Waypoint 2 - Revenue Growth Association

Architecting the AI-Powered Revenue Organization

Waypoint Overview

AI is much more than just a sales execution enhancement technology—it is a catalyst for rethinking the entire Operating Model for the revenue-generating function within an enterprise. Operating Models are built on core pillars: People, Processes, Technology, Data, and Metrics. AI fundamentally changes one of these pillars—Technology—which means it has the power to reshape the entire Operating Model.

This transformation goes beyond automating the 'as is' with better 'intelligence'; it demands a new approach to structuring and orchestrating revenue operations. Instead of simply layering AI on top of existing CRM-based tech stacks, the opportunity is to design a modular, composable architecture that integrates AI-driven intelligence, automation, and decision-making in a way that adapts to an evolving landscape.

In Waypoint 1, we developed a Strategic Visual Blueprint™ (SVB)—not as a deep dive into AI technology, but as a visioning tool to explore what the future business landscape might look like. This included defining potential shifts in markets, customer expectations, competitive dynamics, offerings, ways of working, cost structures, roles, skills, and talent strategies. In expedition terms, this was where we envisioned the destination and started charting a path to to get there.

Now, in Waypoint 2, we introduce the Big Brain AI Architecture—a framework that will be an essential navigation resource as we set off and progress. Just as it wouldn’t be wise to embark on an expedition without knowing what is already loaded on the wagons, what is still needed, and what should not be acquired unnecessarily, the reference architecture helps organizations assess their current technology stack, determine essential AI capabilities, and avoid unnecessary complexity. This ensures that investments align with the long-term journey rather than adding short-term clutter that could slow progress.

A key benefit of aligning around a reference architecture like the Big Brain is that it allows stakeholders to talk in a common language, abstracting away underlying technical complexity. Rather than immediately jumping into “tool talk or tech talk” when evaluating new AI capabilities, leaders can instead discuss needing additional ‘Data Hoover’ capability to access new or incremental data, or expanding Orchestration Layer capabilities to ensure seamless workflow integration. This abstraction makes AI decisions more strategic, helping enterprises avoid a patchwork of disconnected tools.

This reference architecture helps enterprises:

Avoid the pitfalls of monolithic AI sales platforms that lock organizations into rigid models.
Understand how AI agents, LLMs, and automation interact in a flexible ecosystem.
Ensure AI investments align with the future-state operating model and remain adaptable to technological evolution.
Prepare for continued advancements in AI capabilities by considering how AI might absorb features and functions that exist in stand-alone tools today.

Point of Interest: Preparing the Organization for the Journey

Change is hard—especially in large enterprises that have lived through multiple “transformations” with mixed results. Employees often ask simple but critical questions:

Where are we headed?
Why now?
How will this be better for me and my team?
What’s changing first?
Will I have to wait years before I see any benefit?

The Strategic Visual Blueprint™ and Big Brain AI Architecture from Waypoints 1 and 2 aren’t just technical deliverables—they are tools for creating clarity and alignment. CROs must be able to communicate the vision confidently, secure stakeholder buy-in, and demonstrate short-term wins that prove AI-driven transformation isn’t just another disruptive initiative.

Key Takeaway: Change management isn’t just an execution issue—it starts with setting expectations, communicating why the journey is happening, and ensuring that employees see tangible benefits at each milestone.

Detour: The Pitfall of Isolated AI Tools

Many enterprises have already purchased AI-enabled tools to enhance discrete capabilities within their revenue operating model. While these tools may provide some immediate benefits, each new tool comes with a learning curve and a need for integration into an already complex and often disconnected system.

Whether it’s an AI-based lead generation tool, a coaching platform, or a forecasting engine, each addition must be evaluated not just by its functionality, but by its impact on the ultimate goal:

A measurable increase in revenue
A measurable increase in seller productivity

If these outcomes cannot be directly tied to the tool’s use case, then the real outcome may just be more disconnected ‘noise’ that doesn’t deliver the expected ROI.

The Big Brain AI Architecture helps enterprises avoid these unnecessary detours by ensuring that AI investments:

Integrate across the entire revenue model rather than functioning as isolated enhancements.
Enable proactive, contextualized execution rather than adding disconnected layers of complexity.
Support a long-term, AI-centric operating model rather than short-term fixes.

This doesn’t mean enterprises should avoid AI tools—rather, they should evaluate them in the context of a future-state AI-centric operating model and reference architecture. Without this, organizations risk investing in short-term solutions that may not fit into their long-term strategy.

This is precisely what the Big Brain framework provides—a structured, modular approach to AI adoption that aligns with business objectives and future-proof revenue operations.

Breaking Down the Big Brain AI Architecture

Breaking Down the Big Brain AI Architecture

The Big Brain AI Architecture is composed of seven key elements that work together to create an integrated, intelligence-driven revenue engine. Each component is modular, enabling enterprises to evolve at their own pace without being locked into a rigid system.

