Episode 138
Why Judgment Still Wins in an AI-Assisted Sales World
Faster tools, smarter research, and why the rep who brings real value still wins
Episode summary
Hunter Starr, CPMR, is President of Performance Technical Sales, a manufacturers rep firm serving OEM engineers and purchasing teams across the Carolinas and the Southeast. He works at the intersection of engineering and business, helping customers navigate component selection across industrial, off-highway, defense, medical, and agricultural markets.
In this episode, Hunter talks about how access to engineers has shifted, why the old shotgun approach to outreach no longer works, and what it actually takes to get through to customers in a noisier, more digitally mediated world. The conversation explores how AI is changing the front end of the rep workflow — from part cross-referencing to marketing messages — while the real differentiator remains unchanged.
Hunter shares how his team at Performance Technical Sales is using ChatGPT to speed up research, build industry-specific messaging templates, and evaluate tools like Copilot for deeper integration. He also gets specific about what happens after the AI does its part: the human review, the relationship follow-through, and the judgment calls that determine whether an opportunity moves forward.
This episode is for reps, distributors, FAEs, and sales leaders in electronics and manufacturing who want to understand how to adopt AI practically without losing what actually builds business.
You Will Learn
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- How the rep role has changed over the last decade and what it takes to adapt
- Why AI is useful for part cross-referencing and how to constrain it to get better results
- How to build industry-specific messaging templates your whole team can use without reinventing the wheel
- Why follow-through and persistence still outperform speed in technical sales
- How manufacturers are building AI tools that give reps and customers faster access to accurate product data
- Why you don’t need to have every answer in the room — and what to do instead
- How continuous learning through ERA, CPMR, and peer networks keeps reps sharp
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Key takeaways
Adapt to how your customer wants to communicate
Getting face time with engineers is harder than it was five years ago. Hybrid schedules, locked lobbies, and inbox overload mean the old walk-in approach works only some of the time. Hunter’s team has learned to read each customer and meet them on their terms, whether that’s in person, virtual, or via a well-timed email that actually says something useful.
“There’s not a one-size-fits-all answer for every customer. It really just requires you to have an understanding of your customer, their preferences, and kind of being a chameleon to meet each individual customer where they like.”
AI speeds up the front end — judgment handles the rest
Cross-referencing parts used to take 10 to 15 minutes per item. With AI, Hunter’s team can get to the right product series faster, especially when they constrain ChatGPT to manufacturer-specific datasheets instead of the open internet. But the output still gets reviewed by a human before it goes to a customer. The tool gets you 75 to 90 percent of the way there. The rep closes the gap.
“It’s not a perfect solution, but isolating our manufacturers’ publicly available data into a defined channel within ChatGPT has provided better results. Is it perfect? Absolutely not. But it’ll get us 75, 90% of the way there.”
Templates let your whole team move faster without losing quality
Hunter’s team maintains a library of industry-specific messaging templates covering industrial, off-highway, defense, and other verticals. When one rep develops a strong message for a particular customer type, the whole team benefits. Nobody reinvents the wheel. The message gets tailored at the edges, not rebuilt from scratch every time.
Persistence is still the most underrated skill in technical sales
For every seven or eight people who say no, one or two say yes. Hunter is direct about this: you have to be okay with rejection and keep moving. The rep who follows through consistently, gets back with answers quickly, and shows up again after a no is the one who builds real pipeline over time..
You don’t need the title to lead
One of the sharpest moments in the conversation comes when Sannah asks Hunter how you know if you’re a good leader. His answer: your team comes to you with honest feedback. If the people around you trust you enough to bring you problems, challenges, and hard questions, that’s the signal. The open Monday morning meeting at Performance Technical Sales, where the team shares what went well and what didn’t, is a direct expression of that culture.
“Being able to have your team comfortable enough to come to you with honest feedback — that’s critical. It shows they trust you and want you to help them grow.”
The industry ecosystem is a competitive advantage, so use it
Hunter is active in the ERA Next Gen group, completed the CPMR program, and regularly collaborates with other reps in his market. He describes the rep community as unusually collegial compared to other industries he has worked in. The shared health of the rep model means that reps who invest in each other and in the industry’s professional networks grow faster than those who go it alone.
Why this matters
The rep role is not disappearing, but it is changing faster than most firms are adapting. AI tools are compressing the research and outreach workflow in ways that used to take hours. Reps who learn to use those tools well will cover more ground with the same effort. But the tools are only as good as the judgment behind them.
Hunter’s conversation makes a point that gets lost in a lot of AI discussions: speed creates opportunity, but it doesn’t close it. A cross-reference that comes back in two minutes still has to be verified. A marketing email that gets drafted in thirty seconds still has to say something real to get opened. The reps who understand this will use AI to amplify their effectiveness. The ones who don’t will use it to automate their way to irrelevance.
