One of the highest-leverage skills in any sales process, especially in technical sales, is the ability to uncover customer pain. The kind that’s beneath the surface, not listed on the website or in the RFP. That skill is becoming even more essential as buying committees grow and sales cycles stretch. And while there’s no replacement for experience and curiosity, I’ve found that AI can be a real accelerant to this part of the process if you use it well.
In the past six months, I’ve been working with my Solutions Engineering team on using AI more intentionally to uncover and clarify customer pain earlier and more effectively. We're not doing anything that compromises trust or digs where we shouldn’t. But we are being smart about how we prepare, question, and follow up with AI as a thought partner.
Here are a few ways we’re putting that into action:
1. Pre-Call Research That Goes Deeper Than “About Us” Pages
Before discovery calls, we’ll feed public materials—annual reports, blog posts, LinkedIn content—into AI with prompts like:
➡ “What challenges might a company like this face in [industry] when it comes to [category]?”
This helps us walk into calls with better hypotheses and sharper questions. It’s not about showing up with answers. It’s about being ready to listen for the right signals.
2. Sharpening Discovery Questions with Scenario-Based Prompts
We use AI to help reframe generic discovery questions into context-rich ones. For example:
➡ “How would you rephrase ‘What’s your current process?’ to make it resonate with a VP of Operations in manufacturing?”
This practice helps our SEs tailor their approach, and it’s made our discovery conversations more efficient and engaging.
3. Post-Call Debriefs with a Focus on Gaps
After discovery, reps will ask AI to review transcripts (we use internal call recording tools) and prompt it with:
➡ “What business pain points were mentioned, and what questions could we have asked to go deeper?”
This has been especially helpful for newer team members who are still learning how to listen between the lines.
4. Building Reusable “Pain Libraries” by Persona
We’re using AI to help us build internal libraries of common pain points by persona: CIOs, Heads of RevOps, Security leaders, and so on.
Not only does this help in prep, but it also gives SEs a better vocabulary to draw from when summarizing impact.
5. Coaching Curiosity, Not Just Tools
The tech is helpful, but the real unlock has been coaching the mindset around it. We talk often about the difference between checking a box (“Did I ask about pain?”) versus being genuinely curious (“What’s happening behind that challenge?”). AI is there to support that curiosity and not replace it.
We’re still learning, and we’re careful not to let AI replace the art of selling. But when used the right way, it’s a multiplier. And in this environment, anything that helps us get to the “why now?” faster is worth exploring.
Curious to hear how other teams are weaving AI into their discovery motion and hearing what works for you.
