What the Words in Your AI Strategy Actually Reveal
Adopting AI
Every organisation rushing to adopt AI is producing documents, strategies, ethical frameworks, governance policies, stakeholder communications. What almost none of them are doing is reading those documents forensically.
The language an organisation uses around AI reveals far more than its intentions. It reveals its assumptions, its blind spots, and, most critically, its exposure.
The problem with AI language
AI documentation is a breeding ground for three things I look for in every document: trigger words, ambiguity, and sentence structure designed to obscure rather than clarify.
Consider the phrase “algorithmic bias will be minimized.” Minimized by whom? By what standard? By when? That single word – minimized – sounds responsible. Forensically, it commits to nothing. It is the kind of language that reads well in a press release and collapses under legal scrutiny.
Or “our AI systems will operate transparently.” Transparently to whom? Customers? Regulators? Investors? Each audience has a different definition of transparency, and a document that doesn’t specify is a document that protects no one.
This is not accidental. Much AI language is engineered to sound accountable while remaining uncommitted. The problem is that regulators, courts, and increasingly, the public, are getting better at reading it.
What language forensics finds in AI documents
When I examine an AI strategy, ethical framework, or governance policy, I look for:
Trigger words: terms like “responsible,” “ethical,” “transparent,” and “fair” that carry significant weight but zero legal or operational definition. Every one of these is a liability waiting to be tested.
Structural ambiguity: sentences constructed so that accountability is grammatically unclear. “Decisions made by the system will be reviewed” tells you nothing about who reviews them, how often, or what happens when a review fails.
Passive voice as a deflection tool: “mistakes were made,” “errors were identified,” “the system produced an unexpected output.” Passive constructions are the grammatical equivalent of looking away. In AI governance documents, they are a red flag.
Who this matters for
If your organisation is implementing AI, you are producing language about it, to your board, your employees, your customers, and your regulators. That language is a legal and reputational document whether you treat it as one or not.
Scale-ups writing their first AI policy. Enterprises managing AI rollout across business units. Organisations in regulated industries where AI decisions carry heightened scrutiny. All of them are producing documents that will be read, eventually, by someone with an adversarial eye.
Better that eye is mine first.
The window is narrowing
Regulatory frameworks around AI are hardening. The language that passed unchallenged two years ago is being tested in courts and hearings today. Organisations that built their AI governance on vague, well-intentioned language are discovering that good intentions are not a legal defence.
A forensic review of your AI documentation now costs a fraction of what imprecise language costs later.
Interested in a forensic review of your documentation?
Dissectra 