Category: AI

AI-generated creative is already changing how advertisers approach visual production. With Google’s Gemini platform rolling out deeper integrations across Google Ads, its built-in image generation model, Nanobanana, gives marketers a practical tool for creating scalable, brand-aligned ad visuals quickly and affordably.

Whether you’re an e-commerce brand looking to improve product presentation or a service-based business focused on generating leads, AI-generated imagery can support nearly every stage of your campaign. The key is knowing how to write prompts that get results. This post covers everything you need to know to get started, including prompt structures, use cases, and real examples from campaigns we’ve tested.

What Is Nanobanana and How It Works

Nanobanana is Google’s native image-generation model built into the Gemini AI platform. It’s been integrated directly into tools like Performance Max, the asset library, and Product Studio. You enter a written prompt into Gemini, and Nanobanana renders an image based on your description. You can guide lighting, product placement, image size, setting, and even edit elements after generation. Need to remove the background, change the scene to a holiday setting, or make the lighting warmer? Just ask.

Be creative, think outside of the box, and embody your inner graphics designer/photographer. The more descriptive you are in defining what you want, the better the system works in recreating your prompts.

Because it’s built for marketers, Nanobanana also supports ad-safe formatting and understands basic design intent, like how to frame a product or keep the brand’s color palette intact.

Nanobanana access is available through two main Gemini tiers (Gemini is integrated into Google Workspace for most business, enterprise, and education plans):

• Free Tier: Good for basic testing and prompt experimentation, but image generation may be limited or unavailable depending on your region.
• Gemini Advanced ($19.99/month): Includes full access to Nanobanana Pro, supports high-resolution outputs, and is best suited for businesses creating visuals regularly.

For consistent use, marketing teams should opt for the Gemini Advanced plan. Product Studio access within Google Ads is still rolling out and may offer limited Nanobanana functionality depending on your account setup.

Why Marketers Should Care About Nanobanana

Nanobanana isn’t just a shortcut to get more visuals. It’s a way to remove creative bottlenecks entirely. Case studies from early adopters show a 20% lift in click-through rates and a 15% decrease in cost-per-lead, demonstrating the tangible impact on marketing efficiency. Here are a few of the ways brands are already using it:

• For E-commerce Brands: If your current product images are plain white background shots, Nanobanana lets you generate realistic lifestyle backgrounds that make your product stand out. You can transform a generic pack shot into a scene with lighting, environment, and props that align with your brand. Need visuals for your website, a carousel ad, a seasonal promotion, or a last-minute social post? You can create it all in-house with a little patience and the right prompt.
• For Service-Based Businesses and Lead Gen: Don’t have a product? No problem. You can use Nanobanana to illustrate your service visually by showing a technician in action, a friendly team photo, a metaphor like a glowing shield for security, or an outcome like a customer smiling in a freshly cleaned home. This helps you build trust and clarify what you offer, without relying on stock photos or custom shoots.
• For Marketing Teams of Any Size: You don’t need a design background to make great visuals. With the right prompts, anyone can create ad-ready images. You can also edit previous generations or quickly create visual variations for A/B testing or seasonal updates. This saves time, cuts costs, and helps smaller teams scale creative output without hiring or outsourcing.

How to Write Prompts That Work

Think of a Nanobanana prompt like a creative brief. The clearer your instructions, the closer the AI gets to your vision. A good prompt describes what the image should show, how it should look, and what kind of emotion or action it should convey.

A simple structure to follow:

Subject | Action | Setting | Style | Lighting | Mood | Optional Details

Focus on:

• Naming the exact object or subject
• Defining the context (where it is, what it’s doing)
• Choosing a style (photorealistic, editorial, flat lay, etc.)
• Explaining the desired light source and mood

Avoid vague language like “beautiful” or “aesthetic.” Use specific visual cues like “soft shadows,” “shallow depth of field,” “overhead camera angle,” or “sunlight from the left.” The AI takes each part literally.

An important thing to remember is that if you don’t deliberately say “create an image,” make sure to select the Nanobanana “Create Image” button at the bottom of the prompt.

Nanobanana Prompt Strategies for E-commerce

For e-commerce advertising, strong visuals can increase click-through rate, build brand trust, and improve conversion. Here’s how to structure prompts based on campaign type or asset need.

• Studio Product Shots: These work best for Shopping feeds, product listings, or clean performance creative.
• Lifestyle Product Scenes: Great for Performance Max ads, Meta ads, hero banners, or social posts where you’re trying to show the product in use.
• Promotional or Seasonal Assets: Quickly update product visuals with holiday themes or new backdrops.
• Multi-Product or Bundled Creatives: Use one prompt to feature multiple SKUs or cross-sell opportunities.

