I’ve been exploring how Microsoft’s Copilot is revolutionizing search advertising by transforming our daily conversations into actionable insights for advertisers. It provides a window into user intent, reducing wasted spend, and boosting ROAS significantly.
In fact, Microsoft reports a 13-fold increase in ROAS when users interact with Copilot before conducting a search. By tapping into billions of first-party data across platforms like Bing and LinkedIn, Copilot can identify high-value audiences and help advertisers make every dollar count.
The mechanics of conversational search are intriguing. Users tend to provide AI like Copilot with more detailed queries, offering richer context compared to traditional search bars. This shift creates multiple ad opportunities from a single detailed conversation, potentially transforming the advertising landscape.
A recent campaign I ran for a university highlights this transformation in action. Shifting from broad keywords to detailed, conversational queries allowed us to sharply decrease wasted impressions and costs, while significantly boosting engagement.
It got me thinking about how advertisers can transition to this model effectively. Besides technological integration, it requires a strategic realignment to capture the conversational demand using structured data and cross-channel strategies.
Especially with Gen Z, addressing authenticity concerns becomes crucial. They value real interaction, so ads need to feel native and relevant, not generic or intrusive. Using behavioral data from platforms like Activision, we can target more effectively without crossing into ‘stalker-ish’ territory.
As we relearn how to engage with this audience, I see the balance between utility and authenticity as the key to long-term success. The rise of AI in advertising continues to create an exciting new economic landscape, driven by precision rather than sheer volume.
Have you ever felt the pressure of rising expectations in marketing while budgets stay flat? It’s certainly a dilemma I face regularly. In 2025, marketing budgets have plateaued, averaging 7.7% of company revenue. However, our goals continue to grow, prompting us to seek efficient solutions. Enter AI – not as a futuristic possibility, but as the answer to today’s challenges.
Let me walk you through how AI, particularly tools like Artlist AI, is revolutionizing our workflow by cutting down costs and speeding up production, all while maintaining our brand’s creative integrity.
1) Video Production Challenges
As a marketer, I know how pivotal video content is, yet it often becomes a bottleneck. We’re looking to deliver more, faster, without breaking budgets. Luckily, with AI, we’re finding ways to do just that.
By utilizing Artlist AI, our team rapidly converts scripts into screen reality, making video production cycle much less burdensome. From quick concept storyboards to instant variations and voiceovers, AI is a game-changer.
2) Consistent Brand Voice
Maintaining a uniform brand voice across different markets and languages is daunting. AI voiceover allows us to deliver a steady tone and pacing, ensuring our brand’s message is consistent and recognizably ours.
Artlist gives us the tools to tailor our tone for different cultural contexts without losing brand integrity, refining messaging quickly and at scale.
3) Agile Creative Testing
Today’s social media landscape demands rapid creative cycles. With AI, producing and testing multiple versions becomes feasible, allowing us to adapt quickly and maintain engagement.
I’ve seen firsthand how using AI to test creative variations leads directly to improved ad performance and deeper insights into what resonates with audiences.
4) Meaningful Metrics and Feedback
The true impact of creative elements can often feel elusive. AI provides real-time analytics, correlating creative inputs with engagement metrics. For me, this means turning subjective creative decisions into data-driven strategies.
By leveraging these insights, I can ensure that every marketing move is not only creative but also grounded in real effectiveness.
The Takeaway
Integrating AI doesn’t require an overhaul of our processes. Instead, it enhances what we already do, offering efficiencies that allow us to meet increasing demands without expanding budgets.
If you’re keen to elevate your marketing game, Artlist’s suite of AI tools might just be the solution you need. I’ve experienced the difference they make, turning what once seemed like bottlenecks into seamless workflow elements.
I’ve been diving into some recent updates from Google regarding keyword match types, especially for those of us working with AI Overviews (AIO) and AI Mode ad placements. It’s crucial to understand these changes, particularly for those testing AI Max and using various match-type strategies. Let’s break it down so we can all optimize our ad reach effectively.
Why this matters to us. As the digital advertising landscape embraces AI-powered placements, it’s more important than ever to grasp which keywords are ready to serve ads and avoid unintentionally limiting our ad reach or misjudging performance metrics.
