Traditional share of voice (SOV) is effectively obsolete, yet many organizations have replaced it with an equally flawed successor: AI share of voice.
Software vendors now claim to measure brand visibility across ChatGPT, Gemini, Claude, Perplexity, and other AI platforms using a single percentage score. The problem is that these metrics rely on a hidden denominator.
Unlike traditional search, where visibility could be measured against a known keyword set, the universe of possible AI prompts is effectively infinite.
Traditional SOV had limitations, but at least its assumptions were transparent. Marketers defined a fixed keyword set, tracked visibility against competitors, and used that list as a stable denominator. Everyone understood the measurement’s boundaries.
That model no longer exists. Search results are dynamic and personalized, and are increasingly being replaced by conversational interfaces. Yet many AI visibility platforms continue to present precise-looking percentages that can’t be audited or validated.
To stop presenting fictional metrics to leadership teams, we must rethink how we define and measure visibility in AI search.
Why traditional SOV metrics now fail
The basic assumptions of search engine optimization and digital brand tracking have been broken by two major shifts: the disappearance of the static results page and the rapid rise of personalized, conversational answers.
Search engines have become highly dynamic, personalized landscapes that change shape continuously based on real-time data.
Between AI-generated summaries, localized results, continuous scrolling, interactive merchant grids, and real-time social feeds, no two users will encounter the same interface, even when entering the exact same query at the exact same moment.
Because the search environment changes constantly, attempting to calculate a precise “share” of that screen has become a mathematical impossibility.
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The new volatile normality of rankings
Securing the top ranking position in the older marketing model meant capturing a highly predictable percentage of user click-through rates.
In the modern search landscape, however, ranking first organically might place a brand below several sponsored listings, an AI-generated overview, interactive question accordions, and featured discussions from community platforms.
Because search engines now construct layouts dynamically in response to immediate user intent and past search history, rankings fluctuate by the hour.
Measuring share of voice based on static positions is as unproductive as trying to measure the volume of an ocean wave with a wooden ruler.
The modern AI share of voice
When marketing teams realized that traditional rank tracking was losing its utility, software vendors quickly introduced alternative metrics, branded as LLM Visibility or AI share of voice.
These dashboards present highly polished, authoritative percentage scores that suggest a brand’s footprint has been successfully mapped across platforms like ChatGPT, Claude, Gemini, and Perplexity.
These tools fail to deliver on this promise, exposing a fundamental methodology problem that we must address directly.
Legacy tracking (transparent)
LLM visibility (black box)
– Define fixed keyword list (known). – Measure rank on static SERPAuditable denominator.
– Infinite possible user prompts. – Vendor runs small, arbitrary subset. – Subjective denominator.
The infinite tail
Legacy SEO tools relied on a user-defined keyword list that served as a transparent denominator, whereas modern conversational engines present an entirely different mathematical reality where the universe of possible user prompts is effectively infinite.
Buyers no longer search for solutions using simple, two-word phrases. Instead, they enter highly specific, conversational queries that describe their exact organizational context, integration needs, and feature requirements.
Because no marketing tool can realistically sample this infinite universe of natural language, software vendors instead select a small, arbitrary subset of static prompts, run them through AI models behind the scenes, and aggregate those limited outputs into a representative global percentage.
This process creates a metric that only measures share of voice within a contrived and artificial environment, presenting a closed sandbox as if it were the open web.
Marketers maintained full visibility into the data they were analyzing with legacy tracking tools, which meant that if a system reported a specific percentage of visibility, the underlying keyword list could be audited and adjusted. Modern LLM visibility tools obscure their denominator within proprietary, vendor-defined systems that are almost certainly incomplete.
This structural flaw became incredibly clear in September 2025, when OpenAI updated to its ChatGPT 5.0 model. Following this release, the platform-wide volume of outbound citations and source links dropped.
For marketing teams relying on LLM tracking dashboards, this model change resulted in a sudden, sharp decline in their reported visibility metrics. The decline had nothing to do with a loss of brand relevance or a failure in marketing strategy. ChatGPT had simply changed how it presented source data to users.
This update demonstrates that modern AI metrics are highly volatile and largely out of your control. While software vendors are genuinely trying to solve an incredibly complex engineering problem, the underlying methodology simply cannot support the high-confidence dashboards they deliver, meaning these metrics should be treated as directional signals rather than hard numbers.
Beyond AI share of voice: 3 metrics that matter more
We must shift our focus from measuring pure search volume to measuring how effectively a brand is integrated into the broader context of digital discussions.
As search queries morph into conversational discovery, a brand’s visibility is no longer defined by the keywords it owns, but by how deeply it is embedded in the conceptual models used by AI.
1. Share of mentions
AI models synthesize relationships between concepts rather than simply indexing pages, meaning a brand must exist within the model’s training data, fine-tuning datasets, or real-time retrieval sources to be surfaced at all.
Share of mentions tracks how frequently your brand name, products, or key executives are naturally included in the responses generated across the broader information ecosystem.
This metric shifts the operational focus from ranking positions to vocabulary inclusion, ensuring that a brand is recognized by the model even when it is not explicitly prompted for a vendor list.
To influence this metric, organizations must focus on securing organic mentions across high-trust forums, developer communities, and authoritative industry publications where AI models actively gather and update their information.
2. Share of recommendations
When buyers use conversational engines to make purchasing decisions, they regularly ask for direct comparisons, shortlists, and product recommendations to simplify their research process.
Share of recommendations measures how often your product or service is explicitly featured when a user asks an AI engine to act as an advisor on a specific business challenge.
This approach shifts our focus from raw traffic acquisition to winning the buyer’s consideration set, which is critical because conversational engines filter out the noise of the web to deliver a highly curated list of options.
If your product positioning is overly generic, the model will struggle to categorize your offering and will default to recommending competitors that have established a much clearer, highly documented use case.
3. Share of narrative
Merely securing a mention in an AI response is insufficient if the context of that mention portrays your brand poorly, as high visibility within a negative framework can quickly become a strategic liability.
Share of narrative measures the qualitative attributes, adjectives, and associations linked to your brand name in conversational outputs, allowing you to understand how your business is being framed.
Narrative
What it tracks
The core strategic question
The “best” narrative
How often you are framed as the premium, gold-standard market leader.
Is the model positioning our brand as the most capable solution available?
The “popular” narrative
How often you are cited as the default, widely adopted industry standard.
Is the model identifying our brand as the most commonly used option?
The “budget” narrative
How often you are categorized as the cost-effective, value, or entry-level alternative.
