The Most Underrated Skill in AI Product Marketing: Technical Translation
As AI products become more sophisticated, I think one of the most undervalued skills in product marketing is becoming increasingly important:
Technical translation.
Not technical expertise in the sense of writing machine learning models or building infrastructure. But the ability to understand complex technical concepts well enough to translate them into language that different audiences can actually use.
Because right now, many organizations are struggling with a communication gap.
Engineering teams often speak in terms of architecture, infrastructure, APIs, latency, orchestration layers, retrieval systems, or model performance.
Meanwhile, enterprise buyers are thinking about:
workflow impact
operational risk
adoption
governance
integration
ROI
implementation complexity
Those are very different conversations. And increasingly, strong product marketing sits in the middle.
Complexity Is Increasing Across the Entire Stack
One thing that makes AI product marketing especially interesting right now is that the technology stack itself is becoming more layered and interconnected.
A few years ago, many AI conversations centered almost entirely around the model.
Today, enterprise AI involves much broader systems:
model providers
orchestration layers
vector databases
retrieval systems
security frameworks
workflow integrations
governance tools
copilots
agents
enterprise infrastructure
That complexity creates communication challenges. Not because enterprise buyers are incapable of understanding technical products. But because different stakeholders need different levels of abstraction.
A developer evaluating APIs needs a very different conversation than a CFO evaluating organizational risk. A customer success leader cares about different outcomes than a security team. One of the hardest parts of product marketing is helping those conversations remain aligned.
Translation Is Not Simplification
I think there’s sometimes a misconception that technical translation means “dumbing things down.”
It doesn’t.
Good technical translation preserves complexity where complexity matters. The goal isn’t to remove nuance. The goal is to create clarity. That’s a very different thing.
Strong product marketers understand:
which details matter to which audiences
where technical precision matters
where abstraction is more helpful
how to preserve credibility while improving understanding
This becomes especially important in enterprise AI because trust matters. If messaging feels overly simplified or overly hyped, sophisticated buyers lose confidence quickly. At the same time, if messaging becomes too technical, organizations struggle to connect product capability to operational value. Technical translation helps bridge that gap.
Enterprise Adoption Depends on Shared Understanding
One reason I think technical translation matters so much is that enterprise adoption rarely happens inside a single department.
AI products increasingly affect:
engineering
operations
security
procurement
legal
customer support
sales
executive leadership
Each group evaluates products through different lenses. If organizations cannot build a shared understanding of:
what the product does
how it works
where it fits
where risks exist
how workflows change
…adoption slows down.
This is one reason why product marketing is becoming more strategically important in technical organizations. The role is no longer just about launches or messaging frameworks. Increasingly, it’s about organizational alignment.
AI Messaging Is Still Often Too Abstract
One pattern I am seeing in AI marketing is messaging that stays at an extremely high level.
Phrases like:
“unlock productivity”
“transform your business”
“AI-powered innovation”
“revolutionary workflows”
…sound impressive, and may generate some awareness and interest at the top of the funnel. But i quickly needs to go deeper. This high level messaging doesn’t help buyers understand operational reality. Enterprise buyers want specificity. They want to know:
what changes inside workflows
how employees interact with the system
where human review exists
how outputs are validated
how integration works
what governance controls exist
how onboarding happens
That requires more thoughtful communication. And in many cases, product marketers become the people responsible for connecting technical capability to practical organizational impact.
Translation Also Requires Listening
One thing I’ve learned over the years is that good product marketing is not just about explaining. It’s also about listening.
Strong technical translation requires understanding:
what customers are confused about
what terminology creates friction
where expectations diverge
what concerns remain unresolved
how different audiences interpret the same product differently
That kind of listening becomes increasingly important as products become more complex. Especially in the adoption of AI.
Because the technology itself is evolving so quickly that many organizations are still developing their own internal language around adoption.
Final Thoughts
As enterprise AI matures, I think technical translation will become one of the defining skills of strong product marketing organizations. Not because product marketers need to become engineers. But because modern enterprise technology requires people who can:
move across technical and business conversations
preserve credibility with multiple audiences
reduce ambiguity
support adoption
create organizational alignment
The companies that communicate complex technology clearly will likely have a major advantage. Especially as products become more sophisticated and workflows become more interconnected.
Because in the end, even powerful technology struggles when organizations don’t fully understand how to operationalize it.