A thought on titles and timelines

I keep getting connection requests on LinkedIn from "Healthcare AI Experts" with "10 years of AI experience" who were still in college when ChatGPT was launched.

Maybe I'm too humble. Maybe it's lingering impostor syndrome. But after 20+ years building enterprise integration systems, digital transformation platforms across logistics, manufacturing, retail, and finance. After working with neural networks before they were cool, shipping ML-powered products before we called it "AI," deploying real-time anomaly detection while OpenAI was still fundraising, I 𝘴𝘵𝘪𝘭𝘭 hesitate to call myself an AI or a digital transformation expert.

Because the field moves so fast. What I knew this morning is already dated.

Either I'm missing something fundamental about LinkedIn, or we've collectively decided that confidence matters more than experience, and job titles are aspirational rather than descriptive.

Here's what troubles me most: 𝗶𝗻 𝘁𝗶𝗺𝗲𝘀 𝗼𝗳 𝗲𝘅𝘁𝗿𝗲𝗺𝗲 𝘂𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆, 𝘄𝗲 𝗻𝗲𝗲𝗱 𝘁𝗵𝗲 "𝗲𝗹𝗱𝗲𝗿𝗹𝘆" 𝗺𝗼𝘀𝘁.

Call me biased (some might say I'm hallucinating like a typical LLM), but when technology is immature and projects are failing left and right, you don't need people who can recite buzzwords. You need people who survived Y2K and the panic of legacy system failures. Who watched the dot-com bubble burst and learned what happens when hype outpaces value. Who navigated the messy reality of SOA and ESB architectures when "integration" was the magic word. Who lived through the Big Data gold rush and saw Hadoop clusters gathering dust. Who survived cloud migration wars, microservices chaos, blockchain fever dreams, and the IoT/Industry 4.0 promise that's still half-delivered.

People who remember when "digital transformation" meant putting up a website, then mobile apps, then APIs, then... well, it keeps changing, doesn't it?

In healthcare especially, where AI projects face uniquely turbulent waters, experience and perception of time matter infinitely more than prompt engineering skills acquired last Tuesday.

Because here's the thing: healthcare AI projects aren't failing due to innovation issues. 𝗧𝗵𝗲𝘆'𝗿𝗲 𝗳𝗮𝗶𝗹𝗶𝗻𝗴 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗱𝗲𝗯𝘁, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗴𝗮𝗽𝘀, 𝗮𝗻𝗱 𝗰𝘂𝗹𝘁𝘂𝗿𝗮𝗹 𝗿𝗲𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲 𝘁𝗵𝗮𝘁 𝗴𝗼 𝗯𝗮𝗰𝗸 𝗱𝗲𝗰𝗮𝗱𝗲𝘀. The same interoperability nightmares, the same siloed data architectures, the same change management failures we've been wrestling with since HL7 was considered cutting-edge. AI doesn't fix those, it just makes them more expensive and visible.

The ability to say "I've seen this pattern before" or "here's where similar initiatives failed in 2008" or "slow down, we're repeating the same mistakes from the ERP era" or "this reminds me of when everyone thought RFID would solve everything", that's the expertise that actually prevents expensive failures.

Is it just me who finds this disorienting?