Val Swisher Portfolio
Founder and CEO of Content Rules, which has applied natural language processing to client content for over 16 years, working with Google, Meta, Cisco, and Visa; publishes a framework distinguishing "content problems" AI can fix from "process problems" it cannot.
What makes it work
A breakdown of the choices that make this portfolio stand out.
A binary framework that tells you what NOT to use AI for
Splitting issues into "content problems" (AI can help) and "process/governance problems" (AI cannot) gives a decision rule that actively discourages misusing AI.
A specific tenure claim for a specific technology
Stating "16+ years" applying natural language processing situates her current AI work as a continuation of much older NLP practice, not a reaction to the recent generative-AI wave.
Naming enterprise clients across unrelated industries as proof of generality
Google, Meta, Cisco, and Visa span software, hardware/networking, and finance — using them together argues the content-versus-process distinction holds regardless of industry.
What AI Content Strategists can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Build your framework around what your approach explicitly does NOT recommend AI for, not just what it's good at.
If your AI-adjacent expertise predates the recent generative-AI wave, state the specific number of years and the specific older technology.
List named clients from clearly different industries together when arguing your framework generalizes.
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