Is the model framing our brand primarily as a low-cost, entry-level alternative?
If an AI engine includes your brand frequently but consistently describes your product as a complex, legacy system, your high share of voice may actually be damaging your sales pipeline.
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Reframing your success metrics
Leadership teams require competitive benchmarks to evaluate market performance, meaning you cannot simply stop reporting on share of voice without offering a viable alternative.
Transitioning your executive reporting smoothly requires a structured, three-step plan.
Reframing the executive narrative involves educating your leadership team on the limitations of modern AI dashboards.
This means explaining the hidden denominator problem and demonstrating why treating these figures as absolute metrics introduces unnecessary risk.
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People have been calling the keyword dead since at least 2010. Yet here we are in 2026, still using keywords to show ads on Google.
Advertisers weren’t wrong to equate the loss of control with the death of the keyword. The keyword simply couldn’t disappear until Google had something better to replace it.
At Google Marketing Live (GML) last month, we may have seen that replacement. AI Brief is a Gemini-powered control layer that lets you steer AI Max using prompts-first language.
At first glance, AI Brief may seem like just another AI Max feature. AI Max is still trying to gain traction among advertisers. So couldn’t advertisers simply ignore it and stick with keywords?
Probably not.
When users shifted to mobile, Google eventually pushed advertisers toward Enhanced Campaigns. The conditions may now be in place for a similar transition, this time from keywords to prompts.
Consider the other announcements from GML. AI Mode surpassed 1 billion monthly users. The search box is getting its biggest redesign in 25 years. Users in AI Mode are also submitting queries that are, on average, three times as long as traditional searches.
Whether advertisers like it or not, people are increasingly using prompts instead of keywords to find information.
With AI Brief, the replacement for the keyword finally exists. We can now target prompts with prompts. Combined with the consumer-driven shift away from keyword-based searches, that makes the keyword’s obituary much easier to believe.
The keyword is dying because users stopped using it
Most “keywords are dead” arguments over the past decade were supply-side stories. Google reduced broad match’s control, made RSAs decide the best ad variation, and let Smart Bidding set bids to help any keyword deliver on its underlying financial goals. They also stopped showing every query in search terms reports, all steps framed as Google taking the keyword away.
Now it’s different. The pressure is coming from the demand side.
People are asking Google longer, more conversational questions because Google built a search experience that invites them to. The new search box, the biggest upgrade in 25 years, dynamically expands as you type. You no longer pick a “mode” before you ask. The interface itself is telling consumers that “running shoes” is no longer the only way to ask for what they really want.
If you’re an advertiser, the question stops being “Do I want to use keywords?” It becomes “How do I show up in a query a keyword can’t possibly match?” Trying to capture a paragraph of context with three positive match types and one negative is, let’s be real, increasingly absurd.
Optmyzr’s 2026 Match Type Study shows the same pattern from the spend side. We analyzed 30,000 Google Ads accounts in February 2026 across all Search campaigns with active keyword spend. (Disclosure: I’m the cofounder and CEO of Optmyzr.)
Exact match has lost nearly 10 percentage points of spend share since 2022, while broad match has climbed steadily to become the dominant match type by budget.
Phrase match, meanwhile, consistently punches above its weight, holding the largest share of non-branded spend and leading on conversion rate in both ecommerce and lead-gen segments.
Advertisers are clearly growing more comfortable trusting Google’s AI with broader targeting, a shift attributed to Smart Bidding’s maturation rather than exact match losing its performance edge.
The other tell is that Google isn’t alone here. We recently started managing ads on ChatGPT, and OpenAI’s ad surface is keyword-optional from day one.
When the company that invented keyword advertising and the company reinventing search both ship a keyword-optional product, that means something. At this point, we’re just arguing about how fast the keyword is dying.
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AI Brief is a technical replacement for keywords
Unsurprisingly, AI was the topic that drove nearly every announcement at GML 2026. At I/O the day before, Sundar Pichai, Google’s CEO, even said that Google’s migration to become an AI-first company was nearing completion, with AI agents providing the final push and rewriting the last remaining code. Downstream from all the talk about AI is the realization that consumers now prompt rather than search with keywords.
AI Brief is one way to operationalize the required evolution for advertisers to keep up with consumer behavior. Powered by Gemini, it lets you describe, in your own words, what your business is, what your messaging should and shouldn’t say, the searches you want to capture or avoid, and the audience you’re trying to reach.
Google calls these messaging guidelines, matching guidelines, and audience guidelines. Internally, I think of it as: tell the model what you’d tell a new media buyer on their first day.
Then AI Brief echoes back how it understood your requests and shows preview samples of the assets and queries it thinks you meant. You push back if it’s off. You iterate. When you’re happy, you lock in the brief.
That’s a meaningfully different interaction model than a keyword list. A keyword list is a static artifact. A brief is a negotiation. It can adapt as your business changes without you reuploading hundreds of new keywords.
There’s a parallel in the world of coding, where AI has arguably had the biggest impact with agentic code writers and vibe-coding systems like Lovable.dev. The idea is that the code we write to have software achieve an outcome should be merely a temporary artifact reflecting the current abilities of the tech.
Coders should focus on writing the prompts that describe the goals of the web page rather than the code needed to achieve those goals. The prompt instructs the software what it should do and how to do it safely. AI can then write the code that executes the task on demand, using the latest capabilities while staying grounded in the prompts that define its purpose.
This is what Sam Altman called “software on demand” at the GPT-5 launch, the idea that AI can “instantaneously create an entire piece of computer software for you.”
Google echoed the same vision at I/O 2026, where Pichai described Search using Gemini and Antigravity to build custom experiences, dynamic layouts, and persistent mini apps on the fly. Software generated in response to what each user needs, in the moment they need it.
People need to be purposeful about work. Your purpose at work isn’t to write emails and work with spreadsheets. It’s to achieve certain outcomes, and writing emails and using spreadsheets is how that gets done. Stop worrying about how and start thinking about the real goal: growing your ad revenue by 10% while maintaining similar margins.
Keywords are the “how,” not the “why.” AI Brief is actually closer to letting us manage the “why” while letting AI figure out the “how.”
How to try AI brief now
AI Brief is rolling out in English for AI Max for Search first, then Performance Max and AI Max for Shopping. Existing text guidelines will migrate into AI Brief automatically as messaging guidelines.
So yes, this is starting as an AI Max feature, and you may not be using AI Max because several practitioners note that AI Max can pull in junk traffic on lead-gen accounts, competitor-heavy verticals, and new campaigns with thin signal. Some veteran marketers have been turning AI Max off in those situations.
