Podcast Summary: AI Explored

Episode Title: Automating Lead Nurturing With AI Agents
Host: Michael Stelzner (Social Media Examiner)
Guest: Noelle Russell (Founder, Agentic AI; Author, Scaling Responsible AI)
Date: September 16, 2025

Overview

This episode dives deep into how businesses and marketers can practically implement AI agents for automating lead nurturing. Host Michael Stelzner is joined by AI business strategist Noelle Russell to break down misconceptions, core processes, and actionable steps for leveraging AI-powered agents that can text, email, or otherwise interact with cold leads to move them towards becoming qualified sales opportunities. The discussion is rooted in Noelle’s hands-on experience and her accessible framework for building and deploying these solutions, particularly for non-developers.

Key Discussion Points & Insights

1. Noelle Russell’s AI Journey & Passion (03:16–04:58)

• Background: Early exposure to science fiction developed her interest in human-machine collaboration.
• Professional Leap: Moved from cloud architecture into AI via Amazon Alexa to help her son with Down syndrome communicate, stressing the practical, human-centered potential of AI.

2. Why Marketers Should Care About AI in Lead Nurturing (05:22–07:16)

• AI's Unique Value: Automating the tedious, repetitive “21 touches” traditionally needed to nurture a lead—work most humans prefer to avoid.
• Always-On Advantage: “AI never sleeps…we could actually, for almost nothing, have these AI agents working on our behalf and bringing us qualified prospects…” —Michael Stelzner [06:03–06:28]

3. Defining “Agentic AI” & Addressing Misconceptions (07:12–11:32)

What Is Agentic AI? (07:17–08:19)

• More than chatbots—true “agents” can connect with tools (email, CRM, social, etc.), triage info, take action.
• Example: Email inbox triage, CRM integration, cross-platform actions.

Common Myths

• Difficulty: Modern interfaces mean “your superpower is to really get clear on what you want and how you want to build it.” —Noelle Russell [08:26]
• Security & Reliability: Many now offer robust safety features (see safe, responsible design below).

Jobs & AI

• AI is changing work: Not using AI puts you at risk, but those who add AI to their workflows (“you + AI”) become irreplaceable.
• Key Quote: “AI isn’t going to replace you, but someone with your skill set using AI is definitely going to replace you…” —Noelle Russell [11:08–11:32]

4. The Practical Steps to Building an AI Agent for Lead Nurturing (12:11–24:08)

a. Identify the Right Problem

• Choose a “minimum remarkable product” (MRP): Start with a clear, high-impact use case (e.g., reactivating cold leads by getting them onto sales calls).

b. Choose Your Tools & Model

• AI Platform: Noelle recommends Chatbot Builder AI for simplicity and free use, with zero coding required. [15:36]
• LLM Options: ChatGPT, but also possible to use Google Gemini, Claude, etc.
• Easy integration—no need for developer API keys at first.

c. Demo Example: Mindvalley AI Assistant (18:03)

• Use case: Website chatbot to engage visitors, collect info, and drive sales in a dynamic, conversational “human-like” manner.

d. Omnichannel Reach (20:02–22:13)

• “One brain to rule them all” — design one intelligent agent that can deploy via SMS, email, DMs, voice, multilingual conversations, etc.

5. The 3 Ps of Prompt Engineering (22:52–33:28)

1. Purpose

• Define the bot’s job: crystal-clear instructions ensure consistent, relevant behavior.
• Prompt-writing tip: Ask ChatGPT to draft a safe and responsible system prompt based precisely on the outcome you want.

