“We need to implement AI yesterday, but nobody knows where to start.”
This was the opening line from a CMO during our kickoff call last month. She'd just come from a board meeting where every other portfolio company was showing off their AI wins – automated content generation, predictive lead scoring, potentially meaningful campaign optimization. Meanwhile, her team was still manually writing email nurtures and hoping for the best.
Sound familiar?
I've had this exact conversation with probably 6 different marketing leaders in the past quarter. Everyone knows AI is table stakes now. Everyone wants to be an "AI-native" organization. But when it comes to actually getting started? Crickets. 🦗
The paralysis is real, and honestly? It's totally understandable.
We're drowning in AI tool announcements. We're bombarded with use cases ranging from "write better subject lines" to "completely automate your entire GTM motion." And we're terrified of picking the wrong tool, breaking something that's working, or worse – investing time and budget into something that doesn't move the needle.
Because one thing I’ve really also been experiencing is trying to leverage AI to become more productive, but then spending so much time trying to prompt engineer the right process, that it probably would’ve just been faster to do it the old way the first time around.
But here's what I've learned after helping dozens of teams start to navigate this transition: The companies winning with AI aren't the ones with the fanciest tools or biggest budgets. They're the ones who started small, learned fast, and built momentum through quick wins.
👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. If you’re one of the 17 people that have subscribed since last Sunday, hello! So glad you’re here—you’ve just joined 2k+ marketers who read Looped In every Sunday.
Marketing Teams Are Stuck
Last week, I held a kickoff call with a new client I’m onboarding. When I asked them to list their biggest barriers to AI adoption, here's what came up:
"We don't have time to learn new tools"
"What if AI-generated content hurts our brand voice?"
"The ROI is unclear"
"Too many tools to evaluate"
"Our team isn't technical enough"
"We're worried about data privacy"
All valid concerns. But you know what the real blocker was?
They were trying to boil the ocean.
Instead of picking one specific problem to solve with AI, they were trying to figure out a comprehensive AI strategy that would transform their entire marketing function. No wonder they were paralyzed.
So we flipped the script. Instead of asking "How do we become an AI-powered marketing team?" we asked "What's one repetitive task that's eating up 5+ hours per week that we could automate with AI?"
That shift changed everything.
Start Here: 7 High-Impact AI Projects for Demand Gen Teams
After working with multiple teams on AI adoption, I've identified a few projects that consistently deliver quick wins. ⚠️ Warning: these aren’t the prompts you hear about on LinkedIn from all the influencers who’ve built an agent that replaced their 42 person marketing team, but they work and it’s a practical place to start:
1. Email Subject Line Optimization
Time saved: 1-2 hours/week
Tool: Claude or ChatGPT
Prompt: "Generate 10 email subject lines for [paste email content]. Target audience: [describe ICP]. Optimize for open rates in B2B SaaS. Include 3 with personalization tokens, 3 with urgency, and 4 focused on value proposition."
2. LinkedIn Ad Copy Variations
Time saved: 4-5 hours/campaign
Tool: Jasper or Copy.ai
Prompt: "Create 5 LinkedIn ad variations for [product]. Pain point: [specific challenge]. Target: [job title] at [company size] companies. Format: Headline (max 70 chars), description (max 100 chars), CTA options."
3. Webinar → Blog Post Conversion
Time saved: 6-8 hours/webinar
Tool: Descript + Claude
Process: Upload webinar recording to Descript → Export transcript → Feed to Claude with prompt: "Transform this webinar transcript into a 1,500-word blog post. Maintain conversational tone, add subheadings, pull out key quotes, and create a TL;DR summary at the top."
4. Competitor Content Analysis
Time saved: 10+ hours/quarter
Tool: Perplexity + custom GPT
Prompt: "Analyze the content strategy of [competitor domain]. Identify their top 10 performing pages, main content themes, keyword focus, and content gaps we could exploit."
5. Sales Email Personalization at Scale
Time saved: 20+ hours/month
Tool: Clay
Process: Enrich prospect data in Clay → Build Claygent to enrich any signals custom to your org/industry → Leverage Clay’s Signals to enrich for things like recently company news, job postings, tech stack changes, etc. → Build 2nd Claygent to generate personalized opening lines based on those signals → Leverage Twin (or custom agent) to generate a multi-step sequence tailored to your findings
6. Campaign Performance Analysis
Time saved: 5-6 hours/week
Tool: ChatGPT Advanced Data Analysis
Process: Export campaign data → Upload CSV → Prompt: "Analyze campaign performance. Identify top 3 insights, performance trends, and provide 5 specific optimization recommendations based on the data."