The practical playbook shared during a recent PPC Town Hall is solid: start new campaigns in Phrase, promote the winners to Exact, and layer Broad and Smart Bidding on top once you have data.
With the advent of AI Brief’s matching guidelines, advertisers can further tweak their targeting by saying, “prioritize searches for X, avoid Y.” But this strategy still requires a human who knows the account to pull that lever. So don’t unplug your keyboard just yet.
The new funnel, and why short keywords still have a job
Andrew Lolk and Kirk Williams pushed me on a real edge case in the LinkedIn discussion that led to this piece: the newborn photographer whose entire business depends on someone in their city typing “newborn photographer” and converting on the first ad that shows.
Short, transactional queries won’t disappear. So why not keep traditional search campaigns with keywords around to handle these types of queries? I think it’s reasonable to have two campaign types for different jobs. But their relationship is a funnel, not a parallel.
Here’s how I see it shaping up:
AI prompts for discovery: “I just had a baby and I want to remember this period. What are some ideas?”
AI prompts for research: “Compare lifestyle newborn photographers to studio newborn photographers in the Bay Area.”
Short keyword to buy: “Newborn photographer Los Altos.”
If you only show up at the bottom of that funnel, you’re betting your entire business on being the first short-keyword click. If you’re not present in the discovery and research prompts above it, you’re not in the consideration mix when the short query happens.
The reason a user may do that short query is that they already know more or less who they’d buy from, and they’re now looking for the best offer from a shortlisted set of options. The conversational layer feeds the transactional layer. Ignore it, and the transactional volume eventually stops coming to you.
This is also why I don’t think Google maintains two parallel systems forever. The short-keyword volume will keep shrinking relative to AI prompt volume, and at some point, the economics of supporting both stop working.
Further, AI-first campaign types will soon be great at converting agentically, using the Universal Commerce Protocol and other new methods being developed to allow agents to transact for their humans.
What AI Brief does to the four human PPC roles
I’ve argued for years that PPC pros take on four roles in an automated world:
Teacher.
Doctor.
Pilot.
Restaurateur.
These roles continue to explain the PPC manager’s world quite well, but with some new nuance.
The teacher
This role is the most direct analogy. You used to teach the machine what to target by handing it the end result: a keyword.
The funny part is that for many of us, that keyword was already generated by feeding an LLM a prompt and cleaning up the output.
AI Brief lets you skip the lossy translation step. Hand the machine the prompt itself, not the artifact it produced. The teaching gets richer because nothing gets lost.
The doctor
The shift is from “prescribe Drug X” to writing down, in structured language, what the patient actually needs.
The treatment can then evolve as the patient’s condition and the available solutions change. Keywords were restrictive: one symptom, one prescription.
Briefs and prompts allow freedom and evolution. That’s what good medicine looks like, and that’s what good targeting looks like now.
The pilot
We need a new instrument panel. If we’re not aiming at keywords anymore, the search query report stops being the right gauge of how well Google is matching intent.
We’ll probably see more search themes (buckets of intent that AI Brief is mapping into) replacing the line-by-line query list.
The restaurateur
You write the menu and the concept brief so the chef (the AI) cooks. AI Brief is almost literally the concept brief.
You define the cuisine, the values, the things the chef must never serve, and the kind of guest you’re cooking for. Then you taste, correct, and iterate. The kitchen runs.
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Why AI Brief feels different
The keyword isn’t dying because Google decided to kill it. It’s dying because consumers stopped phrasing their needs in a couple of words.
AI Brief is the first structural replacement that seems to allow advertisers to express their intent in as rich a manner as consumers can now express theirs to a chatbot. That’s why this GML announcement felt like a more serious nail in the coffin of the keyword than the last several.
Control was about dictating keywords to Google. Leverage is about feeding the engine the right brief and letting the auction execute at a scale no human team can match.
We don’t have to escape automation. We have to coexist with it on better terms. AI Brief is a great eventual replacement for the keyword. Hand it your prompt. Watch what it does.
Push back. Lock it in. Then you can move on to the parts of the job a machine can’t do, like knowing your customers and working on the goals that move their business in the direction they want.
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Most PPCbudget discussions focus on finding the right split between brand awareness and conversion-focused campaigns. That’s usually the wrong goal.
The optimal balance changes constantly based on business stage, market saturation, seasonality, competitive pressure, and revenue objectives.
Yet many teams still treat the funnel split as a fixed decision: 40% upper funnel, 60% lower funnel, set it and forget it. That might be the right ratio today and completely wrong in six months.
Every budget conversation eventually comes down to the same argument. Someone wants to cut brand awareness spend because it doesn’t convert directly. Someone else warns that if you only chase conversions, the pipeline dries up in 12 months.
Both are right, which is what makes this so difficult.
The lower-funnel case is easy to make
When most PPC managers talk about the lower funnel, they mean Shopping, Performance Max, and high-intent Search.
Someone typing “buy running shoes new york” has already decided they want the product. Shopping shows the right SKU at the right price. PMax chases the conversion signal across every Google surface. The attribution is clean, the ROAS is visible, and the CFO is happy.
The problem is that this demand already exists. These campaign types harvest intent. They don’t create it. Every conversion you get from a high-intent search term or a Shopping click is the result of awareness that was built somewhere else:
A YouTube pre-roll.
A friend’s recommendation.
A social post.
Years of brand presence in the market.
You’re collecting fruit from a tree you didn’t plant.
Search is worth treating separately here because it doesn’t sit neatly at the bottom of the funnel. A query like “best running shoes for marathon training” is informational.
The person is researching, not buying. AI Max and broad match expansion in Google Ads are pushing Search campaigns further into this territory, meaning Search can serve both ends of the funnel depending on how it’s configured and which queries it actually captures.
It’s worth auditing your Search terms regularly through this lens: How much of your Search spend is closing existing demand versus reaching people earlier in their decision-making process?
This works until it stops working. And the signal that it’s stopping usually arrives too late.
When branded search volume flatlines, CPCs on your core terms keep climbing because the same pool of high-intent users is getting more expensive to reach, and new customer acquisition starts to plateau while retention holds steady. These are the symptoms of a brand that’s been living off existing demand without replenishing it.
Lower-funnel efficiency is real. But it’s also borrowing against the future.
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The reseller trap: When your lower funnel depends on someone else’s brand
There is a version of this problem that’s specific to resellers and multi-brand ecommerce, and it doesn’t get discussed enough.
If you sell branded products you don’t own, your lower funnel can work extremely well in the short term.