Real Client Example of a Nanobanana Prompt

We’ve had the opportunity to work with amazing brands like Jura, known for their premium coffee machines. For this example, I’m using their GIGA 10 Diamond Black as the featured product.

Alongside the prompt below, I’ll also upload Jura’s standard product image – typically a white background studio shot – which helps Nanobanana better understand the product design and preserve accuracy during generation.

Example Prompt: “Photorealistic image of a luxury Jura GIGA 10 Diamond Black coffee machine placed on a high-end kitchen counter with subtle under-cabinet lighting, marble backsplash, early morning sun casting a warm glow from the left, slight steam rising from a nearby cup, and a shallow depth of field.”

After about 30 seconds, this was the generated image:

Prompt Strategies for Lead Gen and Services

If you’re advertising services, software, or anything intangible, image prompts are about evoking emotion, trust, and clarity.

• Human-Focused Trust Visuals: Show real people helping others, using your service, or smiling post-service. This works well in local and B2B ads.

Example Prompt: “A digital marketing strategist sitting at a desk reviewing a Google Ads dashboard on a dual-monitor setup, natural lighting from a nearby window, JumpFly branding subtly on a notebook or mug, clean and modern workspace, focused and confident expression.”

This prompt promotes JumpFly’s Google Ads management services by visually showing the kind of dedicated, data-driven attention a client’s account receives.

• Outcome-Driven Prompts: Display the end result: clean homes, happy clients, streamlined dashboards, or completed work.

Example Prompt: “A Google Ads dashboard showing rising performance graphs on a laptop screen in a bright workspace, with a happy business owner in the background giving a thumbs-up, brand colors softly integrated in the environment.”

• Conceptual Metaphors: For services that are harder to visualize, like data protection or consulting, use metaphors. Shields, bridges, puzzle pieces, and clean paths all communicate value without showing the service directly.

Example Prompt: “A stylized scene of a clean, futuristic workspace where data streams shaped like light trails converge into a glowing, upward-pointing arrow. The arrow is composed of digital elements like search bars, ad charts, and KPI icons, symbolizing campaign growth. Soft gradients of JumpFly blue subtly frame the path, giving the sense of organized momentum and professional clarity.”

How to Scale and Test Efficiently

Once you have a few base prompts that work, you can iterate quickly by changing key variables: the setting, lighting, background, or color treatment.

Save winning prompts. Build a small library of structured formats that align with your campaigns: promos, product launches, testimonials, seasonal refreshes, and remarketing.

You can also test multiple versions side by side. Try a clean product image against a lifestyle one. Add human faces to one version, and remove them from another. Nanobanana gives you the power to test creativity at scale with minimal effort.

Final Takeaways and Things to Remember

Writing effective image prompts takes some trial and error, but it’s already proving to be a valuable skill for modern marketers. Whether you need a clean product photo, a relatable lifestyle visual, or something more conceptual for lead generation, Gemini and Nanobanana give you a flexible way to create those assets quickly.

Start by understanding the structure, test different variations, and fine-tune as you go. With a little creativity and a clear idea of what you want to express, anyone on your team can use this tool to generate visual content that drives results.

OpenAI announced in January 2026 that ads would begin appearing in ChatGPT. This is a big deal. The company that changed how millions of people search for information is now entering the advertising business, and with 800 million weekly users, marketers need to pay attention.

Access is currently limited to select enterprise partners, but that won’t last forever. Here’s what you need to know to be ready when the platform opens more broadly.

How ChatGPT Ads Work

ChatGPT ads appear below the chatbot’s responses, clearly labeled as sponsored content. Unlike traditional Search ads, where users scroll through a list of links, these placements integrate directly into the conversational flow.

Think about it: someone asks ChatGPT for laptop recommendations and sees a contextually relevant ad right after receiving their answer. That’s advertising at the exact moment of decision.

OpenAI has made several commitments around trust and transparency. Advertising won’t influence organic responses. User data and conversations won’t be sold to advertisers. Users can see why they’re being shown specific ads, dismiss irrelevant ones, and turn off personalization entirely.

Who Sees Ads

Only free users and ChatGPT Go subscribers ($8/month) will see ads initially. Premium tiers, including Plus ($20/month), Pro ($200/month), Business, and Enterprise, remain ad-free. Users under 18 won’t see ads, and OpenAI is avoiding placements near sensitive topics like politics, health, and mental health.

This mirrors what we’ve seen from streaming platforms: a free or low-cost option supported by advertising (think Google Shopping ads at the very beginning, AKA Froogle), with premium ad-free experiences for those willing to pay. For advertisers, this means the initial audience skews toward price-conscious users who haven’t committed to a paid subscription.

Premium Pricing, Limited Measurement

OpenAI is targeting a Cost-per-thousand impressions (CPM) of around $60.

