, it does everything required to maximize conversion.

To understand the synergy between Gemini and ad tools, it is essential to know how AI functions at each level in the marketing funnel. A campaign is directly related to the entire funnel, and it needs optimization at every juncture.

Top-of-the-funnel: Awareness & Demand Generation

When exploration brand The North Face wanted to understand what customers were looking for in each market, it deployed AI to do the groundwork. Usually, the brand relied on constantly monitoring customer searches for items on its website and tweaking customer experience initiatives accordingly. 

What they did next was use Google Tag Manager 360 in combination with Analytics 360. The company discovered that their customers were searching for a new term—midi parka. This was an opportunity untapped for the brand. So, they renamed one of their products to midi parka and ended up driving 3x more conversions and revenue.

This is how AI can help optimize your campaigns. AI tracks KPIs and also provides real-time feedback and actionable insights. This is a golden opportunity for marketers to discover top-performing channels, pinpoint roadblocks, and gain insights on trends worth tapping into. Imagine doing all these manually? Those were some painstaking days! 

AI tools do more than automate. They can adjust campaigns tailored to set KPIs, maximizing conversion and revenue spikes. 

Middle-of-the-funnel: Lead Nurturing & Qualification

This is where AI gets involved in personalized multichannel nurturing, lead scoring, and routing. If you think AI isn’t a popular choice in the MOFU stage, the reality is different. According to Salesforce’s State of Sales Report, atleast 98% of sales teams agreed that automated lead scoring plays a crucial role in improving lead prioritization and eventually successful sales. 

AI lead scoring uses algorithms to track and analyze user interactions. This information is then used to predict user behavior and tag user profiles as ready to purchase, need nurturing, low possibility, and so on. This improves the hand-off to sales teams, who focus on leads that actually matter. 

Something similar happened when the U.S. Bank wanted to deploy predictive lead scoring to help its sales team focus on promising leads only. The U.S. Bank chose Salesforce’s Einstein, an integrated set of AI and ML technologies. With Einstein’s lead scoring capabilities, the U.S. Bank saw a 25% increase in closed deals, a 260% conversion rate, and a 300% increase in qualified marketing leads. 

Botom-of-the-funnel: Conversion & Sales Enablement

Sales enablement is not easy. You need to stir the right emotions in your users and connect with them on a personal level before they decide to purchase from you. Sales are the final consequence of building trust, connecting with users, and making sure you deliver on your promises. 

We know AI cannot offer emotions. The catch is to enable AI to stay true to human experience. AI can gauge user intent, analyze market needs, and help a brand align with the ultimate goal—triggering sales. 

If you are unsure if this is something that works, here’s a real story of how AI can enable sales (in millions!). 

Standing out in a dynamic ad world is tough, but Nutella demonstrated it with its one-of-a-kind AI-driven jar designs – an ad that not only turned heads but also increased revenue by millions. For this campaign, it partnered with Milan-based design agency Ogilvy & Mather.

“Nutella Unica” leveraged AI to create 7 million unique jar designs. Each jar was different from the other, with no repetitions at all. This campaign took aesthetics to a new level, creating a psychological trigger and tapping into humans’ FOMO nature. Everyone wanted the “exclusive” jar. 

When Nutella announced the unique ad in Italy, the goal was simple—to create a jar design as unique and expressive as the people in Italy. Of course, people rushed to own something unique, pushing users to participate in the rush to gain exclusivity. The fact that this was rare and available only once triggered loyal users to grab their favourite design. Result? The jars sold out instantly. 

Not only did Nutella create record sales, but it also generated massive buzz, elevating Nutella’s status as a brand. 

What worked here? 

• Exclusivity in marketing—People love to be part of something exclusive, and Nutella just tapped into that. 
• Tieing AI with creativity—AI can go beyond analyzing sales calls and recommending the next course of action. Nutella just proved how AI and creativity can create powerful marketing campaigns. 
• Telling a story that connects with users—Sales are rooted in how a brand tells a story that resonates with its target audience. The emotional connection is essential, and Nutella leveraged AI to do exactly that. They made their users feel more than mere consumers. Instead, their users became their narrative. 
• Leveraging social media and rooting in virality—Thanks to social media’s deep penetration in our lives, things can go viral in no time. Nutella used this to make their “unique offering” viral and let social media do the rest. Creating a frenzy and influencing others to join—social media did it best for Nutella. 
• Leveraging omnichannel experience—Nutella did not design this campaign for any one channel. It was about creating a seamless experience across digital channels and offline stores. The brand created a seamless experience across all touchpoints for maximum impact. 

Yes, agentic AI can analyze user behavior, track user conversations, and design sales strategies tailored to your brand. But it can also help you take sales and conversions one step up. For a campaign like Nutella’s to yield results, AI tracks user behavior, interactions, desires, ongoing trends, and much more than just demographic details. It helped the brand create a campaign that went above and beyond. 

The ability to function across multiple channels shows that Agentic AI in the marketing stack can implement cross-tool orchestration. This goes beyond just insights; it translates insights into actions across various marketing tools and platforms.

Let’s see how.

