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Buyers now ask ChatGPT, Gemini, and Perplexity for recommendations before they ever open a search results page, and marketers need a way to see whether their brand shows up in those answers. That’s why Profound vs Semrush for AEO lands on so many shortlists.
The way people search for information is changing. Instead of scrolling through a list of blue links, more users are turning to AI-powered tools like ChatGPT, Google’s AI Overviews, and Perplexity that deliver direct answers to questions.
Claude skills help teams save repeatable workflows as instructions that apply automatically — without pasting a long prompt into every chat. A team writes the workflow once, and Claude applies the skill every time.
If you’re comparing Profound vs. Scrunch, the buying decision usually comes down to where your AEO workflow stalls, not which tool tracks more answer engines. Both Profound and Scrunch measure how AI answers cite your brand, but they’re built for different jobs: Profound leans toward market analysis and content creation, while Scrunch leans toward technical optimization and task handoff.
Building an AI agent is a multi-step process. Marketers need to define the agent’s task, connect approved data and tools, and set limits on its actions. Once they master the basics, anyone — marketers, sales reps, or customer teams — can build agents with ease.
Companies without the internal resources or budget to build a dedicated search department still need to know what options exist for running AEO without a large team. Answer Engine Optimization is no longer optional for companies that rely on search to generate leads and revenue. AI Overviews now appear at the top of search results before a single organic listing — and sometimes above paid ads.
When our blog team made the switch to an AI-first writing model in June 2025, my reaction was split. There was excitement on one side: I could see the inefficiencies in our process that AI might actually fix. On the other, anxiety. And while plenty of that anxiety was about the technology itself (how well these tools could actually write, how much we could trust them), my primary concern was my team.
The best Claude skills for marketing and sales teams turn recurring work into reusable processes with defined instructions, inputs, and outputs. Claude skills are reusable instruction sets for tasks such as campaign planning, sales call recaps, persona research, content outlining, proposal drafting, and performance reporting. Claude skills save marketers, sales leaders, and revenue teams from rebuilding the same instructions every time a task recurs.
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When we reviewed our Japan marketing automation programs last year, the picture wasn’t pretty. Our automated emails were generating about 12 qualified leads per month against a goal of 160.
That’s a huge gap.
Transactional emails are automated messages triggered by a specific action a customer takes, sent to help that person complete or follow up on something already in progress. An order receipt, a password reset confirmation, or a shipping notification are all examples of transactional emails because each one delivers information tied to an action the recipient just took.
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AI Overviews have shifted how audiences discover brands. Gone are the days when users would click through multiple search results until they found what they’re looking for. Now, AI Overviews answers questions directly on the results page, meaning brands are either cited or ignored without users ever visiting a website.
If you’ve opened Claude’s Customize settings and weren’t sure whether a task called for a Skill, a connector, or a Plugin, you’re not alone.
Digital PR has always been about earning coverage that influences the audience you can’t reach through paid channels. While the value proposition of digital PR hasn’t changed, the definition of “showing up” has.
Clients are asking about AEO. Some have seen competitors show up in AI results and want to know what it means. Others have read that AI is changing how buyers find vendors and want to know what their agency is doing about it.
Enterprise brand teams face a complex version of AEO that most tools weren’t built for. A single brand might span a dozen product lines, operate in multiple languages, and compete in markets where local answer engines have their own citation patterns.
Buyer behavior has changed since the advent of artificial intelligence. Now, before ever filling out a form or clicking an ad, prospects are likely asking AI for recommendations, comparing vendors, and gathering answers. Those interactions shape which companies make the shortlist, but they often happen outside the systems marketing operations teams use to measure performance.
Demand generation has been built around the assumption that buyers will come to a brand when they are ready to learn about its category, whether through search, ads, content, or other channels. The funnel starts when a prospect arrives somewhere the business can see, measure, and influence it. But AI is changing what happens before that point.