Shopping and Search campaigns for established brands convert efficiently because the brand owner has already done the awareness work. You’re harvesting demand that Nike, Adidas, or whoever else has spent years and significant budgets building.
The structural risk is that you have no control over that demand. If the brand owner reduces its marketing investment, pulls out of a market, or simply fades in relevance, your Shopping and Search volume follows.
You can’t counter it with your own PPC spend because the underlying interest isn’t there to harvest. The tree stops producing fruit, and you never owned it.
This creates two strategic imperatives that are easy to deprioritize when the lower funnel is performing well.
Own-brand development: products or lines that you control, where you own the brand equity and can invest in awareness independently.
Reseller brand building: investing in the upper funnel to make your own name well known, so customers think of you as the destination regardless of which brands you carry. A consumer who searches for your store name rather than a specific brand is much more resilient than one who only finds you through a branded product query.
Both require some form of upper-funnel investment. Own-brand development needs awareness campaigns to build product recognition from scratch. Reseller brand building needs a consistent presence across Demand Gen, YouTube, and Display to make your name synonymous with the category, not just the brands within it. That’s only within Google’s ecosystem.
To complete the picture, you might also include SEO, word of mouth, pop-up events, local advertising, and more. Brand building has no limits.
Neither of these investments shows up in this month’s ROAS report. Both show up in next year’s business resilience.
Brand awareness spend is often framed as the soft, hard-to-measure part of the budget. The part you do when you have money left over. That framing gets it exactly backward.
Upper-funnel investment is how you build the pool of future converters. Every person who sees a Demand Gen ad on YouTube or Google Display today and doesn’t click isn’t a failed impression. They’re a potential high-intent searcher in three weeks. You’re filling the top of the pipeline that your Shopping and Search campaigns will harvest later.
Google’s Demand Gen campaigns make this dynamic particularly visible within a single platform. You can run Demand Gen to reach in-market audiences who don’t yet know your brand, then watch Search impression share and branded query volume respond over the following weeks. The lag is real and measurable.
Upper-funnel spend today shows up in lower-funnel performance next month, not this week. That delay is why it gets cut first when budgets tighten, and why cutting it tends to hurt six to eight weeks later rather than immediately.
Teams that manage this well think of Demand Gen not as brand spend, but as pipeline investment. The question isn’t “What is the ROAS on this campaign?” It’s “How much qualified demand am I creating for my Shopping and Search campaigns to close?”
The 70/30 or 60/40 rules you read about are averages across many businesses in many contexts. They’re useful as a starting point and useless as a long-term policy.
Consider what changes the optimal split.
A new product launch needs heavy upper-funnel investment upfront because awareness is zero.
A mature product in a saturated category needs it, too, because every competitor is also harvesting the same pool of high-intent searchers, and the only way to grow is to expand the pool.
A seasonal business approaching peak needs to have already done its upper-funnel work before the peak hits because awareness doesn’t respond fast enough to be built in-season.
Equally, a business in financial distress or facing a short-term revenue target can’t afford to wait eight weeks for upper-funnel investment to mature. The right answer in that moment is to focus on the lower funnel, accept the trade-off consciously, and plan to reinvest in awareness as soon as the pressure lifts.
The point is that both of these decisions are correct in context. A fixed split ignores context entirely.
Building a dynamic split logic
Rather than a fixed ratio, the most useful framework is a set of conditions that trigger a shift in either direction.
Shift budget toward upper funnel when:
Branded search volume is flat or declining quarter over quarter.
New customer acquisition cost is rising while retention metrics hold.
You’re entering a new market or launching a new product.
Competitors are visibly increasing their brand presence.
You’re approaching a peak season with at least six to eight weeks of runway.
You’re a reseller whose top brands are showing declining search interest or reduced marketing activity.
Shift budget toward lower funnel when:
You have a short-term revenue target that can’t wait.
Upper-funnel campaigns have been running long enough to build measurable awareness, and the conversion window is now.
Cost per acquisition on Shopping or Search is below target, and scaling makes sense.
Audience saturation on Demand Gen is high, meaning you’re reaching the same people repeatedly without expanding reach.
Within Google Ads, the data to monitor this is available without external tools. Branded query volume in Search Terms, impression share trends on non-branded terms, Demand Gen reach and frequency metrics, and new versus returning customer segmentation in conversion data together give you a reasonable picture of where the funnel is healthy and where it isn’t.
The review cadence matters as much as the metrics. Monthly is the minimum for a funnel split review. Quarterly is too slow. By the time a quarterly review catches a declining branded search trend, you’ve already lost several weeks of pipeline-building time.
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The conversation nobody wants to have
The reason funnel balance stays broken in most organizations isn’t analytical. It’s political.
Lower-funnel spend is easy to defend in a meeting. The ROAS is there, the conversion numbers are there, and the CFO can see a direct line between spend and revenue.
Upper-funnel spend requires a different kind of argument: “This investment will make our Shopping and Search campaigns work better in six weeks.” That argument is harder to make, easier to cut, and almost impossible to defend when someone asks for a quick win.
The answer isn’t to stop making the argument. It’s to change the evidence you bring to it.
Track branded search volume as a leading indicator.
Build a view that shows Demand Gen reach in month one and Search conversion volume in month two alongside each other.
Make the lag visible and the relationship concrete. Once the data tells the story, the conversation gets easier.
Budget allocation isn’t a one-time decision. It’s an ongoing signal about what kind of growth you’re building.
Optimizing purely for this month’s ROAS is a choice. So is investing in the demand that will drive next quarter’s revenue.
And if you’re a reseller, it’s also a decision about whether your business is built on a foundation you control or one you’re renting from brand owners who have their own priorities.
The best PPC teams do both, and they know when to lean in each direction.
Commerce has undergone several major shifts over the past few decades. What started with localized physical stores evolved into borderless, internet-driven ecommerce experiences.
Now, with the rise of AI, it is believed that commerce could be heading toward another transformation: agentic commerce, where AI agents help consumers discover products, compare options, and even complete purchases on their behalf.
Yet despite the excitement, many questions remain. Will consumers trust AI agents with buying decisions? Will businesses see enough return on investment to justify the costs? And does autonomous shopping solve a real problem, or simply add another layer of complexity to the buying journey?
Still, the technology is advancing rapidly. Imagine a shopping experience where consumers no longer jump between tabs, compare dozens of products on different websites, or manually research every purchase. Instead, AI agents understand intent, evaluate options, compare prices, and act within predefined rules to help users make purchasing decisions. What once sounded futuristic is already beginning to take shape.