For the manufacturing rep community specifically, this moment is less about technology adoption and more about professional identity. The best reps have always been the ones who show up prepared, follow through without being asked, and earn trust over time. AI changes the tools. It does not change what the customer actually needs from the relationship.
“Speed alone doesn’t create value. Judgment does. The rep who brings both will be the one customers call back.”
Episode highlights
Why the shotgun approach no longer works the way it used to
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- Walking into an office without an appointment used to be a reliable way to get in front of someone. Today, many facilities don’t have front desk reception, and some won’t open the lobby at all. Hunter describes this shift plainly: the tactics that worked a decade ago still work sometimes, but you can’t build a pipeline on them. Knowing your customer well enough to reach them the way they actually want to be reached is the new baseline.
How constraining AI to curated data improves cross-reference accuracy
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- Rather than letting ChatGPT search the open internet for part matches, Hunter’s team feeds it manufacturer-specific datasheets within a defined channel. The output is more reliable, more relevant, and easier for the team to verify quickly. It is not a perfect system, and it requires manual updates when new products come out, but it represents a practical Phase 1 of AI integration that is already saving meaningful time.
Why Hiring for Attitude and Aptitude Outperforms Hiring for Experience
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- One of Chris’s early mentors gave him a hiring principle he has carried ever since: look for attitude and aptitude first, not deep experience. A motivated, creative thinker with the right instincts will often outperform a highly experienced hire who lacks the drive or the mindset. Chris applies this filter across geographies and org sizes, and it consistently produces teams that think more clearly and move more effectively.
What manufacturers are building that will change how reps work
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- Several manufacturers on Hunter’s line card are developing AI agents that go beyond basic chatbots, giving customers direct access to open order reports, backlog data, and more granular product information in real time. Others are building FAE-level AI tools for product selection behind the scenes. Reps who understand what their manufacturers are building will be better positioned to leverage those tools in the field.
Why follow-through is the rep’s most durable competitive advantage
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- Hunter puts it simply: the rep role is not rocket science. It comes down to doing what you said you would do, getting back to people quickly, and being consistent. In an environment where customers are being contacted by more and more people using more and more automation, the rep who follows through personally stands out quickly.
How open Monday meetings build team trust and culture
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- Every Monday, Hunter’s team meets to share what went well last week and what did not. No filters, no politics, full disclosure across the board. Hunter calls it “open kimono.” The result is a team that is willing to bring problems to leadership early, collaborate on solutions, and hold each other accountable without it feeling adversarial.
Practical Tip
Constrain your AI input to get better output
If you are using ChatGPT or a similar tool for part cross-referencing or product research, do not let it search the open internet freely. Instead, upload your manufacturers’ publicly available datasheets into a defined project or custom channel, and instruct the tool to pull only from that source.
- Gather datasheets for your core product lines and load them into a ChatGPT project
- Set the instruction: only reference these documents, not external sources
- Review every output before it goes to a customer, treat it as a first draft, not a final answer
- Update the source documents when manufacturers release new products or revisions
The result is faster, more relevant output that your team can actually trust — and a workflow that makes the human review step faster too.
About the guest
Hunter Starr, CPMR
President, Performance Technical Sales
Hunter Starr is President of Performance Technical Sales, a manufacturers rep firm serving OEM engineers and purchasing teams across Eastern North and South Carolina.
With nearly a decade in manufacturer representation, Hunter works at the intersection of engineering and business, supporting applications in industrial, off-highway, defense, agriculture, and appliance markets.
His focus is on practical, proven technologies including user interfaces, power solutions, sensing, circuit protection, connectivity, and manufacturing services that reduce risk and improve long-term product performance.
Hunter is a Certified Professional Manufacturers’ Representative (CPMR), was recognized as a Rising Star by the Electronic Representatives Association, and is known for a straightforward, service-driven approach built on trust, responsiveness, and technical understanding.
Who this episode is for
This episode is built for:
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- Manufacturers reps and rep firm owners navigating AI adoption and changing customer access
- Field application engineers and technical sales professionals in electronics and component distribution
- Sales leaders in manufacturing and distribution managing teams through a shifting landscape
- Product marketers and GTM leaders who work with rep channels and need to understand how reps operate today
- Early-career reps looking for a practical framework for persistence, follow-through, and professional development
- Engineering and operations leaders who want to understand what the best rep partners are building
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What you will be able to do after listening
After listening to Episode 138, you will be able to:
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- Understand how to adapt your outreach approach to match how each customer actually wants to communicate
- Use AI tools for part cross-referencing with a practical method for improving accuracy
- Build industry-specific messaging templates your team can share and scale
- Recognize the difference between using AI to amplify judgment and using it to replace it
- Apply a follow-through discipline that builds trust and pipeline over time
- Evaluate professional development resources in the ERA and ECIA ecosystem
- Identify what good team leadership looks like without relying on a formal title
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