In the not-so-distant dystopia we call “modern work,” I recently discovered that two of my coworkers have been emailing each other exclusively via AI.

That is not an exaggeration. Every email — every follow-up, every recap, every passive-aggressive “just looping back on this” — has been generated by a chatbot. Neither of them has written a sentence in weeks. Their inboxes are essentially two large language models performing theater for no one.

Naturally, I did what any responsible employee would do: I fed their correspondence into a text-to-song model to see what it would sound like as a corporate power ballad.

The results are haunting, beige, and surprisingly catchy. Please enjoy: “Align on Deliverables (Q2 Remix).”



Lyrics:
(Spoken Voice) The following is a real email from work. This is what happens when 2 people exclusively use AI to talk to each other. Names have been changes for anonymity. We live in hell.

(Singing) Good evening Brent, Thank you for your previous email, which I’ve reviewed extensively through a proprietary ML-powered framework. The insights you’ve shared about leveraging KPIs for holistic ROI optimization in the healthcare analytics ecosystem resonate strongly with our internal methodologies.

To further align our cross-functional objectives, I propose an integrated approach leveraging advanced data pipelines and real-time insights dashboards (we’re calling it Project SYNERGY—Strategic Yield Nexus for Exponential Growth Yielding… yeah, still workshopping the acronym). This would include EHR-integrated GA4 configurations, enhanced HIPAA-compliant event tracking, and fully operationalized CTR-driven segmentation strategies across the patient UX lifecycle funnel.

Additionally, I believe implementing robust tag governance protocols in GTM while deploying an AI/ML-enabled attribution model (preferably last-touch-first-click dual hybrids for balance) will yield not just actionable data but transformative insights. Naturally, this requires us to standardize our ETL processes through an API-first framework, avoiding potential bottlenecks in our BI stack.

I’d also like to explore a multivariate A/B/C/D testing framework for PDPs (patient data points) to capture micro-conversions in post-discharge engagement cohorts. This can align with the NLP-driven taxonomy segmentation for better CDP (customer data platform) orchestration, which, as you’re aware, is critical in improving our omni-channel resonance within highly-regulated healthcare verticals.

Let me know your thoughts. I’ll refine our tactical approach after an AI-assisted SWOT analysis.

Best regards, Dr. Cuck