From Stack to System: Why Gartner's RAO Category Signals a Shift in Revenue AI Strategy

Why “more tools” isn’t the answer, and what to build instead

This article reflects how we think about Revenue AI at RevBuilders AI, and why Gartner’s new RAO category marks a structural shift in how GTM teams should design their systems.

We’ve been noticing two overlapping trends among B2B GTM teams, and from what we're seeing, they’re a major driver of today’s tool sprawl.

Trend #1: Teams evaluate point solutions in silos to solve their immediate problem. Marketing optimizes for coverage and conversion. SDRs optimize for activity and meetings. Sales optimizes for pipeline movement and deal progression. RevOps optimizes for data integrity and forecast confidence. Each function asks different questions and, with separate budgets, buys tools to answer them.

Trend #2: Suite vendors continue to expand. Capabilities that used to be “a point tool” are now bundled into platforms, creating overlap (suite vs. point solution) and across suites.

Here’s the underlying issue: most companies don’t have a single owner of the end-to-end GTM operating system — the workflows, handoffs, KPIs, and incentives across the customer journey. Each department ends up buying “their” tools to optimize “their” slice, and the overall system becomes more fragmented.

It’s like building a city’s transportation network one neighborhood at a time. Every team adds a road, a ramp, or a shortcut to improve their commute. And, without an urban planner and shared traffic rules, you don’t get speed and flow… you get inefficiencies and congestion.

And that's why this seemingly quiet entry stood out: In December 2025, Gartner published its first‑ever Magic Quadrant for Revenue Action Orchestration (RAO), marking a clear definition of RAO as a new category for AI and sales tech that unifies data, engagement, and automation with human‑in‑the‑loop controls.

To me, that’s a milestone signal, not because it’s “another category,” but because it reflects a shift from tools that inform to systems that coordinate action.

1) Why RAO matters amid a sea of AI updates

Every day, there’s a new AI feature, model, “agent,” or tool announcement. Most of it is incremental.

RAO shows up right now because a lot of organizations hit the same wall:

• We have plenty of data, dashboards, and “signals.”
• We still get surprised at the end of the quarter.
• We still struggle to scale consistent execution.
• We still spend too much time translating insight into action.

RAO is the market response to that gap: the missing layer between what GTM teams know and what they do next.

2) Buyers changed. Your system must keep up.

B2B buyers are more digital, more self-directed, and more selective about when they want humans involved.

Which means the modern requirement is not “add AI."

It’s about building a Revenue AI system that supports how buyers buy.

A system that scales:

• self-serve speed early
• human expertise when it counts
• clean handoffs between digital and human moments

3) Stack vs. System

Most teams can list their tools. Yet, it seems almost nobody can explain how those tools work together as a closed-loop system.

And that gap creates a very real, yet mostly invisible tax on daily work and output.

When the stack isn’t designed as a system, typically two things occur:

1. GTM teams waste time level-setting each other's understanding of purpose, value, and ROI of each tool across roles (Marketing, SDRs, Sales, RevOps).

2. And a constant “taping together” of fragmented workflow steps between teams.

The cost compounds quickly: more meetings, more manual handoffs, more rework, less momentum.

When the stack is designed as a system, it has:

• inputs (signals + data)
• decisions (priorities + recommended actions)
• outputs (plays + touches + follow-ups + coaching)
• feedback loops (what worked, what didn’t, what changed)

4) So, where does RAO actually fit?

The obvious question is:

“Okay… so where does RAO go in the stack?”

RAO isn’t “just another tool.” It’s what you could call the revenue execution hub: the layer that turns signals into prioritized actions and helps ensure those actions actually happen.

If the GTM stack is the orchestra, RAO is the conductor keeping every section aligned on timing, next moves, and the score.

Historically, teams tried to solve this gap with:

• CRM as the source of truth
• Sales engagement platforms to run sequences
• Conversation intelligence to capture calls
• Dashboards to inspect pipeline
• Enablement tools to train and coach

But “seeing” isn’t the same as orchestrating execution.

5) Revenue AI assumes an account-based lens

⚡️ Quick reality check: some teams are still running a legacy lead-based model, while others run hybrid ABM or full ABM.

