A 4-Step Framework Revenue Teams Can Use to Adapt to AI-Directed Buying Committees
AI isn’t just changing tools: it’s changing how buyers research, build trust, and make decisions. In this pop-up session, New Breed’s Jonathan Burg shares early insights from the State of HubSpot research and a practical, repeatable framework for re-architecting your go-to-market strategy around the AI-driven buyer.
The session walks through a four-step process: remapping the buyer journey, identifying gaps in your current experience, prioritizing the highest-impact use cases, aligning tools and data to support AI-powered execution, and building a culture that enables teams to adopt AI responsibly and quickly. You’ll leave with a clear set of questions to ask internally, examples of where AI is already reshaping buying behavior, and guidance for where to start, especially if your org is still in the “learning and testing” stage.
What you’ll learn:
- How buyer behavior is shifting from self-directed to AI-directed research
- A 4-step approach to modernizing your GTM for buying committees
- How to identify gaps and prioritize AI use cases by impact and speed
- What it takes to make your data and tech stack “AI-ready”
- How to build a culture of experimentation with clear guardrails
TOC
Table of Contents
1 Why Revenue Teams Feel Behind—and Why that’s Normal
2 Step 1: Remap the Buyer's Journey for AI-Directed Research
3 Step 2: Identify Your Biggest GTM Gaps and AI Use Cases
4 Step 3: Align Tools and Data to Execute with AI
5 Step 4: Build a Culture that Accelerates AI Adoption
1Why Revenue Teams Feel Behind—and Why that’s Normal
Many revenue leaders feel “more behind than ever” because AI is evolving daily and showing up across every part of go-to-market execution. New Breed’s State of HubSpot research reinforces this—one of the biggest reported challenges is keeping up with new technology and integrating AI into GTM processes.
Tthis as an opportunity: teams that are learning and applying AI now are early adopters who can help their organizations move from experimentation to repeatable impact. The poll results in the session reflect that reality: most organizations are still learning, testing, or exploring rather than fully mature and ready for autonomous AI and agents
2Step 1: Remap the Buyer's Journey for AI-Directed Research
Starting your migration journey
The first step is acknowledging the disruption: today’s buyers aren’t just self-directed—they’re increasingly AI-directed.
Key changes:
- Buyers use AI summaries, peer reviews, influencer content, and “dark social” to gather information
- Buying committees set the speed, not vendors
- Personalization expectations are rising (context matters more)
- Legacy signals and lead-based processes are now table stakes—and less effective than they used to be
To remap the journey, you need to address internal questions:
- Who is on the buying committee, and what role does each person play?
- Where do different stakeholders go to learn and evaluate?
- What timeframes do buyers prefer—vs. how your process forces them to buy?
- When do buyers want human interaction vs. self-service?
- What rational and emotional criteria drive decisions?
How to operationalize this: analyze customer and pipeline data, interview teams and customers, evaluate how your brand appears in large language models, and map an updated buying journey that your sales process can actually support.
3Step 2: Identify Your Biggest GTM Gaps and AI Use Cases
Once you understand how buyers buy today, the “magic” is identifying where your current GTM falls short—especially around personalization, speed, and relevance.
The session calls out common constraints:
- Different AI skill levels across the team
- Disconnected tools and disconnected data
- Trust concerns
- Overload of options and ideas without prioritization
To pinpoint gaps, take a diagnostic approach:
- Where are you failing to reach buyers across journey phases?
- What prevents personalization at the right moments?
- Where are manual research and engagement processes slowing teams down?
- What content is missing to educate, inspire, and convert buying committee members?
- Are your processes and measurement aligned to buying committees vs. individuals?
- Does your team have the competencies to leverage AI responsibly?
From there, translate gaps into concrete use cases (e.g., awareness, inbound follow-up, outbound research, committee engagement), then prioritize based on the intersection of impact and speed.
4Step 3: Align Tools and Data to Execute with AI
This step focuses on preparing your systems to support the use cases you prioritize.
Key questions include:
- Do you have the right intent signals to drive action at the buying committee level?
- Is your structured data clean and integrated across tools?
- Is your unstructured data usable by AI (call recordings, sales notes, knowledge base content, playbooks)?
- Are there AI capabilities already in your stack that you aren’t using?
- What tools are missing entirely?
A central takeaway: be deliberate about getting your data “AI-ready” and ensuring your systems are integrated across marketing, sales, and service. HubSpot users are more likely to feel supported in keeping up with AI and integrating it into GTM processes—reflecting HubSpot’s increasing investment in embedded AI capabilities and agents.
5Step 4: Build a Culture that Accelerates AI Adoption
The “last step” could actually be the first: AI adoption depends heavily on culture.
- Assess your team’s AI maturity (embracing tools vs. hesitance)
- Reduce “shadow AI” by building transparency and guardrails
- Create a culture of experimentation aligned to clear use cases and company direction
- Iterate quickly—experiential learning matters right now
One example discussed is running an internal hackathon: bring teams together around specific GTM use cases, encourage problem-solving, and turn experimentation into practical outcomes (including internal tools that help guide adoption).
Frequently Asked Questions
How mature are most organizations with AI today?
Most organizations are still in early stages—learning, exploring, and testing—rather than fully mature and prepared to capitalize on autonomous AI and agents.
What’s the best place to start if we feel behind?
Start by remapping the buyer journey and identifying where your current process fails to meet buyers with speed, relevance, and personalization. Then prioritize AI use cases based on impact and speed.
Where is AI having the biggest impact on the buyer journey?
There are two major shifts: where buyers go to gather information and the speed at which they can digest it. AI is also reshaping expectations for how and when buyers want to interact with vendors.
What kinds of data matter most for AI-ready GTM?
Both structured data (clean CRM fields, integrated systems) and unstructured data (call notes, recordings, knowledge bases, playbooks) need to be accessible and usable by AI tools and agents.
Why does culture matter so much for AI adoption?
Without a culture of experimentation and clear guardrails, teams adopt AI inconsistently—or hide usage through “shadow AI.” A deliberate culture speeds learning and keeps adoption aligned to real business outcomes.