Cross-Functional Orchestration with AI Agents 

While agentic AI in ad platforms like Google Ads can streamline campaigns, the real power lies in its ability to orchestrate actions across disparate marketing tools and data sources. Google’s vision to infuse AI agents directly into everyday productivity and analytics tools like Docs, Sheets, and Analytics has come full circle now. They have turned into intelligent data-driven assistants that act contextually and cross-functionally. 

This is a big leap in how AI agents sit at the core of the marketing stack. Docs, sheets, and Google Analytics have moved beyond static platforms. They have become dynamic environments where AI understands your goals and proactively assists in achieving them. These are data-driven assistants that can understand context, act across the entire stack, and translate insights into actions. 

AI agents now function beyond commands. They understand and interpret the surrounding data, the history of your work, and your objectives. For instance, an AI agent in Google Sheets won’t just sum a column; it might identify a sudden spike in a specific metric and proactively flag it as a potential anomaly needing investigation. 

The real magic begins when these agents break down traditional departmental silos. For instance, data from analytics can inform content on Docs, or insights in Google Sheets can trigger actions in the ad platform. All of this happens without any manual data transfer or human intervention at every step. This seamless agent orchestration is marketers’ dream, where a network of specialized AI agents collaborates to automate complex workflows. 

Agentic AI is moving marketing beyond isolated tasks to holistic, intelligent campaign management. However, the intelligence of these agents, their ability to deliver precise insights and execute impactful actions, hinges entirely on the quality and accessibility of the data they consume.

This pinpoints an essential foundational element—first-party data. Marketers are now rooting for first-party data to adhere to security compliances and acquire user data that is genuine, clean, and consent-based. With first-party data, advertisers and marketers are not just adapting to a new privacy landscape but are building the essential fuel for agentic AI engines to thrive. 

First-party Data: Founding Element for Agentic Success

Marketers are impressed by agentic AI’s prowess in streamlining workflows. The actual friction is the base on which these AI agents function—data. In an increasingly privacy-first world, marketers are slowly (but steadily) realizing the need for first-party data. 

Privacy-Forward Tech Updates and the End of Third-Party Cookies

A lot of transitions are happening simultaneously in the marketing world. From how campaigns are created to how they are tracked, marketers have reached a point where AI is their co-worker. The challenge, however, begins long before a campaign is in the making. 

Marketers need data to run any campaign. For years, brands have utilized third-party cookies to track user behaviour across multiple digital platforms and tie it to users’ demographic data (which is also captured through these cookies). Detailed user behavior tracking has helped brands deliver ads that make sense and trigger conversions. 

While personalized ads did establish a brand-to-user connection, they have repeatedly raised security concerns about how brands handle user data. Despite the promises of secure encryption and safe data usage, security concerns are rising by the day. 

The alarming cases of data breaches in 2025 alone have pushed marketers and stakeholders to make a seismic shift in how user data is handled. The new transition is to adopt cookieless tracking. Brands are eager to implement server-side tagging and first-party data tracking to ensure user data security without compromising on end-to-end user experiences. 

Why Third-party Data is Causing More Damage?

Typically, data aggregators or ad tech companies collect user information that has no direct relationship with the user. This is called third-party data, which is crucial for audience segment building, cross-site tracking, and powering programmatic ad targeting. For example, when Google or Meta places cookies on a site, example.com, it is a third-party cookie. 

External advertisers often place third-party cookies or tracking pixels on various websites that collect user data such as user behavior, demographics, device information, and browsing history. This data is then shared (sometimes sold) across platforms to create detailed user profiles.

This has repeatedly raised concerns about the safety of user data. With increasing privacy regulations (like GDPR and CCPA) and browser changes (like Safari’s ITP), brands are slowly stepping away from relying on third-party data. Instead, marketers and stakeholders are shifting to first-party data and server-side tagging to retain targeting accuracy while remaining compliant. 

While third-party cookie tracking is great for marketers, it is not so for users, especially in terms of user privacy and consent. Third-party cookies gather data automatically from various sources and tie it to each user’s browsing history to create a unified user profile. This is done without the direct consent of users. Consequently, when brands reach out to these users, it may feel impersonal. 

Cookieless Tracking: Marketers’ Goldmine for Better User Experience

In 2023, a Deloitte poll revealed that almost 48.8% of C-suite and other executives had predicted more frequent and larger cyber threats in the coming years. 

Fast-forward to 2025, and we already have regulatory compliances like GDPR and CCPA frameworks cracking down on third-party data to combat cyber threats. 

Apple’s Safari and Mozilla’s Firefox browsers have already blocked third-party cookies by default. Google’s Chrome browser initially had plans to phase out third-party cookies, but recently, Google announced that they will stay, but with a twist. Google introduced Privacy Sandbox, which gives users a choice to opt out of third-party cookies. 

Eventually, we have stepped into a time when third-party cookies are no longer the norm. Marketers and advertisers are now creating strategies to target customers without cookies. 

Cookie-less tracking or first-party data refers to collecting information from users on your own website—information that users consent to share. There is no third-party vendor or external source here. 