For context: Google Display Network ads average around $3 CPM. Google Search ads sit closer to $38 CPM. Premium connected TV inventory from Hulu and Netflix’s ad tier typically commands $40 to 65 CPM. OpenAI is pricing at the very top of the digital advertising spectrum.

But why? ChatGPT captures users at moments of genuine intent. When someone asks about running shoes for flat feet, they’re expressing unfiltered purchase consideration before narrowing their choices. That’s the high-intent moment advertisers have always coveted.

The trade-off is measurement. Unlike Google and Meta’s sophisticated attribution systems built over two decades, OpenAI is launching with basics: total impressions and total clicks. No granular conversion tracking. No demographic insights. No purchase attribution.

OpenAI says more detailed data may come over time, but for now, performance marketers face real challenges justifying spend without visibility into what’s actually driving results.

The Early Advertiser Program

OpenAI is reaching out to advertisers directly through its enterprise partnerships team, not through traditional agency channels. The initial trial targets companies with significant spending commitments over several weeks, with ads launching in early February.

This enterprise-first approach differs significantly from how Google and Meta built their ad businesses on the backs of small businesses using self-serve tools. There’s no self-serve buying interface yet, and OpenAI isn’t working with ad tech firms to manage placements.

For agencies and smaller advertisers, this means watching from the sidelines initially, but that’s exactly when smart marketers start doing their homework.

What This Means for Your Strategy

ChatGPT’s entry into advertising signals a shift in how consumers discover products. As more people turn to conversational AI instead of traditional search, advertising dollars will follow. The implications depend on your current approach:

• Search advertisers should view ChatGPT as a new high-intent channel that could capture queries currently going to Google. The conversational format suggests users may be further along in their decision-making process. However, the lack of keyword-level targeting and bidding controls makes optimization difficult compared to Search campaigns, where we’ve spent years refining strategies.
• Brand advertisers may find the premium pricing and brand-safe environment appealing for awareness campaigns. Being associated with ChatGPT’s helpful, authoritative persona could carry positive brand associations, particularly for companies looking to align with innovation.
• Performance marketers face the biggest hurdle with limited attribution data. Without visibility into which ads drove conversions, campaign optimization becomes guesswork. Early adopters should approach with clear testing frameworks and realistic expectations.

Our Take on ChatGPT Advertising

At JumpFly, we see ChatGPT ads as one of the most exciting developments in digital advertising in years.

With hundreds of millions of active users turning to ChatGPT for recommendations, research, and discovery, OpenAI has built an audience that most platforms would envy. The opportunity to reach consumers in a high-intent, conversational environment, right at the moment they’re actively seeking solutions, is significant.

We’re eager to get our hands on this platform. Understanding how to create compelling ads for a conversational interface, learning what optimization levers exist, and discovering how to drive measurable success for our clients on an entirely new channel is exactly the kind of challenge our team thrives on. New platforms reward early adopters who invest the time to learn their nuances, and we intend to be ready.

Yes, measurement is limited today, and pricing is premium. Like any responsible agency, we’ll approach testing with appropriate caution and clear expectations. But limited reporting at launch is the norm for emerging platforms. Meta and Google weren’t built in a day either.

What matters is the potential. ChatGPT ads have the potential to become a core component of many brands’ digital advertising strategies.

Our recommendation: lean in. Work with partners committed to understanding this new frontier. Test early to build institutional knowledge. Position your brand to capitalize as the platform matures. Conversational AI advertising isn’t a passing trend; it’s where the industry is heading. The brands that engage now will have a meaningful advantage as ChatGPT’s ad product evolves.

Looking Ahead

OpenAI’s move into advertising validates that AI-powered interfaces have become significant enough to warrant their own advertising ecosystems. The next several months will be telling:

How do early advertisers perform? How do users react to ads in their conversations? Will OpenAI develop the measurement and targeting capabilities that performance marketers need?

These questions will determine whether ChatGPT becomes a meaningful part of the media mix or remains a niche experiment. What we know for certain is that the way people discover information is changing, and advertising will adapt to follow.

ChatGPT ads are just the beginning of what promises to be a fundamental shift in how brands connect with consumers in an AI-first world.

The conversation around AI in paid Search has shifted. It’s no longer about whether you’re using these tools; it’s about how much control you’re willing to hand over. 

We’ve all seen the headlines promising that AI will replace the creative department. We’ve watched how easy it is to generate “limitless” ad content. But now that the dust has settled, a different reality has emerged: the biggest challenge isn’t making AI smarter. The real issue is figuring out exactly when a human needs to step in and stop the machine from being generic, or even from being downright wrong. 

To win today, you must become a Cyborg Architect. Let the machine do the heavy lifting and the grunt work, but you stay firmly in the driver’s seat.

