Microsoft released its 2025 Annual Work Trend Index this week.
The report claims this is the year companies will move beyond AI experiments and rebuild their core operations around AI.
Microsoft also introduced several new terms that it believes will shape the future of the workplace.
Let’s look at what Microsoft wants to add to your work vocabulary. Remember, Microsoft has invested heavily in AI, so they have good reasons to make these concepts seem normal.
The Microsoft AI Dictionary
1. The “Frontier Firm”
Microsoft says “Frontier Firms” are organizations built around on-demand AI, human-agent teams, and employees who act as “agent bosses.”
The report claims 71% of workers at these AI-forward companies say their organizations are thriving. That’s much higher than the global average of just 37%.
2. “Intelligence on Tap”
This refers to AI that’s easily accessible whenever needed. Microsoft calls it “abundant, affordable, and scalable on-demand.”
The company suggests AI is now a resource that isn’t limited by staff size or expertise but can be purchased and used as needed, conveniently through Microsoft’s products.
3. “The Capacity Gap”
This term refers to the growing disparity between what businesses require and what humans can provide.
Microsoft’s research indicates that 53% of leaders believe productivity must increase, while 80% of workers report a lack of time or energy to complete their work. They suggest that AI tools can fill this gap.
4. “Work Charts”
Forget traditional org charts. Microsoft envisions more flexible “Work Charts” that adapt to business needs by leveraging both human workers and AI.
These structures focus on results rather than rigid hierarchies. They allow companies to use the best mix of human and AI workers for each task.
5. “Human-Agent Ratio”
This term refers to the balance between AI agents and human workers required for optimal results.
Microsoft suggests that leaders need to determine the number of AI agents required for specific roles and the number of humans who should guide those agents. This essentially redefines how companies staff their teams.
6. “Agent Boss”
Perhaps the most interesting term is that of an “agent boss,” someone who builds, assigns tasks to, and manages AI agents to boost their impact and advance their career.
Microsoft predicts that within five years, teams will be training (41%) and managing (36%) AI agents as a regular part of their jobs.
7. “Digital Labor”
This is Microsoft’s preferred term for AI-powered work automation. Microsoft positions AI not as a replacement for humans, but as an addition to the workforce.
The report states that 82% of leaders plan to use digital labor to expand their workforce within the next year and a half.
However, this shift towards AI-powered work automation raises important questions about job displacement, the need for retraining, and the ethical use of AI.
These considerations are crucial as we navigate this new era of work.
Behind the Terminology
These terms reveal Microsoft’s vision for embedding AI deeper into workplace operations, with its products leading the way.
The company also announced updates to Microsoft 365 Copilot, including:
New Researcher and Analyst agents
An AI image generator
Copilot Notebooks
Enhanced search functions
Jared Spataro, Microsoft’s CMO of AI at Work, states in the report:
“2025 will be remembered as the year the Frontier Firm was born — the moment companies moved beyond experimenting with AI and began rebuilding around it.”
Looking Ahead
While Microsoft’s terms may or may not stick, the trends it describes are already changing digital marketing.
Whether you embrace the title “agent boss” or not, knowing how to use AI tools while maintaining human creativity will likely become essential in the changing marketing workforce.
Will Microsoft’s vision of “Frontier Firms” happen exactly as they describe? Time and the number of people who adopt these ideas will tell.
Stanford University’s latest AI Index Report reveals a significant increase in AI adoption among businesses.
Now 78% of organizations use AI, up from 55% a year ago. At the same time, the cost of using AI has dropped, becoming 280 times cheaper in less than two years.
More Businesses Than Ever Are Using AI
The latest report, now in its eighth year, shows a turning point for AI in business.
The number of organizations using generative AI in at least one business area more than doubled, from 33% in 2023 to 71% in 2024.
“Business is all in on AI, fueling record investment and usage,” the report states.
In 2024, U.S. companies invested $109.1 billion in AI, nearly 12 times more than China’s $9.3 billion and 24 times more than the U.K.’s $4.5 billion.
AI Costs Are Dropping
One reason more companies are using AI is that it’s becoming increasingly affordable. The report indicates that the cost of running AI queries has decreased significantly.
The report highlights:
“The cost of querying an AI model that performs like GPT-3.5 dropped from $20.00 per million tokens in November 2022 to just $0.07 per million tokens by October 2024.”
That’s 280 times cheaper in about 18 months.
Prices have dropped between 9 and 900 times per year, depending on the use case for AI. This makes powerful AI tools much more affordable for companies of all sizes.
Regional Differences and Business Impact
Different regions are adopting AI at different rates.
North America remains the leader, but Greater China has shown the most significant jump, with a 27-point increase in company AI use. Europe was next with a 23-point increase.
For marketing teams, AI is starting to show financial benefits. About 71% of companies using AI in marketing and sales report increased revenue, although most say the increase is less than 5%.
This suggests that while AI is helping, most companies are still figuring out how to use it best.
What This Means for Marketers & SEO Pros
These findings matter for several reasons:
The drop in AI costs means powerful tools are getting more affordable, even for smaller teams.
Companies report that AI boosts productivity and helps bridge skill gaps. This can enable you to accomplish more with limited resources.
The report notes that “smaller models drive stronger performance.” Today’s models are 142 times smaller than the 2022 versions, so more AI tools can run on regular computers.
The 2025 AI Index Report clarifies that AI is no longer an experimental technology, it’s a mainstream business tool. For marketers, the question isn’t whether to use AI, but how to utilize it effectively to stay ahead of competitors.
The industry is divided down the middle: Half believe that optimization for large language models (LLMs) requires new strategies, while the other half insists good SEO already handles it.
This division has spawned new acronyms like GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization) – terms that are equally loved and hated.
