What started as manual keyword bids and spreadsheet-heavy optimisation has become a machine-led system where platforms predict intent, generate assets, expand reach, and recommend next actions in real time. Google, Meta, and Microsoft now treat AI as a core campaign engine, not just an add-on feature. Google’s own messaging at Marketing Live 2025 made that clear: the future of advertising powered by AI is already here, and the company is expanding ad opportunities into AI-powered search experiences like AI Overviews and AI Mode.
That shift matters because digital ad budgets are still growing fast. IAB and PwC reported U.S. digital ad revenue reached $258.6 billion in 2024, up 14.9% year over year, and IAB’s 2025 outlook projected another 7.3% growth in overall U.S. ad spend. In other words, the market is getting bigger, but managing it manually is getting harder.
This is exactly where the next transition begins: from AI-assisted optimisation (smart bidding, auto-generated assets, audience expansion) to agentic systems that can diagnose, decide, and execute multi-step media tasks with guardrails.
For brands and growth teams, the question is no longer “Should we use AI in paid media?”
It is now “How do we use it well, with control?”
The first shift was not agentic AI. It was a prediction at auction time
Before the recent generative AI wave, the biggest leap in paid media was machine learning-based bidding.
Google’s Smart Bidding is the clearest example. Google describes it as a set of automated bid strategies that use AI to optimise for conversions or conversion value, and it operates with auction-time bidding, meaning bids are adjusted during each auction using contextual signals. Google also explicitly lists signals such as device, location, time of day, language, operating system, browser, and user interaction context.
That changed the operating model for media buyers:
- You stopped setting every keyword bid manually
- You started training the system with cleaner conversion data
- Your leverage shifted from bid edits to strategy inputs (goals, budgets, audiences, creative, landing pages)
This was the real beginning of AI-driven paid media. Not ChatGPT-style interfaces. Not generative ads. Just better predictions, faster than humans could make them.
Why Smart Bidding became the foundation
Smart Bidding worked because it solved a hard problem humans could not solve at scale: evaluating thousands of micro-signals across millions of auctions. That made it especially powerful once accounts reached enough conversion volume and had stable tracking. Google also pushed the ecosystem toward combinations like broad match + Smart Bidding, making automation more effective when intent matching and bidding worked together.
The tradeoff, of course, was control. Advertisers gained performance efficiency, but lost some line-by-line visibility and manual precision.
That tradeoff still defines AI media buying today.
The second shift was AI across the whole campaign, not just bids
Once platforms proved they could bid better than humans in many scenarios, they expanded AI into the rest of the workflow:
- query expansion
- creative assembly
- audience expansion
- placement selection
- budget allocation
- landing page selection
This is where paid media moved from “smart bidding” to system-level automation.
Google: from keywords and bids to AI-powered campaign orchestration
Google’s product stack reflects this evolution clearly.
- Responsive Search Ads (RSAs) use machine learning to test and assemble combinations of headlines and descriptions to show more relevant ads. Google’s RSA help documentation explains that you provide multiple assets, and Google tests combinations to improve performance.
- Performance Max goes further by using Google AI to optimise performance across channels from a single campaign based on a conversion goal. Google positions it as a goal-based campaign type designed to access all Google Ads inventory.
- AI Max for Search (introduced in 2025) packages targeting and creative automation into a single feature suite. Google’s documentation describes AI Max as a combination of search term matching and asset optimisation that uses Google AI to optimise ads in real time. It also expands reach via broad match and keywordless matching while adding new controls and reporting transparency.
This is a major step because it moves optimisation beyond bidding and into what gets shown, where users land, and which search opportunities get captured.
Google also reported (platform internal data) that advertisers activating AI Max in Search campaigns typically saw 14% more conversions or conversion value at similar CPA/ROAS, with higher uplift for campaigns still heavily dependent on exact and phrase match keywords. That does not mean every account gets those results, but it shows where the platform is steering advertisers.
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Meta accelerated the same trend with Advantage+ and retrieval AI
Meta’s Advantage+ ecosystem is another strong example of paid media becoming AI-native.
In Meta’s engineering write-up on Andromeda, the company explains how AI sits at the core of its ad recommendation system, and how advertiser automation and generative AI are increasing the number of eligible ads and creative variants in the system. Meta specifically notes that the growth of Advantage+ increases automation in areas like audience creation, budget allocation, placements, and creative generation.