AI is an incredible concept engine. It can spit out 50 search headlines in the time it takes you to open a new Google Doc. But letting the AI hit “Publish” on its own is a dangerous game. When we treat AI as a vending machine rather than a brainstorming partner, we get a heap of AI Slop, that soulless, blah ad copy that searchers have already started to tune out.

The competitive advantage no longer goes to the person with the best prompts. It goes to the person who knows how to curate, polish, and stress-test the results AI produces.

A Reality Check on “Hallucinations”

In the early rush to use AI for creating ads, people started using LLMs to pull final ad copy directly from website URLs. The promise was speed, but the reality was a mess of “hallucinations,” where AI confidently produced things that were just flat-out wrong.

I saw this firsthand recently. I asked ChatGPT to generate headlines for a client’s Responsive Search Ads (RSAs). The copy looked professional and polished, claiming the company had been in business since 1973.

Something felt off. I checked the site; the business was actually founded in 1978. When I called the AI out, it doubled down, insisting the date was on the landing page. Only when I asked for the specific sentence did it cave: “Apologies, you are correct!” This isn’t an isolated glitch. For another client, the AI invented a “Free Shipping Over $65” offer. The problem? That client never offers free shipping. If those ads had gone live, we would have burned through the budget while destroying the brand’s reputation. Around our office, we have a mantra: “Trust, but verify.”

This is why the Cyborg model is so important. An AI can process data, but it doesn’t always get the truth right. Without a human ‘pilot’ to perform a manual override, speed simply becomes a faster way to fail.

Escaping the “Sea of Sameness”

Beyond the risk of lying to customers, AI-only copy leads to a sea of sameness. Think of AI as a giant blender of the entire internet. It’s just spitting back the mathematical average of what everyone else has already written. It’s designed to be ‘normal,’ but in PPC, ‘normal’ is just another word for invisible.

You’ll get the same tired tropes: “Elevate your run,” “Unlock performance,” “Solutions for you.” In fact, our team has grown to genuinely loathe the word “elevate.” AI seems to be obsessed with it. In a world where you only have seconds to grab attention, being average is a death sentence for your ROAS.

The Solution: The “Sandwich” Workflow

Instead of viewing AI as an “Author,” view it as a “Research Assistant.” We use a Sandwich Workflow to balance machine speed with human judgment.

1. The Bottom Bun: Human Architecture

You don’t start with the AI. You start with the human strategist who sets the gears in motion. This is where you define the goals, the brand voice, whatever guardrails that need to be in place, and the actual strategy. 

2. The Filling: The AI Rapid Volume

This is where you let the machine off the leash. As humans, we’re wired to stop once we hit a “good enough” idea so we can move on to the next task. AI doesn’t get tired. It’s a limitless engine of unfiltered variations. It can spit out 100 different angles you may have never thought of in mere seconds. While 90 of them might not be what you’re looking for, it does drastically increase the odds of finding that one creative outlier that piques your interest.

3. The Top Bun: Human Curation & Deployment

This is where we bring order to the chaos. This isn’t just “editing”; it’s about logic, empathy, and a “vibe check” that AI simply can’t simulate. The human’s job is to sift through the noise, filter out the hallucinations, and ensure the final ad actually resonates with a living, breathing person on the other side of the screen.

Sandwich Layer	Mode	Role	The Cyborg Synergy
Bottom Bun	Architecture	Human	Sets the goals, brand voice, and guardrails.
The Filling	Rapid Volume	AI	Generates 100+ variations and creative angles.
Top Bun	Curation & Execution	Human	Trust but verify. Filters for truth and “soul.” Hacks away the generic fluff to create high-performance ads that actually sound like a person wrote them.

The Rise of the Cyborg Architect

The future of high-performance paid Search isn’t a choice between human intuition and machine speed. It’s the synthesis of both.

We are entering the era of the Cyborg Architect. This isn’t about handing the keys to a bot and crossing our fingers; it’s about using AI as a power-up. Let the machine handle the grunt work of churning through thousands of ideas. Your job is to provide the judgment, the ethics, and the soul of the brand, the “stuff” a machine can’t even begin to replicate.

The most successful brands won’t be the ones using the most AI. They’ll be the ones who know exactly where the machine ends and the human begins.

A Tactical Breakdown of Bridging the Gap Between Data and Reality

In the current digital advertising landscape, a “gate” has opened. We have moved past the era of manual button-pushing and entered a phase where a massive, algorithmic Shadow (a dimension of trillions of signals) does the heavy lifting. The atmosphere is tense and unpredictable; the platforms we use are becoming more secretive, and the insatiable demand from the systems for data often pushes our resources into areas that lack transparency. Though this is improving as we begin to get more of this black box data (like PMax search terms and channel performance report).