In May’s developments. When I followed the conversation between Marketing Director Yoav Eitani and Google’s Ads Liaison, Ginny Marvin, it was clarified that ads can serve either above or below an AI Overview—or appear within—but not in both placements simultaneously. Marvin stated, “Your ad could trigger to show either above/below AIO or within AIO, but not both at this time.”
When we talk about ad placements, it turns out both exact and broad match keywords can trigger ads above or below AIO. However, only broad match keywords (or those using keywordless targeting) have the privilege to appear within the AI Overviews.
What’s different now. In a later discussion with Paid Search specialist Toan Tran, Marvin provided further insight into Google’s updated eligibility criteria. Before this update, the presence of an exact match keyword could block a broad match keyword from filling AIO spots. But thanks to Google’s revisions, that’s no longer an issue.
Marvin detailed, “The presence of the same keyword in exact match will not prevent the broad match keyword from triggering an ad in an AI Overview, since the exact match keyword is not eligible to show Ads in AI Overviews and hence not competing with the broad match keyword.”
This adjustment means that with exact and phrase match keywords not qualifying for AI Overview placements, they won’t compete with broad match keywords in those auctions. So, a broad match can still trigger successfully even if its exact match counterpart is present.
The broader perspective. Google’s strategic update strengthens the distinction between traditional keyword matching and AI-powered intent matching. Ads in AI Overviews now depend on a keen understanding of both user queries and AI-generated content, requiring broader targeting signals.
The takeaway for us. If you, like me, are pushing into AI Max and AIO placements, it’s clear that broad match and keywordless strategies are key to tapping into Google’s AI-driven ad spaces. Exact and phrase match keywords might not appear in AI Overviews, but crucially, they won’t stop us from leveraging broad matches.
I’ve noticed that Google’s AI-powered bidding can truly be enticing. It promises to optimize my campaigns if I just feed it my conversion data and set a target, allowing me to focus on the bigger picture of strategy.
The idea is that machine learning will take care of everything else. But, what Google doesn’t really highlight is that its algorithms prioritize Google’s outcomes, which might not align with my goals.
As I delve into 2026, it’s clearer than ever that with Smart Bidding becoming more opaque and Performance Max absorbing more campaign types, discerning when to direct the algorithm—and when to take charge—has become an essential skill for exceptional PPC managers.
AI bidding can yield impressive results, but there’s also a risk of it undermining profitable campaigns by prioritizing volume over efficiency. The key isn’t in the technology itself but in knowing when the algorithm requires direction, tighter constraints, or a complete override.
This article will guide you through:
How AI bidding actually operates.
Recognizing the warning signs when it’s failing.
The intervention points where human judgment is crucial.
How AI Bidding Actually Works – And What Google Doesn’t Tell You
Smart Bidding offers various strategies, such as Target CPA, Target ROAS, Maximize Conversions, and Maximize Conversion Value. Each uses machine learning to predict conversion likelihood and adjusts bids in real time.
The algorithm evaluates numerous signals during auctions—device type, location, time of day, and more—to determine an optimal bid. During the “learning period” of typically seven to 14 days, the algorithm probes the bid landscape to understand the conversion probability curve.
Although Google advises patience during this phase, sometimes campaigns get stuck in perpetual learning and fail to stabilize.
Google’s Optimization Goals vs. Your Business Goals
The algorithm optimizes for metrics that increase Google’s revenue, which might not align with my profitability goals. For instance, setting a Target ROAS at 400% might prompt the system to maximize total conversion value, focusing on spending the full budget rather than understanding the varied nuances of my business.
My business goals might require a different approach, such as a specific volume threshold or maintaining varying margin requirements across products. The algorithm doesn’t account for these intricacies, like cash flow constraints.
Key Signals the Algorithm Can’t Understand
While AI bidding is effective, it has its limitations. Without intervention, several factors may go unaccounted for, like seasonal patterns, product margin differences, and changes in market conditions.
For example, the algorithm might not recognize the distinction between products with different profit margins. A $100 sale on Product A with a 60% margin is distinct from a sale on Product B with a 15% margin, yet the algorithm treats them equally, highlighting the need for profit tracking and margin-based segmentation.
