“AI” is an ecosystem: research (new methods), applied product (features powered by models), enablement (data, platforms, MLOps), and governance (evaluation, safety, compliance). You don’t need a PhD to contribute, but you do need comfort with experimentation, data quality, and responsible deployment.
If you like building, consider ML engineering, AI product, or prototyping agents and RAG systems- if you enjoy discovery, data science, evaluation, and causal inference may fit- if your strengths are communication and systems, AI program management, policy, and risk are expanding. Your best entry wedge is a small, measurable proof: a retrieval-augmented bot that cuts support handle time, a simple classifier that reduces manual triage, or an evaluation harness that catches drift. \n\nAI is shifting from headline to infrastructure.
Foundation models will be accessible via APIs- value will concentrate in data stewardship, safety, domain expertise, and making models reliable in production. Expect more emphasis on monitoring (bias, robustness, latency, cost), privacy, and human-in-the-loop workflows. Practical skills: Python, SQL, notebooks, versioned experiments, prompt design with evaluation, and integration patterns (RAG, fine-tuning, tool use).
Career reality: there’s hype, but durable impact comes from pairing AI with a clear business problem and a feedback loop. Be honest about when not to use a model, document assumptions and guardrails, and you’ll build trust-and a resilient career-faster than those chasing demos without outcomes.