Most companies are getting messaging wrong before they even write a single word. They jump straight to creating copy without understanding the difference between positioning and messaging, lead with features instead of customer pain points, and treat AI as a magic solution rather than a research amplifier.
The real power of AI in copywriting isn't replacing human insight—it's scaling it. When done right, AI can help companies conduct deeper customer research, create more accurate personas, and test messaging at speeds impossible with traditional methods. But only if they start with the foundation that 70% of effective copywriting is actually research.
Here's a framework for using AI to turn customer insights into copy that actually converts, without losing the human touch that makes messaging resonate.
The first mistake most companies make is confusing positioning with messaging. As conversion copywriter Chris explains, positioning is understanding "what you do, who you do it for, and how you do it better, uniquely, or in a different way." Messaging, on the other hand, is how you communicate all of that across different touchpoints—your pitch, strategic narrative, value propositions, and brand voice.
Most companies skip the positioning work entirely and jump straight to creating creative assets. They treat messaging as the actual deliverables instead of the underlying architecture that should drive every marketing initiative. Without this foundation, companies end up with inconsistent messages that confuse rather than convert.
Even worse, companies that do attempt messaging often lead with features instead of matching customer intent. They land visitors on pages that immediately start talking about the company rather than addressing what brought the visitor there in the first place.
"As soon as someone lands on your page and the first thing they do is start talking about you yourself rather than themselves, they immediately experience some kind of friction," Chris notes. This friction shows up as higher bounce rates, lower engagement, and skeptical prospects who struggle to see themselves in the messaging.
The solution isn't complex: start by matching visitor motivation, intent, and awareness level. When the first thing people read speaks directly to their pain points and desired outcomes, the decision to keep reading becomes automatic.
Another common mistake is creating separate messages for every possible audience. Companies end up with messaging for accountants, HR professionals, mortgage loan officers, and dozens of other segments without any unifying thread.
The problem isn't having multiple audience-specific pages—those are useful for matching search intent. The problem is missing the overarching strategic narrative that ties everything together. This narrative should reflect the unique insight that led to building the product in the first place, the market problem only the company has identified, or the customer mistakes only they've seen.
With this strategic narrative as the foundation, companies can adapt pain points and benefits for specific personas while maintaining a consistent point of view across all touchpoints.
Smart competitive research isn't about copying what others are doing—it's about deconstructing their approach to find gaps and differentiation opportunities. A useful framework divides competitor analysis into four sections: motivation (matching user outcomes and pain points), value (features and benefits), proof (backing up claims), and anxiety (social proof and friction reduction).
This analysis reveals what level of customer awareness competitors are targeting and where opportunities exist. Are they focusing on problem-aware customers while leaving solution-aware prospects underserved? Do their reviews reveal features customers consistently complain about?
The goal is understanding the message landscape prospects encounter when researching solutions, not to copy it but to stand out within it.
Customer interviews remain the gold standard for gathering authentic language, but they're not the only option. Email surveys with open-ended questions often generate surprisingly detailed responses, especially when customers have strong product-market fit. Support chat transcripts, sales call recordings, and review sites like G2 and Capterra provide additional sources of customer language.
The key is building a searchable database of customer language. As Chris explains: "Maybe I'm writing copy and I'm kind of debating, should I use this word or that other word? Then I jump into my voice of customer bank and I literally do a Command+F search and I see what kind of words did they use for this specific use case."
Sometimes entire phrases can be lifted directly from customer feedback. Other times, it's about choosing between two similar words based on which one customers actually use. Either way, the voice-of-customer research provides the raw material for authentic messaging.
One of the most striking examples of voice-of-customer research in action involved a B2B SaaS company selling software to portable toilet and septic container management companies. The challenge was unique: selling software to blue-collar business owners in their 50s and 60s who "hated software" and rarely used anything beyond spreadsheets.
Instead of traditional feature-focused messaging, the company conducted customer interviews and built their "how it works" page like a diary of a typical workday. The copy walked prospects through their daily routine: "When you get to work, this is the first thing that you do with the software. You jump in, you log in your route for the container... And then your driver takes on the software and then this is what happens."
By using the specific language customers used and reflecting their actual workflow, the company increased conversions by 20% across the site. The success came from meeting customers where they were instead of forcing them to learn new terminology or processes.
The PATH framework starts with comprehensive research across three areas. Internal research includes team insights, product knowledge, and support chat transcripts. External research covers current customers, prospects, and importantly, non-buyers who can explain why they chose alternatives. Market research examines competitors and their customer reviews.
This preparation phase creates the foundation for everything that follows. Without solid human research, AI personas become sophisticated guesswork rather than accurate customer representations.