To settle this debate, I analyzed Similarweb data comparing Google organic traffic with ChatGPT brand visibility across four product categories.
The result?
The top search players are not always the top ChatGPT players, and the overlap varies between product categories.
This is important to understand because:
There are real optimization opportunities for some categories, and
You might miss them if you dismiss optimization for ChatGPT as “just doing good SEO.”
At the same time, we can apply the same tactics to ChatGPT as to Google.
I like to think of how we describe the differences between SEO and GEO/AEO like this:
SEO and GEO/AEO are like pianos and guitars.
They’re both instruments. They both make music. And they both share fundamental principles (notes, scales, harmony) that a musician must master to properly play both.
About The Data
Big thanks to Similarweb, especially Adelle and Sam, for sharing the data with me.
Here’s what I reviewed for this particular analysis:
Organic search traffic vs. AI chatbots visibility across four product categories:
Credit cards (finance).
Earbuds (tech).
CRM (software).
Handbags (fashion).
Methodology: Similarweb categorizes ChatGPT conversations based on their content and identifies the most common brands in ChatGPT’s response.
In total, the data covers 69.9 million clicks.
SEO Vs. GEO/AEO: Same, Same, But Different
If GEO/AEO and SEO were the same, the same sites getting organic traffic would also get the most citations/mentions in LLMs.
That’s only true in a few cases, but not for the overall picture.
Credit Cards
Image Credit: Kevin Indig
chase.com — 6.6 million clicks | 13.6% ChatGPT visibility
reddit.com — 5.2 million clicks | 0% ChatGPT visibility
capitalone.com — 4.1 million clicks | 10.3% ChatGPT visibility
citibankonline.com — 3.2 million clicks | 4.4% ChatGPT visibility
comenity.net — 2.9 million clicks | 0% ChatGPT visibility
The data shows that the top organic domains (by clicks) are not the ones getting the most mentions in ChatGPT.
As a result, just doing good SEO is not enough for LLM visibility when we look at specific domains.
Broad relationships between organic clicks and ChatGPT mentions tell a more nuanced story.
Whether or not “just doing good SEO” will be successful for LLM visibility can depend on the vertical or category.
Image Credit: Kevin Indig
In some verticals, AI chatbot optimization can really move the needle. In others, it might not help much.
Earbuds and CRM have a strong correlation between clicks and ChatGPT visibility.
Credit cards and handbags have a weak one.
In other words, credit cards and handbags are a much more open playing field for LLM optimization.
So clearly, that’s where optimizing for LLMs has the biggest payoff.
What Makes A Category Worthy Of Visibility Optimization?
The differentiator is unclear.
The factors that likely play a role are:
Product specs.
Reviews.
Developer docs.
Regulatory language, and/or
Ad spend.
But ultimately, we need more data to understand when product categories have a high or low overlap between AI visibility and organic search.
Image Credit: Kevin Indig
Besides the high correlation between organic and AI traffic, some categories have a higher degree of winner-takes-it-all dynamics than others.
In the CRM category, for example, three brands get almost 50% of visibility: Salesforce, HubSpot, and Google.
These dynamics seem to reflect market share – the CRM space is heavily dominated by Salesforce, HubSpot, and Google. (Google even thought about buying HubSpot, remember?)
After matching many metrics with AI chatbot visibility, I found one factor that stands out more than anything else: brand search volume. The number of AI chatbot mentions and brand search volume have a correlation of .334 – pretty good in this field. In other words, the popularity of a brand broadly decides how visible it is in AI chatbots.
This effect is reflected here as well, and contextualized by market share.
Plainly, the more fragmented a category is, the higher the chance of gaining ChatGPT visibility.
This is great news for organizations or brands in emerging industries or products where there is plenty of room for competition.
However, categories that are dominated by a few brands are harder to optimize for LLM visibility, probably because there is already so much content on the web about these incumbents.
If you’re thinking, “Well, that’s not new, Kevin. That’s true of SEO, too.” I get it.
This information might feel fairly intuitive, but I’ve seen smaller brands or startups that heavily invest in high-quality SEO practices be able to find their way at the top of search results.
What the data I’m discussing today shows us is that it’s going to be even more challenging to optimize for LLM visibility in verticals or industries that are well-established and have long-time trusted incumbents dominating the vertical.
And depending on what vertical your site sits in, you’ll need to develop your organic visibility strategy accordingly.
So, here are the main takeaways from my findings:
It’s risky to dismiss AEO/GEO altogether. You could assume that no action is needed when you’re winning in “classic SEO,” but that would open the door to competitors taking your spot in ChatGPT.
Don’t pivot or panic if you’re already winning. It’s also not helpful to reflexively change tactics or practices in attempts to optimize for ChatGPT when you’re already doing well. Start brainstorming plans for changes (algorithms do change, after all), but no need to reinvent the wheel just yet.
Prioritize content and PR investments for ChatGPT when the overlap with organic search is low across your most prompts. Now’s the time to get the ball rolling on this. Record your actions and your results, and find out what works in your vertical.
The Biggest Differences Between SEO And GEO/AEO
Half of the community wants to put a new label on SEO; half says it’s the same.
Here’s where I think the disconnect stems from:
The fundamental principles overlap, but the implementation and context differ significantly.
Both SEO and GEO/AEO rely on these core elements:
Technical accessibility: Both require content to be easily crawlable and indexable (with JavaScript often creating challenges for both, though currently more problematic for LLM crawlers).
Content quality: High-quality, comprehensive, and accurate content performs better in both environments.
Authority signals: While implemented differently, both systems rely on signals that indicate trustworthiness and expertise.
Despite these shared foundations, how you optimize is different:
User intent and query patterns: AI chatbots handle longer prompts where users express detailed intent, which requires more specific content that addresses nuanced questions. Google is moving in this direction with AI Overviews, but it still primarily serves shorter queries.