The Human
- AI serves to augment and assist sellers, managers, and other revenue leaders by providing proactive insights, guidance, and automation to help them focus on what matters most.
Orchestration Layer
- The orchestrator acts as the connective tissue across all AI components, ensuring seamless communication between systems, aligning workflows, and automating processes in real-time to remove friction from revenue operations.
The Big Brain (AI Intelligence Layer)
- A collection of multiple LLMs and AI agents, each designed to optimize different revenue functions. The Big Brain continuously learns, ingests enterprise data, and delivers actionable recommendations to improve sales execution.
Data Hoovers & Governance
- AI is only as good as the data it ingests. Data Hoovers pull information from structured and unstructured sources, ensuring that insights are based on the most relevant and high-quality data available. Governance mechanisms ensure accuracy, compliance, and reliability.
Brain Trainers (AI Optimization & Customization Layer)
- AI must continuously learn and improve. Brain Trainers refine AI models by incorporating enterprise-specific methodologies, sales playbooks, and proprietary knowledge to ensure recommendations remain aligned with business goals.
Process & Workflow Engines
- Existing CRM, ERP, and other business platforms act as workflow engines today. However, over time, many of these processes will be absorbed by AI-driven automation, reducing complexity and enabling a more seamless operating model.
Enterprise Data
- The foundation of the entire architecture. Enterprise data is drawn from systems of record, customer interactions, and external market intelligence. AI leverages this data to generate insights, automate processes, and drive smarter decision-making.

RGA Guide Services: Designing an AI-Centric Revenue Architecture

Traditional tech stacks for revenue operations typically revolve around a CRM platform, surrounded by countless additional tools aimed at addressing specific seller pain points. Some of these tools are well-integrated into the revenue process, while others operate as standalone solutions—forcing sellers to navigate multiple systems, workflows, and interfaces.

Most sellers don’t want to learn and manage yet another tool—especially when it distracts from actual selling. Every additional tool adds another layer of complexity that often slows them down rather than accelerating performance. Sellers want a system that proactively helps them succeed—without forcing them to hunt for insights across disconnected platforms.

The Big Brain AI Architecture takes a fundamentally different approach. It is designed around the seller and other key revenue stakeholders, ensuring that AI works for them rather than adding to their workload. It does this by:

Decomposing the traditional CRM platform into its core components—CRM data, workflow logic, intelligence, and the user interface—allowing for more flexible and modular AI integration.
Orchestrating AI agents that interact seamlessly with both existing and new data, tools, and workflows.
Enabling modular, composable AI adoption, allowing enterprises to buy, build, or combine AI components as needed, when ready.

As part of this waypoint, we conduct an architecture mapping exercise—evaluating an enterprise’s current tech stack against the Big Brain AI Architecture to help answer critical questions:

How do our current tools and systems fit into the AI-powered future?
Where do we have gaps, redundancies, or opportunities to improve?
Which AI components might we buy, build, or replace over time?
What current tools and systems might we be able to retire?

For example, if a company has already invested in a SaaS-based AI sales coaching tool, we evaluate:

Does the tool’s underlying IP and intelligence align with the Big Brain framework?
Would its capabilities be better deployed as an AI coaching agent that delivers insights at the right time and in the right context, rather than existing as a standalone tool?
Could an LLM be trained on the coaching tool’s expertise, allowing for more contextual, dynamic coaching inside the seller’s workflow?

Travel Time: How Long This Takes

This waypoint is a 2-4 week exercise, as it requires:

Inventorying existing tech stack components and mapping them to the reference architecture.

Identifying gaps, redundancies, and disconnected systems that may impact AI adoption.

Discussing key dependencies, such as data pipelines and governance frameworks.

Ensuring the essential gear is in place before setting off on the journey. This includes making sure foundational elements are ready—such as the right data infrastructure, AI environments, and necessary tools. While some elements can be acquired quickly, others, like setting up a new contract with a hyperscaler or integrating enterprise data sources, may require additional planning and lead time.

Assessing current AI-related technical skills and capabilities within the organization to identify key skill gaps that may need to be addressed before deeper AI implementation begins. Some enterprises may already have experienced AI teams ready to execute, while others may need to build internal expertise before advancing further.

What’s Next? Measuring Success in an AI-Driven World

With an AI-Centric Reference Architecture framework in place, enterprises now have:

A common language for discussing AI in sales and revenue operations.
A structured reference model to assess new AI tools and investments.
An inventory and assessment of how components from the current tech stack align with an AI-centric technical architecture.
A modular, composable design that adapts as AI capabilities evolve.

However, transformation is not just about building the right architecture—it’s about ensuring that every step taken delivers measurable, meaningful progress toward the future-state vision. This means rethinking how success is defined and measured in an AI-driven world.

At the next waypoint, we focus on new ways of thinking about ‘metrics’ that allow us to measure our expedition progress, as well as the sales execution and outcome oriented metrics we defined in waypoint 1.