I’m excited to share that Google is enhancing its Direct Offers with AI-generated bundles, native checkout features, and enticing travel deals. This announcement, made at Google Marketing Live 2026, marks a significant upgrade for the platform.
Driving the news. Google aims to make promotional offers more visible within AI-powered Search experiences.
Brands will soon have the ability to upload a variety of promotional types:
– Discounts
– Giveaways
– Local coupons
– Product bundles
Google’s Gemini will assist in creating personalized offers that align with search intent. This means tailored promotions based on user queries and browsing habits.
How it works. Advertisers can upload eligible promotions and campaign guardrails through Google Ads. Gemini will then curate relevant offers like bundles and discounts that resonate with the shopper’s search and browsing behavior.
Additionally, Google is introducing native checkout support for merchants using the Universal Commerce Protocol (UCP), enabling users to complete purchases directly within AI-driven shopping experiences.
Travel partners such as Booking and Expedia will soon showcase travel offers directly within AI-assisted trip planning features, enhancing the overall travel booking experience.
Why we care. This integration is transforming promotions into an integral part of conversational shopping, steering away from conventional deal extensions and static offers.
Advertisers now need to optimize their promotions to fit within AI-powered discovery and recommendation systems.
The introduction of native checkout options is expected to streamline the transition from product discovery to conversion, potentially boosting sales.
What to watch. It’s worth observing how Google’s shift towards AI-assisted promotional commerce influences conversion rates and consumer shopping patterns.
Availability. Currently, Direct Offers is available as a pilot for advertisers in the U.S.
Dig deeper. Stay informed with more updates from Google Marketing Live 2026:
When I hear about Microsoft rolling out its latest AI-powered features for advertisers, I can’t help but feel excited about the potential ease it could bring to multi-platform ad campaigns.
The unveiling of the new Import Center really caught my attention. It’s designed to streamline the way we can transfer campaigns from Google Ads and Meta Ads into Microsoft Advertising.
This impressive hub offers me the ability to search and filter campaign imports, edit or pause them as needed, access those imported campaigns with ease, view troubleshooting guidance, and even get performance recommendations once the imports are done.
Microsoft assures that this is all about minimizing the hassle of manual troubleshooting and simplifying how we manage campaigns across different platforms.
I find the expansion of AI-powered bidding capabilities particularly appealing as it includes cross-account portfolio bidding for both Search and Shopping campaigns. This addition allows me to handle portfolio bid strategies efficiently across various accounts, optimizing my budget by pooling significant signals.
The enhanced bid strategy reporting metrics such as Avg. Target ROAS, Avg. Target CPA, and Avg. Target impression share are promising tools that let me comprehend bid performances better and adjust targets from within the UI.
Reporting has become even more flexible thanks to the new custom column capabilities. This expansion gives me access to all conversion metrics in custom columns, allows segment reports by goal name, and lets me dive into additional metrics like CPA and ROAS, enhancing transparency and optimization insights.
In my perspective, these updates make campaign management far more seamless across all platforms, including Google, Meta, and Microsoft Ads, while expanding AI-powered bidding and automation.
I’m also catching up with two previously announced updates from Microsoft that are now widely available: seasonality adjustments for portfolio bidding and shared budgets, and the data-driven attribution for automated bid strategies.
By assigning conversion credit across the customer’s journey in campaigns that use Maximize Conversions, Maximize Conversion Value, and Enhanced CPC bidding strategies, these features could be transformative.
I’ve always found it exciting when Google Ads updates its features. Now, they’ve integrated Gemini into Ads dashboards, transforming data analysis into an engaging, interactive experience.
What’s happening. Google Ads is introducing a new Dashboards feature, designed to provide advertisers with performance data through visually appealing charts, graphs, and tables, all powered by Gemini.
What makes this even more fascinating is how users can effortlessly customize their views by typing prompts. The dashboard dynamically updates in real-time based on these input queries.
Why we care. Traditionally, data analysis in Google Ads required manual setups and navigating countless reports. This update shifts towards a more intuitive approach, letting advertisers ask questions and receive immediate visual feedback.
