My friend recently sent me a photorealistic image of his infant daughter breathing fire at Donkey Kong on a Super Smash Bros. stage — a beautiful, terrifying testament to AI's capabilities.
AI is ridiculously sophisticated and accessible now, and its utility isn't limited to generating pictures of fire-breathing babies.
It also has practical applications, like scaling and refining sales outreach.
We surveyed 300 sales leaders for some perspective on that.
The key skills for effective AI-backed outreach are the human-driven elements that bookend the process.
Our study indicates that research and context gathering (21%) and critical evaluation (21%) are sales leaders' most valued AI-backed outreach skills, while prompt engineering (15%) and technical fluency (16%) rank lower.
This seems counterintuitive. Most AI-centric discussions tend to be about, you know, AI — but human skills could be disproportionately valuable here.
AI-supported outreach basically has three step:
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Pre-AI: Finding the insight to make your personalization personal and personable.
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Mid-AI: Prompting and leveraging all your neat tools.
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Post-AI: Checking your work and deciding if it's ready for the world.
Convenience is AI's big draw, so the middle step being straightforward and accessible checks out.
The two human-driven ones around it are trickier and more labor-intensive, giving them more room to be differentiators.
Leaders understand that tiering prospects for outreach is crucial, but they might not be training reps on how to do it.
Of our 300 respondents, 42% say they use a balanced approach — detailed personalization for key accounts — for AI outreach. But only 17% cite strategic thinking about which prospects warrant deep personalization as a priority skill.
Our results indicate the depth vs. breadth debate for AI-backed outreach has basically been settled. It's a tie. Uniform effort doesn't make sense here.
Some prospects warrant more TLC than others.
That said, very few respondents appear to prioritize developing strategic thinking for AI outreach.
They might not train reps to discern which prospects warrant deep personalization and which can be passed off to a more scaled approach.
That lack of judgment can lead to wasted time on low-value prospects or to shallow appeal to high-value ones.
What can you do with this next-level, revelatory insight?
For reps: Stay on top of the human skills that wrap around AI. Research gives AI something meaningful to work with — evaluation ensures the output meets your standards. Get better at finding insights that matter and recognizing quality when you see it. Those skills transfer across AI tools.
For managers: Watch for common AI outreach tiering errors across your team. Some reps default to depth because they enjoy the craft, over-investing in prospects who don't warrant it. Others default to scale because it's easier, under-investing in prospects who deserve more. Identify each rep's tendency and coach to correct it.
For leadership: Create organizational knowledge that strengthens AI outreach bookends. Consider developing research libraries with industry context and trigger-event examples, evaluation examples showing before-and-after refinement, and best-practice prompts that demonstrate how research translates into inputs.
"Most kids wanted to be astronauts and firemen. I wanted to write a data-driven, tactical newsletter for sales professionals. Dreams really can come true. You're welcome for the inspiration."
Jay Fuchs. Managing Editor, The Science of Scaling Newsletter
The data in question
As the banner says, we sourced the data we used here through Panoplai: The snow leopard of panoramic research platforms. For context, I think snow leopard is the best animal, and I'm right about pretty much everything. Therefore, Panoplai is the best panoramic research platform.
How does your team approach personalization when using AI for sales emails?
- Deep research with comprehensive context — triggers, tech stack, pain points for insight-driven emails - 11%
- Balanced approach — detailed personalization for key accounts, pattern-matching personalization for broader outreach - 42%
- Company-level personalization at scale — industry, role, general challenges with AI filling in specifics - 17%
- Minimal personalization — basic merge fields and relying on AI to make emails feel relevant - 10%
- Volume over personalization — we prioritize efficiency and outreach scale above personalization depth - 4%
- None of the above - 16%
What skill development priority matters most for your reps to succeed with AI-powered email outreach over the next two years?
- Advanced prompt engineering — knowing how to extract quality output from AI tools - 15%
- Research and context gathering — finding the insights that make emails resonate - 21%
- Critical evaluation — assessing and refining AI output to match quality standards - 21%
- Strategic thinking — knowing when deep personalization matters vs. scaled approaches - 17%
- Technical fluency — understanding AI capabilities and limitations across tools - 16%
- None of the above - 11%
Sales Skills