Meta’s engineering post is useful because it shows the infrastructure side of the same trend marketers see in the UI:
- more automation
- more creative variants
- faster decisioning
- larger candidate pools
- stronger reliance on ML retrieval and ranking systems
Meta also shared several platform-reported outcomes tied to AI-led automation in that post, including:
- A 22% increase in ROAS in one Advantage+ creative-related example when advertisers turned on AI-driven targeting features
- An estimated +7% increase in conversions for businesses using image generation
- More than 1 million advertisers are using Meta generative AI tools to create 15+ million ads in a month (at the time of writing)
Even if every brand should treat platform-reported benchmarks as directional, the strategic message is clear:
Creative volume + targeting automation + ranking AI now work as a connected system.
The hidden layer that made all of this possible: better measurement inputs
AI bidding and campaign automation only work if the conversion signal is reliable.
That is why measurement infrastructure quietly became one of the most important parts of AI-driven paid media.
Data-driven attribution changed optimisation signals
Google’s data-driven attribution (DDA) uses account conversion data to assign credit based on how different ad interactions contribute to conversion paths, rather than relying on fixed rules like last-click. Google explains that DDA compares the paths of converting customers with similar paths that did not convert to estimate contribution.
For media teams, this matters because AI bidding models optimise toward the conversion and value signals they receive. If attribution is oversimplified, the bidding system can optimise in the wrong direction.
First-party signal quality became a performance lever
Google’s Enhanced Conversions is another example. Google describes it as a feature that uses hashed, first-party customer data (such as email or phone collected on your site) to improve conversion measurement and unlock stronger bidding performance when standard tags miss some conversions.
On Meta, the same measurement shift appears through server-side tracking approaches like the Conversions API (CAPI), which Meta defines as a business tool that creates a direct connection from a marketer’s server to Meta systems for marketing data.
The pattern is consistent across platforms:
- Weaker browser-only tracking created gaps
- Platforms responded with privacy-aware modelling and first-party signal recovery
- Advertisers who improved data quality gave their AI systems a real advantage
MMM is back because AI made media more complex
As channel automation increased, many brands also revived marketing mix modeling (MMM) for strategic budget decisions.
Google’s Meridian is a strong signal of this shift. Google describes Meridian as a MMM built on Bayesian causal inference and launched it as open-source, specifically to help marketers measure channel impact and budget allocation more effectively.
This is important for one reason:
As platforms automate execution, brands need independent measurement to guide strategy.
Search behaviour is changing again, and that is pushing AI further
The next stage of AI-driven paid media is not only about better optimisation. It is about adapting to new user behaviour.
At Google Marketing Live 2025, Google said it sees over 5 trillion searches per year, and highlighted how AI features like Lens, AI Overviews, and AI Mode are changing how people discover information. Google also announced expansion of ads in AI Overviews to desktop and ad placements in AI Mode.
That changes paid media planning in three ways:
1. Intent is less linear
Users do not always search in short, direct keywords anymore. They explore, compare, and ask broader questions.
2. Query matching needs to be more semantic
This is why Google is investing in keywordless and AI-assisted matching features in AI Max.
3. Creative needs to adapt faster
If discovery moments are broader and more varied, static ad copy sets become a bottleneck.
This is also why Google introduced Smart Bidding Exploration, which it described as its biggest bidding update in over a decade. Google said the feature helps advertisers pursue higher-value, less obvious queries, and reported average gains in unique converting query categories and conversions (platform internal data).
The direction is obvious: platforms want AI to help advertisers capture incremental intent, not just optimise known demand.
So what are “agentic systems” in paid media?
This is where the next evolution starts.
OpenAI’s agent documentation defines agents as systems that can intelligently accomplish tasks from simple goals to more complex workflows, and it highlights core building blocks such as tools, guardrails, knowledge, control flow, and tracing/evaluation. The Agents SDK documentation also describes agentic apps as systems where models can use tools, hand off to other specialised agents, and keep a full trace of what happened.
That framework maps surprisingly well to paid media operations.
In practical paid media terms, an agentic system is:
Not just a chatbot inside your ad account.
Not just auto-generated ad copy.
Not just Smart Bidding.
It is a system that can:
- read campaign performance and diagnostics
- identify a problem (e.g., CPA drift, low impression share, creative fatigue)
- propose a fix based on goals and constraints
- execute approved changes across tools
- log what changed and why
- monitor results and iterate
This is already starting to show up in platform experiences.
Early signals from ad platforms
Google’s Ads announcements around Marketing Live 2025 explicitly reference “agentic solutions” and describes products like:
- Your Google Ads Expert, which can help with setup/troubleshooting and identify opportunities
- Marketing Advisor in Chrome, which can surface issues and suggest actions across websites and campaigns
Microsoft is showing a similar path through Copilot in Microsoft Advertising, which supports conversational guidance, asset recommendations, asset generation, performance snapshots, and diagnostics that can inspect campaign setup and propose what to do next.
