What's up everyone! Welcome to the ForgeX Files, our monthly newsletter.
This is where we share our latest research, insights and benchmarks around all things ABM, AI and GTM.
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The "AI" hype cycle is in the midst of an identity crisis.
Everyone knows they need to spend time keeping up with AI (which is impossible), and everyone knows the organizations that actually invest in embedding it will win.
Last week I joined Propensity at their booth at HubSpot's Unbound, where 13,000+ people showed up.
In this ForgeX File, I'm going to break down my 5 takeaways from the event in more depth.
Propensity is an AI-powered lifecycle marketing platform for personalized B2B orchestration.
Folloze AI can draft your ABM campaign in an hour. Folloze deploys it live, per account.
Reachdesk helps B2B teams deliver moments that matter with global gifting and swag.
1. Recursive GTM Platforms Are COMING
The scramble for tech market positioning at Unbound was real. Are you a platform for "GTM AI"? "Revenue AI"? "Agentic GTM"??? Every booth had a slightly different answer.
Merely having "agentic capabilities" or an "MCP" connection is quickly becoming a prerequisite instead of a differentiator.
My POV is that the future of GTM platforms will be recursive in nature, powered by a centralized AI brain. The most forward-thinking organizations and GTM tech companies are already starting to think this way… how do you build a model that improves itself?
Here's what "recursive" looks like in practice:
1. Ingest: The model continuously takes in context, both structured and unstructured.
2. Execute: It acts across the orchestration of your campaigns.
3. Measure: It captures the performance data from everything it runs.
4. Feed back: Those outcomes go straight into the next decision, so your strategy and execution self-improve and get stronger across your GTM orchestration over time.
The output is an AI model that tells you what to double down on, which messaging needs to change, where to lean in harder on orchestration, and which accounts (and which contacts inside those accounts) deserve more $$$$$ in your investment strategy.
2. Static Account and Contact Scoring Should Obviously Be AI Augmented
Manually assigning "weights" across engagement scores isn't going to hold.
The wealth of additional contextual data that can now be layered in allows for far greater accuracy AND better timing.
Our 2026 AI in ABM Benchmark Report backs this up as high-performing teams are 2.4x more likely to report that AI improves their targeting and signal accuracy.
The OLD way: Marketing Op manually assign fixed weights to actions such as webinar attendance, pricing page visits, and content downloads to determine who qualifies as an MQL or warrants sales outreach. And predictive models operate as black boxes, leaving teams with a score and little explanation of what drove it.
The NEW way: Your AI model brings far more context into the decision than campaign engagement. It can draw on what buyers say in recorded sales calls, the questions and concerns they raise in emails, who else enters the conversation, intent signals, firmographics, and MORE. The context helps uncover and map the buying group, where an account is across buying journey stages, and why the AI model scored it that way. As opportunities advance, stall, or close, the model learns which signals actually predicted progress and improves its next recommendation.
This was already on the rise before Unbound, too. In our early-2025 AI + ABM Inflection Point research, “Predictive analytics for account selection and prioritization (46%)” and “Capturing/analyzing buying signals (44%)” were already two of the top four AI use cases.
3. You Can Actually Operationalize Buying Groups in Tech Now
Practitioners have been begging for this in user groups for YEARS, and vendors are finally starting to deliver.
GTM tech is now beginning to support buying groups by:
Identifying the contacts inside of buying groups that exist inside each account
Associating and tagging contacts to the right buying group
Visualizing which buying groups are more engaged and which ones need more attention
Instead of merely highlighting full ACCOUNTS that reach a specific threshold to be justified as an “MQA” or Marketing Qualified Account.
Or every marketer’s favorite, MQLs. Which we all know, in complex enterprise sales, one person does not make the actual buying decision. It’s done in a group.
4. ABM ≠ Pushing Paid Ads to a List of Target Accounts
I had a few conversations at Unbound where either "ABM platforms" or marketing leaders still hold this perception of ABM.
You're building a unified Account-Based GTM strategy that spans marketing, sales and CS, which includes:
Multi-channel orchestrated campaigns: Paid is one channel among MANY (examples: events, direct mail, personalized experiences, sales plays, CS expansion motions).
