The AI-Enabled Shift from ‘Signal-Led’ to ‘Scenario-Led' Outbound
Why signals have become a commodity, and how to use "Signal Stacks" to win the inbox in the AI era.
Today it seems like every AI outbound platform is selling the same promise: ‘Scrape the web for signals to reach out to the right accounts at the right time.’ Easy, right? Just watch for things like:
Funding announcements
Promotions and new hires
Job openings
Tech stack mentions
Social engagement
G2 reviews
Well then why aren’t orgs who’ve doubled down on this signal-led approach booking more outbound meetings?
The problem is that these signals have become commodities. When every rep in an industry has the same “why now”, every message sounds the same. In a world of infinite signals, the signal itself becomes noise.
The competitive edge is shifting.
The winning go to market teams in the AI era aren’t the ones with more ‘signals’. They’re the ones who are identifying ‘signals’ unique to their industry and treating these ‘signals’ like evidence of specific buying scenarios, at scale.
In other words, they’re taking a ‘scenario-led’ approach instead of a ‘signal-led’ approach.
Focus on ‘Scenarios’ Instead of ‘Signals’
A ‘signal’ is just a data point, which is only useful if it points to a reason to buy. Every company, even in complex industries like observability, can distill the core reasons companies buy their software or services into a handful of reasons.
So before you build a ‘signal’ library, you should build a ‘scenario’ library.
A scenario can be defined as: A situation where a buyer feels pain and wants a new outcome that your product (ideally uniquely) solves.
For each scenario, write:
The current state.
Negative consequences of the current state (why do anything?).
What they’re trying to do.
How you uniquely help them achieve it.
Here’s an example scenario I use at Grafana Labs, “First Pane of Glass”. We sell an observability platform that helps engineering teams maximize the performance and uptime of their software:
Current State: Observability data is siloed across several tools.
Negative Consequences: Limited system visibility and difficulty troubleshooting is resulting in platform slowness, downtime and customer churn.
Desired Future State: A first pane of glass to identify issues proactively and streamline troubleshooting.
How Grafana Labs Helps: Grafana’s plugin architecture provides a ‘first pane of glass’ across observability data without any migrations.
Note: During the prospecting phase, it’s important to note these scenarios are just hypotheses. It’s the seller’s job to validate/adjust these hypotheses during the discovery phase.
‘Signals’ Are Just Observable Evidence of a ‘Scenario’
Once you have key scenarios mapped out, signals become easier to find because you’re no longer brainstorming random intent.
You’re asking a more precise question: “What observable evidence would exist if a company was experiencing this scenario?”
That evidence can show up in a lot of places beyond the standard outbound signal stack.
Here are the four categories of signals most teams underuse and a few examples of each:
Situational (your basic static signals):
Competitive and adjacent tech stack
Application instrumentation frameworks
Execution (they’re trying to achieve an outcome):
Social posts about tackling specific projects
Infrastructure modernization (i.e., cloud migrations, Kubernetes adoption)
New product launch (traffic + risk + complexity)
Pain (evidence of challenges with the status quo or achieving an outcome):
Incidents or outages (status pages, postmortems, social posts)
High turnover in the department you sell to
Margin pressure or cost cutting initiatives
Active search (in-market for a solution):
They’re comparing vendors on G2 / Gartner-style pages
They’re viewing your docs, enablement material and implementation guides
They’re asking questions in communities (Reddit, Slack groups, GitHub issues)
The ‘signals’ companies choose to monitor for will differ drastically across industries, business models and unique value propositions.
The Real Unlock Is Signal ‘Stacks’
The real magic is when you combine signals to come up with an informed hypothesis about the buying scenarios you’ve defined.
Single ‘signals’ are weak because they’re ambiguous.
A job opening could mean growth… or churn… or backfill… A bad G2 review could mean they’re starting to look at alternatives… or just switched vendors and now feel comfortable sharing their experience publicly.