Warning Signs Your AI Bidding Strategy Is Failing
The Perpetual Learning Phase
Extended learning periods are a major red flag. If my campaign’s “Learning” status persists for over two weeks, it indicates a problem. The causes could range from low conversion volume to frequent changes that reset the learning phase.
When to Intervene
Boost the budget to speed data collection.
Relax the target for higher conversions.
Switch to a less aggressive strategy, like Enhanced CPC.
Budget Pacing Issues
Healthy AI campaigns show smooth budget pacing. If I observe erratic patterns like front-loaded spending or consistent underspending, it signals a lack of algorithm confidence.
The Efficiency Cliff
This refers to when performance starts strong but then deteriorates. It’s usually visible in Target ROAS campaigns where, month after month, the ROAS declines as the algorithm exhausts efficient segments and expands into less qualified traffic.
Traffic Quality Deterioration
Even when metrics seem fine, qualitative signals might suggest otherwise. I might notice a drop in engagement or shifts in geographic targeting, indicating the algorithm is prioritizing cheaper clicks which don’t necessarily convert better.
The Search Terms Report Reveals the Truth
Regularly exporting the search terms report helps identify issues. I look for irrelevant expansions or low-intent queries that consume budget with little conversion value, such as a luxury retailer finding clicks for “free furniture donation pickup.”
Strategic Intervention Points: When and How to Take Control
Segmentation for Better Control
When it comes to AI bidding, a one-size-fits-all approach might not work for diverse business models. By segmenting my campaigns, I can tailor algorithms to meet specific goals, using separate campaigns for high- and low-margin products or different regional performances.
Bid Strategy Layering
Sometimes, a hybrid approach serves better. I might run a Target ROAS under normal conditions and adjust it manually during peak times to capture volume, or use Maximize Conversion Value with bid caps to honor unit cost constraints.
The Hybrid Approach
Pairing AI with manual campaigns can optimize effectiveness. Allocating a percentage of the budget to each allows for capturing valuable traffic through manual efforts while still leveraging AI for broader campaign management.
COGS and Cart Data Reporting (Plus Profit Optimization Beta)
Google now supports reporting cost of goods sold and cart data, allowing a clearer view of profitability within Ads reporting. Although still in testing, this feature could soon enable profit-focused bidding rather than revenue-focused, enhancing performance analysis.
AI bidding thrives under solid fundamentals, such as sufficient conversion volume and a stable business model with clear margins. In these contexts, AI often surpasses manual bidding by processing more variables than a human possibly could.
This tends to hold true for mature ecommerce accounts, stable lead generation programs, and SaaS models with predictable conversion paths.
Preparing for AI-First Advertising
As Google continues to simplify advertisement management through automation, my role has evolved from bid management to being an AI strategy director. My focus is now on setting clear goals, providing context, and intervening when needed.
Despite the reduction in advertiser control, certain strategic decisions remain human-driven, ones that require intelligence beyond what an algorithm alone can provide.
Master the Algorithm, Don’t Serve It
AI-powered bidding is a remarkable tool for optimization that delivers unparalleled results when conditions are optimal. However, the key lies in mastering it, ensuring that my business context informs the algorithm’s decisions, and knowing when to take control to align it with my strategic goals.
The strongest PPC leaders today are those who don’t just manage bids but helm the systems that manage them.
I’ve come to realize that social platforms are revolutionizing how we discover information. TikTok, Reddit, YouTube, and AI engines are now essential in shaping the way we search.
Understanding this shift, I’ve explored how brands can enhance their visibility by optimizing for social-driven Answer Engine Optimization (AEO). Let’s dive into these exciting developments!
In 2026, I’m witnessing an exciting transformation in the world of public relations. New trends are shaping how I approach PR in this AI-driven era.
Among these trends, Answer Engine Optimization (AEO) stands out, changing the way I prepare for search queries that don’t require clicks. I’m also adapting to the rise of zero-click searches, which demand more sophisticated strategies.
Additionally, I’m personalizing my pitching techniques more than ever before, ensuring that my messages resonate on a personal level. Newsletters are becoming a critical tool for me, offering direct and reliable communication channels with stakeholders.
