Most GTM teams have implemented some version of AI: email writing, AI SDRs, intent tools, signal-based outbound, automated research, deal reviews, and so on.
AI should be making these teams dramatically more productive by now: measurably higher rep efficiency, pipeline and closed won revenue.
But for most teams, the results are still underwhelming.
Ask AI to work an account and you’ll get something like:
“Acme is hiring SDRs and had a closed-lost opportunity last year. Reach out about renewed pipeline growth.”
Technically accurate, but completely generic. It generates the exact kind of message a buyer deletes instantly, and gives us a snapshot of why AI isn’t moving the needle: reps are still forced to do manual research, prioritization remains a guessing game, and the outreach feels predictably synthetic.
The issue is that most teams are asking AI to make GTM decisions without the two things it needs to make those decisions well: context and logic.
GTM’s North Stars
Before getting into the weeds, it’s worth clarifying what AI should actually improve.
If we start from first principles, go to market teams should be focused on three things: a) more pipeline, b) faster pipeline progression, and c) more closed-won revenue.
For the sake of this article, we’ll focus on a) pipeline generation.
Going one layer deeper, the “outcome” of a sales organization’s pipeline generation efforts comes down to three “inputs” they can control (ignoring demand, market awareness, etc.).
Targeting: which accounts and people you focus on
Hypothesis: what problem(s) you articulate and offer to solve
Execution: how well you turn that hypothesis into outreach, calls, presentations, etc.
Each of these three areas is a place AI could add leverage. The question is where it’s actually showing up today – and that’s where things start to break down.
The Problem: most GTM AI tools focus too much on the third layer, “Execution”.
They help write emails, summarize accounts, generate call scripts, or automate activity. That can be useful, but it is not where the real leverage is.
The Reality
The real “alpha” in go to market is actually upstream.
The quality of your a) targeting and b) point of view matters significantly more than the quality of the email you send.
If you pick an account based on a commoditized signal and craft a weak hypothesis (see my post on the Shift from ‘Signal-Led’ to ‘Scenario-Led’ Outbound), a “great” email does nothing.
Whereas if you target the right account with a sharp hypothesis, the copy does not need to be perfect. It just needs to be relevant.
That is precisely why AI GTM initiatives underperform. Today’s agents aren’t experts at deciding:
Which account matters
Why that account matters now
Which person is most relevant
What pain is most likely
What message would actually be credible
And I believe there are two different, but related root causes here:
Context: The agent does not have the right GTM context.
Logic: Teams are outsourcing the logic that should be their in-house edge.
Let’s unpack these.
Problem One: AI Doesn’t Have the Right Context
Most people know this by now – GTM stacks are fragmented.
Good sellers know exactly what signals drive their buyers’ decisions, how to spot them, how to prioritize them, and how to understand relationships between them.
And they use all information available to them to carefully craft their targeting strategy, hypothesis, and messaging:
They’re digging through the CRM, call recordings, intent activity, mutual connections, job postings, reddit, online forums, and more, to decide who to target and what to say.
An agent is no different.
If an LLM is asked who to target or what to say, and it’s working with either a) limited pieces of the puzzle or b) doesn’t know they fit together (or worse, both) – it’s not going to be effective.
Here’s an Example
Imagine two companies in your territory recently posted SDR roles:
An agent that isn’t equipped with the right context or logic would detect the same hiring signal at both accounts, prioritize both, and generate similar outbound.
In reality, the fit, intent, situation, and therefore prioritization of these two accounts may be entirely different:
Company A might be hiring SDRs because they’re scaling outbound, use tools you integrate with, have pain points your product solves well, recently visited your website, and just hired a past champion.
Company B might also be hiring SDRs, but already uses an incumbent tool you struggle to displace, has a workflow you do not integrate well with, and told an SDR that cold called them that they signed a 3-year agreement last month.
If your agents don’t have access to all data, know where you win, where you fall short, how you compare to incumbent tools, what systems you integrate with, which pain points you solve best, and which buying scenarios are actually worth pursuing, it’s impossible for them to be effective.
That is the gap.
Feeding signals to AI is the easy part, the hard part is ensuring it understands your business well enough to know which signals matter, how to rank them, and what to lead with.