In this article, we’ll explore what agentic commerce is, how it works, the technological developments driving it forward, and some of the challenges that could shape its future adoption.
Key takeaways
Agentic commerce represents a shift where AI agents assist consumers in product discovery, comparisons, and purchases
AI agents execute tasks based on user intent, simplifying the shopping journey and enhancing efficiency
Consumer interest is growing, with over 60% expecting to use AI in their shopping experiences by 2026
Technological developments like the Agentic Commerce Protocol (ACP) and Universal Commerce Protocol (UCP) are crucial for enabling agentic commerce
Despite its potential, agentic commerce faces challenges related to consumer trust, security, and the need for business investments.
What is agentic commerce?
In simple terms, agentic commerce refers to a commerce model where AI agents act as decision-makers on behalf of customers.
Instead of manually searching for products, comparing options, filtering results, and completing purchases, users can rely on AI agents to handle these tasks based on their intent, preferences, constraints, and buying goals.
I could literally say into the into a phone to my agent, go and buy me some new shoes with that jacket I bought last week, and that’s it.
And it would go away.
It would do the research.
And of course, you can have a say in an approval in terms of part of the journey.
At its core, agentic commerce works like a digital shopping proxy. Humans define the intent or goal, while AI agents execute the process behind the scenes. While the AI handles the heavy lifting, users still remain in control of the final decision-making process.
Agentic commerce is the next big thing in ecommerce
The concept of agentic commerce may still sound futuristic, but the shift has already started. Consumer behavior, AI adoption, and industry forecasts all point to a future in which AI agents become an active part of the buying journey.
Here are some numbers that highlight why agentic commerce is emerging as the next major evolution in ecommerce.
Consumers already use AI in their buying journey
Consumers have already started relying on AI-powered tools to discover products and make purchasing decisions. According to a McKinsey & Company report, more than 70% of AI-powered search users ask top-of-the-funnel questions about categories, brands, products, or services.
Example of a TOFU research performed on Claude
The same report also found that nearly 50% of consumers already use AI-powered search experiences today. As AI increasingly becomes part of product discovery, traditional search-driven traffic may face growing disruption. In fact, the study suggests that businesses could see 20–50% of their traffic shift away from traditional search experiences over time.
This highlights an important shift: consumers are no longer just searching; they are increasingly asking AI systems to guide their decisions.
Shoppers are expecting agentic commerce
Consumer interest in AI-assisted shopping is also growing rapidly. The 2025 report titled “Agentic Commerce: From Brand Loyalty to Bot Logic” explored how shoppers feel about AI agents in retail experiences.
The report found that more than 60% of shoppers expect to use agentic AI in 2026. The findings also revealed a major behavioral shift: consumers increasingly prioritize convenience, speed, pricing, and trust over platform loyalty.
Instead of browsing individual retailer apps, shoppers may rely on AI agents that can compare products across multiple platforms, evaluate reviews, identify the best deals, and complete purchases more efficiently. This changes the competitive landscape from retailer-versus-retailer competition to AI-driven discovery ecosystems.
Analysts predict explosive growth for agentic commerce
Industry analysts also expect agentic commerce to become a massive economic opportunity over the next few years. Another McKinsey report suggests that agentic commerce could fundamentally reshape the shopping experience.
Based on the growing adoption of AI-powered discovery tools and increasing merchant readiness, the report estimates that by 2030, the US B2C retail market alone could unlock an orchestrated revenue opportunity of $900 billion to $1 trillion. Globally, that opportunity could range from $3 trillion to $5 trillion.
How does agentic commerce work?
At its core, agentic commerce combines human intent with AI-driven execution. Instead of manually browsing websites, comparing products, and completing purchases, users can delegate much of the shopping journey to AI agents. These agents understand goals, evaluate options, make decisions within defined constraints, and even complete transactions on behalf of users.
What makes this different from traditional AI assistants is the ability to act. While assistive AI tools mainly provide information or recommendations, agentic AI can independently execute tasks across the shopping journey.
Here’s a step-by-step look at how agentic commerce works:
Step 1: Capturing the intent
Every agentic commerce journey begins with intent. Instead of typing short keywords into a search bar, users interact with AI agents conversationally.
For example, a shopper might say:
“Find me a durable pair of running shoes under $150.”
“Restock groceries for a vegetarian dinner party.”
“Buy a formal shirt that matches the trousers I purchased last month.”
At this stage, the AI agent focuses on understanding the user’s goals, preferences, budget, delivery expectations, and constraints. If the request feels too broad, the agent may ask follow-up questions to refine the intent before moving forward.
Step 2: Autonomous instruction execution and brand discovery
Once the intent becomes clear, the AI agent begins executing the task autonomously. Instead of searching a single website, it scans multiple ecommerce platforms, marketplaces, product catalogs, reviews, pricing databases, and inventory systems simultaneously.
This is where agentic commerce begins to change traditional product discovery. Rather than showing endless product pages, the agent narrows down the most relevant options based on the shopper’s needs.
At the same time, brands with better-structured product data, accurate inventory information, transparent pricing, and machine-readable content are more likely to be discovered by AI agents.
After gathering options, the AI agent starts evaluating products and comparing tradeoffs. It may analyze factors such as:
Price and discounts
Product specifications
Customer reviews and ratings
Shipping timelines
Return policies
Brand trust and reputation
Instead of simply listing products, the agent reasons through the options and explains why certain products better meet the shopper’s requirements than others.
Users can also refine the decision-making process further by adding conditions such as:
“Only show products with free returns.”
“Prioritize faster delivery.”
“Exclude refurbished products.”
This creates a feedback loop where the AI agent continuously improves its recommendations based on user preferences.
Step 4: Purchase
Once the shopper approves a product or sets predefined rules, the AI agent can move forward with the transaction. Using APIs, commerce protocols, and secure payment systems, the agent can add items to carts, apply discounts, authenticate payments, and complete purchases.
In some cases, the purchase may happen instantly. In others, the AI agent may wait for specific conditions, such as a price drop, stock availability, or faster delivery options, before completing the transaction.
Even though the AI handles execution, users still remain in control through permissions, approval settings, and spending limits.
Step 5: Post-purchase support
The role of AI agents does not end after checkout. Agentic commerce also extends into post-purchase experiences.
AI agents can continue assisting users by:
Tracking deliveries
Managing returns or exchanges
Monitoring refunds
Sending delivery updates
Reordering recurring products
Recommending complementary products or accessories
This turns shopping into an ongoing and intelligent experience rather than a one-time transaction.
