Hey all. I’ve been talking with a few marketers and RevOps peers who love using AI tools but hate the mess they create. Between prompts, docs, dashboards, and random chat threads, it feels like everyone’s building ideas and insights in ten different places at once. Curious how you are handling this right now. Do you have a system that works, or are we all just living in AI junk-drawer land? I’m trying to understand what “organized thinking” looks like for ops teams who are already neck-deep in AI workflows.
Following. I'm curious to hear if anyone has truly designed and implemented something that is simple. Based on what I read of people's solutions, it still sounds complex...start in platform 1 and do X, then it runs through platform 2, before landing in final destination of platform 3. I'm personally coming to the temporarily conclusion of allowing the early AI adopters to live this pain, before ideally tech tools catch up with their AI plans to built solutions natively within their tools to avoid all the pit stops across multiple tools. Am I a fool? Maybe. But until I learn/read about a solution that doesn't sound complex, I'm not diving in. That's not to say I don't use nor experience the great benefits AI has brought to my work and teams in how we use it, but I think we are in this uncomfortable transitional stage right now where the dust hasn't quite settled and I'm happy to wait till that time...for now.
Hi Brandi Z., Cassie W. -- Thanks for bringing this up. This is something people are thinking about, sometimes even over weekends, but aren't speaking about. I really empathise with what you both are saying. Watching from close quarters and picking up what’s working is a smart and deliberate strategy. I’d still call that a proactive plan. "Between prompts, docs, dashboards, and random chat threads, it feels like everyone’s building ideas and insights in ten different places at once" -- This needs to be controlled within GTM as much as vibe coding needs to be controlled for Development. "start in platform 1 and do X, then it runs through platform 2, before landing in final destination of platform 3" - is kicking the can down the road for some hardworking, selfless RevOps person to do the clean up. Not sustainable. This prompt chain reaction needs guardrails, and preserved context to remain useful over longer periods of time for a team/org. That said, the temptation to tinker was real, especially after years in BizOps and ProductOps, seeing these gaps up close. I started looking at areas where older tools had hit their limits:
When I couldn’t punch through the fuzziness of data.
When my GTM team was spending hours researching instead of doing timely action for prospects or customers.
When wiring external signals at scale was either costly or too clunky.
I began as a skeptic, but more as a curious tinkerer. Early LLMs made basic mistakes like misreading numbers in tables or failing to calculate a median (give this a try with group by, you'll know), but they improved faster than expected. Through hundreds of conversations with founders and CROs made one thing clear: dashboards and research have become commodities, and adding to the cognitive load. The real gap isn’t in finding insights, it’s in getting them to the right people at the right time, without adding noise. We’re living through this pain ourselves while building an antidote to these “jumping through the hoops” point solutions. The easy fixes always shift the burden back to users. We decided to take the harder route—figuring out how to move data to insights to action in a repeatable, cross-department way. It’s not plug-and-play, but it’s the only approach that’s actually reducing chaos instead of adding more. Sometimes the hard way ends up being the only sustainable way. We start by using AI to fix data; cleaning what’s messy or incomplete and making unstructured data useful. Then we create templates that make insights repeatable and easy to share. Finally, we layer external signals on first-party data so it becomes richer and more actionable. Most teams already do parts of this in silos. The real unlock comes when it works seamlessly across teams. That’s where value compounds and real, durable revenue impact begins to show. DM me if you are interested in knowing what exactly we have been up to, and if you could gain from any of our hard work. I can share some recordings of real world, durable impact.
Hi Cassie W., Personally, I have a production workflow that works for a client of mine which is a marketing agency, the system extracts on a weekly basis all of the Google Ads data and then extract all of the conclusions and insights from the numbers. It generates this beautiful PDF document and sends it to my client's digital team. The system monitors on a daily basis for specific conditions like high impressions but low conversions or clicks to identify hidden issues for example but overall if I'm honest, I would not use AI currently for visual content generation as it's not there yet. It's not reliable enough, I'd say that. My overall advice regarding using AI is to use it for small, specific things that you'll verify are fine before using. Human confirmation in the current steps is crucial. Regarding the system I explained above if it's valuable. Just tell me I'll provide you with the JSON for free, and I'll attach the PDF example of how the document looks like to get the idea of what it provides. I was thinking about adding keywords optimization to the reports. Not sure if it's useful. Would love to hear your thoughts if you think that's something that could help or is relevant.
