This should be redesigned immediately with AI
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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Transcript generated automatically by AI and may contain errors.
What is the core problem enterprise marketers face with outdated buyer behavior data?
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.
How does Liza Adams define the AI‑enabled visibility‑sentiment‑recommendation framework?
I'm Jordan Cooney, and joining me today is Lisa Adams, AI advisor and go-to-market strategist at Growth Path Partners. One pattern recognition that we should use to redesign our workflows in AI.
To begin to redesign workflows, we need to think about capabilities of AI that's very difficult for human beings to do. And the reason I say that is, as I mentioned before, we're like the Avengers, right? Humans and AIs have complementary superpowers and we overcome each other's weaknesses. There are things that are really hard for us to do, but it's easy for AI. So, for example, really hard for people to uh distill information, lots of information from various sources, as I mentioned previously. So look at our work. That's one of the key roadblocks of why we didn't do certain things because it was hard for us.
Why are AI’s super‑powers like constant pulsing and memory crucial for workflow redesign?
Maybe now AI can do it, right?
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What are the limitations of AI—such as lack of moral compass and context—that teams must guard against?
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The other thing that AI is really good in doing is constant pulsing. Of the market or whatever it is that it that we're trying to get information on, like whether it's scheduled or real time. And then the other thing is um consistent context. Regardless of how many documents or how many pieces of data we've got, it's got consistency in in applying certain standards across all those.
How does the three‑layer trust architecture determine whether a brand gets recommended to the right customer problem?
And then last but not least is memory. We have super bad memory. AI has a lot better memory than we do. So, with all these things, if we can find our limitations, see if we can get AI to use it, uh, to do it. And then on the other hand, AI has limitations. So, for example, it doesn't have a moral compass. Right. It doesn't know our context.
What is the “people‑first AI forward” methodology and how does it prioritize upskilling over workforce reduction?
We still need to check its work. But when we combine those two, we can have complementary superpowers and overcome each each other's weaknesses to achieve better outcomes.
Okay, that's all for today, but until next time, remember the answers are always in the data.
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Chapters
6 chapters
1
What is the core problem enterprise marketers face with outdated buyer behavior data?
0:00–0:42
2
How does Liza Adams define the AI‑enabled visibility‑sentiment‑recommendation framework?
0:42–1:38
3
Why are AI’s super‑powers like constant pulsing and memory crucial for workflow redesign?
1:38–2:31
4
What are the limitations of AI—such as lack of moral compass and context—that teams must guard against?
2:31–3:35
5
How does the three‑layer trust architecture determine whether a brand gets recommended to the right customer problem?
3:35–3:59
6
What is the “people‑first AI forward” methodology and how does it prioritize upskilling over workforce reduction?
3:59–4:14