Unlike third-party data, first-party data is owned by the business. In a survey by Nielsen, 86% of companies recognized the importance of first-party data. This shift is already visible in traditional marketing tactics like retargeting, lookalike audiences based on third-party data, and granular multi-touch attribution across external sites. Marketers are rethinking how they gather insights and connect with their audiences. 

A Gartner report shows that 80% of marketing leaders agree that AI is essential for success. Companies using AI in marketing and sales have already experienced a 3-15% uplift in revenue and 10-20% sales ROI. These numbers are bolstered by the fact that 75% of B2B marketers have already transitioned to first-party data strategies, mitigating risks and improving customer-brand relationships. 

First-party data is at the core of an improved brand-user relationship. 

Unlike third-party data, this comes directly from your audience collected from channels that you own, like – 

• Website visits
• CRM interactions
• Content downloads
• Customer feedback
• Email engagements

For instance, global sportswear brand PUMA aimed to deliver personalized experiences across all user touchpoints. However, they faced roadblocks in unifying data and scaling their personalization efforts. 

PUMA partnered with SAP Emarsys to integrate customer data across web, in-store channels, and mobile. They leveraged AI and deployed automation to create personalized user journeys based on customer behavior and preferences, including product recommendations and targeted offers. 

Data from local site behaviour, store purchase receipts, language settings, and loyalty-tier status flowed into a single profile, making sure every campaign reflected the currency, size, and style preferences. With first-party data, PUMA achieved a 5% increase in email revenue in 6 months and 10x growth in weekly subscribers. 

In a nutshell, AI agents need clean, structured, and compliant data to function autonomously and give desired results.

Benefits of Investing in First-party Data for Marketers

Brands that have prioritized investing in building robust first-party data pipelines are already at an advantageous spot. This includes:

• Direct customer relationships:

Brands are fostering a direct value-driven relationship through email signups, loyalty programs, gated content, and direct customer feedback through customer surveys and preference centers. They are collecting data from customer interactions on their website and mobile app, email signups, purchase history, in-store interactions (if applicable), and direct feedback. This direct interaction instills a sense of trust and familiarity with the brand, enhances hyper-personalization, and moves marketing beyond generic messaging.

One prime example of leveraging first-party data is Starbucks, a globally loved coffee brand. The Starbucks Rewards mobile app is an example of how first-party data should be leveraged. Each purchase made or paid for via the app earns stars that eventually convert into vouchers/offers tailored to customer preferences.
For instance, I enjoy having a Grande size for my beverages and usually order their Sweet Cream Cold Brew in the summer and a Latte in the winter. Incidentally, all offers on my app revolve around these preferences! I am no longer surprised when I see messages like “Your Favourite Cold Brew at 25% off today!” Additionally, my customizations often lead to product recommendations that suit my taste. That’s the power of first-party data.

• Enhanced Data Accuracy & Control with Server-Side Tracking:

Server-side tagging offers more control, better accuracy, and resilience over data compared to traditional client-side (browser-based) tracking. Since the data is directly sent from your web server to your analytics and advertising platforms, it mitigates issues like ad blockers, browser Intelligent Tracking Prevention (ITP), and cookie consent fatigue. You get a more complete and reliable dataset for your agentic AI to learn from.

This is why beauty retail giant Sephora has started investing in server-side Google Analytics and conversion tracking. With the deprecation of third-party cookies, like many ecommerce leaders, Sephora also moved to first-party data tracking. Its Beauty Insider loyalty program is a testament to successful first-party data tracking. Every purchase, product view, and cart events are accurately captured irrespective of browser settings or ad blockers. Their agentic AI-powered recommendation engines and personalized marketing campaigns are rooted in precise, real-time behavioral data from their website.

Investing in first-party data isn’t just about survival in a privacy-first world; it’s about building a robust, intelligent, and highly personalized marketing engine that empowers Agentic AI to deliver unparalleled results. This brings us back to where we started – Google’s I/O 2025’s main highlight that Agentic AI is not a fleeting trend but a foundational shift that will redefine the very essence of modern marketing.

The Future is Agentic: Implications for Modern Marketers

While agentic AI is redefining marketing, the shifting roles do not mean human marketers are going obsolete. Rather, agentic AI is more like a co-worker who handles repetitive and complex workflows while marketers invest in strategic activities. With a creative human mind and a competent AI system, brands can achieve marketing goals faster with more precision and desired outcomes.

It is not a random statement. Data shows:

• 83% of marketers admit AI frees up their time for more creative aspects
• AI automation saves 12.5 hours/week, which translates to 26 extra working days per year

Marketers now have the scope to delve deeper into fine-tuning brand storytelling, creating innovative campaigns, and building customer relationships with longer lifecycle value. Embracing agentic AI early and strategically can offer a competitive edge like no other.

With agentic AI in your marketing campaign, you can amplify several critical areas like:

• Hyper-personalization at scale
• Dynamic creative optimization and testing
• Predictive analytics and conversion optimization
• Real-time budget allocation and efficiency

In conclusion, all we can say is – Agentic AI is no longer an option for modern marketers. It is a MANDATE to thrive and stay ahead in the competition. Embrace the shift, invest in first-party data, upskill your teams, and prioritize ethical AI.