Building the AI-Ready GTM Engine: Clay in Real-World Workflows
This webinar was designed for GTM leaders and operators who want to see what “AI-ready” really looks like in practice.
Unlocking RevOps with AI: Watch the Replay
Watch the full webinar with Jessica Burke to learn how AI can unify data across CRMs, call systems, and enrichment tools, enhance account scoring models in Clay, and streamline sales coaching and preparation. Jessica also shares practical lessons on balancing AI automation with human oversight, building trust in AI-driven scoring, and embedding AI insights into everyday workflows, giving you a clear look at what works, what doesn’t, and how to scale responsibly.
Key Topics and Learning
AI strengthens data management but doesn’t replace oversight. Jessica stressed that while AI can unify CRMs, call systems, and enrichment tools into a single “source of truth,” it still needs human guidance for nuanced cases and data hygiene.
Dynamic scoring gets smarter with AI. By layering in complex signals, contextual analysis, and transparent explanations, AI scoring models in Clay can give teams both accuracy and trust in the results.
AI enhances sales prep and coaching. Centralizing call transcripts and account insights into Salesforce helps managers send tailored prep emails, assess call quality, and provide actionable feedback.
AI democratizes CRM insights. Natural language queries and visualizations can unlock data access for teams beyond RevOps, but Jessica cautioned that proper guardrails and validation remain essential.
Human-in-the-loop is non-negotiable. Successful AI personalization requires structure (guides, subcategories, rules) and gradual rollout. Jessica advised treating AI “like a new intern,” capable, but needing direction.
Clay adoption lessons. Costs scale with use cases, not company size; always consult your security team for setup; start small with pilots, and blend AI automation with targeted, human-QA’d approaches for the best outcomes.
See How This Works for Your Team. Book a Strategy Call with an Al/RevOps Expert
Industry Areas of Expertise: Artificial Intelligence, SaaS, Analytics
Al Tools Expertise: Clay, Relevance Al, Zapier
RevOps Areas of Specialty: GTM Engineering, Sales Ops, Marketing Ops, CS/Support Ops, Sales Enablement, Business Systems, Business Ops
Industry Areas of Expertise: Artificial Intelligence, Advertising, Cloud Computing, Cybersecurity, FinTech
Al Tools Expertise: Clay, Copy.ai, LaGrowthMachine, Zapier
RevOps Areas of Specialty: GTM Engineering, Sales Ops, Marketing Ops, CS/Support Ops, Sales Enablement, Business Systems
Industry Areas of Expertise: Cybersecurity, FinTech, SaaS
Al Tools Expertise: Clay, LaGrowthMachine, n8n, Zapier
RevOps Areas of Specialty: Sales Ops, Marketing Ops, Deal Desk, GTM Engineering, Business Systems
Q&A from the Webinar
How can you backtest lead scoring accuracy in Clay?
Clay doesn’t have a native backtesting engine. However, you can work around this by uploading historical data, such as Closed Won opportunities, and testing scoring against it. While not built in, Clay is flexible enough that you can create custom methods to iterate and refine your scoring.
How do you manage security risks when using AI and large language models (LLMs)?
Never put company data into free or public LLMs. Even with paid models, always follow your security team’s guidance. At Jessica’s company, they use a secure setup where data is stored externally, and the LLM only references it. Guardrails and clear context ensure models remain secure, but policies should be driven by your internal security team.
What does a Clay implementation cost for small vs. large organizations?
Pricing depends more on use cases than company size. Costs rise when using Clay credits for tasks like complex email writing or deep research. Simpler actions, like enrichment or CRM connections, cost little to nothing. Many teams save on credits by using their own OpenAI keys for analysis. Clay’s pricing page is public, so you can calculate based on credit need. Start small, then scale as use cases expand.
How has the team responded to AI adoption?
Reactions were mixed. Some employees jumped in eagerly, even asking for Clay access. Others were more hesitant. Success came from meeting people where they are:
– Simplifying workflows so AI outputs land in tools they already use.
– Human-in-the-loop setups (e.g., sales reps using a GPT trained to answer like the CRO).
– Feedback loops with regular check-ins, ensuring teams feel heard and see value.
This mix of personalization and change management has helped adoption spread across the company.
How do you motivate slower adopters?
By embedding AI into tools people already use instead of asking them to learn new systems. Regular feedback sessions also build trust, showing employees that AI is there to support, not replace, them.
How do you get Gong data into Clay?
Two main ways:
1. Use the Gong API to pull call data directly.
2. Sync Gong transcripts into Salesforce or HubSpot. Once synced, the transcripts appear as activity records, making them accessible to Clay without pulling directly from Gong.
About the presenter
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