By Alison French, Founder of LTO and Creator of ShowScout
After countless conversations with founders about how they're using AI, I realized something crucial - we're all figuring this out as we go, but we're not all sharing what's working. As someone focused on helping B2B companies identify and capture revenue opportunities, I've spent the last few months experimenting with AI tools to streamline my workflow, turning one sales call into multiple pieces of content, and using AI as my personal copy editor. The big shift came from treating AI as a practical tool to get things done, not a magical solution.
What I'm about to share isn't theoretical - it's my actual workflow for transforming complex business challenges into practical execution frameworks. I started with basic tools like Fireflies for call transcription and Claude for analysis, then built a systematic approach to maximize AI's effectiveness. Whether you're working on sales/marketing integration, trade show strategy, or scaling revenue, these are the practical strategies I use every day to drive measurable ROI while maintaining authenticity.
The content below defines the processes I follow for using AI to create content. If you'd like to get a copy of the exact frameworks and prompts I'm pasting into Claude you can download that eGuide here - The Practical Guide to AI-Powered Content for B2B Leaders
Core AI Tools I'm Using
Fireflies + Claude for call transcription and analysis
ChatGPT for Excel work (especially deduping and cross-referencing prospect data with CRM data)
Google Docs for prompt management and iteration
Setting Up Your AI Workflow
Let's talk about something that took me way too long to figure out – how to actually organize your AI workflow so you're not starting from scratch every single time. After months of trial and error (and honestly, a lot of messy first attempts), I've landed on a project organization system that's been a game-changer for my business.
The key was realizing that AI tools work best when they have consistent context about you, your business, and your clients. Think of it like training a new team member – you wouldn't expect them to nail your tone of voice or understand your product positioning without proper context, right? So I've broken this down into four core project areas that help maintain that crucial context while making sure your AI outputs actually sound like you and align with your business goals. Here's exactly how I structure it...
Project Organization
Create separate projects in your AI tool to maintain context and improve output quality:
Tone of Voice Project
Writing samples
Meeting transcripts
Professional bio
Brand voice guidelines
Company Messaging Project
Core value propositions
Product positioning
Key benefits and features
Customer pain points
Client-Specific Projects
Individual client context
Project requirements
Communication history
Product Demo Analysis
Call transcripts
Voice of customer data
Feature requests
Pricing feedback
Pro Tip: Store your prompts in Google Docs for easy reference and iteration. Review and refine them as you learn what works best.
Practical Use Cases
After getting the foundation set up, let's dive into the real reason you're here – putting AI to work in ways that actually move the needle for your business. I've tested dozens of use cases, but these are the ones that have consistently delivered ROI while saving me serious time. Each of these is something I use in my own business, not theoretical examples.
Use Case 1: Turning One Demo Call into Seven Content Pieces
Deciding what to write about can be the biggest challenge when sitting down to produce content. I've found demo calls are my greatest inspiration for content.
Time Investment: About 1 hour
Output: 7 unique content pieces
Process:
Start with your demo call transcript
Ask Claude to analyze:
Key customer challenges
Product benefits that resonated
Solution alignment
Take the learnings from this chat and copy them into a new chat for each of the items below and then ask Claude to run the corresponding prompt based on this information.
The ask Claude to generate multiple content pieces:
Sales follow-up email
Follow-up nurture message
LinkedIn article
LinkedIn post promoting the article
3 reshare variations
Blog post version (SEO optimized)
Pro Tip: I have my AI prompts for each of these content pieces saved as a google doc so it's just a quick copy and paste when I'm working through each of these prompts.
Use Case 2: AI as Your Copy Editor
Here's how I practically use AI as my copy editor—and it's completely transformed my writing process. Instead of agonizing over every word as I draft, I now write freely knowing I have an editing partner on stand-by ready to help refine it.
The key is being strategic with your editing requests. Rather than asking AI to "make this better" (which rarely works well), I use targeted prompts to address specific issues like awkward transitions or sections that don't quite sound like me. Scroll down to the bottom of this post for additional statements I use to refine my content..
I've found I get the best results when I focus on one aspect at a time – whether that's tightening up wordy sections or adding more narrative detail where needed. And here's an important reality check: don't expect perfection on the first round of edits. I typically use these prompts iteratively, refining piece by piece until it hits the mark.
Step-by-Step Approach:
Write your first draft without worrying about perfection
Use your tone of voice project for context
Key Prompts to Refine Content:
"Bridge these two sections"
"This doesn't sound like me - revise to match my voice"
"Make this more concise"
"Add narrative detail to this section"
"Review for grammar and flow"
Pro Tip: Expect 50-70% accuracy on first drafts. Use targeted prompts to refine specific sections rather than trying to perfect everything at once. I included my process for creating prompts in the article below.
Use Case 3: Demo Call Analysis for Product-Market Fit
Over the past few months, I've been conducting dozens of sales demos, deliberately using each conversation as a testing ground for product-market fit. Instead of just hoping to remember key insights, I'm feeding these call transcripts into AI to systematically uncover patterns—everything from pricing reactions to feature requests to workarounds prospects are currently using.
This isn't just market research; it's real-time product validation that's already helped me pivot pricing models, refine positioning statements, and prioritize product roadmaps based on actual customer feedback.