Signal weighting and ranking factors: AI chatbots give significantly more weight to overall brand popularity and volume of mentions. Google has more robust ways to measure and incorporate user satisfaction (Chrome data, click patterns, return-to-search rates). In another study I’m working on, trends indicate search results are more stable and the emphasis on content freshness is higher.
Quality and safety guardrails: Google has developed specific criteria for YMYL (Your Money Your Life) content that AI chatbots haven’t fully replicated. LLMs currently lack sophisticated spam detection and penalty systems.
Rich results: Google uses a variety of SERP features to format different content types. ChatGPT only incorporates rich formatting for some content (maps, videos).
And like I mentioned at the start, SEO and GEO/AEO are like pianos and guitars.
They share fundamental musical principles, but require different techniques and additional knowledge to play both effectively.
And essentially, classic SEO professionals will need to train as multi-instrumentalists over time.
Strategic Adaptation, Not Reinvention – Yet
Despite the different dynamics, both SEO and GEO/AEO have the same optimizations:
Create better content.
Provide unique perspectives.
Increase your brand strength.
Ensure your site is properly crawled and indexed.
The difference lies in how much attention you should pay to certain content categories and how resource allocation works.
Rather than creating an entirely new practice, it’s about understanding when and how to prioritize your efforts.
By the way, I also think it’s too early to coin a new acronym.
The AI and chatbot landscape is evolving rapidly, and so is search. We haven’t reached the final form of AI yet.
In some verticals with low correlation between search and AI visibility, there’s a significant opportunity to stand out.
In others, your SEO efforts may already be giving you the visibility you need across both channels.
But I do expect GEO/AEO to differ more from SEO over time.
Why? The signals OpenAI gets from interaction with its models and from the richness of prompts should allow it to develop its own weighting signals for brands and answers.
OpenAI gets much better inputs to train its models.
As a result, it should be able to either:
Develop its own web index that it can use to ground answers in facts, or
Develop a whole new system of grounding rules.
What Should You Do Right Now?
Focus on understanding your category’s specific dynamics.
Are the SEO leaders in your category also dominating prompts on ChatGPT?
If so, focus on becoming a leader in search results.
If not, focus on becoming a leader in search and invest in monitoring and optimizing your visibility across relevant ChatGPT prompts with targeted content, PR campaigns, content syndication, and content repurposing across different formats.
And until we all see this technology evolve and distinguish itself further from traditional organic search, I say we just all stick with SEO as our agreed-upon acronym for what we do…
…at least for now.
Featured Image: Paulo Bobita/Search Engine Journal
But the problem with hacks is that, as soon as they’re discovered, changes will be made that make those hacks ineffective.
Consider the Rank Or Go Home Challenge, where Kyle Roof managed to get his website a top ranking for the string “Rhinoplasty Plano,” despite 98% of the site being “lorem ipsum” text.
Within 24 hours of Google hearing about this, the site was de-indexed.
The same holds true for AI search, but here, the system is changing at a breakneck pace. What works today may well not work a month from now.
Understanding GEO
Generative Engine Optimization (GEO) is the emerging field of optimization for AI search. This includes optimizing to appear in Google’s AI Overviews, Gemini, ChatGPT, Grok, and others.
This field is evolving rapidly, meaning that tactics used today may not work in a year.
Here are a few examples of how quickly generative AI evolves, according to a key benchmark analysis by Ithy about OpenAI’s o1 to o3 models.
Mathematical reasoning: AIME 2024 benchmark accuracy rose from 83.3% to 96.7%, a 13.4% improvement.
Scientific reasoning: Using the GPQA Diamond Benchmark, ChatGPT’s “o3 scored 87.7% accuracy compared to o1’s 78.0%, demonstrating a stronger capacity to handle complex, PhD-level science questions with greater precision and depth.”
Coding: ChatGPT has significantly improved from o1 to o3, with o3 “achieving a 71.7% accuracy rate, a significant increase from the o1 model’s 48.9%.”
This means that, in the long term, hacking the system simply won’t be cost-effective. Any hack you uncover will have a very limited shelf life.
Instead, we should turn to a tried and true tactic from SEO: aligning consensus.
What Is Consensus, And How Do You Align With It?
Put simply, consensus is when a variety of high-quality sources align on a topic.
For example, if you ask Google if the earth is round or flat, the resulting snippet will tell you it is round because the vast majority of high-quality sources agree on this fact.
Screenshot from search for [is the earth round or flat], Google, February 2025
The highest-ranking results will be sites that agree with this consensus, while results that don’t align rank poorly.
AI search works in much the same way. It can identify the general consensus on a topic and use this consensus to decide which information is most relevant to a user’s search intent.
Building Consensus Through PR
So, then, building consensus is key for GEO. But, how can you help build consensus?
The answer is through the use of experts.
How Experts Build Consensus
Let’s take an example from Mark Cuban, a financial expert and Florida resident.
When discussing the topic of the housing crisis in Florida on the platform Bluesky, he stated that a major issue is the affordability of home insurance.
This, was then cited by a variety of articles on sites like GoBankingRates.
Further articles may then also cite this article, perhaps bringing in other experts to comment.
Soon, a consensus forms: Florida’s housing crisis is due at least in part to homeowners’ insurance rates. And if we ask Google this question, the AI snippet reflects just that.
Screenshot from search for [what are the factors in florida’s housing crisis], Google, February 2025
Even a single expert’s opinion can have a major impact on consensus, especially for smaller, more niche topics.
Positioning Expertise To Build Consensus
The important thing to keep in mind is consensus cannot be faked.
Building consensus requires convincing people. And to convince people, you’ll need to establish your expertise and credibility and get a conversation going to establish consensus on a topic.
