AI-assisted outreach drafting and sales content generation

People have been using Codex wrong.

Most people use it to write code faster. I use it to increase our sales firepower.

Instead of doing manual prospecting, I drop a spreadsheet of brands into my Codex project and let it analyze the dataset: company signals, category, metrics, sometimes even checking their socials.

Then it tells me something much more useful than “good lead / bad lead”: why this brand might actually be a fit for us.

Once it finds a match, it updates the sheet with the results and can drafts a message specifically for that company. Not a generic template, a note grounded in their business.

The result is a very different workflow. Outreach starts to feel less like blasting emails and more like running analysis.

Sales starts looking a lot like engineering:
Dataset → reasoning → action.

The real shift is this: AI isn’t just helping write the message. It’s helping decide 𝘄𝗵𝗼 𝗶𝘀 𝘄𝗼𝗿𝘁𝗵 𝗿𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝗼𝘂𝘁 𝘁𝗼 𝗮𝘁 𝗮𝗹𝗹.

Your agent is your firepower. Use it everywhere you can.

I’ve been using Perplexity Computer every single day for my job.

I have it go through my LinkedIn, identify every lead relevant to the tech I’m selling, at the exact organizations I’m targeting.

Then it digs through all publicly available information about those companies. Press releases, documentation, interviews, product announcements, anything that references initiatives related to the technology I’m selling.

After that, I have it craft highly personalized outreach messages to each lead using the research it compiled.

Guys…

Sales Development Representative and Business Development Representative roles are cooked.

ABSOLUTELY COOKED.

I’m sitting here watching AI do hours of prospecting, research, and personalization in minutes.

It genuinely feels like I have a team of multiple sales reps doing all the dirty work for me.

I’m mind blown.

🤯🤯

STOP writing cold emails from scratch.

This free Claude prospecting playbook replaces 45 minutes of research per prospect.

Most SDRs are still:

- Googling prospects for 20 minutes before writing one email
- Copy-pasting the same generic opener to 200 people
- Getting 1-2% reply rates and blaming the list

I spent 6 months building Claude workflows that my team of 6 SDRs used to hit 140% of quota. Collectively.

Then I documented every prompt, every workflow, every template.

Here is what is inside The Claude Prospecting Playbook:

- The cold email prompt that writes like a human, not a bot
- ICP research prompt that replaces 45 minutes of Googling
- Subject line generator hitting 45%+ open rates
- 7-step cold email sequence builder, full workflow
- Objection handling script generator
- Account prioritization prompt that ranks by likelihood to reply
- 5 bump email prompts for follow-ups 2 through 5
- The system prompt I use for every prospecting conversation

I tested this against ChatGPT head to head for cold outbound.

Claude won. The data is in the playbook.

Want it?

- Connect with me
- Comment "CLAUDE"
- I will DM you everything.

P.S. Repost and I will send you the exact system prompt my top SDR uses daily.
We must be connected or LinkedIn blocks the message.

A quick and dirty outreach system for founder-led sales. Bonus, it won't make you hate yourself:

1. Create a project in Claude or ChatGPT and call it something like “sales and business assistant.” Drop in a detailed explanation of your ICP, strategy docs, transcripts of sales calls. This becomes your operating system for outreach, planning, and prospecting.

2. Request a data export from LinkedIn (settings -> privacy -> data). Takes them about 24 hours to send you a CSV of your connections.

3. Drop it into your AI project alongside your targeting framework and ask it for a list of people you should reach out to; ask it to include a score for each. Half the results will be wrong. Tell the AI why. You will eventually get your first 10 to 20 people to reach out to, and your AI project will get smarter.

4. Start a chat inside your project called “leads” or “prospects.” When you’re ready to message someone, screenshot their LinkedIn profile, drop it in, and ask the AI to draft a message. Refine heavily because the draft will make you want to claw your eyes out. Again, the AI will get smarter over time. Human edit always.

5. On the message itself: don’t do the fake “I saw your post about X and it really resonated with me” thing. That is insufferable. Just be human and make the ask clear.

