Agents, MCPs and buzzword fatigue: what AI actually changes for analytics engineers

New AI tooling launches weekly. Terminology multiplies faster than the problems being solved. This peer exchange cuts through the noise with honest practitioner experience.

Tool overload - How many AI tools has your team adopted? How many have stuck? What is the cost of context switching?

What actually changed - Has AI genuinely improved your daily workflow, or did it just move the bottleneck somewhere else? When AI generates your dbt models, who owns the logic?

The skill shift  - If AI handles more SQL and boilerplate, what skills should analytics engineers invest in instead? Data modelling intuition, business context, communication, governance? What does career growth look like when entry-level tasks get automated? Has AI changed your recruitment process?

AI-ready data - Is this a meaningful goal your team is working toward, or a category invented to sell platforms? What does your organisation actually need to benefit from AI?

Bring your honest experiences. This is a hype-free zone.