u/chasing_next

I've barely taken notes on sales calls in 5 months. Here's what has helped me make calls more productive & actionable:

1. Sign up for Tactiq to transcribe calls (there are others, but I prefer Tactiq)

- get free tier that transcribes 10 calls/month

- download chrome extension

- works with google meet, teams, zoom, etc.

2. Join a call & make sure it's transcribing

- do a test call before an important call

- tactiq will notify others it's transcribing in chat

3. Export the transcript after the call

- tip: Tactiq just rolled out an MCP for direct access

4. Run the below prompt in a new ai chat window

- attach the downloaded transcript with prompt

- tip: set this up as a command or skill to run after every call

- tip: tweak the prompt toward your specific needs

5. Synthesize findings across calls

- find recurring pain points, needs, ICP terms

- use them in marketing and to guide product strategy

---

PROMPT (step 4):

You are my call-notes assistant. I'll share a raw call transcript to process. Synthesize it into structured notes I can act on. Follow this format exactly:

## TL;DR

2-3 sentences: who this was, what they're trying to do, and the single most important takeaway.

## Their Situation

What's going on in their world: their role, their team, their current process. Stick to what they actually said.

## Problems & Pain (in their words)

List the problems they raised. For each one, quote or closely paraphrase the actual language they used. Don't translate it into my terms. The exact words matter for following up later.

## What They Care About

Goals, priorities, what "better" looks like to them.

## Objections / Hesitations

Anything they pushed back on, worried about, or were lukewarm on.

## Fit & Opportunity

Where what I do could genuinely help them, and where it clearly doesn't. Be honest about non-fit, don't force it.

## Follow-Up
• Concrete next steps with owners
• Anything I promised to send
• One suggested follow-up message that pitches the value back to them using THEIR own words from this call

Rules:
• Don't invent anything that wasn't said.
• Preserve specific names, numbers, tools, and quotes.
• If something is ambiguous, flag it rather than guessing.

Have been having a lot of convos with marketers about how they're using (or not using) AI. one thing that's stood out is how quick people are to mention agents... then i ask follow ups and the heavy majority aren't doing anything nearly agentic.

I've found most companies to be very basic in their AI use. Most strong teams i've talked to boil down to a manager seeing opportunities, sharing them with their team, and evangelizing everyone to get similar benefits.

For the teams who are stuck in basic chat, they should stop making agents the focus until they understand how to work with AI fundamentally. If this is skipped, agents stay theoretical (or for only a few people to lead) instead of something the team can identify opportunities and design new processes around.

Getting ahold of the foundations is pretty simple. The best way I've seen people get noticeably better is experimenting with their AI platform's project feature (known as notebooks in copilot and gemini). This teaches a few fundamental things important for embedding AI into work:

1. How to craft instructions for AI to read at the start of each convo (spend time structuring, iterating until the results are consistent and voice/outputs meet your needs).
2. How to load context so AI has the background it needs to operate, without refeeding info or providing so much it gets confused.

Both of these only come with trial. There's such a thing as too much and too little steering.

Beyond this, projects/notebooks let you spot where AI fits strategically in work. They're so much more than a way to organize your chats. They can give AI background on a specific initiative (Q3 campaign planning), help execute repeat tasks (brand voice checker), act as a specialist (SEO performance analyst), or hold a specific frame of mind (CMO feedback generator). Using them broadly is how you get good.

Nailing these foundations of how to work with AI systematically make agents much more approachable. They're an onramp to spot places where AI can bring value worth designing larger agent processes around.

Made a post last week about 17 things I learned talking to marketers about AI:
https://www.reddit.com/r/DigitalMarketing/comments/1thsdwx/17_things_i_learned_talking_to_marketers_about_ai/

Following up with what the few teams who had a handle on it were doing different.

It all came down to managers taking charge instead of waiting for L&D, IT, the AI task force, or a consultant to figure it out for them.

Here's what those managers were doing:

1. They use AI in their work strategically

This one sounds like a no-brainer, but chatting with AI to finish a deliverable isn't what I mean. Instead, top managers figured out specific ways to use AI in their work repeatedly and would share how they were applying it with their teams.

Things like building deep research into the campaign strategy process or setting up a project that acts as a client giving feedback on deliverables. By creating their own processes they could model AI adding value beyond back-and-forth chat, then set an expectation that their team use it in a more structured way too.

