Start With One Repetitive Workflow, Not a Grand AI Strategy

Most AI initiatives fail because teams try to “AI-ify” everything at once instead of proving value on one painful workflow.

A better approach is to start with a task your team already does manually and can clearly measure. Examples from the episode:

- Sales research before calls - CRM updates after calls - Website and messaging reviews - Podcast or content research

Pick one workflow that:

1. Happens every week 2. Has a clear definition of “good enough” 3. Is currently done in docs, sheets, or ad hoc by humans

Then design an AI agent to follow the same steps your teammate would:

- Give it a simple job description: what to do, why it matters, what “good” looks like - Connect the right tools: CRM, data warehouse, call recordings, YouTube, Reddit, Slides, etc - Run it on a small sample first, review output, and refine the prompt

Do not expect perfection on day one. Treat the first version as a junior hire: useful, needs feedback, and gets better every week. Once the agent is consistently delivering value, then scale it and move to the next workflow.

Turn Your CRM And Lifecycle Into An Actual Growth Engine

Most “CRM” setups are just glorified contact databases with linear drip sequences. AI agents let you finally run the lifecycle programs you have talked about for years.

Instead of hard-coded journeys that assume everyone is the same, use AI on top of your product and engagement data to:

- Score accounts and users dynamically based on behavior, recency, frequency, and spend - Detect intent signals like feature drop-off after a support ticket, or power usage before expansion - Trigger the right next action automatically: email, in-app message, Slack to a CSM, task for sales

A simple starting blueprint:

1. Pipe key signals into a warehouse or CDP (signups, product events, tickets, meetings, invoices) 2. Use an AI agent to classify customers into a few clear states, such as “new”, “healthy”, “expansion-ready”, “churn risk” 3. Define what should happen when someone enters or exits each state 4. Let the agent monitor changes and push actions back into your CRM or marketing platform

You do not need to rebuild your entire lifecycle map. Start with one segment that obviously matters: high-intent free trials, expansion-ready accounts, or churn-risk customers.

Over time, layer on personalization. Use AI to generate email and in-app copy that references the exact features a customer has used, tickets they have filed, or content they have engaged with. You get both more relevance and less manual copy work.

Use AI Agents To Unlock New Growth Plays, Not Just Save Time

The most interesting use cases from the episode are not about shaving a few minutes off a task. They are about shipping growth plays that were previously impossible.

A few concrete examples you can borrow:

1. Competitive and customer research at scale - Agent scans Reddit and other forums for conversations about your category or competitors - It summarizes what users love and hate, then auto-generates a slide deck with themes, quotes, and opportunities - Your product and marketing teams get an always-fresh market pulse without a manual research sprint

2. Podcast and partner prospecting - Agent reviews thousands of podcast transcripts on YouTube - It flags shows that match your ICP and where your founder or subject-matter expert would be a relevant guest - Outreach becomes volume plus relevance instead of random cold pitches

3. Website and messaging optimization without A/B test volume - Agent reviews your site using a clear framework (claims, proof, social evidence, differentiation) - It identifies weak claims, missing proof (like reviews, G2 badges, case studies), and suggests improvements - You iterate copy and structure based on qualitative insight instead of waiting for a statistically significant test

4. Competitor engagement farming - Identify social posts in your space that ask people to comment for a resource - Use an agent to collect the engaged accounts and enrich them - Run targeted outbound or retargeting to people who have already raised their hands as interested in your problem space

In each case, the pattern is the same:

- Tasks that were “nice to have” but never prioritized - AI agents doing the heavy lifting across large data sets - Humans focusing on judgment, messaging, and relationship-building

This is where AI marketing automation shifts from cost-saving to revenue-generating.

Key Takeaways

- Start with one repetitive workflow and prove value before you try to “AI everything” - Treat AI agents like junior hires: give them clear instructions, tools, and frequent feedback - Use AI to transform your CRM from a static database into a dynamic, signal-driven growth engine - Focus lifecycle efforts on a few high-impact states first, like expansion-ready or churn-risk customers - Deploy AI agents for net-new plays such as scaled research, partner discovery, and competitor engagement farming

Conclusion

AI marketing automation is not about replacing your team. It is about removing the manual drag so your best people can focus on strategy, creativity, and relationships.

If you are a founder or growth lead, pick one high-friction workflow this week, turn it into an AI-powered process, and iterate. The compounding advantage goes to the teams who learn by doing, not by waiting for a perfect AI roadmap.