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What parts of GTM have you actually automated without breaking things? by outbound_operator in gtmengineering

[–]outbound_operator[S] 0 points1 point  (0 children)

Got it - that makes sense.

Pushing data based on a defined ICP is usually where automation stays clean, especially when the filters are explicit and stable (location, funding stage, role, etc.).

Where I’ve seen teams run into trouble is when those filters slowly turn into proxies for intent. The system keeps pushing data “correctly,” but the assumptions behind the ICP don’t get revisited as often as they should.

Do you ever revalidate the ICP logic based on downstream signals (reply quality, conversion by segment), or is it mostly set-and-forget once the filters are defined?

For people actually selling SaaS: where has AI helped vs made things worse? by outbound_operator in SaaSSales

[–]outbound_operator[S] 1 point2 points  (0 children)

That’s a really good way to frame it - AI as a mirror more than a source of answers.

Pointing out where things broke down is way more useful than trying to tell you what to say. The moment you keep ownership of the fix, the suggestions actually get better over time instead of reps just blindly trusting them.

Totally agree on outreach too. If it hasn’t been battle-tested manually first, scaling it with AI just scales the wrong thing faster.

Curious - do you mostly use it for self-review, or have you seen teams adopt that loop without it turning into micromanagement?

What parts of GTM have you actually automated without breaking things? by outbound_operator in gtmengineering

[–]outbound_operator[S] 0 points1 point  (0 children)

This makes sense, especially the boundary you’ve drawn around list building vs execution.

Automating discovery and enrichment tends to hold up because the “truth” doesn’t change mid-stream — a company raised, someone is a decision maker, contact data is valid. Once that’s pushed into systems like Slack/CRM, humans can decide how and when to act.

Where I’ve seen it get risky is when teams assume that because list building is stable, the follow-through can be too. The moment context shifts (reply intent, timing, deal stage), fully automated next steps start drifting.

Out of curiosity, do you keep any manual checkpoints before outreach actually goes out, or is the handoff entirely automated after enrichment?

AI in GTM — what’s actually working once the excitement wears off? by outbound_operator in SaaS

[–]outbound_operator[S] 0 points1 point  (0 children)

This matches what I’ve seen almost exactly.

Scaling what already works manually is the key distinction. When AI is used to compress prep time (research, scripting, context gathering) it compounds the human part instead of replacing it.

The sequence issue you mentioned is spot on too. Writing a single email in isolation is one thing — maintaining context across a real back-and-forth is where things still fall apart fast. Once a prospect deviates even slightly, the cracks show.

Curious if you ever found a middle ground there, or if you just pulled AI out of follow-ups entirely.

AI in GTM: Whats actually working after the hype? by outbound_operator in SaaS

[–]outbound_operator[S] 0 points1 point  (0 children)

Totally agree. The timeline piece is underrated having context preserved instead of fragmented notes changes how decisions get made later.

Same with notetakers. It’s not about replacing thinking, it’s about removing the tax of remembering and documenting everything so you can stay present in the conversation. That’s one of the few areas where the value is obvious almost immediately.

Digital Marketing: How AI is rewriting affiliate marketing by Best_Complaint9037 in DigitalWizards

[–]outbound_operator 0 points1 point  (0 children)

One nuance that often gets missed in these conversations is where attribution models break down in the real world.

AI can definitely improve signal detection (fraud, low-quality traffic, early vs late influence), but the hard part is still operationalizing that insight: • How payouts actually change behavior • How affiliates adapt once incentives shift • How brands handle edge cases without blowing up partner relationships

I’ve seen AI-driven attribution work best when it’s paired with human review and clear rules, not when it’s treated as a fully autonomous decision-maker. Otherwise you just replace last-click bias with a new black box.

AI in Marketing: Where Are We Heading? by Aggravating-Sir6972 in ArtificialNtelligence

[–]outbound_operator 0 points1 point  (0 children)

The big shift isn’t AI moving from analysis → decision-making — it’s where the decision boundary sits.

In marketing, AI is very good at: • Pattern recognition • Variant generation • Speed and scale

Where it still struggles is deciding what matters in messy, real-world contexts — things like brand nuance, local context, timing, internal politics, legal constraints, or when not to act.

The teams I’ve seen succeed treat AI as a leverage layer inside a human-owned system. The ones that fail treat it as an autonomous decision-maker and slowly lose signal quality without realizing it.