How to plan your AI transformation responsibly

TL;DR: Having AI and being built for AI are two very different things. The gap between them is the thing worth measuring and it’s why we built the Outreach AI Maturity Model. It isn’t just a single score and it isn’t a pitch for more software; it’s a measure of how ready your revenue organization actually is to deploy agentic workflows. Based on the results, my Customer Success organization helps guide our customers to more advanced levels of maturity.

Why do revenue organizations need an AI Maturity Model?

There's a question I hear in almost every conversation with revenue leaders right now, and it's no longer “should we use AI?” It's “how do we get started?” The appetite for agentic workflows is real, and it's justified. But it comes with an assumption worth examining: that going from AI-assisted to agent-driven is a switch you flip once the technology is good enough.

It isn't. Assistive AI you can adopt almost overnight — it drafts, it suggests, it summarizes, and a person stays in the loop for every output. Agentic AI is different, because you're no longer asking AI to help a person do the work. You're trusting it to do the work. Bridging the gap requires a specific operational shift: from teams that simply use AI to an organization that’s built for it — with standardized workflows, trusted data, aligned teams, and the accountability to know what's working.  

That shift doesn't happen all at once and it doesn't look the same for every team. That's exactly why an AI maturity model matters.  

What is the Outreach AI Maturity Model?

The Outreach AI Maturity Model is a framework that maps how ready your revenue organization is to turn AI into measurable results. It plots teams across four stages: Traditional, Connected, Consolidated, and AI-Efficient.  

• The Traditional stage is where sales, marketing, and customer success run on separate systems, processes, and even org structures. Execution is reactive. AI, if it's present, lives in disconnected experiments.  

• In the Connected stage, AI tools and automation are emerging — but adoption is limited and data trust is still developing.  

• In the Consolidated stage, workflows are standardized, data is trustworthy, and signals can reliably drive decisions. This is the foundation for advanced AI use cases in prospecting, forecasting, coaching, and deal execution.

• The AI-Efficient stage is an advanced go-to-market operation where humans and AI agents execute joint workflows together. It’s predictive, governed revenue execution at scale, but only because the foundations are in place. And AI isn't an assistant on the side; it's a teammate executing work on behalf of your team.

“Sales organizations are moving beyond AI experimentation and into operational scaling, but many lack a framework for knowing where they stand or what to prioritize next,” said Michelle Morgan, research manager for Sales Force Productivity and Performance at IDC. “Outreach's AI Maturity Model gives revenue leaders a structured way to benchmark their current state and a concrete path toward AI-driven execution.”

What actually moves you forward

Here's the part that surprises people: advancing maturity has very little to do with how much AI you've deployed. What determines your maturity stage are five underlying drivers: workflow standardization, data trust, inspection and accountability, cross-team alignment, and AI readiness.

1. Workflow standardization

This is whether your team executes revenue work — prospecting, deal management, renewals, coaching, forecasting — consistently enough that it can be supported, measured, and eventually automated. When every seller works their own way, there's no repeatable pattern for AI to learn or act on, so tools stay stuck as optional assistants. Standardizing the work is what turns AI from a novelty into something that can reliably take execution off your team's plate. ‍

2. Data trust

‍This is whether your team actually believes the numbers feeding your systems and reports. AI built on data people don't trust gets ignored no matter how sophisticated the model is, because no one acts on a recommendation they can't verify. As maturity advances, data moves from manually reported spreadsheets to connected, reliable signals. ‍

3. Inspection and accountability

‍This is whether performance is reviewed and owned through consistent, repeatable process rather than reactive spot-checks when something looks off. This also includes clear accountability: Who owns the workflows agents execute? Who approves changes to agent behavior? Without clear ownership, accountability for automated execution diffuses, and when something goes wrong, no one has a clear mandate to fix it.

4. Cross-team alignment

‍This is whether sales, marketing, and customer success operate around shared workflows and signals or run in silos with separate tools and disconnected data. Misalignment shows up as clumsy handoffs, churn risk no one catches in time, and expansion opportunities that fall through the gaps between teams. As alignment increases, agents run in sync across the customer lifecycle — triggered by shared data rather than by whoever happens to be watching. ‍

5. AI readiness

‍Early on, this means getting comfortable with tools that draft and suggest; further along, it means trusting agents to research, personalize, and execute in your highest-consistency workflows while your team stays in control. Readiness is as much about operating structure and governance as it is about the technology, which is why it tends to be the driver that separates teams that experiment with AI from teams that scale it.

To help customers progress towards more advanced stages of the maturity model, we have developed prescriptive playbooks aligned with each of these drivers. Based on your assessment results, these drivers reveal where the friction is and the playbooks provide a prioritized roadmap for where to focus to progress towards more advanced stages of AI maturity.

Each driver that you improve upon moves the needle on the business outcomes that matter most. For example, as your workflows become more standardized, productivity improves as every rep executes from the same playbook — output increases without adding headcount. And as data trust increases, revenue visibility improves as pipeline data reflects actual buyer behavior instead of rep estimates

As you work through your prioritized roadmap with the playbooks, our Customer Success team works with you in lockstep, ensuring every action builds towards the right business outcomes.  

Why the path towards AI maturity is worth it

The climb towards AI maturity is worth it because it ensures every agentic workflow you rollout actually delivers on expected outcomes. No more stalled AI investments.  

“The maturity model gives teams the language to tell leadership, 'Here is where we are, here is what the next stage requires operationally;’ it can turn an AI conversation into a roadmap,” said Heather Dahmer, global head of Demand Generation and Sales Activation, SoftwareOne.  “Having that shared vocabulary across the business drives real alignment, giving a repeatable way to measure progress and keep moving forward.”

The framework shifts the conversation to think through how to adopt and create the right AI infrastructure, not just which AI features to buy.  

Getting it right and moving towards more mature stages has material impact on your revenue organization. For example, Outreach customers who have adopted agentic workflows are seeing a 3x improvement in reply rates, 2x the reply to meeting rate, and sellers are booking 5x more meetings while saving 10 hours of admin time each week. You can learn more in our Agent Productivity Impact Report.  

To help organizations achieve this, we also partner with our customers on building a Revenue Center of Excellence. This provides customers with a framework centered around: people, process, and playbooks. Together, they give you a way to bring scalable, iterative, and measurable success to your AI journey.  

This is a journey you don’t take alone

I lead Customer Success, so I'll be honest about the part that matters most to me: a maturity model is only useful if it helps you do something. A model that tells you you're "Connected" and then leaves you to figure out the rest isn't helpful, it's a label.

We designed this to be a partnership. It starts with our customers taking an assessment that gives you a clear, specific read on your highest-impact gaps. From there, you and your CSM review the results together and align on a playbook that guides you on what to do next and how to set things up. You activate the right capabilities for your current stage and measure progress as your maturity improves. The goal isn't to get every team to "AI-Efficient" by next quarter. It's to make the next step clear, achievable, and worth taking.

The teams that will win the next few years aren't the ones spending the most on AI. They're the ones building their organization to use it. If you're not sure where you stand, take Outreach’s AI Maturity Model assessment and let's figure out the next move together.

‍