Zoom in. These new dashboards will showcase crucial metrics such as impressions, clicks, video views, and costs. You’ll also find them breaking down performance data across various dimensions like devices, audiences, and campaign types.
The main goal is to empower advertisers with a clearer and faster way to understand what’s happening within their accounts.
What to watch. I’m curious to see how broadly this prompt-driven reporting will be adopted and if it will lessen the need for custom reports and additional analytics tools.
Have you ever wondered how Conductor fuels the innovative AI Visibility Dashboard within the Clutch platform? I’ll take you through the fascinating journey of this integration and show you how it enhances visibility and insights.
As I explore the workings of the AI Visibility Dashboard, it becomes clear how Conductor seamlessly powers this tool, providing valuable features directly within Clutch. The dashboard is designed to offer an intuitive and comprehensive approach to analyzing and optimizing your digital presence.
Have you ever wished there was an easier way to optimize advertising spend in real-time? Well, Google is stepping up its game, and I’m here to share all the exciting details with you.
Recently, Google has introduced new, AI-driven bidding and budgeting features across platforms like Search, Shopping, and Performance Max. The goal? To help us advertisers capture more demand with less manual effort.
What’s happening. With updates such as Journey-aware Bidding and demand-led budget pacing, Google is expanding its automation stack. These tools are designed to let our campaigns adapt swiftly to changing consumer behaviors.
Ultimately, the focus is on allowing AI to identify and seize opportunities we might otherwise miss.
Why it matters to us. These updates are about pulling in more conversions without bogging us down with extra manual work. Google’s AI can now find new demand and adjust our spending real-time. By enhancing bid responsiveness and budget adaptability, our campaigns are set to become significantly more efficient.
It’s all about extracting greater value from our budgets while remaining competitive in a rapidly shifting search landscape.
Smarter bidding with better context. With Journey-aware Bidding in beta, advertisers like us can now include more of the customer journey — such as non-biddable conversions — into optimization. This gives Google AI a comprehensive view of factors leading to sales, beyond initial actions like form fills.
Meanwhile, Smart Bidding Exploration is extending beyond Search. Already boosting unique converting users by 27%, it’s about to roll out to Performance Max and Shopping campaigns.
Demand-responsive budgets. On the budgeting front, Google’s innovations allow us to set spend over defined periods without stressing over daily limits. The demand-led pacing takes it further, automatically adjusting spend based on what’s currently demanding attention, increasing our budgets during high-opportunity days and conserving funds when things slow down.
Those of us using total budgets have already enjoyed a remarkable 66% drop in manual budget tweaks.
Why this matters. Historically, budget management has been labor-intensive. Now, with automated pacing, we can reduce constant monitoring and increase campaign efficiency.
Things to watch:
How much control we’re prepared to hand over to automation
If exploration’s incremental gains lead to profitable growth
Over the years, I’ve noticed how digital marketing has settled into a predictable routine. It spans across various channels like SEO, content marketing, social media, and digital advertising. Yet, many of us relied too heavily on a familiar core strategy, often ignoring the potential of using every available channel.
This predictability was comforting. It allowed marketing teams, including mine, to stick to what worked, refining execution within a known framework. However, AI search has upended this comfort, exposing our inconsistencies. To truly succeed with AI SEO, it’s clear that I need to adopt a much broader strategy.
Over the last 15 to 20 years, I’ve observed how digital marketing comfortably fit into a predictable rhythm, with each channel having a designated role.
Content marketing, social media, SEO, and paid advertising followed habitual strategies. But this lack of variation led to a form of laziness in our approach.
This structure offered results, so we let the broader strategies slip away.
The issue? It gave us a false sense of security. We should have employed broader strategies all along, as they now drive real visibility in AI search.
AI has reshaped digital marketing, changing user search behavior and how brands are evaluated.
Traditional search relied heavily on algorithms and singular sources, whereas AI taps into multiple inputs across numerous sources.
These sources ought to be part of your marketing arsenal—representing your brand across social media, third-party directories, press releases, and more. In this new system, your website is just one element among many sources AI uses to comprehend your brand.