7. Customer Story Mining
Time saved: 8-10 hours/month
Tool: Gong + Claude
Process: Export Gong call transcripts → Feed to Claude → Prompt: "Extract customer success stories from these calls. Find specific metrics, pain points solved, and quotable moments. Format as case study outline."
Rip off the Bandaid and Get Started
Here's the framework I've used with multiple teams to go from AI-curious to AI-informed:
Foundation Building (Crawl)
Week 1: Pick ONE project from the list above
Week 2: Test with 2-3 team members, document learnings
Key principle: Start with something that's annoying but not mission-critical. You want quick wins without high stakes.
Momentum Building (Walk)
Week 3: Roll out first project to full team
Week 4: Add second AI project, measure time savings
This is where you'll start seeing compound benefits. That email marketer who saved 2 hours on subject lines? They're now using that time for strategic work.
Scale & Systematize (Run)
Week 5: Document AI workflows and best practices
Week 6: Run your first AI hackathon (more on this below)
By month 2, AI should feel like a natural part of your team's workflow, not a scary new thing.
Build a Hackathon Model
Want to accelerate AI adoption across your team? Run an internal hackathon. Just like Clay’s GTM Hackathon’s you’ve probably seen all over your feeds lately. Not the pizza-fueled, all-nighter kind, but the trap-your-team-in-an-office for like 4 hours kind. Here’s the framework the Clay team is using:
Pre-Hackathon (1 Week Before)
Send this prep email: "We're hosting a 4-hour AI hackathon next Friday. Your mission: Pick one repetitive task you hate and figure out how to automate it with AI. Come with a specific problem in mind."
Set up tool access:
ChatGPT Team account
Claude Pro
One specialized tool (Copy.ai, Descript, Clay, AirOps, etc.)
Create a shared doc with prompt templates and examples
Hackathon Day Structure (4 Hours)
0:00-0:30: Problem sharing session (each person shares their chosen task)
0:30-0:45: Vote + stack rank the problems worth solving today
0:45 - 3:15: Split into groups + build the top solutions
3:15 - 4:00: Invite your company to attend an optional live demos from each team
Post-Hackathon
Document all automations in a team playbook
Assign "AI Champions" for each successful automation
Schedule monthly "AI Show & Tell" sessions
One client ran this hackathon format and discovered 8 hours of weekly time savings per person across their 5 person outbound team. That's a full FTE worth of capacity that just compounds over time. 🤯
Essential AI Prompting Principles
Before you dive into tools, master these prompting fundamentals:
The CLEAR Framework
Context: Set the stage with background info
Length: Specify desired output length
Examples: Provide 1-2 examples of what good looks like
Audience: Define who will consume the output
Role: Tell the AI what expert perspective to take
Example Prompt:
Context: I'm a demand gen manager at a Series B SaaS company selling sales intelligence software to VP Sales at mid-market companies.
Role: Act as a B2B SaaS copywriter with expertise in sales intelligence tools.
Task: Write email copy for a nurture sequence
Length: 3 emails, 150 words each
Examples: [paste your best-performing email]
Audience: VP Sales at 50-500 person companies who have shown interest but haven't booked a demo
Constraints: Conversational tone, no buzzwords, focus on time savings and revenue impactMy Current AI Stack for Demand Gen
After testing 50+ tools, here's what I’m actually using daily (as of now):
Content Creation
Claude: Long-form content, strategy docs, email sequences
ChatGPT: Quick copy variations, data analysis, brainstorming
Research
Perplexity: Competitor research, industry trends
Clay: Signal research, industry news, funding announcements
Workflow Automation
Clay: Data enrichment + personalization at scale
Zapier AI: Connecting AI outputs to marketing tools
Specialized Tools
Descript: Webinar → content transformation
Runway: Quick video editing for social
Start with the free tiers. You don't need paid plans until you're using AI obsessively.
How to Handle the Inevitable Pushback
When you start implementing AI, you'll naturally face resistance. Here's how I might address the most common concerns:
"AI content sounds robotic" Response: Show them this prompt addition: "Write in a conversational tone like you're explaining to a smart friend over coffee. Use contractions, ask questions, and include specific examples."
"What about job security?" Response: Frame AI as a multiplier, not a replacement. "AI handles the repetitive stuff so we can focus on strategy and creativity – the things that actually move the needle."
"The quality isn't good enough" Response: AI output is a starting point, not a finish line. Think 60% done in 20% of the time, then human polish for the final 40%.
"It's too expensive" Response: Calculate time savings. If AI saves 5 hours/week at $50/hour, that's $1,000/month in capacity. Most AI tools cost <$100/month.
So what's it gonna be?
See ya next week,
Kaylee ✌
This was good