Building a complex AI workflow is a significant upfront investment. Teams should first manually validate that a marketing channel, like webinars, is effective before dedicating resources to automating its repeatable components. Automation scales success, it doesn't create it.
Marketers observe a significant disconnect between the sophisticated AI workflows discussed online and the more basic applications happening inside companies, even at the CMO level. This highlights the need for practical, real-world examples over theoretical hype.
Use an AI agent to systematically analyze sales call transcripts. By automatically extracting and categorizing data like competitor mentions and objections into a structured format (e.g., a spreadsheet), product marketers can quickly identify trends and prioritize their roadmap and messaging.
To create bottom-of-funnel content that resonates, analyze raw sales call transcripts for customer pain points. Then, overlay this qualitative data with quantitative data on SEO/AI search queries where your company and competitors are not appearing. This identifies a "blue ocean" of relevant topics.
Technical structure is crucial for AI Search Optimization (AEO). An article with properly ordered HTML headings (H1, H2, H3) is three times more likely to be cited by an LLM compared to a similar Page 1 ranking article with poor structure, making it a critical, low-effort optimization.
For AI Search Optimization (AEO), content freshness is critical. Research shows that content updated within the last three months is three times more likely to be cited by LLMs like ChatGPT compared to content left untouched for six months or more, revealing a steep drop-off curve.
To bridge the AI skill gap, avoid building a perfect, complex system. Instead, pick a single, core business workflow (e.g., pre-call guest research) and build a simple automation. Iterating on this small, practical application is the most effective way to learn, even if the initial output is underwhelming.