In other words, you’ll need:
Credible expertise.
High-quality data or insights.
Enough coverage or references across the web to establish that your viewpoint is widely accepted (or at least seriously considered) by other experts.
Say you want to build consensus around the idea that the best way to pay off debts is to prioritize debts with the highest interest rates.
By publishing original research that shows this to be true, backed by the voice of an established expert, you can start a conversation on this topic.
As further blog posts and online conversations reference your data, your position will gain greater reach. Then, more experts may comment on it and agree with it, over time building that consensus on the topic.
Then, when somebody goes to research the topic with AI search, the AI will find that consensus you’ve built.
Consider the case of blue light.
In 2015, the Journal of Clinical Sleep Medicine published a study:
“Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness.”
This study showed that exposure to blue light suppresses melatonin production, leading to delayed sleep onset and reduced sleep quality.
This research was then cited by experts and major outlets before gaining traction on social media and blogs.
Now, if you AI search “Does blue light affect sleep?” you’ll be given this information (that blue light affects sleep), and it will cite this original research and the websites and experts who wrote about it.
Screenshot by author from Perplexity, February 2025
Collaboration For Building Consensus
Of course, you don’t have to simply wait for a conversation to find its way to other experts. By collaborating directly, you can amplify the establishment of a consensus.
Let’s take the same example as before. But, this time, we make a small change: Instead of authoring studies or guest posts solo, we do so in collaboration with another established expert.
In doing so, you can essentially “hijack” your collaborator’s authority and audience:
Their followers will become aware of your research.
Their peers and fellow experts are more likely to consider your findings.
Media outlets also view collaborations as more credible than a single, lesser-known source, further boosting your reach.
Take the example of David Grossman’s article The Cost of Poor Communications.
Inclusion in an article published on Provoke Media’s The Holmes Report, allowed Grossman to present his ideas to a wider audience.
This information went on to be referenced in a variety of other articles, including sites such as Harvard Business School.
Then, over time, these ideas form part of the consensus on business communication, appearing in AI search results for platforms such as Perplexity.
Screenshot by author from Perplexity, February 2025
Even With The Best Methods, Building Consensus Is A Process
This is, of course, a simplification of the process.
Your study or collaboration may simply go unnoticed, even if you do everything right.
Or, it may go against the existing consensus. In this case, you’ll face a serious uphill battle to try and change that consensus.
Even if you are successful, it may take some time for a new consensus to emerge.
These things don’t happen overnight. But, that doesn’t mean you should give up; every time you publish a study or collaborate with another expert, your reach and authority grows.
And as you continue the conversation through further studies and guest posts, a new consensus can begin to form.
At the heart of that new consensus are your ideas and expertise.
In turn, when providing sources for its search results, AI search will surface your ideas and drive traffic to your website.
Long-Term Success Over Short-Term Hacks
While new hacks are always being found, tried and tested methods will always be the better choice.
Instead of chasing an ever-moving target trying to outsmart constantly evolving generative AI tools, your time is much better spent building consensus around a topic.
This means:
Establishing expertise through studies and guest posts.
Collaborating with other experts to boost your reach and authority.
Continuing this process of authority building over time.
Building consensus takes time, but the payoff is lasting influence, which sees AI search surfacing your content and treating you as a trusted source of information.
Large language models (LLMs) like ChatGPT and Claude are best known for their writing abilities, drafting ad copy, summarizing reports, and helping brainstorm blog content.
However, most marketers still know little about one of their most powerful features: They can write actual code.
First, it talked. Then, it wrote. Now, it builds.
We’re not just talking about basic snippets. These models can generate full scripts, fully functional browser extensions, small web apps, and automations, all from plain English prompts – or any other language you’re most comfortable with.
For marketers and PPC pros, that unlocks a new level of efficiency. You no longer need to know how to program to start benefiting from technical solutions to everyday problems.
In the past, I might have only written a script if it saved me hours of manual work every month.
Now, with LLMs, it’s so quick to build something that I’ll even create one-off tools for tasks that would’ve only taken me an hour or two. That’s how low the barrier has become.
In this article, I’ll walk through the types of problems you can solve with LLM-powered coding, the browser-based tools that make it accessible, and real examples of how marketers are already using this to move faster.
The Real Power: Turning Instructions Into Code
LLMs have ingested much of the world’s knowledge, and that includes scripts and computer code. That means if you can explain a process clearly, they can usually turn that explanation into working code.
Because they’re multi-modal, they can even understand a diagram you’ve whiteboarded at the office and turn that into code, too.
This makes them incredibly valuable for non-programmers who know what they want but don’t know how to build it.
Think of the marketer who understands how data should be formatted for a monthly client report but dreads the repetitive steps of reformatting CSV files. Or the account manager who wants to automate their process of eliminating underperforming search terms but doesn’t have a dev team to help them.
With LLMs, these tasks can be described in a few sentences, and AI can generate Python scripts, JavaScript tools, or even complete web apps that solve the problem.
This isn’t just about saving time. It’s about unlocking experimentation and removing the friction that keeps good ideas from scaling through technology.
What Problems Can LLM-Generated Code Solve?
Let’s break down the kinds of problems where LLM coding can shine. These aren’t hypothetical; they’re pulled from common workflows across agencies and in-house teams daily.
1. Automating Repetitive, Time-Consuming Tasks
You probably do at least one of these on a somewhat regular basis:
Copying Google Ads data into slides for reporting.
Cleaning up GPT’s output before sharing it with a client.
Manually reviewing ad copy for brand compliance.
With the help of an LLM, each of these can be turned into a repeatable, automatable workflow. You describe the task, and the LLM builds the script that does it.
This is especially valuable for marketers who are tired of being “spreadsheet operators” instead of strategists. By turning routine tasks into one-click tools, you free up hours a week and reduce human error.
