Enterprise brands shifting budget from traditional content generation to answer engine optimization investments
episode
Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
4 min
2 speakers
6 chapters
transcribed 1 month ago
Transcript
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Why are enterprise brands shifting budget from traditional content to answer‑engine optimization?
The Voices of Search Podcast is a proud member of the I Hear Everything Podcast Network. Looking to launch or scale your podcast? I Hear Everything delivers podcast production, growth, and monetization solutions that transform your words into profit. Ready to give your brand a voice? Then visit IHear Everything.com. Welcome to the Voices of Search Podcast, a member of the I Hear Everything Podcast Network. Ready to expedite your company's organic growth efforts? Sit back, relax, and get ready for your daily dose of search engine optimization wisdom. Here's today's host of the Voices of Search Podcast, Jordan Cooney. Sorry. Next lightning question. Trend or trash, enterprise brands will shift budget from traditional content generation to answer engine optimization investments.
How does AI‑generated content differ from traditional writing and why does its value depend on data and expertise?
It's I think yeah, I think enterprise brands are gonna shift uh budget from traditional content generation to AI generated content. I think that's trend.
Okay.
You need to really do it well. AI generated content is not meant to just be this like no meat on the bones. Here's some written words. The written word is basically it's not it's priceless in the wrong way. It's zero. The price to the written word is nothing. Yeah. The value of your content now is literally only the things you bring with you to that prompt, the data, the opinions, the expertise. All the SEOs in the world are like saying E A T.
What role does E‑A‑T play in AI discovery and how can brands demonstrate expertise to LLMs?
Yes, E A T. That's what matters in the space right now. It's like built making a necklace. I always say this. You know, the necklace is like stringing all these beads together and tying it together.
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Which AI‑driven frameworks help brands identify the 3‑5 word queries LLMs actually execute?
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If you have all these beads in your hand, all these insights, all these opinions, all these facts, all this data that you've assembled, you've run studies, you've talked to experts, you've got all this work done. The point of the AI model in creating the content should just be the ifs, the ands, the ors, the ths, the theys, the thems. It shouldn't matter that the content's AI written.
How can the ‘necklace’ analogy guide the creation of high‑utility, answer‑focused AI content?
The AI should just be stringing the necklace together, all the beads you've already assembled, and then tying it up. I can't stand slop content and and that's not what profound does either. So definitely not gonna wanna see like there is no magic string. There's no magic token you can put in to get AI to like you more. It is still gonna be exceptional content that's really, really high utility, optimized to provide answers to users at the context they're seek seeking it.
Love it. Okay, that wraps up this episode of the Voices Search Podcast. A huge thank you to Josh Bliskell from Profound for joining us today. If you'd like to get in contact with Josh, you can find a link to his LinkedIn profile in our show notes or on the voicesofsearch.com.
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Chapters
6 chapters
1
Why are enterprise brands shifting budget from traditional content to answer‑engine optimization?
0:00–0:58
2
How does AI‑generated content differ from traditional writing and why does its value depend on data and expertise?
0:58–1:33
3
What role does E‑A‑T play in AI discovery and how can brands demonstrate expertise to LLMs?
1:33–2:14
4
Which AI‑driven frameworks help brands identify the 3‑5 word queries LLMs actually execute?
2:14–3:32
5
How can the ‘necklace’ analogy guide the creation of high‑utility, answer‑focused AI content?
3:32–4:28
6
What are the final takeaways and next steps for listeners wanting to implement answer‑engine optimization?
4:28–4:32