AI is reshaping B2B GTM with signals you’re still ignoring

AI tools are already significantly impacting go-to-market (GTM) teams. From auto-generated emails to deal forecasting and call summarization, AI-powered features are being rolled out across sales and marketing processes and generating meaningful value. According to a 2024 survey, 93% of GTM leaders reported using AI in some capacity, with 78% planning to increase their AI investments in 2025.

We’ve only just scratched the surface. What’s coming next will require more than task automation. Businesses will need to embed AI in their end-to-end GTM workflows to understand what drives buyer behavior, capture signals in real time, and use that context to drive execution. That context lives in what we call metadata: the signals generated by sales activity and customer interactions. And it’s this new layer that will define the next generation of GTM platforms, enabling systems that learn from context, act autonomously, and improve with every buyer interaction.

AI is also addressing another major blind spot: visibility into the full B2B buying journey. Research shows that up to 80% of the B2B decision-making process happens before a buyer ever engages directly with a vendor, and that share is growing. Buyers are doing more independent research, across more channels, before reaching out or connecting with a brand. And when they do engage, AI is poised to automate much of the remaining 20% of the buying journey, from qualification to follow-up to proposal. This gives AI the potential to illuminate the invisible majority of the journey, capturing early intent signals, interpreting buyer behavior, and shaping strategy before any direct interaction takes place.

In this article, we’ll break down where AI is adding value today, what’s missing, and why the future of B2B go-to-market, encompassing sales, marketing, and full-funnel engagement, will be shaped by platforms that collect the right behavioral signals and get better with every cycle.

Recent applications of AI in GTM

AI is already driving meaningful improvements across GTM workflows, especially in areas that were historically manual, repetitive, or hard to scale. While most tools today focus on generating outputs, they’ve also shown early ROI around helping teams move faster, personalize more deeply, and convert deals more efficiently.

Some of the more promising use cases we’re seeing include:

• AI for outbound orchestration: Clay and Bluebirds automate personalized outreach across channels, blending real-time enrichment, persona-based sequencing, and dynamic copy.
• Buyer intent tracking: Vector captures mid-funnel behavior—like demo engagement or product interaction—and triggers timely follow-up.
• Warm pipeline activation: Unify surfaces and activates previously engaged leads, helping convert latent interest into qualified pipeline.
• Live demo personalization: TestBox delivers hands-on product experiences tailored to each buyer’s role and use case, helping convert interest into intent.
• Full-funnel content generation: Tofu and Gradial help marketing and sales create landing pages, scripts, and case studies adapted to each funnel stage and persona.
• Customer success automation: Hook gives CS teams visibility into churn and expansion risk, recommending proactive interventions based on usage data.

Below is a market map of some of the tools we’ve been tracking:

Data, metadata, and the new foundations of AI-powered GTM

While AI tools can automate tasks like writing emails or summarizing meetings, they still depend on disconnected inputs that reveal little about how and why B2B buyers actually make decisions, especially across the parts of the journey that happen outside the funnel. Most GTM platforms are built around surface-level inputs like lead lists, basic engagement tracking, and firmographic filters—data that describes the buyer, but doesn’t explain their intent or behavior. The result is a shallow view of the buyer, one that’s hard to learn from, adapt to, or scale. What’s missing is the why behind each decision.

That information lives in metadata, the context-rich buyer signals generated by running the GTM motion itself. This includes:

• Which rep reached out to which persona, through what channel, and why?
• What product feature did a user adopt before expanding usage?
• How did a buyer react after receiving a certain message, and did it create momentum?
• What are some common patterns across cohorts that can enable rapid scaling?
• What are the attributes and behaviors of the most effective sellers?

This metadata isn’t just helpful, it’s foundational. Copilot-style tools can use it to personalize output, while agents rely on it to function altogether. Autonomous systems need structured, declarative metadata to make decisions that humans would make based on gut feel, tribal knowledge, or Slack backchannel messages.

A huge portion of what drives exceptional GTM execution still isn’t logged anywhere. Most reps still make decisions based on intuition, experience, and industry knowledge, not structured inputs. Why one account gets prioritized over another or how a message is worded for a specific buyer rarely lives inside the CRM.

It’s the same story on the buyer side. Remember, 80% of their journey happens before they ever talk to a vendor. Most tools track what happened, not why. Why was this vendor chosen? Why now? What internal debates or priorities shaped the decision? Who was the ultimate decision maker? None of that shows up in activity logs. Yet these are the signals that actually drive outcomes.

If AI is going to move from automating outputs to orchestrating decisions, it needs access to this hidden context, the metadata that lives outside the screen, across both sides of the transaction.

If you control the workflow, you generate the metadata. And if you generate the metadata, you can automate intelligently, learn continuously, and own the end-to-end motion.

