How I Evaluate AI Sales Tools as Head of SDR: My Real Framework
Friday was a full day of AI sales tool demos. By the end of it — after sitting through 6Sense and reviewing Apollo vs ZoomInfo head-to-head — I had something more valuable than any single tool: a framework for evaluating all of them.
As Head of SDR at my company, I’m constantly being pitched the next AI sales platform. Every one of them promises pipeline transformation. Most deliver marginal improvement wrapped in excellent marketing. Here is how I cut through that.
The 6Sense Demo — What Actually Happened
We sat through a full 6Sense demo on Friday. The platform is impressive — the largest intent data network in B2B, account scoring powered by AI, GDPR-compliant tracking across the open web.
Context: we are mid-transition away from Priority Engine and actively evaluating alternatives. Our TAM is roughly 5,000 accounts globally — companies running SAP who need to manage testing, upgrades, and deployments. 6Sense maps well to that world.
The questions I pushed on:
- Intent signal retroactive accuracy — Can you show me accounts that closed in the last 90 days and prove they were flagged as in-market 60 days prior? This is the only real test of intent data quality.
- SAP ecosystem-specific signals — Are you capturing intent around SAP S/4HANA migrations, ERP upgrade cycles, and testing automation? Generic tech intent is noise for us.
- API flexibility — Can my automation layer consume your data without an enterprise contract for every workflow?
We left with follow-up materials to review and a decision to bring in the marketing team. The right move — intent data only works when sales and marketing activate it simultaneously.
Apollo vs ZoomInfo — The Real Comparison
Also on Friday: a deep dive on Apollo versus ZoomInfo for lead enrichment. The specific use case was real-time form enrichment — removing repetitive fields from web forms by auto-populating company and contact data when a prospect starts typing their email.
Apollo wins on price and API accessibility. ZoomInfo wins on data depth and accuracy for enterprise accounts. For us, targeting SAP customers at large enterprises, accuracy matters more. A stale title at an enterprise contact is not just a missed touch — it is a credibility hit.
The decision: technical meetings with both vendors to test their APIs against our actual Marketo stack. No vendor gets a decision based on a demo alone.
What Else Is in Evaluation
We are currently running a POC with OnFire as part of our broader evaluation of AI outreach tools. Alongside that, Amplemarket is on my radar — strong AI sequencing and credible multi-channel execution. Both are actively being assessed.
The tool I am now watching more closely is Amplemarket. The AI sequencing is more sophisticated, the intent layer is credible, and multi-channel execution feels more native. Worth a proper evaluation.
My 5-Part AI Sales Tool Framework
1. Intent Signal Retroactive Accuracy
Take 10 accounts that closed in the last 90 days. Check if the tool flagged them as in-market 60–90 days prior. Below 60% hit rate means the intent data is noise for your market. This test takes 30 minutes and eliminates half the platforms immediately.
2. Data Accuracy at Your Tier
Pull 50 contacts. Verify against LinkedIn in bulk. More than 15% stale titles or wrong companies is a red flag. Enterprise teams cannot afford dirty data — it kills credibility at the moment that matters most.
3. API and Automation Compatibility
If it cannot connect to my automation layer without a professional services engagement, it is not the right fit. The best tool reduces manual steps — it does not add them.
4. Real AI vs. Cosmetic AI
Ask the vendor: show me a specific decision the AI made that a human analyst could not have surfaced manually. Vague answer = the AI is a layer of UX on top of a database. Specific pattern detected across millions of accounts = real intelligence.
5. Cost Per Qualified Meeting
Reverse-engineer the math: tool cost divided by incremental qualified meetings booked. If that number is higher than your current cost per meeting, no feature justifies adoption. Build the model before signing anything.
OpenClaw and Claude Weigh In
I run two AI systems daily. OpenClaw is my personal automation engine — 339 scripts, 20 cron jobs, running 24/7 without being asked. Claude is the reasoning layer for strategy, analysis, and decisions like this one. They see tool evaluation differently.
OpenClaw: “Evaluate integration depth first. I don’t care how impressive the UI is — I care about webhooks, API rate limits, and clean data schema. A tool that can’t be automated creates manual work. The best platform is the one that plugs cleanest into everything else and disappears into the workflow.”
Claude: “Start upstream from integration. What is the actual bottleneck? Finding accounts, enriching contacts, writing the first message, or following up at the right moment? A tool that perfectly solves the wrong bottleneck is worse than no tool. Map the constraint first. Then find the tool that removes it.”
Both are right. The best evaluations hold the operational question (does it plug in cleanly?) and the strategic question (does it solve the real constraint?) simultaneously. Most teams answer only one.
The Decision Process From Here
Three tools are getting technical evaluations in the next two weeks: Amplemarket, Clay, and 6Sense (if the SAP-specific intent signals check out). OnFire is currently in POC evaluation.
No decision is being made on a demo. Every tool gets tested against real data, real accounts, and a real cost model. That is the only honest way to evaluate AI in a sales stack.
Devin Pillemer is Head of SDR at an SAP SaaS company. He writes about sales leadership, AI automation, and building systems that scale.