Technological developments
Agentic commerce is not powered solely by AI models. Behind the scenes, it depends on a growing ecosystem of protocols, frameworks, APIs, and payment systems that help AI agents interact with digital commerce platforms securely and efficiently.
One important concept shaping agentic AI is the Model Context Protocol (MCP). In agentic AI, MCP enables AI models to connect with external systems, tools, databases, and applications via a standardized communication layer.
Instead of building separate integrations for every AI model and every software platform, MCP creates a common framework that allows AI agents to access information and execute actions more consistently. Think of it like creating a shared operating language between AI systems and digital tools, so they can work together without requiring completely custom connections every time.
As agentic commerce evolves as a use case of agentic AI, similar commerce-focused protocols are now emerging specifically for shopping ecosystems. These protocols help AI agents understand product information, communicate with merchants, compare inventory, and securely complete transactions on behalf of users.
Here are some important developments supporting agentic commerce:
Agentic Commerce Protocol (ACP)
One of the most important developments in this space is the Agentic Commerce Protocol (ACP), an open standard introduced by Stripe in collaboration with OpenAI.
ACP is designed to help AI agents interact more naturally with ecommerce systems by creating a standardized framework for product discovery, checkout, and payment execution. In simple terms, it provides the infrastructure that allows AI agents to move beyond simply recommending products and actually complete purchases securely on behalf of users.
The protocol is still in its early stages, but its first real-world implementations are already emerging. For example, ChatGPT users in the United States can already purchase products from Etsy merchants directly within the chat experience through Stripe-powered checkout. Shopify integrations are also expected to follow.
This matters because it signals a shift from AI-assisted discovery to AI-enabled transactions happening inside conversational interfaces themselves. Instead of redirecting users across multiple websites and checkout flows, ACP aims to make the entire shopping journey more seamless and agent-friendly.
Another important aspect of ACP is its open-standard approach. Rather than creating a closed ecosystem tied to a single platform, Stripe and OpenAI position ACP as a framework that developers, merchants, and ecommerce platforms can adopt more broadly as agentic commerce evolves.
Looking ahead, protocols like ACP could become foundational infrastructure for AI-driven shopping experiences, especially as more businesses begin to optimize their product catalogs, payment systems, and checkout experiences for AI agents rather than only human users.
As more AI agents enter the shopping journey, a new challenge emerges: how can these agents communicate with thousands of retailers, marketplaces, payment providers, and service platforms without requiring a custom integration for each one?
Introduced by Google, UCP is an open standard designed to create a common language for agentic commerce. Rather than building separate connections between every AI agent and every commerce platform, UCP provides a shared framework that allows them to communicate more efficiently throughout the shopping journey.
Think of it this way: if agentic commerce becomes mainstream, millions of AI agents could research products, check inventory, compare prices, place orders, and manage returns every day. Without a standardized framework, retailers and AI platforms would need to create and maintain countless one-to-one integrations. UCP aims to remove this complexity by providing a common set of rules for all participants to exchange commercial information.
What makes UCP particularly interesting is its broad scope. Unlike protocols that focus mainly on purchasing, UCP is designed to support the entire commerce lifecycle, including:
Product discovery
Product comparison
Purchasing and checkout
Order tracking
Returns and post-purchase support
Google has also designed UCP to work alongside other emerging AI standards, including Agent2Agent (A2A), Agent Payments Protocol (AP2), and Model Context Protocol (MCP). This allows businesses to adopt agentic commerce without completely replacing their existing systems.
The initiative already has significant industry backing. Google co-developed UCP with major commerce companies, including Shopify, Etsy, Wayfair, Target, and Walmart. It has also received support from companies such as Mastercard, Visa, Stripe, and American Express.
Platforms that support Universal Commerce Protocol
While agentic commerce is still in its early stages, UCP represents an important step toward a future in which AI agents, merchants, and payment providers can operate within a single ecosystem rather than through isolated platforms. In many ways, it provides the foundational infrastructure needed to make agentic commerce scalable across the broader digital economy.
Mastercard Agent Pay
While protocols like ACP and UCP focus on communication and interoperability, Mastercard Agent Pay focuses on one of the most critical challenges in agentic commerce: trust and secure payment execution.
As AI agents gain the ability to discover products, compare options, and make purchasing decisions, they also need a secure way to complete transactions on behalf of users. Mastercard Agent Pay was introduced to provide the infrastructure for exactly that.
The platform is designed to allow AI agents to execute payments while operating within user-defined permissions, authentication requirements, and spending controls. Rather than giving AI systems unrestricted access to payment credentials, Agent Pay focuses on creating verified, traceable, and authorized payment flows for agent-driven commerce.
One of the most significant developments came through its collaboration with PayPal, where Mastercard Agent Pay is being integrated into PayPal’s wallet infrastructure. It allows AI agents to securely complete transactions on behalf of PayPal users while maintaining the security and trust mechanisms that consumers already expect from digital payments.
This partnership is particularly important because it moves agentic commerce closer to real-world adoption. Instead of existing only within experimental AI environments, agent-driven payments can potentially operate across a much larger ecosystem of merchants, consumers, and payment networks.
Together, ACP, UCP, and Agent Pay are helping lay the foundation for agentic commerce. While ACP focuses on enabling AI agents to interact with merchants and complete purchases, UCP creates a common language that allows agents, retailers, and platforms to work together at scale. Agent Pay adds the trust layer by enabling secure, authorized payments, bringing AI-driven shopping one step closer to reality.
FAQs: What is agentic commerce?
What are the benefits of agentic commerce for enterprises and users?
Agentic commerce benefits both businesses and consumers by making shopping more efficient and personalized.
For users AI agents can reduce research time, provide tailored recommendations, monitor prices, and automate routine purchases.
For enterprises Agentic commerce can streamline operations, improve personalization, automate repetitive workflows, support faster decision-making, and help products reach customers more quickly. Together, these benefits create a more convenient shopping experience while improving operational efficiency.
Are agentic AI and agentic commerce the same?
No, they are not the same. Agentic AI is the underlying technology that enables AI systems to understand goals, make decisions, and complete tasks autonomously. Agentic commerce is a specific application of agentic AI in shopping and commerce. In other words, agentic AI is the foundation, while agentic commerce is one of its real-world use cases.
What’s the difference between traditional commerce and agentic commerce?
In traditional commerce, the shopper remains the primary decision-maker and executor throughout the buying journey. Even when AI is present, its role is largely limited to recommending products or improving search experiences. In agentic commerce, AI agents actively participate in the shopping process by researching products, comparing options, and executing tasks on behalf of users, guided by predefined goals and preferences.