The AI shift: Our predictions for the future of Enterprise GTM

The next generation of GTM platforms will be defined by workflow ownership. Own the workflow, and you capture the behavioral signals. And if you generate those signals, you unlock intelligent automation, continuous learning, and full-funnel control.

This shift isn’t just happening on the seller side. Buyer behavior is evolving just as fast. AI-native platforms like ChatGPT and Perplexity are replacing traditional search. 

As SEO declines, entirely new demand surfaces are emerging, many of which are outside the reach of today’s GTM playbooks. 

It’s also becoming harder to rank in search as the web is flooded with AI-generated content, making quality discovery more difficult and increasing the need for new intent signals.

This dual transformation will define the future of B2B sales: AI-enhanced sellers on one side and AI-influenced buyers on the other. Below are three key shifts we believe will reshape how companies reach, engage, and convert customers.

1. AI will reshape the role of the sales rep, from execution to orchestration

The early generation of AI agents focused on automating grunt work, such as logging CRM entries, sending sequences, and qualifying leads. But the endgame is more ambitious: systems that handle outreach, surface buyer intent, negotiate pricing, and draft proposals autonomously. We're starting to see glimpses of this from players like Artisan and Regie.ai, but we're far from full automation. 

What this means for GTM orgs is a redefinition of the human rep’s role. As agents handle more of the transactional layer, reps move upstream to manage trust, navigate politics, and shape strategy. Sales becomes less about execution and more about orchestration. The winners here won’t be the tools that replace the rep, but the ones that reshape what the rep actually does.

2. Hyper-personalization will evolve into full-funnel orchestration

The best GTM teams are no longer just personalizing outbound, they’re orchestrating relevance across every buyer touchpoint. That means modular content for landing pages, role-specific demos, personalized sales scripts, dynamic case studies, and smart sequencing based on live buyer behavior, tailored to the needs of different stakeholders in the buying committee.

• A CFO might see a personalized ROI calculator on LinkedIn. 
• A technical buyer gets product docs tied to their stack. 
• A mid-funnel champion enters a demo built around their exact use case. 

These touchpoints reflect how the best teams engage the full buying committee; each persona sees a message mapped to their priorities and decision stage.

Tools like Clay and Bluebirds are enabling this on the outreach side, while companies like Tofu are doing it for content: generating and distributing assets tailored to specific personas and moments in the funnel.

In this new world, personalization becomes central to the GTM strategy, not just a last-mile tactical step.

3. The new front lines of GTM: Where activation begins before outreach

Traditional channels like search, outbound, and paid ads are saturated and losing effectiveness. Cold emails have become background noise, and ads are expensive, with most teams targeting the same buyers with the same generic messages. This has created diminishing returns on standard outbound motions and forced teams to look for new, higher-signal surfaces to drive more pipeline.

The next wave of GTM activation will come from surfaces that many teams aren’t tracking well: product usage, in-app behavior, support interactions, community engagement, even Slack threads and Zoom chat. These are high-context moments that often get missed, and when tracked, are often misread or ignored.

Some of this is already unfolding. Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO), which involves making your product visible to buyers querying platforms like Perplexity or ChatGPT, is an early sign of how discovery is changing. GTM will be less about pushing messages and more about surfacing and capturing signals and demand from new sources.

We don’t see a world moving back to high-touch sales cycles. If anything, GTM is becoming more buyer-led, defined by less friction, fewer meetings, and greater autonomy. The systems that win will be the ones that engage early, stay invisible when needed, and surface at just the right moment with precision and relevance to the buyer.

Why incumbents won’t win by default

Incumbents like Salesforce, HubSpot, and Outreach are moving quickly to keep up with new AI tools saturating the market. They’re layering in generative features, embedding copilots, and acquiring startups. But speed alone doesn’t solve the deeper issue.

These platforms were built as systems of record. They capture what happened (emails logged, calls made, stages updated), but not why it happened. They don’t control the workflows where real signals are created. That context lives upstream: in product usage, rep behavior, internal champion dynamics, and buyer reactions to specific messaging.

Without access to that full feedback loop—behavior, action, and outcome—AI tools remain reactive. A copilot might flag that a deal has stalled, but it won’t know that the champion never involved procurement, or that similar deals in this segment often stall unless security documentation is sent within a day of the demo. These systems can summarize, but they can’t guide. They can enhance, but not orchestrate.

This is the opportunity for startups. If you control the workflow, you generate the metadata. And if you generate the metadata, you can build intelligent systems that improve with every cycle. That’s how you compound product value and why incumbents, even with distribution, won’t win by default.

Context is king in the next era of AI GTM

The first wave of AI GTM tools is already delivering value by improving workflows, reducing reps' grunt work, and enhancing personalization. But the bigger opportunity is still unfolding. The next generation of platforms will be built around metadata: learning from context, capturing intent earlier, and orchestrating actions end-to-end.

Building something that fits this thesis? I’d love to hear about it. Feel free to email me at varun@signalfire.com.