Built from the data up
Bad data doesn't announce itself. It just costs you — wrong accounts, bounced emails, contacts who left six months ago, AI outputs that need fact-checking before anyone uses them. Every bad record that flows through your stack costs you time, API calls, and credits too.
This is the framework for fixing the foundation, and what becomes possible when you do. Walk through the mental models behind AI GTM that actually works, the two data layers, the GTM hierarchy, the maturity model, and the five questions to identify your highest-impact next move.
Where every GTM team loses time, money, and API calls
Most revenue teams aren't losing because they lack tools. They're losing because the tools they have are running on bad data.
The fix isn't another tool. It's a verified data foundation that every tool and every AI model runs on.
Start with truth
AI doesn't fix bad data. It amplifies it. The teams winning with AI GTM didn't start with the best prompts. They started with verified data.
"In the AI era, trust and data accuracy are the real growth moats."
Yoni Tserruya, CEO, Lusha
When a language model gets bad data, it returns bad answers — confidently. A stale CRM record produces generic advice. A verified account picture with live signals produces something a rep can use in the next 30 minutes. The model doesn't know the difference. You have to give it the right input.
Claude, GPT-4, Gemini — every company has access to the same models at roughly the same price. The gap between teams isn't the model. It's what the model is working with. Verified contacts, named buying signals, and ICP scoring built from closed-won data will consistently outperform a team running the same model on a stale CRM export. That gap widens with every deal cycle.
One prompt, two data layers
Here's what comes back.
This is a mid-market SaaS company. Here are some general discovery questions to reopen the conversation and understand their current priorities...
R.M. was promoted to VP of Sales 6 weeks ago and is scaling the SDR team. 12 open roles across EMEA and North America. The intent signal on prospecting data is live. Lead with the SDR expansion angle. Reach out this week — the timing window is open now.
Two layers. One data foundation
Most B2B tools stop at Layer 01 — a database you search. Lusha goes further. The data layer gives you 300M+ verified contacts, buying signals, and enrichment, globally compliant and ready to use. The intelligence layer sits on top of it — predictive scoring, buying committee mapping, and lookalike discovery built from your specific won deals, not a generic model. Both layers connect to wherever your team already works. That's the difference between a contact database and a GTM data foundation.
Everywhere.
The right data foundation changes what your team and AI agents can do. Lusha data lives wherever your team works — not locked inside one tool.
Deep intelligence is what separates a data vendor from a GTM platform
Every B2B data tool gives you a contact. A verified email, a direct dial, a company profile. That's table stakes.
What changes the output — for AI and for your team — is intelligence shaped by your specific business. Not a generic ICP model built from industry averages. Not intent data scored against anonymous topic clusters. Intelligence built from your closed-won deals, your target personas, and your actual buying patterns. That's what Lusha Deep Intelligence does.
Lusha Deep Intel starts from your closed-won data. It finds the patterns in the accounts you've already won — the signals that showed up before those deals closed — and surfaces new accounts that match those patterns. The model learns from what converted, not from what looks good on paper.
Anonymous intent tells you a company is researching a topic. Named signals tell you something happened — a VP joined six weeks ago, a Series B closed last month, 12 SDR roles posted this week. Verifiable, time-stamped, tied to a specific person. That's the difference between a guess and a reason to call.
Deep Intel maps the full buying group — Champion, Economic Buyer, Technical Evaluator, Influencer, Blocker — verified via Lusha, classified by role, and audited for gaps. A deal with no verified Economic Buyer at Stage 3 isn't a pipeline problem. It's a data problem. Deep Intel surfaces it before it costs you the deal.
Give Lusha your best customers. Deep Intel finds more companies that look like them — built from your closed-won data, not a generic similarity model. The result is a prospecting list that starts from what converted, not from what fits a broad category filter.
"The teams pulling ahead aren't just using better AI. They're feeding it better intelligence — intelligence shaped by their specific business, their specific wins, and their specific buyers."
Yoni Tserruya, CEO, Lusha
Four forces, sequential, non-negotiable
The hierarchy doesn't bend. Build on bad data and every layer above it is wrong. The teams that skip straight to outreach generation are spending money to send better messages to the wrong people.
Where are you today
Locate your organization in the model, then identify your highest-impact next move.
Three teams that built the foundation
"Lusha's direct contact information is worth more than gold. Data is our bread and butter. Lusha is focused on data, not bling and bells and whistles."
Jeremy Levine, Director of Business Development, WalkMe
"With Lusha, SDRs spend 87 additional hours prospecting each month. We've tripled the number of outbound meetings generated."
Florence Broderick, VP Marketing, CARTO
"Lusha has directly contributed to our revenue growth, helping us generate £1.4M in 2024 through better data and smarter outreach."
Chris Coghlan, Performance and Development Manager, Empiric
Remove the friction, keep the judgment
Sellers don't lose deals because they lack AI. They lose time to the work that isn't selling — research, enrichment, context-switching, admin. Give that work to the machine. Keep the judgment, the relationships, and the last mile with the human.
"Traditional sales tools force you to search and filter endlessly. Sales streaming is the opposite — the right accounts, contacts, and signals come to you."
Yoni Tserruya, CEO, Lusha
Five questions to locate yourself
Answer honestly and you'll know exactly where your data foundation stands — and what to fix first.
Verified data, named buying signals, and deep intelligence shaped by your business. Connect once, run anywhere.
