The 4 stages of becoming an AI-powered organization

When I ask executives if their company is using AI, most of them say yes. When I ask if their company has an AI strategy that will drive impact, they often say no.

Why the disconnect?

It’s partly the result of how AI got in the door at most companies — through individual users finding and adopting AI tools on their own. This bottoms-up entry wasn’t team-oriented, so more often than not, the use cases focused on reducing frontline users’ tedious tasks and making unwieldy ones easier. This first wave was exciting and created a groundswell of interest in AI, but it led to the ad hoc and fragmented use of multiple AI tools. Value was at the individual level, and focused on simple use cases.

That’s now changing inside leading companies.

Executive leaders — often driven by their CIOs, CTOs, CROs, and RevOps leaders — want to harness the full potential of AI by weaving it into their GTM teams’ key workflows. The goal is to embed AI seamlessly into each team’s daily operating rhythms. It’s the best way to standardize AI’s impact and ensure the entire revenue org consistently uses best practices and reaps AI’s rewards.

There are four steps companies can take to get the most out of this next wave of AI adoption:

This first stage centers on AI’s core strength: efficiently searching and summarizing vast quantities of information.

This is where most GTM teams begin their AI journey, as it addresses their fundamental need to make sense out of more data than humans can easily process.

Today, GTM teams commonly use AI to answer some version of “What do I need to know?” AI excels at understanding and distilling complex information, delivering summaries, and flagging risks. Common use cases include AI-generated meeting summaries and handoff briefs; highly searchable account and pipeline data; and natural-language queries about any deal, contact, or account.

This stage delivers value by allowing teams to quickly interpret and use large datasets. While the initial impact manifests as individual productivity gains, this stage represents the foundational use case for how companies can begin to derive value from AI. The limitation is that while it’s valuable, it’s often fragmented across team members who are making queries and searching for information ad hoc. AI’s insights aren’t standardized or woven into the same process for everyone.

Building on information processing, this second stage shifts your AI use to gaining insights by comparing deals and assessing how your whole team and pipeline are progressing against your best practices playbook. This is where AI begins to provide critical insights and clarify what’s working across your revenue teams, how you’re doing compared to your goals, and whether your teams are using preferred behaviors.

At this stage, you’ll take AI beyond basic data processing and integrate it into your core revenue processes to provide context drawn from your key workflows. You and your teams can use it to answer key questions about deals, contacts, and accounts:

• Which key behaviors and best practices are reps using and which ones are we missing?
• Are there risks in this deal that we haven’t spotted?
• Are there opportunities to grow this account?
• Have we addressed this prospect’s objections?
• Which of my team’s deals are most likely to close on time?

When AI is integrated in this way, it can enable deal health assessments, pipeline risk analyses, coaching scorecards, and more. This creates significant, team-level value by establishing visibility into the performance patterns across your revenue organization. You can more easily understand what good looks like and build in data signals that inform your process.

Once you’re comfortable using AI to gain performance context, you’re ready for actionable recommendations, where AI provides guidance around what to do next.

At this level, AI becomes your strategic advisor, embedded directly into your revenue workflows. It can assess — based on historical data and your current state — how to efficiently execute on a process or core role. AI can recommend and prompt specific actions, ensuring that your team members take the next best step. They’ll do so with the confidence that it’s the most effective path forward.

This stage is all about achieving consistency across your processes and your team. AI makes it much easier to standardize best practices across your GTM teams and scale top performers’ behaviors. For example, AI can:

• Prompt people to use selling behaviors that should be replicated across the team
• Recommend targeted and relevant coaching and training for reps, based on what they’re already doing and what’s effective
• Prioritize tasks and prompt users on what to do next
• Suggest tailored messaging and on-target responses in customer communications

By enabling this consistent, guided execution with AI, you’ll gain value from individual insights and from lessons that apply to your entire revenue org. You’ll leverage AI to automatically guide your entire team to consistently execute on best practices.

This four-stage process culminates in AI’s most advanced capability: doing tasks for you.

This stage, often referred to as autonomous execution or agentic AI, is where the AI takes actions on its own, within the parameters you allow. For instance, It doesn’t simply identify at-risk deals; it sends out manager notifications that enable better coaching. It doesn’t just suggest that a follow-up email needs to be sent; it drafts one based on the contextual signals in an account. That’s only a smattering of its capabilities, all of which you can tailor to your team’s needs.

Although most GTM leaders want to move into autonomous execution as quickly as possible, you should only enter this fourth stage once you have effective best practices and the right visibility, and have a clear sense of the outcomes you want to generate.

You’ll shift from using AI as a tool to leveraging it as a true revenue enabler for the whole organization.

Bring your revenue organization through these stages systematically and you’ll create a sustainable competitive advantage — one that only comes with embracing AI across every deal and interaction. That’s the difference between lightly using AI and running an AI-powered organization: It’s omnipresent in your workflows and instills consistent, scalable precision across your organization.