Why Most Companies Aren't AI Ready

Barr Moses—co-founder and CEO of Monte Carlo—thinks we’re headed for an AI reckoning. Companies are building fast, but most are still managing data like it’s 2015. In this episode, she shares high-stakes failure stories (like a $100M schema change), explains why full-stack observability is becoming essential, and breaks down how LLM agents are already transforming data debugging. From culture to tooling, this is a sharp look at what real AI readiness requires—and why so few teams have it.

🔧 Everyone’s building AI on shaky infrastructure.

100% of data leaders feel pressure to build with AI, but only ~1/3 believe their data is actually AI-ready.

💥 Small mistakes now have enterprise-scale consequences.

Examples like Unity’s $100M schema issue and Citibank’s $400M fine show that even minor failures can explode.

🔍 Observability is the foundation of trustworthy AI.

Data quality isn’t just about alerts — it’s about end-to-end visibility into data, code, systems, and model output.

🤖 Agents aren’t just for users — they’re for your data team.

Monte Carlo is building LLM agents that automate data triage and troubleshooting across upstream dependencies.

📉 Most orgs still manage data like it’s 2015.

Despite the GenAI hype, many teams rely on manual checks, dashboards, and “pairs of eyes” instead of scalable systems.

📊 The real moat isn’t the model — it’s your ability to trust the output.

With access to models increasingly commoditized, the differentiator is how well you manage the entire stack* that feeds and governs those models.

⚠️ Reliability isn’t just technical — it’s emotional.

Fire drills, Slack pings, and trust-destroying metrics still define the lived experience of many data teams.

🧱 AI readiness is a cultural transformation.

This isn’t just a tooling problem. It requires executive sponsorship, shared metrics, and org-wide accountability.

You can read the full transcript here.

00:00 The Evolution of Data and AI in Organizations

00:43 High Stakes of Data Quality Failures

01:18 Introduction to Bar Moses and Monte Carlo

02:08 The Growing Gap Between AI Ambitions and Data Readiness

03:59 Challenges in Data Quality and Observability

06:43 Real-World Examples of Data Failures

12:33 Strategies for Improving Data Management

18:07 The Future of Data and AI Integration

27:03 Fundamental Truths for Success

27:30 Exciting Applications of AI in Data Quality

27:46 Monitoring and Troubleshooting Agents

31:03 Challenges and Innovations in AI

34:33 The Future of AI and Data Observability

43:15 The Importance of Cloud Solutions

48:57 Final Thoughts and Takeaways