These are not fully autonomous media agents yet.
But they are the bridge between:
- platform automation
- generative assistance
- multi-step decision support
That bridge is what becomes an agentic system.
What the next paid media operating model looks like
For brands and growth teams, the future is not “AI runs everything.”
It is a hybrid operating model where humans set strategy and guardrails, while AI systems handle analysis, execution, and monitoring at machine speed.
A likely agentic paid media workflow
A mature agentic setup for paid media could include:
1) Planning agent
- Pulls historical performance by channel
- Reads seasonality and promotions
- Builds channel budget scenarios
- Flags risk areas before launch
2) Campaign build agent
- Draft campaign structures
- Suggests audience segments and exclusions
- Maps landing pages to intent clusters
- Generates first-pass creative variants
3) Optimisation agent
- Monitors pacing, CPA/ROAS, and impression loss
- Detects anomalies
- Suggests bid/budget/creative shifts
- Applies pre-approved changes within limits
4) Measurement agent
- Checks conversion health
- Detects tracking breaks or signal drops
- Reconciles platform vs analytics numbers
- Triggers alerts when data quality affects optimisation
5) Reporting and insights agent
- Produces weekly executive summaries
- Explains what changed and why
- Separates signal from noise
- Recommends next tests with expected impact ranges
The point is not to automate everything blindly.
The point is to reduce manual busywork so media teams can focus on strategy, messaging, and business outcomes.
The biggest mistakes brands will make in the agentic era
As AI capabilities grow, the risk is not “using too much AI.” The real risk is using AI on top of weak foundations.
Mistake 1: Treating AI as a replacement for strategy
AI can optimise toward your goal. It cannot choose the right goal for your business.
If your conversion actions are wrong, your funnel is broken, or your landing pages are weak, agentic systems will scale the problem faster.
Mistake 2: Poor conversion and value signals
Smart Bidding, AI Max, Advantage+ style automation, and future agents all depend on signal quality. If your tracking is incomplete or delayed, performance will drift, and recommendations will be unreliable. Google’s DDA and Enhanced Conversions guidance makes this especially clear.
Mistake 3: No guardrails
OpenAI’s agent docs explicitly emphasise tools and guardrails for a reason. Paid media agents should not be allowed to:
- overspend beyond budget caps
- publish creative without approval
- change naming or taxonomy rules
- alter measurement definitions
- run experiments without a test plan
Mistake 4: Optimising only inside platforms
Platform AI is powerful, but platform AI optimises inside platform boundaries. Brands still need independent measurement and business context. That is where MMM, incrementality, and cross-channel analysis remain critical. Google’s Meridian launch reinforces this need.
How to prepare now, before agentic systems become standard
You do not need to wait for a fully autonomous media agent to start preparing.
The best teams are already building the foundations.
1) Clean up your conversion architecture
- Audit primary vs secondary conversions
- Fix duplicate or noisy events
- Pass revenue values correctly
- Align platform goals with business outcomes
2) Improve first-party data flow
- Strengthen tagging and server-side signal coverage
- Use privacy-safe enhancements where appropriate
- Validate CRM-to-ad-platform feedback loops
3) Standardise campaign structure and naming
Agents need clean patterns. If your account structure is messy, AI recommendations become harder to trust and harder to automate.
4) Build creative systems, not one-off ads
AI-powered campaign systems perform better when they have:
- clear offer hierarchy
- landing page relevance
- strong asset libraries
- reusable messaging frameworks
5) Define human approval layers
Decide in advance what AI can do automatically vs what requires signoff:
- budget shifts
- new asset launches
- audience changes
- geo expansions
- bid strategy changes
This is how you move from “AI features” to an AI operating model.
Why this matters for Y77.ai clients
The winners in the next phase of paid media will not be the brands using the most AI tools.
They will be the brands that combine:
- strong measurement
- clean data
- clear goals
- controlled experimentation
- fast execution
That is the real shift from Smart Bidding to agentic systems.
Smart Bidding taught the industry to trust machine prediction.
AI campaign systems taught the industry to trust machine execution.
Agentic systems will require brands to trust machine decision support, but only with the right guardrails.
That is where expert partners still matter.
Not because AI replaces human media teams.
Because AI raises the standard for how media teams should operate.
Want to future-proof your paid media before the agentic shift becomes the norm?
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