A #research #scientist, turned #salesman, turned #datascientist. Or more appropriately, I'm just a dude playing a dude disguised as another dude!
San Francisco, CA
Joined December 2013
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Opting out of AI in education sounds principled until you realize what happens in 2030 when graduates compete against peers who actually know how to work with these tools. The gap isn't neutral. It's professionally disqualifying. The real question is pedagogy, not presence. 📊
Use this link. bppe.consulting/blog/ai-read…
By 2027, 75% of hiring will test AI proficiency. The gap between adoption and actual capability keeps widening. Training and collaboration close it. Not hype, not speed. Real skill building. That's where the work is. 🎯
Nearly 80% of data teams spend half their time prepping data instead of finding insights. That's workflow friction, not a skills issue. AI is starting to solve this. Have you looked at where your analysts actually spend their days? 📊 Details in link.
Click the link. strategy.com/software/resear…
AI pilots stall in production not because models fail, but because the data behind them isn't ready. Fragmentation, poor quality, missing governance. You can have the best model in the world and still lose if your foundation isn't solid. 🚀
80% of data teams spend more than half their time on data prep, not insight generation. That's not a tools problem. It's a workflow problem. Most teams are still treating data as a pipeline instead of a strategic asset. Shift the process, shift the value.
Most organizations still think the solution to AI in their workflows is detection and restriction. Wrong approach. The real move is designing processes where AI without human judgment produces obviously inferior results. That's when you actually solve the problem.
78% of organizations deploying AI lack comprehensive bias detection frameworks. That gap shows in the data: 23% performance disparity across demographic groups. Skip the checkbox audits. Run structured bias tests on your datasets before they power decisions. Link in the reply.
Bias detection tools alone don't guarantee fair outcomes. You need diverse annotation teams and continuous human review paired with your technical frameworks. The best systems I've seen treat this as ongoing work, not a one, time audit.
Most institutions are still betting on AI detection tools to solve their assessment crisis. They're fighting the wrong battle. The real move is designing evaluations where the thinking process matters more than the polished output. That's where integrity actually lives. 🎯
Seat, based pricing is about to get a reality check. AI agents are shifting the entire value equation from how many users you have to what outcomes actually get delivered. Software vendors who don't adapt will find themselves competing on price alone. The winners are already r...
California's AI transparency law kicks in January 2026. High, impact models now require documented training data and safety testing. This isn't a compliance burden if you've already mapped your data lineage and validation protocols. Link in the reply.
Use this link. webpronews.com/global-ai-reg…
AI pilots stall before production not because models fail, but because data behind them isn't ready. Fragmented silos, missing metadata, weak governance. The problem isn't the technology, it's the foundation underneath. Build for resilience first. What's your data readiness lo...
Your content strategy just got obsolete. AI search engines don't care about keyword density anymore, they care about whether you actually understand what people are asking. If you're not optimizing for intent, you're invisible to LLMs. Time to rethink everything. 🔍
Your SEO ranking still matters for AI visibility, just in a different way. LLMs pull from top 10 search results 4x more often than lower, ranked pages. The game changed, but the fundamentals didn't. 📊 @diwasiuk Link below.
Check out the link below for more details. searchtides.com/how-llms-cho…