A Target Account Portfolio: defines the level of budget, resources and capacity allocated to each account in your list.
The Best ABM deployment model(s) for your organization: 87% of organizations run two or more ABM deployment models simultaneously.
Source: ForgeX 2026 State of AI in ABM Benchmark Report (n=189)
Target Account Portfolio (TAP) = the prioritized set of accounts an organization has committed to pursue, segmented by Account Economics and assigned to an ABM deployment model, with an explicit allocation of budget, resources and capacity.
5. Literacy Around Your Account Economics Is Critical
You probably shouldn't be deploying intensive 1:1 ABM across 8 named accounts if your ACV is $50k…..
Account Economics is a new term we'll be pushing on at ForgeX, especially when you consider the amount of investment that goes into each account.
I've heard it over and OVER again the past few weeks… Demand Gen leaders and VPs of Marketing who have been running low on pipeline are now considering "doing ABM."
ABM principles should be part of your underlying strategy to begin with. The harder (and more important) part is understanding the economics behind the accounts in your Target Account Portfolio, which drives two decisions:
Which ABM Deployment Model to leverage based on your Account Economics
Which accounts get placed into each model
A quick gut-check on how the models map to economics:
Deployment Model | Typical Economics | Coverage |
|---|---|---|
Enterprise ABM (1:1) | $1M+ ACV (ForgeX recommendation), 9–18 month campaigns | 3–8 accounts per practitioner |
Enterprise ABM (1:Few) | High ACV, clustered by shared need | Clusters of <25 accounts |
Growth ABM (1:Many) | Scaled, tiered (1, 2, 3) investment | 50 to 1,000s of accounts |
Deal-Based ABM | Extra resources on an active opportunity | Live pipeline only |
[In-Person] AI in Marketing Innovation Tour: San Francisco
Our AI in Marketing Innovation Tour, part of our new FutureX event series, kicks off in person at the Autodesk Gallery in San Francisco on Thursday, November 12.
Here what you’ll hear:
The State of AI in B2B Marketing: New ForgeX benchmark data and a roadmap for advancing AI across capabilities, data, tooling, governance, adoption and organizational design.
Modernizing Marketing Measurement and Attribution: How to operationalize buying groups and connect tactical activity, account engagement, pipeline and revenue in one measurement framework.
Building an AI Center of Excellence: Real-world examples from Autodesk and an inside look at building agents, connecting tools and enabling teams to scale AI across marketing.
Every request is reviewed and the room is capped, so grab your spot early.
***Austin, New York, London and Seattle stops are coming through 2027. See all tour stops.
[On-Demand] AI for ABM: What You Need to Know About MCPs, Agents & Prompts
Missed our virtual session last week? Trey Harnden, GTM Engineer at Folloze, walked through what MCPs are and why they matter, how skills, agents and prompting work, and how to use GitHub to organize your AI workflows.
Related conversations::
GTM Orchestration: What It Is and Why ABM Is Driving It with Sumner Vanderhoof, CEO at Propensity (the perfect companion to takeaway #1)
Our latest Revenue Xchange episode, How Loftware's CMO is Rethinking Marketing with AI
Top Conversations in the ForgeX Community
Here's what ABM and GTM leaders were debating in our Slack this week:
#abm-chat-general: My sales leader still expects pre-AI ABM tactics. How do I bring him along?
#vendor-marketplace: Who's the best HubSpot-savvy MOps and RevOps agency?
#job-board: New Senior Demand Gen and MOps roles (ABM experience wanted)
Join 1,000+ ABM and GTM leaders to read every response and chime in (free forever, zero vendor pitching).
Work With a ForgeX Analyst
Teams bring us challenges like:
Choosing the right ABM deployment models
Building a unified Target Account List
Transitioning from MQLs to buying groups
Measuring the impact of ABM and demand investments
Choosing and prioritizing AI use cases
Stuck on one of these (or something tougher)? Schedule a strategy call with ForgeX.
Join 1,000+ ABM and GTM leaders and become a ForgeX member to access our full research library, step-by-step frameworks, benchmark data and community.
Have an awesome rest of the week y'all!
Davis