One of the most significant changes AI has introduced is how it has expanded the digital marketing landscape beyond the website. While having a robust website is crucial, it’s part of a much larger ecosystem now. The marketing strategy must adapt to this expansive landscape.
In the past, maximizing website visibility was often enough to yield results. However, relying solely on this approach no longer suffices. AI aggregates data from a wide range of sources, from articles and brand mentions to third-party profiles and published content, shaping its understanding of who you are.
Focusing exclusively on the website restricts AI’s ability to locate and understand your brand.
Most marketing programs, especially those established before AI’s time, fall short here. To modernize, it’s vital for a brand to be visible across a more extensive range.
AI prefers brands that establish an intentional online presence, showing up with purpose across the internet.
A fragmented marketing approach, which worked in the past, now falls short. Previously, each successful channel felt effective and met our goals, but AI demands more. It looks for consistent messaging and expertise, linking various online signals to assess your brand’s presence.
When these signals are aligned, your brand’s visibility in AI search improves. Inconsistent or weak broader presence translates to weaker visibility.
Lazy marketing approaches—sticking to separate channels using the same old tactics—are now exposed. This approach may have yielded results once, but those days are numbered. It’s crucial now to go beyond that—to present your brand on multiple platforms, so AI can find you.
If your competitors enhance their presence, failure to do the same will leave you behind as they occupy more space in AI-generated responses.
As AI exposes any inconsistencies, it’s time to transition into the era of AI search.
It’s essential now to transition beyond older models and adopt newer strategies suitable for digital marketing. The tactics that always worked like press releases, directory listings, and marketing beyond just your website, should have been in use all along.
AI search doesn’t rewrite marketing rules; it enforces the importance of a comprehensive strategy. This means we can’t afford to do less anymore.
I’m thrilled to share that Yelp has just introduced a powerful AI update, bringing a new level of ease to local searches and bookings with their conversational “Yelp Assistant.” This tool is designed to help me move from searching to booking, ordering, and scheduling—all in one seamless experience.
Discover What’s New. At the heart of this innovation is the Yelp Assistant, a chatbot capable of answering complex questions, recommending businesses, and even making reservations or appointments without me ever needing to leave the app.
How It Works. The assistant taps into Yelp’s vast database of user reviews and photos to offer tailored recommendations, explain why a business is a good fit, and allow me to refine results in a conversational manner. It takes things to the next level by letting me book a table, order food, or request a quote without needing to switch platforms.
What Else Is New. Yelp is also enhancing integrations with platforms like Vagaro, Zocdoc, and Calendly, which streamlines bookings in categories such as beauty, healthcare, and home services. Plus, they’re strengthening their partnership with DoorDash for smoother delivery options.
Spotlight on Menu Vision. Another exciting feature is the revamped “Menu Vision,” which uses AI and visual overlays to display dishes, reviews, and photos in real time while I’m browsing a menu. This makes deciding what to order quicker and easier.
Why This Matters. For someone like me looking for convenience, Yelp is transforming from a simple discovery platform into a full-fledged transaction experience powered by AI. This means that while visibility remains important, businesses need to ensure they’re optimized for conversions right within the platform.
The Bigger Picture. By focusing on AI not just for discovery but also conversion, Yelp is turning intent into transactions without redirecting me elsewhere.
Looking Ahead. The assistant is now live on iOS and Android, with plans to expand further across more categories and desktop later this year.
As I look around, it seems like everyone is scrambling to harness AI’s power. However, I’m realizing that fundamental identity gaps and issues like fraud and unreliable inputs are not getting resolved, but rather they are magnified by AI models.
AI has quickly become one of the most confidently discussed items in our modern marketing strategies. Budgets are reallocated, teams restructured, and vendors evaluated primarily by how “AI-powered” they appear. The belief is strong that once the right AI models are in place, performance metrics—such as targeting, segmentation, and conversion—will simply fall into place.
Yet, I’ve discovered a quieter truth. While organizations aren’t necessarily struggling with using AI, they face challenges feeding it adequate data. And often, the data they are supplying AI isn’t nearly as reliable as assumed.