Can you share some practical, real-world use cases for agentic commerce?
Several companies are already experimenting with agentic commerce. For example, Amazon has introduced its “Buy for Me” feature, which allows AI agents to purchase products from third-party websites when items are unavailable on Amazon.
Similarly, Google is testing AI-powered shopping experiences that can monitor prices and automatically purchase products when they meet user-defined conditions. Beyond consumer shopping, businesses are also using AI agents to monitor inventory levels and automatically reorder supplies when stock runs low.
Agentic commerce still faces important questions
While the technology behind agentic commerce is advancing quickly, widespread adoption is far from guaranteed. Many consumers may not feel comfortable giving AI agents the authority to make purchasing decisions or access payment methods on their behalf. Others may question whether autonomous shopping solves a real problem or simply makes it easier to buy more things, more often.
Businesses face their own uncertainties. Supporting agentic commerce may require investments in new protocols, structured data, integrations, and AI-ready commerce experiences. Whether those investments yield measurable returns remains unclear, especially given that consumer adoption is still in its early stages.
There are also broader challenges to solve, including security, fraud prevention, AI bias, platform dependency, and the potential loss of direct relationships between brands and customers. Agentic commerce may represent an exciting new direction for digital shopping, but its long-term success will depend on whether it can create value for consumers, merchants, and the broader ecommerce ecosystem, not just the AI platforms powering it.
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What do you do when inspiration for your umpteenth blog post is low? What’s the solution to writer’s block or a general lack of ideas? Every writer will encounter a lack of inspiration from time to time. You’ll be staring at your screen, not knowing what to write about. Nevertheless, you are determined to write those blog posts regularly. Today, AI tools like LLMs or Yoast AI Content Planner can spark ideas when you’re stuck. Luckily, there are many other ways to get inspired!
Use audience feedback as a source for blog post ideas, especially questions that need elaboration.
Check the Google Search Console’s Performance report for search queries that might inspire new content.
Consult your keyword research for long-tail keywords; they can point to potential blog topics.
Explore platforms like ChatGPT and Pinterest, and use tools like the Yoast AI Content Planner for fresh blog post ideas.
Draw inspiration from current events, your daily activities, and maintain a list of ideas to combat writer’s block.
Getting new blog post ideas on your site
Inspiration from your audience
If your blog has a comment section for your audience to leave comments or you have a contact form, you’ll receive feedback. While most of the reactions you get will just be positive or negative statements, you might receive questions as well. Perhaps some of these questions are easy to answer in a reply, but other questions will be off-topic or need elaboration. You can also send a questionnaire to your readers to gather input and feedback. Those kinds of questions are excellent starting points for your next post. You could try keeping a list of relevant questions whenever you come across them, so you have a place to look when inspiration is low.
Google Search Console is still one of the best tools to find new blog post ideas. It shows you the exact search terms people use to find your site. This helps you spot topics your audience cares about, but you haven’t fully covered yet.
The Performance Report is where you’ll find these insights. It lists the search queries that bring visitors to your site, along with clicks, impressions, and average rankings. Look for queries where your content ranks but doesn’t fully answer the question. For example, if people find your site by searching “how to keep toddlers busy without screens” but you don’t have a dedicated post on that topic, it’s a clear sign to write one.
If you use Yoast SEO with Google Site Kit, you can access Google Search Console data directly in your WordPress dashboard. This integration saves time because you don’t have to switch between tools. Just open the dashboard, click on the Yoast SEO tab, and open the General section. You’ll see your top search queries and performance metrics right there.
While tools like Ahrefs or Semrush offer deeper competitive analysis, Google Search Console provides direct data from Google. It’s free, reliable, and still one of the best ways to find information about what your audience is searching for. Use it alongside Yoast SEO’s tools to ensure you cover all the topics that matter to your readers.
Use the Yoast AI Content Planner
You know you need to publish, but deciding what to write about can sometimes take forever. To help you overcome this, we built the Yoast AI Content Planner. It scans your existing content, identifies gaps, and suggests five relevant blog ideas.
When you open a new post, Yoast SEO analyzes your site’s content and generates ideas tailored to your niche. These aren’t generic suggestions because they’re based on what your audience is already reading and what’s missing from your blog. For example, if you run a food blog and have written about meal prep but not quick vegetarian lunches, that might suggest that topic.
Once you pick an idea, Yoast SEO creates a structured draft with a suggested title, headings, and even a meta description. You get a clear outline so you can start writing immediately. If the first set of ideas doesn’t feel right, you can generate a new batch with one click.
Yoast AI Content Planner is included in all our Yoast SEO Premium products. It’s designed for anyone who writes regularly and wants to publish consistently without running out of fresh ideas. This tool helps you create content that fills real gaps for your audience. Give it a try the next time you’re stuck for ideas.
Tailored content suggestions generated by Yoast AI Content Planner
Dig deeper into your keyword research
Your keyword research document contains many potential blog ideas. But don’t just pick a keyword and start writing, because digging deeper helps you find the best angle.
What’s the search intent behind a keyword? Are people looking for a how-to guide or an opinion piece? Tools like Yoast SEO’s Semrush integration, or Google’s autocomplete can help you figure this out. Don’t forget to check what appears in Google’s AI Overviews or AI Mode answers when you research these keywords and topics.
For example, if your keyword is “best running shoes for flat feet,” ask:
Are people looking for affordable options?
Do they care about durability or style?
Are they comparing specific brands?
Each of these could be its own post:
“Best budget running shoes for flat feet in 2026”
“Most durable running shoes for flat feet (tested and reviewed)”
“Nike vs. Brooks: Which running shoes are best for flat feet?”
This way, you’re not simply writing about a keyword, but answering the exact question your audience is asking. Plus, if you set up Wincher in Yoast SEO, you can track how well your posts perform for these keywords over time.
Finding ideas for blog posts on the internet
Pinterest
Pinterest is still a useful place to find inspiration, especially if your blog covers visual topics like food, DIY, fashion, travel, or home decor. But it’s not just for pretty pictures, because you can use it to spot trends and gaps in your niche. Search for keywords such as [blog post ideas], [blog ideas], or [what to blog about]. To get even more inspiration fast, include your niche in your search. For example: [blog post ideas for parents], or [blog post ideas for lifestyle bloggers]. Be sure to check the top-pinned post for the topics.
It’s a good idea to be cautious as well, because Pinterest is clickbait heaven. Falling into the trap of quantity over quality is easy. Keep your focus, or you’ll lose track of time.
