PR Teams Use AI to Draft Faster, But Speed Doesn't Equal Productivity
Seventy-seven percent of PR professionals now use AI to draft pitches, build media lists and summarize coverage, according to Muck Rack's State of PR 2025 report. The tools work fast-generating full drafts in seconds. But the speed of generation masks a harder truth: most of that output requires substantial work before it's usable.
This gap between what AI produces and what's actually publishable explains why companies across industries report no noticeable productivity gains despite widespread AI adoption. A National Bureau of Economic Research survey found more than 80% of companies saw no meaningful improvement.
In PR, the problem comes down to what gets measured.
The Correction Cost No One Tracks
Most evaluations of AI implementations measure generation speed. In PR, that means timing how quickly AI produces a draft or assembles a media list. What rarely gets measured is the correction cost: the time required to make the output actually usable.
Take a pitch. AI can produce a serviceable first draft in under three minutes with proper prompting. But then it needs editing for tone, checking against the reporter's beat and revision. It also needs verification-AI tools fabricate details regularly, so fact-checking claims, confirming sources and verifying references are non-negotiable steps.
That review typically adds 10 to 15 minutes, sometimes more. The total time saved shrinks considerably once you account for this work.
The real equation looks like this:
Net Productivity Gain = Time Saved on Generation - Correction Cost
Most teams track only the left side of that equation. That's why perceived efficiency and actual gains diverge so significantly.
A Test Your Team Can Run Today
Pick a task your team already performs with AI-drafting a pitch, building a media list or preparing a briefing note.
Time how long it takes to get the first output. Then time the correction phase: every edit, fact-check and revision performed before the output is usable. Finally, compare the total with how long the task takes without AI tools.
The pattern is consistent. A pitch that took 25 minutes to produce manually might now take 20 minutes total. Media research that took 45 minutes might drop to 30. Those are real time savings, but far more modest than the "AI wrote this in seconds" narrative suggests.
When AI output requires significant revision-say, pitches to journalists covering niche beats-the correction cost can nearly erase the time gained entirely.
Where AI Actually Delivers Productivity
Calculating the correction cost reveals which AI applications genuinely reduce workload. The most productive uses involve inputs rather than outputs.
Examples include identifying journalists covering a specific narrative, uncovering new storylines before they gain traction, or building background research to inform a pitch. These applications reduce revision burden because better research produces fewer drafts.
When AI improves inputs, the correction cost on outputs shrinks. This is where productivity gains materialize.
Measure Outcomes, Not Activity
The productivity conversation around AI has stalled because teams are tracking the wrong metrics. Speed of generation is a measure of activity. What communications leaders actually need are outcome metrics: How much did correction time decrease? Did journalist response rates improve as inputs were refined with AI? Are time savings being redirected to strategic work?
Most PR teams already have the data to answer these questions. They just need a different perspective on what to measure.
For more on AI for PR & Communications, or to understand how to measure AI Productivity Training, explore targeted resources for your role.
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