2. Trying Something Entirely New
Unlike the tasks above, which you know exactly how to do but hate how much time they take, there are also some projects you may not have tried because you do not know how.
For my team, that included a quiz to make blog content more engaging. For me, it involved building a browser extension to blur sensitive data on the screen.
These are ideal use cases for LLM-powered coding. They allow you to prototype and test ideas without needing a development team, and if you’re lucky enough to have one, you don’t need to wait for your project to get prioritized.
You can get feedback quickly, iterate faster, and build an entire proof of concept before involving engineering.
Marketing innovation often dies in the backlog. LLM coding makes it easier to try things on your own.
3. Google Ads Scripts
This is one of the most exciting areas for PPC pros. Google Ads scripts are powerful, but let’s face it, they’ve always had a learning curve. Now, LLMs can flatten that curve dramatically.
You can tell a model:
Write a Google Ads script that checks all active campaigns with “Mother’s Day” in the name. If the current date is within seven days of Mother’s Day, increase the daily budget of those campaigns by 20%. Include comments to explain each part of the code so I can understand what the script is doing.
It will return a fully functional script that you can paste directly into your account’s scripts section.
This lowers the barrier to entry for marketers who want to automate common PPC maintenance or build lightweight tools for managing large accounts.
You can go from idea to automation in minutes, no JavaScript experience required.
Tools That Make LLM Coding Accessible
I hope the idea of becoming more efficient through code sparks your interest, especially if you’ve ever found yourself repeating the same task week after week.
Whether you’re managing ad campaigns, cleaning data, or formatting content, the ability to automate even small pieces of your workflow can save hours and reduce errors.
Here’s the best part: You don’t need to be a developer to start.
You don’t even need to install anything, understand programming languages, or know how to set up a server. You definitely don’t need to open a complicated integrated development environment (IDE).
The tools I’m about to show you run entirely in your browser. They’re designed to help you go from idea to functional code with nothing more than a clear description of what you want to achieve.
If you’ve never written code before, this is exactly where you want to start.
Claude (Anthropic)
For marketers, Claude’s ability to write, test, and execute code right inside the interface is a real standout.
No setup is required, no installations, and no APIs to connect.
You describe what you need, Claude writes the code, and you see the results in real-time. This fast, feedback-driven loop makes it easier than ever to experiment and iterate without the usual technical friction.
The 200,000-token context window is another game-changer. You can paste your entire campaign structure, a long analytics report, or even a full landing page copy, and Claude will process it all in one go.
It keeps track of every detail you’ve shared, so nothing gets lost as you build on your ideas.
There is a tradeoff, though. Claude currently runs code in a single-file execution environment. That’s fine for most marketing tasks, but for more complex, multi-file projects, it’s not as flexible as tools like Vercel’s V0.dev, which supports full project structures.
Still, for marketers building scrappy, high-impact tools fast, Claude handles a surprising amount.
Here’s what’s most exciting to me:
It can run JavaScript right in the browser, perfect for quick tasks like data filtering, simple visualizations, or interactive prototypes.
It translates technical concepts into plain, marketing-friendly language, so you’re never stuck decoding dev speak.
It surfaces insights from your data quickly, helping you spot trends and outliers that would otherwise go unnoticed.
One of the benefits of LLMs is that they can adapt to each user’s level.
If you’re not technical, it gives you just enough to feel empowered. If you are, it meets you there and helps you move even faster. Either way, it expands what’s possible without getting in your way.
Below is a view of Claude generating code based on a marketing-focused prompt, with both the prompt and working output visible in the interface.
Users can toggle to the code view if they prefer to see that instead of a preview of the tool.
Screenshot from Claude.ai, April 2025
V0.dev (Vercel)
As much as I’m excited about Claude, V0 takes it to a whole new level.
Vercel, the creator of Next.js, made V0.dev, which is designed to build working software by describing what you need.
Why it stands out:
Generates full React components, HTML, and CSS.
Lets you deploy working projects instantly.
Handles multi-file architecture (great for real apps).
Marketers can use V0.dev to build:
Text reformatting tools.
User interfaces (UI/UX).
Internal dashboards.
Fully working web apps.
It’s like having a front-end developer in your browser.
Here’s a screenshot of what I quickly tried building using V0.dev. I prompted it to create a simple tool for Search Engine Journal readers that takes a blog post and outputs key takeaways in bullet form.
V0.dev generated a clean, on-brand interface with just a single prompt, no coding required. It’s a great example of how fast you can go from idea to working prototype.
What’s especially cool is that you could even launch this tool so anyone can use it.
Screenshot from v0.dev, April 2025
When creating a tool that requires third-party integrations, V0 asks for the required API keys and credentials.
When building something that can’t be hosted online, like a Chrome extension, it explains how to install the files. In short, it helps anyone, regardless of ability, to create a working piece of software.
GPT-4o (OpenAI/ChatGPT)
GPT-4o is the LLM I’ve used the most for building ad scripts, as it was the first one to write an error-free piece of code. It’s also great for:
Creating data transformation scripts.
Debugging code.
Explaining errors.
Translating code from one language to another.
But, GPT is limited in that it can’t run the code it writes directly in the chat window. That means there is a lot of copy-and-pasting required to take the code, install it on a server, test it, and then iterate with GPT to debug it.
While I think GPT is awesome for writing code, if I need something quick and simple, I’ll prefer Claude. If I want something more complex and want to debug it in the LLM, I’ll use V0.
Real-World Example Use Cases
Let’s go deeper into actual examples. These aren’t just ideas; these are projects you can ask an LLM to help build today.
Example 1: Chrome Extension To Blur Sensitive Text
The Problem:
I’m frequently taking screenshots of dashboards or search results but need to hide client names, numbers, or other sensitive data.
