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Article

6 Areas Where AI Adoption Actually Gets Decided

29 July 2026

Becca Eddleman

Most organizations assume AI adoption happens in the big moments like the budget approval, the all-hands announcement, or the tool selection process. But that’s just the decision to start.

AI adoption happens in smaller, more specific moments that most leadership teams aren’t paying close enough attention to. And the organizations that are compounding gains from AI right now didn’t get there by doing something sophisticated. They made six strategic decisions and held to them.

Here’s where adoption actually happens, and what getting each one right looks like.

When someone owns AI
During the pilot (and what it actually means)
When tied to the right metric
In your system’s data
In the first workflow
With the person at the top

1. When someone owns AI

Who should own AI adoption in your GTM organization

Not a committee. Not a shared responsibility that informally spans RevOps, Marketing, and IT. One person. An AI GTM Lead with the business strategy knowledge and AI capability to pull accountability through the organization, with department leads reporting to that single owner.

Without this, AI becomes everyone’s second priority, and nobody’s first. The Pulse survey data is unambiguous: 48% of organizations have no clear or multiple owners of AI initiatives. But ownership ambiguity has a cost, and most initiatives stall because of it. When responsibility is distributed, the organization has no single person to hold, and nothing moves.

Assign this within 30 days as a permanent accountability structure.

2. During the pilot (and what it actually means)

How to run a successful AI pilot that scales

Most organizations use the word pilot to describe wildly different things. In these cases, it could look like a rep testing ChatGPT, a team running a workflow experiment, or an org-wide rollout. Treating them interchangeably is how pilot mode becomes permanent.

A real pilot has a defined scope: one workflow, one team, 30 to 45 days, and a pre-agreed decision at the end. Scale, iterate, or stop. Without that definition, the loop never closes, and organizations stay stuck experimenting indefinitely.

3. When tied to the right metric

How GTM teams should measure the revenue impact of AI

Only 17% of organizations in the Pulse survey have clearly tied AI to metrics like pipeline, conversion, or win rate.

Usage rates and adoption dashboards are not the right metrics. Neither are hours saved or emails generated. The question that matters is whether AI is moving a number that a CFO recognizes, like pipeline velocity, win rate, sales cycle, and net revenue retention. Productivity counts, but it has to be tracked alongside the metrics that actually reflect business performance.

If you can’t answer what AI is doing to your revenue metrics, you don’t yet have a measurement strategy. You have a monitoring strategy, and the difference will show up in your pipeline.

Survey note:

Skaled’s AI GTM Pulse survey was conducted in April 2026 to understand how GTM teams are using and operationalizing AI. In the webinar debrief, we bring those insights together, connecting the dots across usage, automations, ownership, and measurement.

See the full AI GTM Pulse results

4. In your system’s data

How poor data quality affects AI adoption

AI amplifies what’s already in your systems. Clean data produces better outputs faster. Bad data gets automated at scale. (Think wrong lead scoring executed more efficiently, flawed forecasting delivered with more confidence).

Auditing data hygiene before building out your AI-driven workflows doesn’t mean you need to stop everything. It does mean you should focus on sequencing correctly. Fix the foundation, then build on it.

5. In the first workflow

How a GTM team should choose its first AI workflow

There might be a temptation to go broad on AI with multiple use cases and teams, or put all the hope in a fancy tool that promises to transform everything at once. We’ve seen enough of these engagements to know how that plays out.

Start with one repeatable workflow that is either slowing your team down or directly unlocking revenue, like CRM logging, follow-up sequencing, daily deal watches, and next steps. Build real automation, not just AI assistance. Prove it works for one team, then expand. The organizations that move fastest aren’t the ones that start biggest. They’re the ones that start clearest.

6. With the person at the top

How executive leaders drive AI adoption across a GTM organization

This is where adoption is most definitively decided and the place most executives aren’t examining honestly.

According to Skaled’s AI GTM Pulse survey, 86% of GTM teams are already using AI on the frontline. The transformation is stalling at the top, where leaders are still treating AI as a technology deployment rather than a fundamental change in how people work.

That distinction matters more than it might seem. Technology deployments get sponsored. Real transformation means changing not just how work gets done, but who owns it. And that ownership has to sit with someone with enough authority to retrain the organization, reset the definition of good, and hold the business accountable to it.AI is the second category. And most organizations are still treating it like the first.

Leaders need to show public commitment to AI goals, regular review sessions where progress and learnings are expected, and a standing requirement that teams report adoption milestones the same way they report pipeline. These are all signals an organization reads to determine whether this is real or optional.