This realization leads me to the uncomfortable truth about inputs. AI doesn’t produce truths; it magnifies what’s provided. If data is fragmented, outdated, or manipulated, AI doesn’t correct it—it scales it confidently.
Marketers have invested heavily in data infrastructures, only to find that an abundance of data and signals doesn’t necessarily equate to readiness. Large volumes do not guarantee validity. For instance, customer profiles built from various identifiers don’t assure a unified identity, and AI models are not inherently designed to question these flawed inputs.
Identity is at the core of this issue. Every AI-driven marketing effort assumes accurate identity for analysis and targeting, yet identity remains a fluctuating component in our data stacks. Consumers frequently move across devices and change profiles, making it tricky to track accurately over time. However, most systems treat a snapshot identity as a constant, and AI inherits this flawed assumption.
Additionally, not all data issues stem from outdated sources. Some are intentionally deceptive due to evolving fraud tactics, becoming more challenging to distinguish without additional context. Fraudulent behavior can significantly distort model outputs and performance metrics, creating a feedback loop where AI unintentionally perpetuates the very issues it should mitigate.
Traditional data strategies often focus on structure over substance, and clean data doesn’t equate to accuracy. AI demands an in-depth understanding of identity validity, activity authenticity, and risk awareness, which traditional strategies may overlook.
The illusion of AI readiness becomes apparent when dashboards show excellent match rates and models yield seemingly precise outputs. However, metrics of identity reachability and engagement accuracy become crucial yet often disregarded questions.
True AI readiness starts with ensuring that our data inputs are trustworthy. It focuses on verifying identity accuracy, validating meaningful activities, and acknowledging risks rather than simply accumulating data records.
By addressing these foundational elements, organizations can suppress low-value identities, optimize outreach, and mitigate misuse before it skews results. Over time, this creates a structural advantage for AI operations, leading to more reliable predictions and efficient campaigns.
I’ve come to understand that AI’s impact on marketing is undeniable, yet it cannot independently resolve inherent data challenges. Organizations need to prioritize and invest in understanding the integrity of their data systems.
The real question isn’t about applying AI but assessing whether our data is worthy of AI. This deeper level of scrutiny defines true readiness and distinguishes the truly prepared from those merely rushing ahead.
Have you ever felt like you’re living in an ‘AI Groundhog Day’? Despite the wealth of AI tools we can use, many of us find ourselves stuck in a loop, manually prompting AI again and again. If we aim to truly automate PPC tasks, we need to move beyond this cycle.
Picture this: you open a chat window, carefully craft a prompt, and paste in your context. The result is fantastic! Yet, an hour later, the cycle repeats. If this sounds familiar, you’re still entrenched in manual work, albeit with a digital twist.
To harness AI effectively, I’ve realized we must transition from being doers to orchestrators. This means moving away from one-off prompts and starting to build robust systems. My book, “The AI Amplified Marketer,” delves deeper into how the human element remains crucial even as AI evolves rapidly.
Today, I’ll guide you on using Skills, an emerging AI capability, to enhance efficiency in managing PPC.
What’s a Claude Skill?
Many of us marketers have tried ChatGPT’s Custom Instructions—a broad directive for AI behavior. A Claude Skill, however, is more precise, dictating specific instructions to ensure consistent and predictable outcomes aligned with my expectations.
Recently, while rating search terms, I noticed AI’s inconsistency. One session yielded letter grades, another a percentage, and another, a numerical scale. This variability can disrupt workflows, confusing tools and team members alike.
A Skill eliminates this inconsistency, ensuring that every time, the results format remains unchanged. This evolution transforms AI from an unreliable assistant to a steadfast team member.
The latest capabilities in Claude allow a Skill to morph your comprehensive PPC strategy into an executable AI playbook, coordinating tasks among various tools and subagents efficiently.
Whether it’s auditing accounts or analyzing search query reports, Skills encapsulate your expertise into scalable systems for your team to deploy with AI seamlessly.
How to Build Your First AI Skill
Starting a new Skill might seem daunting, but it’s quite straightforward. In a chat with your AI, you can upload an audit checklist, a SOP, or a workflow blueprint, and instruct Claude to formulate it into a Skill.