The LLM Solution:
I asked V0.dev to generate a Chrome extension that adds a blur effect to any numerical values on the page.
It generates all the files needed and explains how to install my custom extension in my Chrome browser. It returns:
The manifest.json file.
JavaScript to inject CSS.
Instructions to package and install the extension.
Screenshot from Optmyzr.com, April 2025
Why It Matters:
This isn’t something most marketers would ever think to build, but with a few prompts, you’ve created a privacy-preserving utility that saves you editing time and protects sensitive info.
Example 2: Web App To Reformat GPT Output
The Problem:
I use Deep Research from ChatGPT to generate research for my team or future blogs, but I don’t love how source references are formatted when I copy the research into a Google Doc.
The LLM Solution:
Use V0.dev to create a web app that:
Accepts pasted text.
Accepts a list of formatting changes I would normally make manually (e.g., finding links and putting them in superscript).
Displays the cleaned version instantly.
Why It Matters:
It streamlines content workflows. Instead of editing output by hand, you get consistent formatting that meets your brand or platform guidelines.
Example 3: Interactive Blog Quiz Generator
The Problem:
We wanted to make our blogs more interactive, and my team had the idea to add quizzes.
The LLM Solution:
Use Claude to generate a quiz engine in HTML/CSS/JS. Feed it five to seven questions, then tie the result to different calls to action (“Download This Guide” or “Talk to an Expert”).
Why It Matters:
Interactive content improves time-on-page, reduces bounce, and personalizes the experience, without needing design or dev support.
Want to see it? Check out how AI is transforming our content about bidding strategies.
Screenshot from Optmyzr Blog, April 2025
Conclusion: Marketers Can Now Build What They Need With AI
Writing utility software is easier than it’s ever been before.
For marketers, the question used to be “What tools should I use?” Now, it might be: “What tools should I create?”
If you’ve ever been bottlenecked by engineering resources, or if your “wouldn’t it be cool if…” idea has sat in a notebook for months, this is your chance.
You don’t need an IDE. You don’t need to understand loops or classes. You just need a problem to solve, a clear description, and the right LLM at your side.
Aleyda Solis’ SEOFOMO published a survey of ecommerce owners and SEOs that indicates a wide range of uses of AI, reflecting popular SEO tactics and novel ways to increase productivity, but also reveals that a significant number of the respondents have yet to fully adopt the technology because they are still figuring out how it best fits into their workflow. Very few of the survey respondents said they were not considering AI.
The survey responses showed that there are five popular category uses for AI:
The survey respondents used AI for important reasons like product listing and descriptions, as well as for scaling meta descriptions, titles, and alt text. Other uses include creating content outlines, grammar checks and other assistive uses of AI.
But some also used it for blog content, landing pages, and for generating FAQ content. There’s no details of how extensively AI was used for blog content but a case could be made against using it for fully generating main content with AI (if that’s how some people are using it) because of Google’s recent cautionary guidance about extensive use of AI for main content. Google’s Danny Sullivan at the recent Search Central NYC event cautioned about low effort content lacking in originality.
The other reported uses of AI was for grammar checking and clarity which are excellent ways to use AI. Care should be used even for these purposes because AI has a style that can get injected into the content even for something as simple as checking for grammar.
Another interesting use of AI is for revising content so that it matches a company’s “brand voice” which is checking for word choices, tone, and even sentence structure.
Lastly, the ecommerce survey respondents reported using AI for brainstorming content ideas which is another excellent way to use AI.
Analysis & Research
The part about keyword analysis is interesting because the report lists keyword research and clustering as one of the uses. Clustering keywords according to similarity is a good practice because it’s somewhat repetitive and spammy to write pages of content about related things, one page for each keyword phrase when one strong page that represents the entire topic is enough.
Focusing on keywords for SEO has been around longer than Google, and even Google itself has evolved from using keywords as a way to understand content to also incorporating an understanding of queries and content as topics.This is seen in the fact that Google uses core topicality systems as part of its ranking algorithm. So it’s somewhat curious that topicality research wasn’t mentioned as one of the uses, unless keyword clustering is considered part of that. Nevertheless, data analysis is a great use of AI.
Technical SEO
Technical SEO is a fantastic application of AI because that’s all about automating repetitive SEO tasks but also for assisting on making decisions about what to do. There’s lots of ways to do this, including by uploading a set of guidelines and/or charts and asking AI to analyze for specific things. Apps like Screaming Frog allow integration with OpenAI, so it’s leaving money and time on the table to not be investigating all the ways AI can integrate with tools as well as just asking it to analyze data.https://www.screamingfrog.co.uk/seo-spider/tutorials/how-to-crawl-with-chatgpt/
For example, one of the uses reported in the survey was for generating an internal linking strategy.
User Experience (UX) & Conversion Rate Optimization (CRO)
Another way ecommerce store owners are using AI is for improving the user experience and CRO.
The survey reports:
“AI-powered product recommendations
Chatbots for product discovery or customer support
CRO/UX audits based on user behavior”
Training & Education
Lastly, an increasing number of the ecommerce respondents reported using AI for generating training documentation for internal use and for creating customer documentation.
The survey reports:
“Less common but growing:
Learning how AI tools function
Using AI to create training material or SEO learning resources”
Not Using AI Or Limited Use
What was surprising is the amount of SEOs that are not using AI in a meaningful way. 31% of respondents said they are not using AI but are planning to, 3% of the survey respondents were digging their heels into the ground and flatly refusing to use AI in any way, while an additional 4% answered that they weren’t sure.