2. People (Audience)

• Specify who the bot serves—as detailed as possible (demographics, psychographics).
• Include who shouldn’t be served if relevant, so the agent can gracefully redirect or disqualify.
• “You want to spend some time really defining…who is your target audience? So…the AI can lead score and focus on the right people.” —Noelle Russell [28:07–29:30]

3. Portfolio (Data Context)

• Feed the agent with company/product data: website copy, PDFs, docs, knowledge bases.
• This makes the bot’s answers accurate and “on brand.” Otherwise, the AI will fill in gaps (risking hallucination).
• Introduces the concept of RAG (Retrieval Augmented Generation): “the more data you give it, the more it’s going to…look at it through the context and lens of what you want it to look at.” —Michael Stelzner [32:06–32:24]

6. Managing Hallucination, Brand Voice, and Sample Q&A (33:54–39:44)

• Test for edge cases/unexpected questions (“AI red teaming”).
• Provide “desirable” and “undesirable” example questions and responses—use the agent to field even left-field queries in a branded, opportunity-creating way.
  • Example: If someone asks a Mindvalley bot about taxes, reply: “Unfortunately, I don't help with taxes, but tax season is stressful. Here’s a class you might want while doing this,” etc.

7. Testing, Optimizing, and Reinforcement Learning (39:44–43:55)

• Break Your Bot: Internal testing phase where you and your team/friends try to trip up the bot and spot coverage gaps.
• Use built-in “inbox” (in Chatbot Builder AI) to review transcripts and optimize.
• Give users a chance to report thumbs up/down or leave feedback—crucial for continued improvement.
  • “The only way AI models get better is if we tell it that it's broken.” —Noelle Russell [41:52]

8. Driving Adoption (43:59–49:37)

• Highlight chatbot’s benefits and “multilingual, conversational experience” to users.

• Automated SMS Example: Agent re-engages cold leads with a simple personalized message, and an LLM takes over to field their replies, ask two key questions, and drive to calendar booking.

  • Sample questions: “Have you already started using AI in your business?” and “How have you trained your team?”

• Encourage constant testing: “If you don’t get more than 30% conversion, change the questions.” —Noelle Russell [48:06]

Notable Quotes & Memorable Moments

• On Human/AI Collaboration:
  “If we are clear about how we want to treat our customers, this machine will do that on our behalf and we can move on to maybe higher value work...closing those deals.”
  — Noelle Russell [06:28]

• On Adopting AI:
  “You now need to become you +AI...AI isn’t going to replace you, but someone with your skill set using AI is definitely going to replace you...”
  — Noelle Russell [11:08]

• On Prompt Engineering:
  “Just asking for what you want, which I think is the moral of prompt engineering...the AI system like ChatGPT does know because it’s built on a safety system.”
  — Noelle Russell [25:33]

• On Continuous Improvement:
  “This job never goes away. This baby never grows up.”
  — Noelle Russell [43:55] (on needing ongoing human feedback and optimization)

Important Segment Timestamps

• Noelle’s AI journey & motivation: [03:16–04:58]
• Why marketers need AI for lead nurturing: [05:22–07:16]
• What is agentic AI; common fears: [07:12–11:32]
• Practical building steps, case study: [12:11–18:03]
• Omnichannel (email, SMS, voice, DM) possibilities: [20:02–22:13]
• Prompt engineering framework (the 3 Ps): [22:52–33:28]
• Red teaming and handling odd questions: [33:54–39:44]
• Testing and continuous improvement: [39:44–43:55]
• Adoption strategies; the two-question framework: [43:59–49:37]

Resources & Connect

• Demo Video, eBook, TED Talk, More:
  agenticAIagency.ai/SME
  (see show notes for direct link)

• Connect with Noelle Russell:

  • Instagram: @NoelleAI (preferred for messages)
  • LinkedIn: Follow (not connect)

Final Takeaway

AI agents enable marketers to reclaim time and increase conversion by automating the laborious, repetitive parts of lead nurturing—while keeping communication on-brand, cross-channel, and even multilingual. The actual building process is now within reach of non-technical folks, thanks to natural language interfaces and new tools. The key: Clear instructions (the 3 Ps), real data, iterative testing, and a willingness to incorporate AI into your workflow so you become “AI enhanced”—not replaceable.

Detailed show notes and demo resources at:
SocialMediaExaminer.com/aipod