COO & Co-Founder, Codd AI
Piet Loubser serves as Chief Operating Officer at Codd AI, Inc., where he is responsible for leading all aspects of sales and marketing. His professional career includes more than 30 years in data, analytics and AI, building and leading GTM teams at industry leaders such as Business Objects, Informatica, Hortonworks, SymphonyAI, Alation, Paxata and Privacera.
Piet Loubser on LinkedIn
Every new AI app quietly rebuilds the same business definitions and guardrails. How semantic governance debt builds up, and why CDOs need one shared foundation.
Anthropic's look at self-service analytics with Claude shows trusted AI analytics needs a governed semantic foundation. Should enterprises build it themselves?
Enterprises deploy copilots and AI agents before building the foundation needed to trust them. Why trust must be engineered into a certified semantic layer.
As AI moves from assisting analysts to doing analytical work itself, data team roles, boundaries, and governance must be redesigned. Here is what changes.
Governed data and semantics solve only part of AI analytics trust. As AI plans multi-step analysis, teams must capture, refine, and reuse the reasoning itself.
Customers want answers, not sensor data. How an EV provider uses a contextual semantic layer and natural language analytics to turn telemetry into value.
LLMs know the world, not your business. Why fine-tuning, RAG, and bigger prompts can't define business meaning, and why it belongs in a governed semantic layer.
AI makes building software easier than ever, yet enterprises keep buying AI apps. A practical framework for deciding which AI capabilities you should own.
Your definitions of revenue, bookings, and active customers live in databases, BI reports, documents, and people's heads. Why capturing them is AI's hard part.
AI governance platforms decide what AI is allowed to do. Shared, governed business context decides whether it does the right thing. Here is the missing piece.
An architectural blueprint for Chief Data and AI Officers who need every copilot, agent, and AI application to reason from the same trusted business context.
The next generation of enterprise analytics captures and automates trusted expert reasoning, moving teams from reading dashboards to making decisions faster.
How a certified, AI-generated semantic layer becomes a reusable engine for business intelligence, data quality checks, and autonomous enterprise AI agents.
LLMs, copilots, and agent frameworks will keep changing. Why enterprise AI needs context portability so certified business meaning survives every model swap.
Natural language analytics succeeds or fails on readiness, not models. The four areas leaders invest in: data, context, AI, and the often-missed organization.
Most conversational analytics builds context at runtime, after the question is asked. Why that doesn't scale, and the case for a governed semantic layer.
Most enterprise AI analytics projects never reach real decisions. The cause is rarely the model. It is missing business context and the trust lost without it.
Conversational analytics is replacing dashboards, but ease of use isn't the story. It's the translation problem BI never solved, and why most AI still fails.
Do you need a knowledge graph or just better context? A framework to sort low-context from high-context AI use cases and see where a context layer pays off.
Every analytics tool now has its own AI copilot. Learn why an AI control plane built on a contextual semantic layer is becoming core enterprise infrastructure.
Analytical drift is when the same question gets different answers depending on who asks. Learn why it makes AI analytics untrustworthy and how to prevent it.
Context layer vs semantic layer: why giving AI context is not the same as understanding it, and why trusted AI analytics needs a contextual semantic layer.
Text-to-SQL copilots write queries but don't understand your business. The missing context layer that separates impressive demos from trusted analytics.
Every enterprise platform now ships an AI copilot. Six risks from fragmented AI reasoning, and how a shared context layer restores trust and consistency.
Six months of Codd AI product innovation: stronger contextual semantic foundations, smarter conversational analytics, governance at scale, faster time to value.
Why analytics vendors retreated from chat-with-your-data promises, the structural flaws of context-free NLP, and how a contextual semantic layer changes that.
Conversational analytics rarely fails because of technology. It fails when leadership habits don't change. Why culture decides success more than AI capability.
GenAI analytics is moving from experiment to infrastructure. Ten predictions on how it will change in 2026, and what they mean for data and analytics leaders.
The term semantic layer has been stretched until it means almost nothing. Why the real divide is between universal and domain-specific approaches to context.
A practical guide to the build vs buy decision for AI-powered analytics: what it really takes to build your own system versus buying a purpose-built platform.
When to fine-tune an LLM and when to invest in context engineering with semantic layers and RAG, and why the most successful enterprise AI strategies use both.
Why context-aware AI is essential for trustworthy GenAI analytics, and why organizations must standardize business context now, before AI fragments it further.
Why every data and AI vendor is now building a semantic layer, how the approaches differ, and why most semantic layer tools fall short of enterprise needs.
ChatGPT and copilots like Databricks Genie are useful, but without enterprise context they guess. Why the next frontier is context-aware, business-fluent AI.
Check your readiness for conversational analytics across five dimensions, and the foundation you need before everyone asks your data questions in plain English.
What is contextual analytics? How it connects AI to business meaning, using a contextual semantic layer to deliver trusted, consistent answers from your data.
How business data products and contextual semantic layers work together to deliver trusted, contextualized analytics that both people and AI agents understand.
How conversational analytics built on a contextual semantic layer delivers measurable ROI by opening data access to everyone and speeding up time to insight.
What is a contextual semantic layer? How it extends the traditional semantic layer with business meaning and domain knowledge so GenAI analytics can be trusted.
Why traditional data literacy training falls short, and how conversational analytics shifts literacy from mastering BI tools to fluency in business context.
Codd AI, the first AI-powered, context-aware semantic layer, is now generally available, turning raw data and business knowledge into instant, trusted insights.
How NLP is changing business intelligence, and why the future needs both conversational interfaces and BI dashboards, united by an intelligent semantic layer.
How conversational AI built on a semantic layer is moving analytics from static dashboards to dynamic dialogue, putting trusted insights in everyone's hands.
Why GenAI gives wrong answers in analytics without business context, and how context-aware AI built on a semantic layer delivers trustworthy insights.
Why enterprise GenAI hallucinates on business-critical questions, and how a context-aware semantic layer turns AI from clever assistant into trusted advisor.
Piet Loubser joins Codd AI as co-founder, COO, and go-to-market leader, bringing decades of analytics experience to help solve GenAI's hallucination problem.