That makes a full 37% that aren’t using AI in any meaningful way. Looked at another way, 31% of respondents were getting ready to adopt AI into their workflow. Many managed WordPress hosting companies are integrating AI into their WordPress builder workflow as are some WordPress builders. AI can be integrated via WordPress SEO plugins as well. Wix has already integrated AI into their customer workflow through their proprietary Astro chatbot and companies like Shopify are also planning meaningful and useful ways to integrate AI.
The SEOFOMO survey makes it clear that AI is a significant part of the SEO and ecommerce workflow. Those who don’t use AI shouldn’t feel like they have to. But if you’re unsure how to integrate it, one way to think about it is to ask: what kinds of tasks would you hand off to an intern? Those are the kinds of tasks that AI excels at, enabling one worker to produce at a level five times greater than they could without using AI.
Google’s John Mueller answered a question about LLMs.txt, a proposed standard for showing website content to AI agents and crawlers, downplaying its usefulness and comparing it to the useless keywords meta tag, confirming the experience of others who have used it.
LLMS.txt
LLMS.txt has been compared to as a Robots.txt for large language models but that’s 100% incorrect. The main purpose of a robots.txt is to control how bots crawl a website. The proposal for LLMs.txt is not about controlling bots. That would be superfluous because a standard for that already exists with robots.txt.
The proposal for LLMs.txt is generally about showing content to LLMs with a text file that uses the markdown format so that they can consume just the main content of a web page, completely devoid of advertising and site navigation. Markdown language is a human and machine readable format that indicates headings with the pound sign (#) and lists with the minus sign (-). LLMs.txt does a few other things similar to that functionality and that’s all it’s about.
What LLMs.txt is:
LLMs.txt is not a way to control AI bots.
LLMs.txt is a way to show the main content to AI bots.
LLMs.txt is just a proposal and not a widely used and accepted standard.
That last part is important because it relates to what Google’s John Mueller said:
LLMs.txt Is Comparable To Keywords Meta Tag
Someone started a discussion on Reddit about LLMs.txt to ask if anyone else shared their experience that the AI bots were not checking their LLMs.txt files.
They wrote:
“I’ve submitted to my blog’s root an LLM.txt file earlier this month, but I can’t see any impact yet on my crawl logs. Just curious to know if anyone had a tracking system in place,e or just if you picked up on anything going on following the implementation.
If you haven’t implemented it yet, I am curious to hear your thoughts on that.”
One person in that discussion shared that they host over 20,000 domains and that no AI agents or bots are downloading the LLMs.txt files, only niche bots like one from BuiltWith is grabbing those files.
The commenter wrote:
“Currently host about 20k domains. Can confirm that no bots are really grabbing these apart from some niche user agents…”
John Mueller answered:
“AFAIK none of the AI services have said they’re using LLMs.TXT (and you can tell when you look at your server logs that they don’t even check for it). To me, it’s comparable to the keywords meta tag – this is what a site-owner claims their site is about … (Is the site really like that? well, you can check it. At that point, why not just check the site directly?)”
He’s right, none of the major AI services, Anthropic, OpenAI, and Google, have announced support for the proposed LLMs.txt standard. So if none of them are actually using it then what’s the point?
Mueller also raises the point that an LLMs.txt file is redundant because why use that markdown file if the original content (and structured data) have already been downloaded? A bot that uses the LLMs.txt will have to check the other content to make sure it’s not spam so why bother?
Lastly, what’s to stop a publisher or SEO from showing one set of content in LLMs.txt to spam AI agents and another set of content for users and search engines? It’s too easy to generate spam this way, essentially cloaking for LLMs.
In that regard it is very similar to the keywords meta tag that no search engine uses because it would be too sketchy to trust a site that it’s really about those keywords and search engines are better and more sophisticated nowadays about parsing the content to understand what it’s about.
How can you structure content so that AI pulls your site into the response?
Do you really need to change your SEO strategy?
For years, SEO teams followed a familiar SEO playbook:
Optimize upper-funnel content to capture awareness,
mid-funnel content to drive consideration,
lower-funnel content to convert.
One page, one keyword, one intent.
But with the rise of ChatGPT, Perplexity, Copilot, Gemini, and now Google’s AI Mode, that linear model is increasingly outdated.
So, how do you move forward and keep your visibility high in modern search engine results pages (SERPs)?
We’ve reverse-engineered AI Overviews, so you don’t have to. Let’s dive in.
What We’ve Discovered Through Reverse Engineering Google’s AI Overviews (AIO)
From what we’re seeing across client industries and in how AI-driven results behave, the traditional funnel model – the idea of users moving cleanly from awareness to consideration to conversion – feels increasingly out of step with how people actually search.
How Today’s Search Users Actually Search
Today’s users jump between channels, devices, and questions.
They skim, abandon, revisit, and decide faster than ever.
AI Overviews don’t follow a tidy funnel because most people don’t either.
They surface multiple types of information at once, not because it’s smarter SEO, but because it’s closer to how real decisions get made.
AIOs & AI Mode Aren’t Just Answering Queries – They’re Expanding Them
Traditionally, SEO strategy followed a structured framework. Take a travel-related topic, for example:
Informational (Upper-Funnel) – “How to plan a cruise?”
Commercial (Mid-Funnel) – “Best cruise lines for families”
Transactional (lower-Funnel) – “Find Best Alaska Cruise Deals”
However, AI Overviews don’t stick to that structure.
Instead, they blend multiple layers of intent into a single, comprehensive response.
How AI Overviews Answer & Expand Search Queries
Let’s stay with the travel theme. A search for “Mediterranean cruise” might return an AI Overview that includes:
Best Time to go (Informational).
Booking Your Cruise (Commercial).
Cruise Lines (Navigational).
AI Mode Example for ‘Mediterranean Cruise’
What’s Happening Here?
In this case, Google isn’t just answering the query.