But as the atmosphere shifts, so must our roles. We aren’t just account managers anymore; we are Strategic Architects. We are the ones standing at the portal, responsible for building the bridge between the cold, mathematical logic of the Rift and the nuanced, emotional reality of our world.

The goal is to build a hybrid workflow that respects the power of the machine while keeping a firm human hand on the controls. I like AI, I like automation, and I like solving workflow issues. These are all things we need to embrace practically, so here is my breakdown of the Green Zone (the automated advantage) and the Red Zone (the human necessity).

The Green Zone: Scaling High-Utility Efficiency in the Light

In the green zone, the machine is your greatest asset. It processes data fluctuations at a speed that leaves human analysis stuck in the 1980s. These workflows allow us to capitalize on opportunities before they vanish back into the Void. A lot of what I discuss below is automating a workflow, but it can easily be adapted to include AI agents to act on your behalf.

1. The Dynamic Budget Bridge

Even with a disciplined pacing strategy, traditional manual budget management often struggles to keep up with the real-time volatility of the Rift (the shifting portal where our strategy meets the high-speed chaos of the live auction). To stay ahead of the curve, you need a workflow that can mirror the shimmering reality of the auction as it happens.

• The Workflow: Use scripts or automated rules to adjust budgets daily based on a weighted look-back at performance (one-day, seven-day, and 30-day intervals). If the one-day data shows a significant surge in efficiency and the 30-day trend is stable, the system should automatically widen the gate to ride the wave. This is a tactic we used throughout 2025, and especially in Q4, with great success.
• The Containment Unit: To ensure the rule doesn’t go wild and create a financial wormhole, always set a hard budget cap. This allows the AI to capitalize on trends quickly before they’re “too late,” while keeping the spend firmly grounded in our world’s reality.
• How We Do It: We establish our bottom-line ROAS at the outset. This metric ensures that our rules continue to drive performance as long as targets are being met. We also implement rules to do the reverse and reduce budgets should we begin to see slight inefficiencies. 

2. The Sentinel of the Feed

If you are managing high-volume product feeds, analyzing every SKU manually is an impossible task. Give it a try, and you will quickly feel overwhelmed. You need a “sentinel” that can detect anomalies in the Shadow realm of the feed faster than any human eye.

• The Workflow: Deploy custom solutions or scripts that monitor for “flare-ups” (trending SKUs seeing a sudden surge in conversions) or “Shadow spenders” (products spending heavily for no apparent reason with zero return).
• The Action: Have the system either take action (pausing the waste or boosting the winners) or, at the very least, send an immediate notification. Being alerted to a trending SKU in the moment is the difference between potential wasted spend, a record-breaking day, and a missed opportunity that gets sucked back into the Void.
• How We Do It: This is ongoing, but it is done with custom alerts to notify us of SKUs surging positively or negatively. At the moment, this is an email notification to quickly help us identify the SKUs where we can then take action in the platform. 

3. N-Gram Frequency and the “Static” of LLMs

The way consumers search has fundamentally shifted. Thanks to the rise of Large Language Models (LLMs) and conversational search, queries are becoming longer, more complex, and more unique. This has led to a surge in single-impression search terms. One-off queries that make up the background static of the Rift. Individually, they look like noise, but collectively, they can drain a budget through a thousand tiny cuts.

• The Workflow: Use a script or custom solution to extract search terms and perform regular N-gram analysis. By aggregating those unique queries into patterns, you can identify underlying waste across thousands of low-volume searches.
• The Benefit: This turns the conversational “noise” into a tactical map, allowing you to build robust negative lists and keep the AI focused on the high-intent signals that actually cross over into conversions.
• How We Do It: The quickest, simplest solution is using an LLM like ChatGPT or Google Gemini. Define your prompt, and have it analyze the csv file to get the N-gram result. I use this data to determine the actual waste and begin adding negative keywords. 

The Red Zone: Where the Algorithmic Shadow Loses Human Resonance

The red zone is where the Rift becomes dangerous. Left unsupervised, the algorithm will take the path of least resistance. If we allow the machine to act for us without a human “tether,” we risk being pulled into a reality that doesn’t serve the brand or account.

The “AI Slop” and Brand Decay

We are currently in a period of ad fatigue, or so it feels that way. Users have become incredibly adept at spotting “AI slop,” content that is glossy and technically proficient but completely deVoid of a human pulse. I have heard the term AI slop more in the last two months as users continue to adopt more AI tools. When a brand leans 100% on AI-generated creative or copy, it creates an uncanny valley that triggers a subconscious “ignore” reflex in the consumer. 

Many months ago, I remember stumbling upon a study by Graphite that details how AI-generated articles officially surpassed human-written content, now accounting for 52% of all new web articles. I can only imagine this continues to grow as more creative tools are released, hence contributing to AI slop. 