Enterprise email marketing software is built for high-volume programs that span teams, brands, and regions. With so much software available, the hard part comes before and after the send — whether governance keeps every team within the rules, CRM-native data surfaces the right consented audience, and attribution connects email engagement to pipeline and revenue.
Today, when a buyer asks ChatGPT, Perplexity, or Google’s AI Mode a question, they rarely see a list of suggested links. They see one synthesized answer that cites a handful of sources. If your brand is one of those citations, you win attention, traffic, and trust. If it isn’t, you’re invisible, even when you rank on page one.
Digital marketing is one of the most process-intensive functions in marketing, and AI agents for digital marketing are changing how teams handle that load. Every campaign involves the same chain of tasks. Building audiences, setting up nurture sequences, writing follow-ups, and monitoring performance all have to happen every time, across every channel.
Link-building and content outreach were built for search engines. Domain authority, anchor text, and referring domains were the signals that moved search rankings, and they translated directly into an SEO playbook you could run at scale. Answer engines don’t work the same way.
Content marketers are used to publishing consistently, earning trust, and measuring how content contributes to demand. But now, buyers also do their research in AI assistants and answer engines, sometimes without clicking through to a website.
Brand marketing has always been about controlling the narrative, so that the story your company tells about itself is the one the market hears. That job has gotten harder.
Search engine optimization (SEO) teams have long used rankings, organic traffic, and conversions to understand how people find and engage with content. Those signals still matter, but they don’t show whether a brand appears in AI-generated answers.
A GTM tech stack is the set of tools a company uses to run go-to-market activities across the customer lifecycle. These platforms can make it easier to connect marketing, sales, and customer teams. But, if the pieces aren’t compatible, a business’ GTM tech stack is just another layer of disconnected tools. The difference comes down to how the stack is built.
If you’ve been asked to diagnose and fix AEO gaps, the first step is to conduct an audit. For most brands, an AEO gap is any reason an AI answer engine can’t (or won’t) use its page as a source.
A vector embedding is a numerical representation created by an embedding model. The model converts text into a list of numbers that can be compared with other vectors, helping a retrieval system find passages with similar meaning even when they use different words. Semantic retrieval is one tool AI systems can use to find source material; modern retrieval can also combine semantic search with keyword search and other relevance signals.
AI search optimization tools help marketers understand where a brand appears in AI-generated answers, which sources earn citations, and what to improve next. They complement traditional SEO tools rather than replace them: SEO measures rankings, clicks, and organic traffic, while AI-search tooling adds visibility signals such as mentions, citations, sentiment, and answer accuracy.
If you’ve searched “Profound versus Athena AI for AEO,” you’ve probably already hit a wall: every comparison you find is written by a competitor, an affiliate, or the vendors themselves.
An AEO checker tells you whether the AI answers your buyers rely on actually mention your brand. People increasingly ask ChatGPT, Perplexity, and Gemini a question and act on the reply without clicking a link, so visibility that once showed up in your rankings can vanish into an answer you never see.
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This Scrunch vs. Peec AI comparison evaluates how both tools measure AI answer engine representation across different buyer segments, price points, and feature scopes.
In 2025, most of the major answer engines made an almost identical update. Claude, ChatGPT, Gemini, and many others started to show what they were thinking.
Few phenomena within the creator economy have moved as fast as AI’s embrace. What was once treated with anxious suspicion is now more widely viewed as a necessary strategy.
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Most email marketing teams know the basics. Authenticate your domain. Clean your list. Write a compelling subject line—test before you send.
Something big just shifted in how people find answers online.
More buyers are skipping the investigation and deliberation of clicking through blue links on Google, in favor of asking ChatGPT, Perplexity, and the like for one direct answer.
Learning how to optimize your website for AI search is one of the hottest skills for marketers right now, because the audience for these tools is growing fast. Monthly unique visitors to the major answer engines climbed from 634 million in Q1 2025 to 904 million in Q1 2026, up more than 40% in a year, according to Wix Studio.