6. Send 15-30 LinkedIn connection requests a week to grow your audience. Build lists of people in Clay on the free tier and enrich your lists with their LinkedIn profiles. Or just search manually on LinkedIn.

7. Track who you’ve messaged. Google doc with name + the last action you took. Mine is ugly but it keeps me honest, and my exec assistant uploads it to HighLevel.

8. You can have Claude Cowork scan your list and set reminders on your calendar to follow up with people if appropriate. It can even include a drafted follow-up message in the event notes to keep you from procrastinating when it's time to send a nudge.

9. Write at least one LinkedIn post a week, ideally two. This is fuel for everything else. Your content is what warms people up after they accept your connection request.

10. Audit your LinkedIn headline and about section. Your profile + content is how people decide if you’re worth paying attention to, and it’s important to the new algorithm.

11. The only metric that matters when you start is “did you do your actions this week.” Fifteen to twenty-five. You can’t control who says yes to a demo but you can control whether you did the work.

If you get into a good groove, keep the momentum going. It’s tough to slow down and have to rev the whole engine back up again.

What am I missing?

Three Mistakes Companies Make When Implementing AI in Sales

I've observed three common mistakes that can derail AI implementations in healthcare and fintech companies.

Mistake #1: Deploying AI without changing behavior
Companies often install tools but fail to modify their processes. Sales representatives log into the system, unsure of how to utilize it, and revert to their old habits. For instance, one fintech company invested $80K in AI but experienced zero adoption by month three. The issue wasn't the tool itself; it was the implementation—no training, no clear workflow, and no accountability for usage. After conducting a 2-hour workshop, assigning a champion representative, and setting a goal to "use AI for every first outreach," adoption surged to 85% within two weeks.

Mistake #2: Expecting day-one perfection
AI tools improve over time with more data and feedback. A healthcare company I collaborated with anticipated that the AI outreach would immediately match their top representative's style. Initially, it didn't perform as expected, but by week four, it surpassed average performance, and by week twelve, it outperformed most. The mistake was discontinuing the tool at week two due to its lack of perfection. Patience and iteration are key to achieving results.

Mistake #3: Not measuring the right things
Many companies focus on whether they implemented the tool, which is the wrong metric. Instead, they should measure:
- Time spent on non-selling work (should decrease)
- Pipeline per representative (should increase)
- Representative satisfaction (should improve)
- Deal velocity (should accelerate)

One finance VP concentrated on tool usage rather than actual revenue impact, resulting in high adoption but no revenue change. We reframed the focus: "Each representative is saving 4 hours weekly. Are those hours producing pipeline?" By measuring pipeline per hour worked, the strategy shifted to deploying freed-up time toward high-value activities, leading to tangible results.

The pattern across successful implementations includes:
1. Change workflow first, deploy tool second
2. Allow 60-90 days for optimization
3. Measure revenue impact, not tool usage

Do those three things, and you'll see 2x+ productivity gains.

Have you experienced this issue?

Ever wondered what powers my daily AI-driven workflow? Here’s the current tech stack that keeps me on top of things and helps our MSP clients sell AI:

ChatGPT – My go-to LLM for content and brainstorming; I've got a small army of customGPTs that each do a different task.

Comet – An AI browser on a Chrome backbone; LOVE the AI assistant feature for summarizing videos or giving me TLDR synopses on long, technical pages.

Perplexity – If I need data to prove (or disprove) a point, this is my go-to tool.

Gemini (Nano-Banana) – AI-generated imagery for engaging visuals.

Descript – AI-boosted video editing.

Replit – Vibe coding for the win! I used this to code our internal SaaS tool, to organize our CustomGPTs and make them user friendly. (I'm also coding a Chrome extension that's proving to be a bit tricky.)

Monday.com – Task management and automation powerhouse (we accidentally maxed out our automation quota last month!).

I'm sure there are a few more that I'm missing, but these tools are how we (currently) deliver our AI sales coaching...and deliver accountability.

Where is your stack different?