Problem Two: Borrowed Logic Can’t Be an Edge
This is the strategic flaw: Teams are outsourcing what should be their core competitive advantage.
They’re buying their upstream intelligence (targeting, hypothesis generation, etc.) from AI GTM vendors.
When you outsource this, you’re now running the same decision logic as everyone using that model or vendor. And by definition, a signal or strategy everyone has access to cannot, by definition, be an advantage.
The only thing that can be proprietary is what you do with it: the layer that decides which signals matter, how they combine, and what they mean for your business specifically. The edge was never the signal. It’s the interpretation on top of it.
So when you also buy that interpretation layer from a vendor, you’ve commoditized the last thing you had left. The signal was already shared; now your reading of it is too.
However, buying makes sense for parts of the workflow.
It’s arguably silly to build tools that enrich accounts, find job posts, scrape websites, generate drafts, summarize calls, route leads, sync data, or send emails.
Those are execution layers.
But the upstream (and most important) parts of your GTM motion should not be outsourced:
Which accounts are worth prioritizing
Which signals actually matter
Which combinations of signals indicate a real buying scenario
Which personas are likely involved
What pain hypothesis should be used
What proof points should be attached
What the system should learn from wins, losses, replies, and meetings booked
This is where your GTM edge (or lack thereof) stems from.
The simple rule:
Buy: tools that execute the work (identifies job posts, enriches contacts, generates email copy, sends emails, etc.).
Own: logic that informs any decision making (what to search for in job posts, which signals to scrape, how accounts are prioritized, etc.).
The Fix: Build a GTM Context Layer
The teams winning with AI have figured out where the edge actually lives. They build an intelligence layer that sits between their raw data and their execution tools – the layer that takes signals and turns them into a point of view only they can produce.
This is your “GTM Context Layer”. A proprietary system that tells humans and agents which signals matter, how to interpret them, what scenario they suggest, who likely cares, and what message fits.
A strong GTM context layer has three parts:
First, a “data foundation”; this is where you bring together the raw ingredients: CRM data, opportunity history, closed-lost reasons, product usage, website activity, enrichment, job posts, news, technographics, call notes, email engagement, partner notes, and rep activity.
Second, GTM decision logic: This is the rules-based layer that defines your ICP, personas, account scoring, signal weighting, routing logic, buying scenarios, disqualifiers, and playbooks.
Third, an AI orchestration layer.
This is the workflow layer that coordinates retrieval, tool calls, prompt routing, agent skills, context assembly, and output generation.
It decides what context to pull, which sources to check, which signals to rank, which playbook to apply, and which skill to run for the task.
Do these three things right, and your agents go from:
“Acme is hiring SDRs and had a closed-lost opportunity last year. Reach out about renewed pipeline growth.”
To:
“Acme is hiring SDRs and RevOps, uses a stack we consolidate well, and lost last time due to timing. Prioritize RevOps with a tooling-efficiency angle, Sales with a pipeline-growth angle, and tailor outreach to the pain each team owns.”
Where to Start
You don’t need to rebuild your entire GTM stack overnight.
Start by auditing three things:
Audit where your Decision Logic lives: Are you letting third-party AI algorithms decide who you target and how you position your value? If yes, move your ICP definitions back internal.
Shift from signals to Scenarios: Stop triggering outreach based on a single isolated event. Instruct your data team to build models that find combinations of events that equal undeniable pain.
Constrain the Orchestration payload: Stop asking your tools to guess what to say. Pass them a highly restricted, hyper-contextual payload for every single prospect.
You don’t have to do all three at once. Even one of these moves a real decision back inside your business, and ahead of every competitor running the same default logic.
Closing
AI for GTM underperforms for a boring reason: teams automate the execution and under-invest in sharpening the upstream judgment behind it.
Everyone has the same models and the same off-the-shelf signals now. What separates the teams that pull ahead is what they own upstream of execution: the custom signals they build and the context layer that knows why this account, why now, who to reach, and what to say. That’s the difference between the Acme email no one answers and the one that earns a reply.
AI doesn’t replace your strategy. It just exposes how good it actually is – and most of today’s implementations are proof of this.
New post time 🗣️