• The Human Hand: AI produces variations; humans produce resonance. Brands that treat AI as a replacement for creative intuition often find their message lost in the static. To avoid sounding like an unauthentic copy of the brand’s identity, every asset needs to be reviewed by a human for brand alignment and tone.
• How We Do It: We use these tools to bridge the gap in our efficiency, but we never blindly accept every output shared. Accuracy and brand integrity remain our essential tethers to reality. We use technology to ideate faster than ever, but we are still the sorcerers who approve what goes live.

Signal Pollution & The Conversion Wormhole

Automated bidding is only as good as the signals it receives. One of the most common pitfalls I see with automated campaigns like Performance Max is the machine optimizing for the wrong “wins.” If you haven’t strictly audited your conversion actions, the AI might find a way to hit its targets by chasing low-quality leads, accidental clicks, or “soft” conversions that have no real-world value.

• The Danger: Once the machine starts learning from poor signals, it creates a feedback loop. It will aggressively bid on traffic that looks like your low-quality leads, funneling your entire budget into a wormhole of useless data. Without a human architect to verify that the “conversions” are actual sales or qualified leads, the machine will double down on bad behavior until the budget is gone.
• How We Do It: One of the most effective ways to manage the Rift is to force your campaign to focus on a single conversion action, rather than scattering its energy across all the actions being tracked. This gives the machine a singular, high-intent target to pursue. From there, we work to clear the noise using negative keywords and signal testing. If the machine’s learning becomes too corrupted by poor data, we don’t hesitate to close the gate and relaunch a duplicate, effectively purging the previous learning and starting with a clean slate.

Architectural Erosion (The Auto-Apply Trap)

The platforms are constantly sending “recommendations” from the Rift, suggestions to remove redundant keywords, switch to broad match, or consolidate campaigns. While these are framed as optimizations, they are often the machine’s way of erasing the strategic boundaries you’ve spent years building.

• The Danger: If you leave “Auto-Apply” settings toggled on, or if you blindly accept every architectural change suggested, you are allowing the machine to rewrite the map of your account. Over time, this causes architectural erosion. You wake up to find your carefully segmented campaigns have been merged into a single, amorphous “hive” structure that the AI finds easier to manage, but you find impossible to control. Without a human architect to defend the blueprints, the machine will eventually dismantle your strategy in favor of its own convenience.
• How We Do It: We treat every platform recommendation as a suspicious signal that must be interrogated. We disable all “Auto-Apply” features as a default, ensuring the gate remains locked until we’ve reviewed the impact of a change. We are the protectors of the account’s architecture; we decide when a wall comes down, or a bridge is built, never the machine.

The Architect’s Final Blueprint

To thrive in this landscape, your workflow must be a balanced bridge. Automate the high-speed math, the dynamic budgets, the feed sentinels, and the N-gram analysis to keep your efficiency ahead of the Rift’s volatility.

But when it comes to the “Human Resonance” of the campaign, the creative integrity, the quality of your conversion signals, and the strategic architecture of your account, you must remain firmly at the controls. Don’t let the machine’s path of least resistance become your accounts’ undoing.

Automate the math. Humanize the meaning. 

AI gets a lot of credit right now for being fast, smart, and endlessly capable, especially when it comes to writing. I’ve heard the same questions many people in digital advertising have heard lately: “Can’t AI just write the ads?” And honestly, with how confidently these tools present themselves, it’s totally fair to wonder.

A few weeks ago, I tested a new chatbot and shared it with our team. This sparked broader conversations about which tools people were using, which ones felt the quickest and most accurate, and whether any stood out in ways that actually made a difference in real PPC workflows. With a background in quantitative sociology, it felt natural for me to approach these questions with structure rather than intuition – so I put together a content analysis and comparison to see how different chatbots actually perform when given the same task.

I wanted to keep this grounded, so I chose a real product where I’m part of the target audience and ran a side-by-side comparison of five commonly used chatbots:

• Google Gemini in Google Sheets
• Google Gemini 2.5 Flash
• OpenAI’s ChatGPT 5
• Google’s Notebook LM
• Perplexity’s Comet Browser Assistant

The product in question was a delightfully niche rat-toy subscription box. As a proud owner of two pet rats, often called “fancy rats,” I knew that I’d be able to see which outputs from these chatbots would resonate with me and the needs of my fellow audience of rat owners.

Prompt Structure & Evaluation Criteria

In any study, it’s important to keep things consistent, so I created one standardized prompt and pushed it through all five models without any follow-up instructions. This would demonstrate each chatbot’s raw starting point. The goal here wasn’t to crown a winner, but to understand the nuances of how each one functions and where human judgment is still essential.

