This week on AI for GTM: - 4 elements every high-converting case study has
- A workflow to go from customer outreach to a published case study using AI
- Latest AI updates from G2, Optimove, and earned media research impacting GTM teams
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The AI playbook for writing SaaS case studies faster |
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Most SaaS companies have a backlog of case studies they need to write. But between the interview, the writing, the design, and the approvals, it's just a lot. I saw this firsthand when working with the Mailmodo marketing team. The team had a fair share of case studies to write. While the result was decent, the effort across all those steps was so high that they stopped making them altogether. Recently, we restarted the whole thing. But this time with AI in the mix. The process is significantly faster, and honestly, a lot more effective. Here is the exact playbook they put together: |
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First, understand what good case studies look like |
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Before using AI, you need to know what sections make a case study effective. After reviewing dozens of examples, we found these four elements very common: - The pre-usage state: What was broken for the company, the cost, and what they had tried before. A buyer needs to see themselves in it.
- The search and decision: How did they find your solution? What alternatives did they consider? What almost made them walk away?
- The after state: What changed after implementing your tool? Include numbers with context: good, bad, or average outcomes.
- The quotable moment: A specific, slightly surprising line only a customer who experienced the problem could say. Sales reps can use it in outreach because it resonates with prospects’ fears.
Once you map these sections, the next step is identifying where AI can help. |
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A 4-step AI workflow to create case studies faster | For this workflow, we used the Claude Projects feature to keep all prompts, context, and instructions in one place. You can use any similar setup, like another AI tool with saved instructions. |
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Step 1: OutreachThe key idea here is to set up an automation wherever the CSM team is assigning a new customer detail to write case study copy. For example, if you have google sheet tracker, you can automate by: When a new row is added → the automation passes the details to Claude, → which drafts a warm outreach email directly into the CSM's Gmail drafts folder. You then add a human on the loop to review it and send it. |
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Step 2: Interview and TranscriptThe next AI usage is in the interview process. We used Claude to generate a tailored question list across five segments: - The before state
- The search and decision
- The implementation
- The after state
- The honest reflection
For each segment, we aimed for three to five specific questions designed to surface vivid, quotable, number-backed answers. Avoid yes/no questions wherever possible. For the transcript itself, we created a single automation that runs all the way from data extraction to writing. We first connected our Fireflies recorder directly to AI tools. So once the call ends, Fireflies generates the transcript, and the automation pulls out four things: the strongest before state, real objections, a specific outcome with a number, and the most quotable line. That extracted output feeds directly into the writing stage. |
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Step 3: WritingWith insights collected, we set a prompt that tells Claude how to write the use case. Here is the outline we set: - Headline: Write a line that reflects the success metric
- Subheadline: one sentence to explain the customer, problem, and result in under 25 words
- Results box : 3 to 5 bullets under 15 words each
- The challenge: describe the before state in specific terms without mentioning the product once
- Why they chose us: Cover the search, the alternatives they considered, the objections, and what resolved them
- The results: share the success metrics and always include what the number actually made possible for the team
- Customer quote: Should be around 30 to 50 words and pulled directly from the transcript
- What's next: A section to conclude where the customer is headed and what they are planning to do next with the product
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Step 4: DesignOnce the copy is approved, the next step is to create your design asset. You can try out the built-in image generation tools that come with Claude or connectors. For this, we used the Canva + Claude connector. The team wanted to highlight some quotes from the interview, so they created a prompt where Claude would extract the strongest quote directly from the case study copy. The system then generated a banner using our Brand Kit, fonts, and colors, and returned it as an editable Canva file ready to tweak and export. |
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AI news: What's happening right now? | - G2 now connects directly with Claude, pulling verified buyer reviews into AI chats. For GTM teams, this means your reviews and case studies are now what determine whether your product shows up in an AI recommendation at all.
- Optimove launched a workspace where AI agents that run campaigns on entirely. Four agents handle journey, offer, content, and send-time decisions, all sharing context with each other. For GTM teams, this directly improves how campaigns are executed. Instead of building and optimizing flows manually, teams define strategy, guardrails, and inputs while agents handle execution.
- New research shows earned media drives 25% of all LLM citations, with non-paid sources making up 94% of links in AI-generated answers. As buyers increasingly use AI for research, visibility in these systems is being shaped by PR, not ads. For GTM teams, rather than changing how distribution works. Thought leadership, press coverage, and genuinely newsworthy content now directly influence whether your brand shows up in AI responses.
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| That's all, folks! I'll see you soon with more tips and ideas. If there is something we can do better for you, please let me know by replying to this email. |
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Until next time,
Aquib
CEO, Mailmodo |
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