What communicators get wrong about AI-assisted measurement
“The best tool in everyone’s tech stack is critical thinking.”
Most communicators using GenAI tools understand the “garbage in, garbage out” philosophy of prompting: if you feed AI inputs that are not engineered with parameters like role, context, constraints, audience and message format, you’re going to get weaker outputs that are statistically safe but often inaccurate.
According to AMEC Managing Director Johna Burke, the same logic holds true with measurement.
In the latest episode of Ragan’s “Signature Voices” series, Burke challenges communicators to consider the whole AI “meal” before asking the tool to measure in the first place.
“The tool always has to follow the decision,” she begins. “Are you diagnosing reputation risk, are you validating message credibility, or are you demonstrating impact on stakeholder behavior?”
“AI tools add speed and pattern detection, but they don’t determine relevance or strategic importance, and that’s where organizations fall behind. The best tool in everyone’s tech stack is critical thinking.”
In the full episode, Burke shares tips for prioritizing data quality at the beginning to build a baseline of transparency, bias control, consistency and clarity.
Watch it below.
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