So instead of ‘signals’, you want ‘signal stacks’: A combination of signals that, when observed together, suggest one of your buying scenarios is present.
In reality, you probably won’t find enough data to have 100% confidence that a given scenario is true, which is why you treat it as a hypothesis.
Here’s an example of what a signal stack looks like.
Pretend you’re selling Grafana Labs’ open and composable observability platform:
Weak single signal: Hiring a Site Reliability Engineer.
Sales angle: “I saw you’re hiring an SRE. We help SREs maximize uptime…”
You don’t have an educated guess as to why the company is hiring the SRE and are forced to default to a generic uptime pitch.
Strong signal stack: Hiring a Site Reliability Engineer + using three observability tools + recent public outage + customer complaints referencing downtime.
Now you have: A plausible pain, urgency, and a great reason to reach out. This is extremely strong evidence of the Grafana Labs ‘Scenario’ I shared earlier (‘First Pane of Glass’):
The sales angle becomes: “I saw the recent downtime and was wondering if you’re hiring SREs as a band-aid for the fragmented stack. We help orgs unify their data so...”
The rep can infer that fragmented tooling is preventing them from debugging effectively resulting in downtime, that they’re trying to fix it by hiring an SRE, and can propose a hyper-relevant solution.
Challenges Connecting Signal Stacks to Buyer ‘Scenarios’
This is the intuition top sellers use naturally, but it’s historically been nearly impossible for junior reps to replicate at scale.
It takes a significant amount of time, intelligence, product expertise and industry knowledge to come up with detailed account hypotheses from subjective cues.
Most reps can observe signals, but don’t change their messaging based on which signals were present.
This is how you end up with the typical emails with “congrats on funding” or “saw you recently joined as CRO” followed by a generic pitch that doesn’t resonate.
This gap is something our RevOps and AI teams are working on addressing at Grafana Labs. The platform is composed of 14 different products that map to different observability personas, problems and initiatives. It’s a ton to learn, and there’s a significant ramp-up time before reps are able to quickly form accurate hypotheses and position the platform accordingly.
AI Finally Makes This Possible at Scale
This type of ‘judgment’ is a perfect use case for LLMs.
AI can now act as the reasoning layer that connects the dots between disparate data points that a human would take hours to correlate.
If you define your scenarios and give the system a baseline (i.e., ‘these signals tend to imply this scenario, and this messaging should be used for this scenario’), LLMs can do the heavy lifting:
Continuously scan the web for signals and monitor first-party intent data.
Turn scattered signals into a ranked list of scenarios with the “why” behind each.
Trigger/recommend the right outbound play at the right moment.
Draft hyper-relevant messaging for reps to review and send.
This is how you craft timely, relevant, expertise-driven outreach that stands out, gets replies and converts to qualified pipeline at a higher rate.
Implementation Checklist
I’m not going to provide an in-depth implementation guide here, but here’s what you need to build out to serve as your foundational data and logic layer:
Start by recording your top 3 ‘Scenarios’.
These are the three most common situations that someone buys your product/service. Be sure to include current state, negative consequences, desired future state and how you can help them get there.
Write down the top signal stacks for each scenario (use AI if you have to).
Break them down into execution, pain, pressure and solving signals and include the scenario relationship logic.
Define 1-2 ‘plays’ (email templates or sequences) per scenario.
Craft email frameworks with variables to be filled in.
Push templates or sequences to reps.
Display in internal tooling (recommended) or Slack, and push to engagement platform via API for reps to send.
In practice, you’ll be searching for custom signals, coming up with a hypothesis based on the stack of identified signals, and reaching out with how you can help in that specific situation.
The Bottom Line
Outbound isn’t dying. Generic outbound is dying because signals are now everywhere. The edge is moving to operators who can define scenarios precisely, build custom signals that reflect real-world evidence, combine them into stacks, and craft relevant messaging at scale.
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-Cam Wright
P.S. - I always love meeting people building in the space. Reach out if you’d like to bounce ideas about outbound, GTM engineering, or AI implementations at scale.