It anticipates what the user will want to know next, acting more like a digital concierge than a traditional search engine.
We started noticing this behavior showing up more often, so we wanted to see how common it actually is.
To get a clearer picture, we pulled 200 cruise-related informational queries from SEMrush and ran them through our custom-built AI SERP scraper. The goal was to see how often these queries triggered AI Overviews, and what kind of intent those Overviews covered.
The patterns were hard to miss:
88% of those queries triggered an AI Overview
More than half didn’t just answer the initial question.
52% mixed in other layers of intent, like brand suggestions, booking options, or comparisons, right alongside the basic information someone might’ve been looking for.
Using a different query related to Mediterranean Cruises, the AIO response acts as a travel agent, guiding the user on topics like:
How to fly,
Destinations with region,
Cruise prices,
Cruise lines that sail to that destination.
While it’s an Information non-brand search query, the AIO response is lower-funnel as well.
Again, less than half of the queries were matched intent.
Here are some examples of queries that were identified as Informational and provided only the top-of-funnel response without driving the user further down the funnel.
The Verdict
Even when someone asks a simple, top-of-funnel question, AI is already steering them toward what to do next, whether that’s comparing prices, picking a provider, or booking a trip.
What Does This Mean for SEO Strategies Moving Forward?
If AI Overviews and AI Mode are blending intent types, content, and SEO strategies need to catch up:
It’s no longer enough to rank for high-volume informational keywords. If your content doesn’t address multiple layers of intent, AI will fill the gaps with someone else’s content.
SEO teams need to analyze how AI handles their most important queries. What related questions is it pulling in? Are those answers coming from your site or your competitors?
Think beyond keyword volume. Long-tail queries may have lower search traffic, but they often align better with AI-cited content. Structure your pages with clear headings, bullets, and concise, helpful language—that’s what AI models prefer to surface.
The Future of SEO in an AI World: Hybrid Intent Optimization
The fundamentals of technical and on-page SEO still matter. But if your content is still built around single keywords and single intent types, you’re likely to lose visibility as AI continues to reshape the SERP.
The brands that adapt to this shift by creating content that mirrors the blended, fast-moving behavior of actual users are the ones that will continue to own key moments across the funnel, even as the funnel itself evolves.
AsAItransformssearchbehavior,it‘scrucialtoadaptyourSEOstrategiesaccordingly.AtDAC,wespecializeinaligningyourcontentwiththelatestsearchtrendstoenhancevisibilityandengagement.Reachouttoustodaytofuture-proofyour strategy with our award-winning TotalSERP approachandstayaheadintheevolvingdigitallandscape.
https://www.dacgroup.com/” class=”btn-learn-more button-green medium-size”>Optimize Your SEO For AI Search, Now
Image Credits
Featured Image: Image by DAC. Used with permission.
In-Post Image: Images by DAC. Used with permission.
Google’s AI Overviews have changed how search works. A TrustRadius report shows that 72% of B2B buyers see AI Overviews during research.
The study found something interesting: 90% of its respondents said they click on the cited sources to check information.
This finding differs from previous reports about declining click rates.
AI Overviews Are Affecting Search Patterns in Complex Ways
When AI summaries first appeared in search results, many publishers worried about “zero-click searches” reducing traffic. Many still see evidence of fewer clicks across different industries.
This research suggests B2B tech searches work differently. The study shows that while traffic patterns are changing, many users in their sample don’t fully trust AI content. They often check sources to verify what they read.
The report states:
“These overviews cite sources, and 90% of buyers surveyed said that they click through the sources cited in AI Overviews for fact-checking purposes. Buyers are clearly wanting to fact-check. They also want to consult with their peers, which we’ll get into later.”
If this extends beyond this study, being cited in these overviews might offer visibility for specific queries.
From Traffic Goals to Citation Considerations
While still optimizing for organic clicks, becoming a citation source for AI overviews is valuable.
The report notes:
“Vendors can fill the gap in these tools’ capabilities by providing buyers with content that answers their later-stage buying questions, including use case-specific content or detailed pricing information.”
This might mean creating clear, authoritative content that AI systems could cite. This applies especially to category-level searches where AI Overviews often appear.
The Ungated Content Advantage in AI Training
The research spotted a common mistake about how AI works. Some vendors think AI models can access their gated content (behind forms) for training.
They can’t. AI models generally only use publicly available content.
The report suggests:
“Vendors must find the right balance between gated and ungated content to maintain discoverability in the age of AI.”
This creates a challenge for B2B marketers who put valuable content behind forms. Making more quality information public could influence AI systems. You can still keep some premium content gated for lead generation.
Potential Implications For SEO Professionals
For search marketers, consider these points:
Google’s emphasis on Experience, Expertise, Authoritativeness, and Trustworthiness seems even more critical for AI evaluation.
The research notes that “AI tools aren’t just training on vendor sites… Many AI Overviews cite third-party technology sites as sources.”
As organic traffic patterns change, “AI Overviews are reshaping brand discoverability” and possibly “increasing the use of paid search.”
Evolving SEO Success Metrics
Traditional SEO metrics like organic traffic still matter. But this research suggests we should also monitor other factors, like how often AI Overviews cite you and the quality of that traffic.
“The era of volume traffic is over… What’s going away are clicks from the super early stage of the buyer journey. But people will click through visit sites eventually.”
He adds:
“I think we’ll see a lot less traffic, but the traffic that still arrives will be of higher quality.”
This offers search marketers one view on handling the changing landscape. Like with all significant changes, the best approach likely involves:
Testing different strategies
Measuring what works for your specific audience
Adapting as you learn more
This research doesn’t suggest AI is making SEO obsolete. Instead, it invites us to consider how SEO might change as search behaviors evolve.
Featured Image: PeopleImages.com – Yuri A/Shutterstock