With this product being so niche, each chatbot was able to demonstrate its ability to capture tone, specificity, and an understanding of the audience within a strict set of parameters. I asked each model to generate 20 short headlines, 10 long headlines, and 10 descriptions. These text assets all had to be “within Google’s character limits,” which tested their ability to interpret vagueness while also assessing their base knowledge. I included a clear objective of driving sales and requested ad copy tailored to adults ages 22 to 60 who own and love pet rats.

Each AI model’s raw output was evaluated across five metrics:

• Speed and workflow (is the output ready-to-use?)
• Output formatting (is the output clean & error-free?)
• Content quality and creativity (is the output eye-catching & unique?)
• Prompt accuracy and relevance (did the output follow instructions?)
• Strategic depth and audience resonance (is the output persuasive to rat owners?)

These criteria speak to the real considerations that determine whether AI-generated copy is usable as is – or whether it serves as a starting point requiring significant human intervention.

What the Study Revealed

An important note to keep in mind here is that this study is a snapshot in time – it’s indicative of the landscape of AI during the time that I conducted this study (late October 2025). AI is changing every single day, and doing the same test again today, next week, or a month from now could yield very different results.

One of the clearest findings was that no single model performed at the top across all metrics. Each chatbot demonstrated noticeable strengths in some categories and really missed the mark in others. The results highlighted a more nuanced reality – different AI tools excel under different metrics, and choosing the right one depends heavily on the task at hand.

Model-by-Model Breakdown

Gemini in Google Sheets

Gemini’s spreadsheet integration stood out for its efficiency and discipline. It delivered fast output, highly consistent formatting, and strong adherence to character limits. For specialists who need clean, rule-following copy quickly, this model performed well. However, its creativity and emotional resonance lagged behind the others. Its outputs read clearly and correctly, but lacked depth. And because Gemini in Google Sheets doesn’t yet have the ability to crawl webpages, the output lacked brand-specific detail.

Gemini 2.5 Flash

Where Gemini in Sheets excelled in speed and discipline, Gemini 2.5 Flash excelled in creativity. This model produced some of the most playful, engaging, and audience-attuned copy in the study. It demonstrated a stronger understanding of what might excite or delight the niche pet audience. The tradeoff here is that this creativity often came at the expense of formatting consistency, frequently exceeding character limits for each text asset. Gemini 2.5 Flash clearly has value, especially in ideation, but usually requires a specialist to refine and tighten the results – all that time spent editing can add up quickly!

ChatGPT 5

In this study, ChatGPT 5 generated the safest, most generic copy overall. It followed the prompt and rarely broke character limits, indicating that it was a dependable, if basic, baseline. Despite this, the phrasing frequently defaulted to broad, repetitive language that didn’t differentiate the product or speak uniquely to the intended audience. It also struggled to fill the character counts efficiently, often clocking in at 60 to 70 characters for a long headline or description. While the copy was technically sound, it lacked the originality and strategic nuance needed for strong performance against competitors on the SERP. ChatGPT also occasionally added extraneous or incorrect information – it wrote ad copy that said “20% off!” when there was no mention of a sale anywhere on the site. The time it can take for specialists to verify outputs from this chatbot can eat up all the time saved by initially using this tool.

Notebook LM

While many use Notebook LM as an AI study-buddy, it showed moments of creativity and thematic variation when used as an ad copywriter. Because it relies solely on the source material you give it, it can be helpful during early brainstorming. However, it did struggle significantly with character limits and consistency, making it less suited for direct application in the tightly structured format of Google Ads. Its strengths leaned more toward conceptual exploration rather than toward ready-to-deploy text assets, making it a great tool for a niche ad where you want the output to be hyper-specific.

Comet Browser Assistant

Of all the chatbots tested, Comet was the most balanced performer across all five evaluation categories. It didn’t take the top spot in creativity or strategic depth, but it avoided major weaknesses and delivered dependable, well-structured output. Its copy blended clarity, accuracy, and audience relevance more evenly than the others, making it the steadiest “all-around” option in the study.

What This Means for Advertisers

This comparison ultimately showed that AI can move quickly, generate volume, and spark ideas, but it doesn’t yet understand the strategic nuances that drive performance. None of the models consistently balanced creativity, accuracy, formatting discipline, and audience insight in a way that replaces human decision-making.

And this is precisely how we approach AI within paid search advertising. We see these tools as accelerators, not autopilots. They help our specialists explore ideas faster and work more efficiently, but the final decisions still come from people who understand the brand, the audience, and the platform dynamics. AI provides momentum, but the direction still comes from experience.

The goal of this study wasn’t to find the perfect chatbot – it was to understand how these tools behave in real PPC tasks and to determine which one is best suited to the task at hand. The results were clear: every model contributed something valuable, but each also required interpretation, refinement, and direction. That’s the space where human expertise remains irreplaceable.