The articulation phase feeds all research into AI personas using platforms with large context windows. The key is creating separate persona chats that "never break character" and include "thoughts" tags that reveal internal motivations before expressing what personas would actually say.
For example, a persona might think: "I'm worried this software will be too complex for my team to learn" before saying: "We need something simple that doesn't require training." These internal thoughts help copywriters understand the emotional drivers behind surface-level objections.
The testing phase runs different messaging scenarios with AI personas, probes for objections, and simulates responses before launching campaigns. As Chris describes it: "It's like having a conversation with any of your customers. You can ask them any questions."
This capability allows teams to test headlines, email sequences, and value propositions at scale. They can explore edge cases, probe for emotional triggers, and identify potential objections before real customers encounter them.
The final phase combines AI findings with real-world testing. If AI personas respond strongly to specific messaging angles, those get tested in sales conversations and marketing campaigns. The results feed back into the system, creating a continuous research flywheel that gets more accurate over time.
This approach turns AI from a one-time tool into an ongoing research system that scales human insight rather than replacing it.
Synthetic research works best for early-stage companies with limited customer access. Even 60-70% accuracy is valuable when the alternative is no customer insights at all. It's also useful for scaling small datasets—turning five customer interviews into 50 detailed personas—or expanding research into questions that weren't asked in original interviews.
The most interesting application is continuous background research. AI personas can monitor market trends, test new messaging angles, and even pre-test ad creative before launch. Platforms like Synthetic Users and Ask Rally are building APIs that could automate these processes entirely.
Synthetic research fails when used as a replacement for customer understanding rather than an amplifier. Companies with extensive customer data and research capabilities likely don't need synthetic personas. More importantly, synthetic research should never be the only research—it works best as a complement to real human insights.
The platforms that do synthetic research well address bias, coordinate persona distribution across roles, and build algorithms that produce realistic rather than overly positive responses.
The copywriters at risk are those who rely on formulas and templates. AI can easily replicate paint-by-numbers approaches to writing. But copywriters who understand that copy comes from research, strategy, and deep customer empathy have a different future ahead.
As Chris puts it: "If you have a strategic vision, so if you know that the copy comes from the research work, then there's the strategy in between, and then you can't really write any word without all of those foundations, then I would say you can still be the effective copywriter using AI."
The role shifts from writer to architect—orchestrating AI systems, knowing when to be the human in the loop, and maintaining the intuition for what resonates with real customers.
Many copywriting critics haven't actually experimented with advanced AI tools beyond free ChatGPT. They're making judgments based on limited experience with basic applications rather than the sophisticated research and persona systems now possible.
The future belongs to copywriters who embrace AI as a research amplifier while maintaining the human skills of empathy, strategic thinking, and taste that determine whether copy actually converts.
AI's real power in copywriting isn't replacing human insight—it's scaling it. The companies that win will be those that use AI to get closer to customers, not further away. They'll conduct more research, create more accurate personas, and test more messaging variations while maintaining the human touch that makes copy resonate.
The framework is straightforward: start with research, not technology. Build comprehensive customer understanding through interviews, surveys, and voice-of-customer analysis. Use AI to scale that understanding into detailed personas and systematic testing. Then maintain the human skills of strategic thinking and emotional intelligence that turn insights into copy that converts.
The future of copywriting isn't human versus AI—it's humans using AI to become better researchers, strategists, and customer advocates.
"If you rely on formulas, templates, when you're writing copy, then probably AI can replace you. But the thing that it can't really replace you now... if you have a strategic vision, so if you know that the copy comes from the research work, then there's the strategy in between." - Chris Silvestri
0:45 - Positioning vs messaging mistakes
4:12 - Avoiding "what we do" friction
14:29 - Research is 70% of the work
26:28 - AI synthetic research tools
33:34 - PATH framework explained
35:52 - AI won't replace strategic copywriters
Get a free scorecard to assess your messaging fit on Chris’ website here: conversionalchemy.net
Connect with Chris on LinkedIn here: https://www.linkedin.com/in/christophersilvestri
Connect with Paxton on LinkedIn: https://www.linkedin.com/in/paxtongray/
AI platforms Chris uses for audience research:
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Chris is the Conversion Alchemist. A SaaS message-market fit specialist and conversion copywriter, he worked 10 years as a software engineer in industrial automation. Then, took a sharp turn to enter the digital marketing world as UX lead at the usability testing startup Conversion Crimes (and previously at the conversion design agency Zeda Labs). Chris has been working as a messaging strategist and copywriter for B2B SaaS brands like Moz since 2016.