Speed in crisis management is no longer negotiable; it’s a necessity. I am constantly enhancing brand authority to build trust and resilience in the face of challenges.
These changes are rewriting the traditional PR playbook, and I’m eager to see how they continue to evolve. Embracing these innovations is key to staying ahead in the rapidly changing PR landscape.
I’ve noticed a significant shift in Google Ads as they now allow us to optimize bidding for view-through conversions (VTC) in Android App campaigns. This change highlights a growing emphasis on video-driven performance.
In the past, VTC was a subtle, behind-the-scenes signal within Google’s system. Now, it’s a visible option that allows me to focus on conversions that occur after an ad is seen, rather than clicked.
The shift. It’s evident that Google is steering app advertising away from traditional click-focused strategies, encouraging an approach centered around influence and incremental results. This is particularly beneficial for platforms like YouTube and in-feed video advertising.
This update means our bidding strategies align more intuitively with the actual ways users discover and install apps today.
Why it matters to me. This flexibility allows me to go beyond mere clicks, enhancing measurement metrics for video-centric app campaigns. It’s an exciting validation for those of us invested in upper-funnel marketing activities.
Who benefits the most? Advertisers who prioritize video content and focus on creating awareness and engagement. This is a game-changer for teams oriented towards long-term growth, not just immediate installs.
What I’m keeping an eye on:
How Google’s attribution models affect campaign reliance
Potential shifts in Cost-Per-Acquisition expectations
The growing importance of creative quality over click-centric strategies
First seen by. I came across this update thanks to Rakshit Shetty, a Senior Performance Marketing Executive who first spotted this change.
Bottom line. Google is elevating view-based data for app campaigns to a priority status, marking a shift towards a performance marketing strategy led by AI and agnostic of sales funnels.
When I think about Google’s journey with artificial intelligence, Sergey Brin’s admission strikes a chord. He candidly revealed that Google ‘for sure messed up’ by not prioritizing AI investments at the right time. Looking back, it seems clear that we released groundbreaking research but didn’t capitalize on it to ride the current wave of generative AI.
Google’s cautious approach always intrigued me. According to Brin, there was hesitation and fear about potential missteps, especially since chatbots could ‘say dumb things.’ It’s fascinating to hear him acknowledge that Google didn’t move quickly enough after publishing the Transformer paper.
The hesitation seemed to stifle opportunities. Brin admitted that while Google was reluctant, companies like OpenAI leapt forward with brilliant foresight. They seized the moment, leveraging insights and even talent, such as Ilya Sutskever, to drive AI innovation.
Reflecting on the past, Brin shared, “In some ways, we for sure messed up in that we underinvested… eight years ago when we published the transformer paper. We didn’t take it all that seriously and didn’t necessarily invest in scaling the compute. And also we were too scared to bring it to people because chatbots say dumb things. And you know, OpenAI ran with it, good for them.” He graciously acknowledged the value of the history we’ve built.
The current landscape still favors Google, as Brin points out. Years of AI research and development, deep learning, and our robust infrastructure continue to provide a competitive edge. This bedrock underlines the control we maintain over key technologies driving AI today.
Why does this matter? Brin’s insights shed light on why Google’s AI-driven changes in search sometimes seem sudden and erratic. Our earlier caution means we now find ourselves speeding ahead, possibly too quickly, to bridge the gap. The fluctuating nature of Google Search is a byproduct of this rapid adjustment process.
When Brin spoke about AI’s future, he characterized the field as highly competitive and ever-evolving. He mentioned, “If you skip AI news for a month, you’re way behind.” It’s uncertain what the ultimate potential of AI is, or if there’s a limit to its intelligence.
On a personal note, Brin shared that he often uses Gemini Live for engaging conversations while driving. Interestingly, he noted that the public version is outdated, with a “way better version” on the horizon.
Looking back and forward, Brin’s remarks at Stanford were part of an event celebrating the School of Engineering’s century-long legacy. The discussion touched on Google’s early days, its culture of innovation, and the present AI ecosystem. You can watch the full video here.