AI can generate a starting point, but it can’t yet determine what will resonate with a specific audience, or when a headline is technically correct but strategically empty – those choices still rely on people. 

It’s not about choosing between human creativity and AI. We’re choosing the best of both: AI for speed and exploration, and humans for insight and strategy. When paired thoughtfully, the two complement each other, and the work is stronger for it.

Generative AI (GenAI) has quickly moved from a buzzword to an everyday marketing reality. What once took weeks of designing, filming, or drafting can now be done in minutes with the right AI tool.

New AI platforms seem to launch almost every week, but this list highlights the ones that stand out for marketers today. They cover everything from idea generation to visuals, video, and even voice content.

Whether you are launching a product, refreshing ad creatives, or managing a multichannel campaign, these nine tools can save time, cut costs, and unlock new creative possibilities.

1. ChatGPT – Research & Content Creation

What it is: A generative text model that helps with research, brainstorming, and writing.

Why marketers love it: Perfect for blog drafts, ad copy variations, or creative prompts in seconds.

Example use: A coffee chain asks ChatGPT for 10 fun, Instagram-ready headlines for a pumpkin spice latte campaign.

2. NotebookLM – Brand-Aligned Copywriting

What it is: Google’s NotebookLM is an AI assistant that uses your own materials, like PDFs, Google Docs, a website URL, or even videos, to generate copy in your brand’s style.

Why marketers love it: Ensures ad copy stays accurate, compliant, and aligned with brand voice.

Example use: A content team uploads past blog posts and their homepage URL, and then uses NotebookLM to generate outlines for new articles in the same tone and style.

3. Canva AI – Visual Content Made Easy

What it is: A design platform with AI features like Magic Write, background removal, and instant resizing.

Why marketers love it: Makes professional design accessible for social posts, ads, and email banners.

Example use: A retailer quickly generates branded “New Arrival” social posts that look polished and ready to publish in minutes.

4. Adobe Firefly – Commercial-Ready Design

What it is: Adobe’s GenAI suite built into Photoshop, Illustrator, and Premiere Pro.

Why marketers love it: Firefly is trained on Adobe Stock, openly licensed, and public domain content. The outputs are commercially ready and safe to use, giving marketers peace of mind while creating directly inside the Adobe tools they already rely on.

Example use: A creative agency generates imaginative visuals, such as a woman with floral headpieces, in Photoshop and uses them in paid campaigns.

5. Midjourney – Artistic Image Generation

What it is: A text-to-image generator that creates highly stylized, detailed artwork.

Why marketers love it: Ideal for producing campaign imagery, mood boards, or product visuals without expensive photo shoots.

Example use: A children’s brand prompts Midjourney for “a whimsical ladybug with an umbrella in the rain” and turns the image into creative assets for social media and packaging.

6. Runway – AI Video Creation & Editing

7. Veo 3 – Cinematic Video Generation

What it is: Google DeepMind’s latest model that creates cinematic-quality clips from text prompts.

Why marketers love it: Delivers quick-turn ad spots and teasers without the need for a production crew.

Example use: A sports brand generates a dynamic 8-second Formula 1 promo to test audience response before investing in a full shoot.

8. Gemini Nano Banana – Fast Image Generation

What it is: Google’s lightweight GenAI image tool, part of Gemini 2.5 Flash.

Why marketers love it: Great for quick edits, mockups, and playful campaign tweaks.

Example use: A marketer replaces all the fruit in a product photo with bananas to create a fun visual refresh for digital ads.

9. ElevenLabs – AI Voiceovers

What it is: A platform for natural-sounding, multilingual voice generation.

Why marketers love it: Adds professional narration to videos, podcasts, or ads in seconds.

Example use: A fitness brand creates English and Spanish voiceovers for its video ads without hiring actors.

What’s Coming Next

These are the latest updates pointing to what’s ahead for these GenAI tools:

• Adobe Firefly: Firefly Boards bring collaborative planning to teams, with new image models offering better realism and deeper video and audio integrations on the horizon.
• Midjourney: Video V1 adds motion to stills, and upcoming updates promise more control over style, movement, and upscaling.
• Runway: Gen-4 delivers more consistent results and stronger storytelling, with further refinements expected for professional ad workflows.
• Veo 3: Advancing toward longer, more realistic clips that push beyond teasers into higher-end commercial video production.
• Cross-tool trend: Marketers are increasingly blending tools like Midjourney, Runway, and Veo 3 in a single campaign, showing that the future is about orchestration rather than relying on a single tool to do it all.

Final Takeaway

Together, these tools highlight how much creative power is now available to marketers. From writing and visuals to video and voice, GenAI is opening new possibilities and making it faster than ever to move from idea to execution.

GenAI is not here to replace marketers; it is here to support them. Those who adopt these tools early will not only stay ahead but also help define the future of digital marketing.