I read a recent blog post by Artyom (CEO and Co-founder of cube.dev) on how AI data engineers capture the industry’s collective ambition: to automate repetitive tasks and democratize data access. We wholeheartedly agree with this direction—but with a crucial caveat.

The current rush to deploy AI teammates risks repeating the mistakes of the “self-service BI” era: empowering business users at the cost of governance, consistency, and institutional knowledge. At Querio, we believe the missing link isn’t better AI—it’s systems that preserve and scale human understanding.

Here’s my take on where the AI data landscape is headed, and why context-first architecture like Querio’s will separate winners from hype cycles.

The AI Data Landscape Today: Automation vs. Understanding

Most AI data tools fall into two camps:

1. Task Automators (e.g., text-to-SQL, pipeline fixers) that speed up repetitive work but tread data as isolated tasks, ignoring business logic. 
   
   

2. Black Box “Solutions” (e.g., end-to-end analytics platforms) that democratize access for non-technical users, but have opaque logic that erodes trust from the data team and creates governance debt. 

The end result with either case is that data teams spend more time firefighting misinterpretations than enabling innovation, while business users get faster answers that lack accountability.

Our thesis is that context preservation unlocks responsible empowerment. We’re building tools that reject the false choice between automation and control. We focus on:

1. Making Business Users Self-Sufficient (Without Anarchy) with context-aware AI assistants.
   If a marketing manager asks, “Why did Q3 CAC spike?” The system explains the metric definition (including recent changes), Related factors (e.g., a broken attribution model in August), and Guardrails (“Uses Finance-approved spend data”).
   
   

2. Turning Data Teams into Architects, Not Help Desks.
   Engineers embed rules using our ontology into the system once and they auto-apply to all user/AI interactions (DRY).s.
   
   

3. AI as a Collaborative Partner, Not a Replacement.
   AI agents and humans operate on the same Querio context layer and share an understanding of metrics, lineage, and rules.

The Road Ahead: From Fragmented Tools to Context-Aware Ecosystems

The next evolution in data tooling won’t be about building smarter AI—it’ll be about building systems that make both humans and AI smarter. We envision a future where:

• Business users ask complex questions without accidentally violating compliance rules.
  
  

• Data teams define logic once, then focus on high-value problems instead of repeating themselves.
  
  

• AI agents handle tedious tasks within guardrails, while humans handle judgment and strategy.

In order to achieve this, it will require a paradigm shift. From rigid semantic layers that centralize metrics like Cube to adaptive context layers that capture how data connects to business outcomes. AI in data is coming, and it is an inflection point. Without context-aware systems AI democratization will create more chaos than value - data teams will drown in low-level work despite “automation”, and companies will lose years of hard-won data knowledge to turnover and tool sprawl. These are all fears we’re hearing from some of the largest data industry leaders like Robinhood and Brex to mid market leaders. 

We’re committed to a different path—one where AI amplifies human expertise instead of sidelining it.

Join Us in Building Context-First Data Ecosystem. If you’re reimagining how your organization balances agility with governance, or if you’re tired of choosing between empowering users and maintaining control, let’s talk.

The future of data isn't just automated - it's intentionally, responsibly human!