Here's exactly how I'm turning sales conversations into actionable product insights:
Analysis Framework:
Compile demo call transcripts
Ask AI to identify patterns in:
Customer challenges
Current solutions/workarounds
Product feedback
Feature requests
Pricing reactions
Documentation:
Create individual summaries for each call
Maintain a running document of insights
Stop when you stop getting new information
Pro Tip: Make sure to include this output in your messaging project. This insight will tremendously improve the quality of the output in Use Case 4 and 5.
Use Case 4: Value Proposition Development
Here's something I discovered that completely shifted how I develop value propositions—let the AI help you iterate, but be strategic about how you guide it.
Instead of trying to nail the perfect value prop in one shot, I use the goldmine of insights from my demo calls as raw material, then let AI help me explore different angles. The key is giving it a clear framework (download my exact framework here) and then treating it like a collaborative brainstorming session.
I always ask for multiple options because I've found that combining the strongest elements from different versions usually creates something better than any single version. Here's exactly how I do it...
Process:
Use demo call analysis as input
Draft value propositions following the Values > Benefits > Features framework
Refine through iterations:
Ask for 5-10 options
Consolidate to top 3
Refine language and structure
Pro Tip: Guide the AI by being specific about what you like and don't like. Combine elements from different versions to create the perfect fit.
Use Case 5: Website Copy Development
I've found that applying the StoryBrand framework through AI is a game-changer for website copy, especially when you combine it with real customer insights from your demo call analysis and value proposition work. The magic happens when you feed in those actual customer pain points and refined value props —your website copy suddenly shifts from generic to laser-focused.
I use the AI to weave together the successful messaging patterns we identified in sales calls with our strongest value propositions, then refine it through the StoryBrand framework. Here's exactly how I blend all these pieces...
StoryBrand Framework Application:
Reference StoryBrand guide (download my exact framework here)
Generate initial homepage copy
Refine through targeted prompts:
Add specific benefit statements
Adjust tone and voice
Test alternative versions
Address customer objections
Pro Tips for Getting Better AI Results
Context is King
Always provide relevant background
Include your persona and audience
Reference previous successful content
Iterative Refinement
Start broad, then narrow focus
Use specific feedback
Combine elements from multiple outputs
Prompt Management
Save successful prompts
Document what works
Regularly review and update
Quality Control
Review AI output carefully
Trust but verify
Maintain your brand voice
Remember: AI is a tool to enhance your workflow, not replace your expertise. Use it to handle repetitive tasks and generate initial drafts, freeing you to focus on strategy and refinement.
How to Create Effective Prompts
Here's one of my biggest learnings about prompt creation – you need to approach it systematically, starting with solid research. Rather than guessing what might work, I've developed a method that consistently produces prompts that get results.
The key is gathering diverse expert perspectives first, then using AI to identify the patterns and best practices before crafting your final prompt. I spent months refining this process through trial and error, and it's transformed how I get quality outputs from AI. Here's my exact research-to-prompt blueprint...
Research Method: Step-by-Step Guide
Start with Google Research
Search for expert articles on your specific topic (e.g., "how to write high-converting LinkedIn posts")
Look for articles from credible sources like LinkedIn experts, content marketers, or industry leaders
Focus on articles with concrete examples and frameworks, not just theory
Compile Research in Google Docs
Copy and paste the most valuable articles into one document
Include 3-5 different perspectives on the same topic
Keep the source links for reference
Add any of your own notes or insights
AI Summary Process
Share the compiled research with your AI tool
Ask it to: "Summarize the key principles and best practices from these articles"
Request identification of common themes and patterns
Have it highlight unique insights from different sources
Convert to Prompt Format
Start with a clear context statement (e.g., "You are helping create LinkedIn content for a B2B founder who...")
Include specific requirements (tone, length, structure)
Add your persona information
Specify desired output format
Common Prompt Types:
For copies of the exact prompts I’m using, go to www.joinlto.com/free-resources
LinkedIn article prompts
Sales follow-up templates
Product messaging frameworks
Blog post structures
SEO optimization guidelines
Remember to include your persona and audience context in each prompt for better results.
Effective Prompts for Refining AI Output
Let me share my tried-and-true prompts for getting AI to nail your content refinements. These aren't just random suggestions – they're the exact prompts I use daily to transform rough drafts into polished pieces that actually sound like me. I've split them into two categories: prompts for improving content flow and prompts for quality control. After countless revisions, these are the ones that consistently get results...
For Content Flow
"Write a bridge to connect these 2 sentences"
"I don't like the way this reads... provide some alternatives"
"This doesn't sound like me. Revise to make it sound more like me"
"This is too flowery / Make it more direct"
"Make this section more concise"
"Add additional narrative to these bullets/section"
For Quality Control
"Review the entire piece for grammar, spelling, and punctuation"
"Adjust the tone to match my voice (reference previous content)"
"Rewrite this section to be more conversational"
"This feels generic - make it more specific to our industry"
Pro Tip: End each refinement session by asking for a final proofread of the entire piece to ensure consistency in tone and style.
I've shared my exact workflow for using AI to create authentic, high-converting B2B content – but there's one crucial piece missing: the specific prompts that make it all work.
After months of testing and refining, I've documented every prompt that consistently delivers results. Instead of starting from scratch like I did, you can grab my proven frameworks and start implementing them today.
Want the complete blueprint? I've packaged all my prompts, frameworks, and step-by-step processes into a practical guide for B2B leaders. No theory, no fluff—just the exact systems I use to create content that drives revenue while maintaining authenticity.
Download the full guide at www.joinlto.com/free-resources and start transforming your content creation process today.
