AI and automation now run the modern GTM engine.
LLMs sit at the front of that stack, driving conversations, routing, qualification, and follow-up at a scale humans could never match.
Teams are pouring money, time, and executive attention into these AI programs because the promise is enormous: faster revenue, shorter cycles, and consistent buyer experiences.
But there’s a problem hiding in plain sight — and it’s derailing even the most ambitious AI roadmaps.
AI is only as accurate as the signal it receives.
And today’s GTM signals are wrong more than they’re right.
This is the core problem in AI-powered GTM.
LLMs shine at conversation, but without accurate upstream intent, they're flying blind on who to engage and how
So without the right input signal, even the smartest AI becomes automated guesswork.
The million-dollar question every LLM still can’t answer is simple: Who is actually in buying mode right now?
The Input Signal Crisis
According to industry research from MIT and Forrester, AI is failing because of bad data. Most buyer intent signals used across GTM are ~20-30% accurate.
And these inaccurate signals are driving our LLMs - which amplify whatever signal they’re given, which creates inaccurate automation at scale.
The result is predictable:
- Sales-ready buyers face friction, delays, and over-qualification
- Early-stage researchers receive aggressive sales messaging
- Outreach fires at the wrong moment and interrupts buyer flow
- Conversations misroute, wasting human time and damaging trust
- GTM systems compound bad data across every tool, from CRM to AI agents
Take the industry’s classic “strong intent” signal: the pricing pageview. Teams treat it as the gold standard intent signal.
But Lift AI data shows:
- Up to 94% of pricing page visitors are not sales-ready
- 88% of high-intent buyers don’t visit the pricing page.
This is the data feeding your LLMs today.
It’s no wonder conversational AI systems feel inconsistent, brittle, or overly aggressive.
Why LLMs Can’t Solve Intent on Their Own
LLMs generate language. They do not accurately understand buying behavior before generating that language.
They’re like a salesperson who's been blindfolded. They have fantastic communication skills, but they can't see who they're talking to or what that person did before talking to them. They don't know if the person in front of them is:
- A tire-kicker doing early research
- A competitor gathering intelligence
- A student working on a project
- An executive ready to sign a deal this week
So the LLM does what it's designed to do: it responds based on the contextual input it receives. It tries to infer the prospect’s buying intent through text, instead of reading all of their behavioral cues first.
This creates a structural limitation:
LLMs operate downstream of the real intent signal — unless you give them one.
Lift AI: The Intent Prediction Layer LLMs Have Been Missing
Lift AI acts as the intent prediction layer beneath every LLM, AI agent, and workflow in your GTM stack.
It predicts true buyer intent in real time with 85%+ proven accuracy, across 100% of visitors using outcome-trained behavioral learning built on billions of journeys and millions of purchases (instead of trained on language).
Coverage includes:
- Anonymous Visitors (~70% of traffic)
- Identified Contacts & Accounts (~30% of traffic)
- Engaged Visitors in chat and forms (1–3% of traffic)
LLMs decide what to say next.
Lift AI decides who is actually ready to buy, when to engage them, how to guide the conversations, and how much friction to apply (if any).
Together, you get more intelligent automation.
How Lift AI Makes LLMs Significantly Smarter
1. The Right Visitor
LLMs stop treating all visitors equally. High-Intent visitors get fast-lane access to sales. Mid-Intent visitors get guided education. Low-Intent visitors get nurtured without draining human time.
2. The Right Moment
Lift AI detects readiness the instant it happens. Your LLM engages at peak intent - not minutes or days too late.
3. The Right Conversation
With accurate intent context, LLMs tailor tone, depth, and next actions based on real readiness. High-Intent visitors avoid unnecessary qualification questions. Low-Intent visitors avoid hard sales pressure.
4. The Right Outcome
High-Intent buyers convert faster. Human resources get focused. Low-Intent visitors stay engaged and informed. Your GTM stack finally operates with accuracy at the center.
Before/After: A Conversation Transformed by Intent
Visitor behavior:
Low-Intent research pattern (landed on blog, skimmed content, looked at educational content).
Visitor asks Chat: “Can you show me pricing?”
❌ Without Lift AI:
“Absolutely! I can walk you through pricing and set up a demo. What’s your email?”
→ Too aggressive. The visitor bounces.
✅ With Lift AI:
“Since you’re comparing options, here’s a guide that outlines how solutions differ. Want me to send it over?”
→ Helpful. Educational. Trust-building.
This is what happens when language generation and behavioral prediction work together.
What’s Your Choice? Guesswork or Precision
Real Proof: Boomi’s LLM Becomes a GTM Growth Engine
Boomi connected Lift AI’s intent scores into their conversational AI agent to adapt routing and messaging in real time.
Results:
- SDR conversation-to-pipeline efficiency jumped from 3.66% to 8.72%
- Opportunities booked doubled
- Strongest quarterly pipeline created from chat + AI agent + SDR collaboration
This is the compounding impact of feeding accurate signals into your AI systems.
Activate the GTM Engine: Predictions + Generation
LLMs can’t tell you who is ready to buy.
Lift AI can with 85%+ proven accuracy across 100% of your website visitors.
Together, they form the modern GTM engine:
ONE Accurate Signal + World-Class Language Generation.
This is the shift that turns AI from automated guesswork into a revenue system —
faster pipeline, fewer wasted cycles, and a GTM stack that finally acts with precision.
Experience the impact of accuracy inside your own GTM engine.
Try Lift AI free for 30 days and experience the difference that accuracy makes across your entire GTM stack.
