The conversation around AI and marketing has been dominated by one narrative: speed. Create more content faster. Publish at scale. Outpace the competition through sheer volume. But this misses something more fundamental—and more valuable—that AI can do.
The real power isn’t in making more content. It’s in making complex ideas comprehensible.
Every marketer working in technical fields, regulated industries, or abstract service businesses faces the same challenge: bridging the gap between how experts talk about what they do and how customers understand problems they’re trying to solve. This translation problem has always been the hard part of marketing, and it’s where AI offers something genuinely useful beyond just efficiency gains.
The Translation Problem in Marketing
Consider the common scenario: a software company builds a powerful tool that solves real problems, but their marketing copy is incomprehensible to anyone who isn’t already immersed in the technical ecosystem. Or a financial services firm offers sophisticated wealth management strategies, but their website sounds like it was written by lawyers for lawyers. Or a healthcare company develops an innovative treatment, but patients can’t figure out whether it’s relevant to their specific situation.
These aren’t failures of expertise. The people building these products and services understand them deeply. The problem is that expert knowledge doesn’t automatically translate into customer-facing communication. What’s obvious to someone who spent years developing a solution is often opaque to someone encountering it for the first time.
Traditional marketing approaches this through multiple rounds of revision. A technical expert writes something. A marketer edits it for clarity. Stakeholders review it. Legal checks it. The message gets diluted, genericized, or both. Six months later, the website still doesn’t clearly explain what the company actually does.
AI changes the dynamics of this translation process in meaningful ways.
From Jargon to Clarity Without Losing Accuracy
One of the trickiest aspects of marketing translation is maintaining technical accuracy while removing unnecessary complexity. Oversimplify and the message becomes misleading or sounds condescending to informed buyers. Stay too technical and most potential customers bounce without understanding whether the offering is relevant to them.
AI can navigate this tension more effectively than traditional editing processes because it can process both the technical source material and vast amounts of customer-facing language simultaneously. It recognizes patterns in how concepts get explained successfully across different contexts and can suggest ways to bridge expert language and customer language without distorting meaning.
Take something like platforms using Blaze for ecommerce websites, where the technical capabilities might involve API integrations, dynamic content generation, and marketing automation workflows. An engineer or product manager might naturally describe these features in technical terms because that’s the precise language for what the system does. But a small business owner considering the platform needs to understand it as “automatically creating product descriptions that match your brand voice” or “sending personalized emails based on what customers actually buy.”
AI can help map the technical reality to the customer benefit without requiring the marketer to become a technical expert or the engineer to become a copywriter. It serves as an interface layer between these different ways of understanding the same thing.
Regulated Industries and the Precision Problem
Financial services, healthcare, legal services, and other regulated industries face an additional translation challenge: everything they say must be both understandable and defensible under regulatory scrutiny. This creates paralysis. Marketing teams become so afraid of saying something wrong that they end up saying almost nothing useful.
The typical result is content that’s technically compliant but practically useless. Disclaimers overwhelm substance. Language becomes so hedged that readers can’t determine what’s actually being offered. Website visitors leave more confused than when they arrived.
AI can help thread this needle by working within constraints. When given clear guidance about what claims can and cannot be made, what language is approved, and what disclaimers are required, it can generate variations that stay within bounds while still communicating meaningfully.
This doesn’t eliminate the need for compliance review—nothing should. But it dramatically expands the solution space. Instead of choosing between one overly cautious draft and one overly aggressive draft, marketers can explore dozens of variations that maintain compliance while testing different approaches to clarity.
The translation becomes: “How do we say this in a way that’s both legally sound and actually helpful to someone trying to make a decision?”
Abstract Services and the Tangibility Gap
Professional services firms—consultants, agencies, advisors—face perhaps the hardest translation challenge of all: making abstract value concrete. When the deliverable is insight, strategy, or expertise rather than a physical product, how do you help potential clients understand what they’re actually getting?
The natural tendency is to fall back on process descriptions (“our methodology involves five phases”) or vague outcome promises (“we help you achieve breakthrough results”). Neither gives prospects enough concrete information to evaluate fit or value.
AI can help translate abstract services into concrete scenarios and specific outcomes by drawing from patterns across case studies, client testimonials, and problem descriptions. It can take a general service description like “strategic advisory” and help translate it into language like “when you’re deciding whether to expand into a new market and need someone to pressure-test your assumptions with relevant data and experience from similar situations.”
This isn’t inventing fake specificity, it’s finding the language that connects what the service provider knows they do well with how potential clients experience the problems that led them to search for help.
Preserving Nuance While Removing Noise
One legitimate concern about using AI in marketing translation is whether it flattens important distinctions or removes nuance that matters to sophisticated buyers. This is a real risk if AI is used carelessly, but it’s not inherent to the technology.
The key is using AI as a collaborative tool rather than a replacement for judgment. The expert provides the nuance—the specific distinctions that matter, the contexts where general rules don’t apply, the edge cases that need acknowledgment. AI helps express that nuance more clearly than the expert might on their own while removing the jargon and insider references that create unnecessary barriers.
Think of it as having a skilled editor who can say, “I understand what you’re saying here, but someone unfamiliar with this field will interpret it differently. Here’s how to make your intended meaning clearer.” That editorial function has always been valuable. AI makes it more accessible and iterative.
Multiple Audiences, Multiple Translations
Many products and services need to speak to different audiences with different levels of expertise and different priorities. A cybersecurity solution might need to appeal to CIOs evaluating technical capabilities, security analysts who’ll implement it, and CFOs concerned about cost and compliance.
Creating separate marketing for each audience traditionally means tripling the workload or settling for one-size-fits-none messaging that fails to resonate with anyone. AI makes true audience-specific translation more feasible. The core information stays consistent, but the framing, emphasis, examples, and language adjust based on who’s reading.
This isn’t about being manipulative or saying different things to different people. It’s about recognizing that a technical differentiator that matters immensely to an implementer might be irrelevant to an executive evaluator, while a business outcome that drives executive decisions might seem too high-level for someone assessing technical fit.
The Human Role in Translation
None of this means AI can operate autonomously in marketing translation. The human role becomes more important, not less, but it does shift.
Instead of spending hours trying to convert technical documentation into marketing copy from scratch, marketers can focus on defining what good translation looks like for their specific context. What analogies resonate with their audience? What level of detail is appropriate? What trade-offs between precision and accessibility make sense for their buyers?
Instead of subject matter experts struggling to write content, they can review and refine AI-generated translations, confirming accuracy and adding the insights that only deep expertise provides.
The bottleneck shifts from “who has time to write all this?” to “how do we ensure what’s being said is both clear and correct?”, which is a more manageable problem with better outcomes.
Translation as Core Marketing Function
Reframing AI’s role from creator to translator changes how it fits into marketing strategy. The question stops being “how can we use AI to make more content?” and becomes “where are we currently failing to translate our value into language our customers understand?”
That’s a more focused, more strategic question. It leads to identifying specific gaps—the product page that technical users love but confuses everyone else, the service description that makes perfect sense internally but generates confused sales calls, the whitepaper that establishes expertise but drives no action because readers can’t connect it to their situation.
These translation failures have always existed. AI simply makes fixing them more feasible at the speed and scale modern marketing requires. The expertise was always there. Now there’s a better bridge between expert understanding and customer comprehension.
And that bridge—that translation capability—might be the most valuable marketing application of AI that’s emerging. Not because it’s flashy or revolutionary, but because it solves a problem that’s been fundamental to marketing communication since the beginning: making complex value clear.