Vol. 34
AI as a Translation Layer
One of the more subtle shifts happening with AI has little to do with models getting smarter and more to do with how work moves inside organizations. Increasingly, AI is being used less as a decision-maker and more as a translator.
Across enterprises, AI is being applied to the work of translation that once sat between teams. It reshapes technical material for executive audiences, interprets regulatory language into operational terms, and helps turn strategy into task structure. The output is seldom final, but it is often sufficient to advance the work.
What stands out for me is where this work appears. It is less visible at the edges of organizations and more present in the spaces between functions, such as IT and the business, risk and product, or data teams and executives. In those spaces, AI is becoming organizational middleware, carrying meaning across silos without forcing structural change.
Translation has always carried a cost inside organizations, often hidden in meetings, reviews, and handoffs. As that cost declines, coordination changes, and work moves more quickly through reduced effort spent aligning on meaning.
Recent industry research reflects this pattern. Surveys of AI adoption suggest that much of the realized value comes from knowledge work support, including summarization, synthesis, explanation, and reframing. These applications tend to reduce friction rather than substitute for human judgment.
There is an organizational implication here that’s easy to miss. When translation becomes cheap, expertise starts to concentrate around framing rather than execution. Knowing how to ask the right question, how to set context, and how to interpret outputs becomes more valuable than producing raw artifacts. AI raises the floor on comprehension, but it also shifts where differentiation lives.
This also helps explain why AI often feels more impactful in informal or shadow use than in formal systems. People use it where translation pain already exists like before a meeting or to produce a draft, (i.e, long before anything hits a production workflow).
From this perspective, AI creates pathways that allow meaning and work to travel across organizational silos.
That may end up being one of its most durable enterprise contributions.
References https://lnkd.in/e5jc6VHq
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