As I dive into the evolving world of SEO, I’ve noticed one thing: the industry is entering its most unpredictable phase yet. With traffic on the decline and AI increasingly handling informational queries, it’s clear that the landscape is shifting beneath our feet.
It’s fascinating to observe how social platforms are now serving as search engines, and Google is transforming from a gateway to a comprehensive answer engine. This transformation leaves many of us in the industry uncertain about what metrics matter, what we should optimize, and essentially, what SEO’s role truly is in this new digital era.
Despite the chaos, I’ve found clarity in one specific marketing metric that cuts through the noise: share of search. This metric offers a straightforward insight into brand health and potential future demand, aligning marketers and SEOs with confidence.
Share of search becomes particularly important as we notice a significant shift in how discovery and measurement need to adapt. The days of accidental discovery through traditional search behavior are dwindling.
AI and platforms like Meta are increasingly providing direct answers without directing traffic elsewhere, shifting the focus towards metrics that provide a clearer indication of consumer interest, like share of search.
Interestingly, share of search, a concept developed by James Hankins and Les Binet, calculates a brand’s search volume against the total search volume for its category. This simple yet powerful metric correlates strongly with market share and future buying behavior.
In our rapidly changing environment, share of search provides a critical signal for marketers, showing whether a brand is being searched for more or less compared to competitors. This insight offers a palpable reflection of underlying consumer interest and demand.
While traffic as a metric is losing its significance because of AI pre-answering queries, share of search cannot be manipulated easily. It stands resilient as a reflection of authentic consumer desire.
Moreover, this metric crosses platforms effortlessly, as people now search across various digital spaces such as Amazon, TikTok, YouTube, and potentially even LinkedIn. Share of search adapts to fragmented discovering behavior precisely.
It’s exciting to see how, even if AI-driven systems like ChatGPT rarely generate clicks, they often trigger brand searches, emphasizing the importance of this metric as a measure of marketing effectiveness.
For SEOs like me, adopting share of search means transforming our roles from content producers into strategic partners, providing deeper insights into consumer behavior and brand demand.
Ultimately, embracing share of search elevates our value within an organization, offering a fresh narrative around brand visibility and performance. As AI continues to reshape the digital landscape, this metric is becoming indispensable for those of us in SEO and marketing. I encourage everyone to learn more about this compelling metric and explore its potential to transform how we measure success in the AI era.
I recently discovered that uncontested ads might be silently eating away at my holiday budget. Even when I’m the sole bidder, my CPCs remain stubbornly high. Here’s how I began to reclaim those wasted dollars.
This holiday season, Google Search and Shopping Ads are projected to surpass a staggering $70 billion in spending. However, many advertisers, myself included, overlook a critical flaw in Google’s auction system that drains our funds—even in the absence of competitors.
The team at BrandPilot identifies this issue as the “Uncontested Google Ads Problem,” a significant yet often ignored source of wasted ad spend during peak times.
During SMX Next, I learned from John Beresford, the Chief Revenue Officer at BrandPilot, about a little-known quirk in Google’s auction logic. It’s fascinating how this can lead advertisers like me to overspend on our brand terms, shopping placements, and category keywords because Google doesn’t automatically lower our CPCs when no one else is bidding.
Instead of enjoying lower costs as the sole bidder, I found myself paying the same high rate as if competitors were still active. It’s a situation that unfolds thousands of times a day for major brands, and like me, many marketers don’t even realize it.
In John’s session, we explored:
Understanding why “competition gaps” are far more frequent than we think.
Discovering how uncontested moments can warp CPCs, even on brand keywords.
The potential of real-time auction visibility—and how AI is revolutionizing the field.
He also shared how advertisers are deftly reclaiming wasted spending and channeling it back into growth, without giving up impression share, traffic, or revenue.
Identify why CPCs are artificially high when competitors are missing.
Calculate the true financial impact of the Uncontested Ads Problem on your budget.
Execute AI-driven bidding and suppression strategies to avoid self-bidding and increase ROAS.
If you’re managing Google Search or Shopping campaigns this holiday season, this session is a must-see. Learn how to keep Google from sneaking off with your budget and start converting those savings into real performance improvements.