Intriguingly, Claude employs a specialized protocol to construct Skills, guaranteeing outputs that are structured, adhere to best practices, and align with Anthropic’s architecture.
Technically, a Skill is stored as a Markdown (.md) file, serving as the playbook for the task at hand. Concerned about data privacy? You can save this locally or opt to share it in a cloud repository for easy team access and updates.
You don’t need to start from scratch. Platforms like GitHub offer pre-built Skills that you can experiment with and tailor to your needs.
How to Use a Skill in PPC
To get started with a Skill, make sure you have some available in your account.
Simply tell the AI the specific task you wish to accomplish. If a suitable Skill exists, the AI will apply those instructions to carry out the task.
Keep in mind, having competing skills could disrupt consistency. For instance, two skills performing Google Ads audits might randomly select different methodologies, thwarting the predictability.
PPC Skills Need Real-Time Data
While a Skill defines powerful logic, without real-time data, its application remains theoretical. Consider crafting an analysis to review search terms over the past 14 days—it’s great in concept, but without active data pulling from Google Ads, it remains incomplete.
Previously, this required manually downloading CSVs from interfaces. It worked, but was slow and the data became outdated immediately.
Enter the Model Context Protocol (MCP), bridging AI Skills to live data sources seamlessly. Using protocols like Optmyzr’s MCP, Skills can dynamically access and apply live Google Ads data, converting static instructions into an adaptive, responsive tool. (Disclosure: I’m the cofounder and CEO of Optmyzr.)
From Grunt Work to System Oversight
Integrating Skills with MCP transforms AI from assistantship into management. Tasks like search term analysis can shift from hands-on processes to automated oversight, with the AI undertaking everything from data pulling to implementing results.
Incorporating capable logic (Skills) with real-time data (tools) nurtures a practical system ready to shoulder routine tasks, enabling me to focus more on strategy orchestration.
4 PPC Skills You Can Build Today
Ready to jump into action? Here are four PPC Skills to inspire you:
1. Search Term Mining
This Skill guides AI in evaluating search query reports to target waste and opportunities.
Without tools, it requires manual CSV uploads and report implementation. However, with MCP, the necessary data is automatically sourced and applied directly in your Google Ads account.
2. Ad Copy Generation
Using a landing page and keywords, this Skill generates ad copy tailored to user intent and value propositions.
Manual editions involve copying assets, whereas MCP integrations can identify underperforming ads, generate new copy, and even initiate ad experiments autonomously.
3. Account Auditing
This Skill performs a checklist to spot issues like missing ad extensions or budget constraints.
Manually, it reports findings, but with MCP, it remedies problems directly, such as applying existing extensions to appropriate ad groups.
4. Budget Reallocation
Analyzing comparative data, this Skill identifies budget shifts to maximize returns.
Without tools, it suggests reallocations; with MCP, it dynamically analyzes and implements these changes, optimizing budgets promptly.
The Future of Your Role: From PPC Doer to PPC Designer
The fusion of Skills and tools allows us to depart from mere AI collaboration to AI-driven responsibilities. Instead of juggling tasks, our focus shifts to designing automated systems, crafting Skills, and setting the course for relentless efficiency.
As technology melds development and user-friendly interfaces, we’re at the cusp of a paradigm where non-developers design systems. It’s time to innovate and welcome AI as a genuine ally.
The End of Endless Prompting
The cyclical nature of endless prompting confines us to manual execution. By harnessing Claude Skills, we’re revolutionizing our approach to PPC—from mundane tasks to sophisticated system design. This transition embodies the essence of an AI-amplified marketer, fostering a dependable, efficient partner that channels our expertise into thriving systems.
The journey begins by viewing your daily routines through a designer’s lens. What process is ripe for crafting your inaugural Skill?
I am thrilled to share the news of an exciting new partnership that is set to revolutionize the way we connect AI visibility data to tangible citation outcomes and impacts.
This collaboration promises to enhance the visibility of AI-generated insights and effectively translate them into actionable citations, thereby amplifying their real-world influence.
In a world where AI continues to drive change and innovation, ensuring that these contributions are recognized and used is crucial, and this partnership is a significant step in that direction.