A modern Revenue AI system assumes an account-based lens, because real deals aren’t “one person fills out one form.” They involve multiple stakeholders, multiple touchpoints, and extensive anonymous research,  which only makes sense when signals roll up to an account (and ideally a buying group).

Good news: you don’t have to be “perfect ABM” to use this map. Many orgs start hybrid. The key is to treat the account as the organizing object for signals, orchestration, and outcomes — and let leads serve as supporting detail.

6) The modern Revenue AI system

Here’s the simplest way to make sense of the modern GTM landscape (and who typically owns each layer):

1. Foundation: CRM + data + integration + governance (your truth layer) (usually RevOps / Systems + Ops; everyone depends on it)
2. Upstream Intelligence: market & buyer intelligence (where to focus and why now) (typically Marketing/ABM + RevOps; Sales consumes the signals)
3. Bridge capabilities: turning signals into execution-ready inputs (enrichment, routing, list building, personalization variables) (RevOps + Marketing Ops; often Sales Ops too)
4. Midstream Engagement: engagement & conversion (what’s happening in real time) (SDRs/BDRs + Sales; Marketing for inbound conversion)
5. Downstream Execution: the revenue execution hub + enablement (what must happen next and how we scale it) (Sales leadership + RevOps; Enablement supports execution)
6. AI layer (copilots + agents): a multiplier across every layer (cross-functional; governance usually RevOps/IT/Security)

7) Three system plays (patterns you can steal)

Here are a few repeatable system pattern examples:

Play 1: Intent → qualified meetings (without spamming people) | ABM + intent → bridge activation → inbound AI SDR/conversion → engagement → RAO guidance

Play 2: The lean outbound engine (GTM engineering style) | bridge activation → enrichment sources → outbound + deliverability → RAO prioritization

Play 3: Save deals already in pipe (where AI ROI often hides) | conversation signals → risk detection → orchestrated actions → enablement + coaching

⚠️ Prereqs (so these plays actually work):

• ◻ CRM data hygiene + clear definitions (account, stage, owner)
• ◻ reliable lead-to-account matching + routing rules
• ◻ instrumentation end-to-end (signal → action → outcome)
• ◻ governance for AI-assisted workflows (permissions, logging, QA)
• ◻ deliverability + compliance basics (especially for outbound)

8) A simple way to apply this map in your org

If you can’t answer these in plain English, you don’t have a system yet:

1. Which signals trigger action, and which do we ignore on purpose?
2. Where do decisions turn into actions today — in people’s heads or in workflows?
3. How mature is our bridge capability cluster (enrichment, routing, activation, personalization)?
4. Which plays are instrumented end-to-end (not just tool adoption)?
5. What do we want AI to do specifically, and how will we measure it?

Closing

RAO matters because it marks a shift from

• tools that report activity

to

• systems that coordinate action

Here's to turning insight into action,
– Nikke

If this “stack to system” lens is useful, subscribe to RevAI Real Talk, a bi-weekly, no-hype set of field notes on what’s changing in Revenue AI, what actually matters, and one practical move you can apply this week to drive pipeline and predictability.

And if your team is done with tool sprawl and ready for a system that drives pipeline and predictability, the Revenue AI Accelerator™ Masterclass is our end-to-end program for designing and operationalizing Revenue AI — from foundation and governance through orchestrated plays that scale.
👉 Explore the masterclass.

Publishing notes: Gartner and Magic Quadrant are trademarks of Gartner, Inc. Gartner does not endorse any vendor, product, or service.

Likewise, I am not endorsing any vendor, product, or service in this article – all names mentioned are for the explicit purpose of providing context and clarity.

🔗 Links to Gartner-defined GTM market categories mentioned in this article:

• Account-Based Marketing (ABM) Platforms
• Conversational Marketing Solutions
• Sales Engagement Applications (or SEPs) (“transitioning to RAO”)
• Revenue Intelligence (also “transitioning to RAO”)
• Revenue Action Orchestration (RAO)
• Revenue Enablement Platforms
• RevOps Data Automation Solutions (maps to above “bridge” cluster)
• Sales AI Assistants (often overlaps with RAO + engagement, but is its own defined market)

🔗 And links to Gartner-defined foundation layer categories (not “GTM tools” per se, but absolutely part of the Revenue AI system):

• Integration Platform as a Service (iPaaS)
• Data Integration Tools
• AI Governance Platforms