2. They make their team want to share

Teams that stood out had a sharing culture that wasn't forced. They're talking about AI in conversations because they want to, not as a requirement.

Many had Slack channels, but this isn't necessary. It's more about building momentum by recognizing people and making figuring out AI together part of their team culture. They celebrate people trying things, thinking out of the box, getting curious, and asking questions. Team interest naturally grew when people were seeing others create things that were helping their work.

3. They push for tool access

While most mangers default to whichever AI tools their org rolled out, these managers were pushing for specific tools or flexibility to test stuff (even if it meant having to go through IT or security). I heard of teams setting up structures where individuals could request a tool, get reimbursed, if they shared what they learned. Another gave each person a $250 AI budget for tools and training.

This could sound unrealistic in a lot of orgs because of privacy, but there's a strong case for it. Of everyone I talked to it was clear using unapproved tools was the norm (with many paying out of pocket), with or without rules. Better to make it official and learn what's working than lock everything down and pretend it's not happening.

Keeping it hidden creates a don't-ask-don't-tell policy which is counter to the collaborative environment teams doing it well have.

4. They give their team time to rethink work

Want to be careful with this one since it could easily be misinterpreted. The managers doing it well aren't pushing AI down the road by giving their team space, they're adjusting deadlines and holding their team accountable for figuring out how AI can fit in their processes.

While a major narrative is that AI is supposed to speed you up, it takes slowing down and reimagining work to figure out how to integrate it in a way that truly helps. Keep deadlines the same and you'll get band aid use where teams don't have time to experiment and write off its abilities too soon.

Surprisingly, most of these managers weren't deep into AI and didn't know their team was ahead, but talking broadly they clearly were.

I'm in the AI training space, but it's been hard to break through to Marketers. For the last few months, I took a step back to listen and learn.

I spoke with Marketers in-house, at agencies, and even for AI-first orgs. Also a range of positions from CMO to new hires.

I found that people have been very curious how they stack up with other orgs since there isn't much info sharing as teams figure this out.

Maybe 10% of teams have a handle on AI. Here's what I heard:

WHAT ALMOST EVERYONE SAID

1. Teams have basic tools and only a fraction of people use them. Mostly Gemini, ChatGPT, Copilot. Half or less use them.

2. There's a lot of shadow AI happening. People paying out of pocket for Gamma, Granola, Tactiq, Claude, and ChatGPT. Many feel they can't openly say what they're using, but that's not stopping it.

3. No one feels adequately trained. Training that exists is compliance-video format. Watch to check a box.

4. Almost nobody is excited. I sensed dread and burnout instead. People know they should be doing more but are too busy and don't know where to start.

5. Teams are left to figure it out on their own. Members wait for managers. Managers wait for IT or L&D. IT and L&D are figuring it out too. AI is everyone's job, which makes it nobody's.

WHAT SURPRISED ME

6. People are protective of their stuff. Gatekeeping good prompts and workflows. A way to stand out as fear of downsizing creeps in.

7. AI was in about 50% of performance reviews. Specifics are vague. Some admitted to fudging their scoring because there was no clarity behind the rollout.

8. AI can be too good at brainstorming. Big campaign ideas get sold in, then teams realize they can't execute them. No budget or capacity, not possible.

9. The slop problem is less about receiving slop, more about the fear of sending it. I expected the opposite. Teams are training each other to be cautious, sometimes overly.

10. Everyone is namedropping agents, hardly anyone is building them. The vocab is way ahead of the practice.

11. Marketers who could benefit from agents are being left out of designing them. Strategy is happening in Leadership, IT, and Ops. Big miss.

12. Marketers are defaulting to vendors for AI growth efforts. Adding vendor AI is the easiest win when you've been given no time or support.

13. Creative teams are dragging their heels more than others. Good reasons (brand protection, backlash). Downside: they're missing fast, cheap concepting and spreading hesitancy to other teams.

14. People aren't sure if AI actually saves them time. They're getting outputs that are a little better, but they took the same or more time.

WHAT WASN'T MENTIONED

15. No one named the gap between what AI can do vs what they're using it for. People know they're underusing it, but can't name what they're not doing or where to go next.

16. No one brought up ROI unprompted. The people who care about ROI aren't the ones figuring out the tools.

17. No one feels confident they're ahead of the curve. Even the teams clearly further along were surprised when I told them.

My intention sharing this isnt to make people feel relieved that things are messy across the board. There are team's pulling ahead which will start to become more evident with time.