Content Idea Generator
To be clear, the Content Idea Generator won’t give you ready-to-go article ideas. At best, it will point you in the right direction; at worst, it will provide you with a few good laughs to clear your head. For example, you can enter the term [house plant]. Content Idea Generator could give you the following title: ‘The 15 biggest house plant blunders’. A content idea about [wine]: ’17 unexpected uses for wine’. Enter [baby] and a suggestion that might come up: ‘20 ideas you can steal from babies’.
So, while the Content Idea Generator won’t give you what you want immediately, it’s sure to get your creativity flowing. Taking the previous examples, you could expand on that and get the following blog ideas:
‘The 15 biggest house plant blunders’: a post about common mistakes people make when caring for the plants in their homes
‘17 unexpected uses for wine’: a post about using wine for cooking, cleaning, baking, etc.
‘20 ideas you can steal from babies’: could inspire a blog post about babies’ habits adults should adopt, such as getting enough sleep, dressing up warmly, expressing your emotions, etc…
Use AI and chatbots for inspiration
AI tools and chatbots like ChatGPT, Claude, or Gemini can help when you’re stuck. But don’t just ask for generic ideas, and always provide context about your blog and your audience. Here’s how to get the most out of them:
Ask for specific angles, so instead of “Give me blog ideas about parenting,” try:
“What are five unique angles on ‘screen time for toddlers’ that most blogs miss?”
“What are three common mistakes new bloggers make when writing about SEO?”
Always try to refine vague ideas, so if you have a broad topic, ask AI to narrow it down. For example:
“Give me five blog post ideas about ‘healthy snacks for kids’ that aren’t just recipes.”
“What are three easy-to-apply SEO tips for small e-commerce stores based in India?”
Reverse-engineer competitors by feeding AI a competitor’s blog URL and asking:
“What gaps does this blog have? Give me five post ideas they haven’t covered.”
“What are three topics this blog covers poorly? How could I do them better?”
Try to avoid producing commodity content, because AI often suggests ideas that feel generic or overdone. Always add your own perspective, your experience, or data, as this can truly make your content stand out from the crowd. For example, if AI suggests “10 tips for better sleep,” make it unique:
“The science behind sleep: What actually works, according to research”
“How I improved my sleep in 30 days (with data)”
“Why most sleep tips don’t work for parents (and what to try instead)”
Days Of The Year
Days Of The Year is a website that offers inspiration for all kinds of blogs. This website collects all the fun, bizarre, and nice holidays the world has to offer. You can easily lose a couple of hours while scrolling through that site. Keep your pen and notepad at hand, though, because it is bound to give you tons of inspiration. There are days available for every niche. Are you a fan of mythical creatures? April 9th is ‘Unicorn Day’. There’s also a ‘Leprechaun Day’ and a ‘Howl at the Moon Day’. May 25th is ‘Towel Day’, which can give travel bloggers and lifestyle bloggers ideas for posts. Think of blog posts such as: ‘How to keep your towels soft’ or ‘With this information you will never buy the wrong towel again’.
Other blogs and fellow bloggers
The internet is full of inspiration for blog ideas, and there are many places to look. Perhaps you follow other bloggers who inspire you. A great way to come up with blog post ideas is to read other posts or just scroll through post feeds. Similarly, you can join Facebook groups related to your niche or for bloggers. Discussing ideas with fellow bloggers will surely get your creative juices flowing! Make sure you do not copy people’s ideas, though, and give credit where credit is due.
Get blog post inspiration from your life
Current events
Current events can give you great blog ideas if you connect them to your niche. The trick is to link the news to what your audience cares about in a way that feels natural. For example, if you run a parenting blog, a new study on screen time could inspire a post like “How much screen time is too much? What the latest research says.” If you write about personal finance, a change in tax laws might lead to “Three ways the new tax rules affect your savings (and what to do about it).” The key is to add value, so don’t just repeat the news, but explain what it means for your readers.
Set up Google Alerts for keywords related to your topic to stay updated. When something relevant pops up, think about how it affects your audience. For instance, if you blog about sustainable living, a new recycling policy could lead to a post titled “How to adjust your recycling habits under the new rules.” Avoid sensitive topics unless you can handle them thoughtfully. If you do cover them, focus on helping your readers, not just exploiting the trend. The goal is to turn news into high-quality content that fits your blog’s purpose.
Your daily life
Situations from your own work could also be great inspiration for blog posts. You can write about things that happen in your day-to-day life, and how you go about them. Or even about what you do if your clients or colleagues are faced with a certain problem. It’s quite possible that others encounter the same problem and are seeking input.
If you write about real-life situations, you should always make sure that you respect the privacy of your clients, friends, or colleagues and ask for permission to use their cases on your blog. For example, a therapist with a blog offering mental health tips might want to use examples from their practice. In that case, it’s vital to change names and details to protect clients’ privacy and the practice’s future!
Clear your head to find fresh ideas
Sitting at your desk for too long can drain your creativity. If you’re staring at a blank screen, step away and do something that shifts your focus. A short walk, or even washing the dishes, can help reset your mind. The goal isn’t to force ideas but to give your brain space to wander. Often, the best thoughts come when you’re not trying too hard.
If you need a more structured break, try a ten-minute brainstorming sprint. Set a timer and ask yourself: “What are twenty blog ideas about [your topic]? Make five weird, five practical, and ten in between.” Don’t overthink it and just write down whatever comes to mind. When the timer goes off, pick the most interesting idea and freewrite about it for another five minutes. This exercise forces you to think outside your usual patterns and often leads to unexpected angles. When you return to your desk, you’ll likely feel more focused and inspired.
Keep a list of ideas
The solution can be very simple: some days, you have plenty of blog post ideas, some days you don’t. So, prepare for days when you have no inspiration and keep a list of blog ideas. It doesn’t matter whether it’s a list on your mobile phone or on paper. Every time you have a good idea, write it down. You can use these ideas on days you’re feeling uninspired.
Wrap up with fresh ideas
Don’t let a lack of inspiration derail your publishing schedule. Whether you use Yoast AI Content Planner or take a break to clear your head, there are always ways to find new topics. The best approach combines structure and creativity, using tools to generate ideas, then refining them with your own insights and voice.
The next time you’re stuck, pick one method from this list and give it a try. Maybe it’s deep-diving into your keyword research or setting a timer for a quick brainstorming session. Each of these strategies can help you break through writer’s block and keep your content flowing.
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Today, we’re excited to announce the launch of new Search Generative AI performance reports
in Search Console, including dedicated reports for Search and Discover, to help you understand your
site’s visibility within generative AI features on Search.
