GenAI assistants like ChatGPT and co-pilots like Databricks Genie are useful, but without enterprise context, they guess. Here's why the next frontier of AI is context-grounded, business-fluent intelligence.
1. The AI Assistant Boom and Its Limits
AI assistants are everywhere. Every major platform now boasts its own "co-pilot," from GitHub and Databricks to Microsoft 365. At the same time, general-purpose conversational models like ChatGPT and Claude have captured the world's imagination, bringing generative AI into everyday workflows.
They're fast, intuitive, and powerful. They can generate code, write emails, summarize reports, or even draft SQL queries in seconds.
But there's a fundamental problem: they don't truly understand your business.
Most AI assistants work within narrow bounds, confined to the tool or data they're attached to, or rely purely on linguistic patterns with no connection to your company's logic, definitions, or relationships. The result? Answers that sound confident, but can't be trusted.
The next leap forward in AI isn't about faster models or bigger datasets. It's about giving AI the context it needs to reason about your business the way your best analysts do.
2. The Current AI Landscape: Two Types of Intelligence
A. The Co-Pilot Generation
Co-pilots like Databricks Genie are task-focused helpers. They automate repetitive work inside a specific platform like writing queries, optimizing code, or generating pipelines.
They're powerful within their domain. Genie can understand your Delta tables, schemas, and Databricks workflows. But its world ends there. It doesn't know what "revenue," "customer churn," or "on-time delivery" mean in your business context. You can augment the lack of business understanding by adding complex business metric definitions into Metric Views and an individual can add lots of prompts and inputs in a Genie session to perhaps get a better business relevant insight.
In short, it can automate tasks, but there is no standardized and persistent business understanding.
B. The General Chatbots
Then we have general conversational models: ChatGPT, Claude, Gemini, and others. They excel at reasoning and communication, but operate in a vacuum. They can talk about anything, yet know nothing specific about your enterprise data, KPIs, or governance rules.
They're fluent in language, but blind to meaning. Again, the user can introduce a database schema and input business rules and logic to assist with a specific task in a session, but there is no standardized and persistent business knowledge available.
These two categories, co-pilots and chatbots, are stepping stones. They help us interact with technology more naturally. But they lack one critical ingredient for enterprise analytics: context, that is standardized, certified and persistent across all user sessions.
3. Why Context Is the Missing Ingredient
Context is the connective tissue of understanding. It's the shared layer of meaning: business definitions, relationships, hierarchies, and logic that lets humans interpret data correctly.
LLMs don't have this context. They were trained on text, not on the semantic models or data relationships that describe how your business works. That's why they often guess, hallucinate, or misinterpret metrics.
Without context, AI can tell you what happened, but not why or how.
For example:
- Databricks Genie can generate a SQL query, but can't explain the business impact of the result.
- ChatGPT can define "revenue growth," but can't calculate it using your company's actual definitions or data.
To move from conversation to comprehension, AI needs context.
4. Enter the Context-Aware AI Era
Context-aware AI represents the next evolution: where language models are grounded in a contextual semantic layer that connects meaning, data, and relationships across your enterprise. We write in detail in our blog "When AI Speaks Your Business Language: The Power of Contextual Analytics" about the power of conversational analytics when matched with a robust context-aware layer in your data stack.
This layer acts as a living blueprint of your organization's knowledge.
It encodes:
- Ontologies and taxonomies: how your business concepts relate.
- Knowledge graphs: connecting structured and unstructured data across systems.
- Business logic and rules: how KPIs are calculated and governed.
- Memory layers: capturing institutional knowledge over time.
By integrating these signals, context-aware AI can interpret questions the way humans do: understanding not just the words, but the intent and meaning behind them.
It's the difference between AI that talks about your data, and AI that understands your business.
5. The Practical Difference
| Capability | Co-Pilot (Databricks Genie) | Chatbot (Claude) | Context-Aware AI (Codd AI) |
|---|---|---|---|
| Scope | Platform-specific | General world | Enterprise-wide |
| Grounding | Technical metadata | Language patterns | Contextual semantic layer |
| Business Understanding | Minimal | None | Deep, explainable |
| Trust & Governance | Platform-limited | None | Full enterprise lineage |
| Example Query | "Write SQL for this table." | "Explain revenue growth drivers." | "Why did EMEA revenue drop in Q3, and what data supports that?" |
Codd AI operates in that third column, translating technical data into trusted, business-ready answers through persistent, contextual understanding.
6. The Business Impact of Context-Aware AI
Context-aware AI doesn't just answer questions. It transforms how decisions are made.
- Trust: Every response is backed by governed definitions and data lineage.
- Speed: Business users can ask natural-language questions without waiting on analysts.
- Scale: A single contextual layer powers analytics, BI, and GenAI across all tools.
- Explainability: Each answer comes with transparency: how it was derived, from what data, and under which rules.
In short, context-aware AI bridges the gap between human reasoning and machine precision.
7. The Path Forward: From Chat to Comprehension
Enterprise AI maturity is evolving in three stages:
- Task Automation (Co-Pilots): Speed up manual work and code generation.
- Conversation (Chatbots): Make analytics more accessible through natural language.
- Comprehension (Context-Aware AI): Enable reasoning, explainability, and trust at scale.
To reach the third stage, organizations must standardize their context by building a contextual semantic layer that becomes the foundation for every AI and BI interaction.
When AI understands not just data, but meaning, it stops guessing and starts thinking.
8. Conclusion: AI That Truly Understands Your Business
Co-pilots made AI useful. Chatbots made AI accessible. Context-aware AI makes AI trustworthy.
The future of GenAI analytics isn't about bigger models; it's about shared understanding. When AI has the context of your business, it can reason like an analyst, explain like a domain expert, and act with confidence.
Codd AI is building that future by turning fragmented data and disconnected systems into a single, context-aware intelligence layer that finally lets AI speak your business language.
Your data has value. Your context gives it meaning.
Discover how Codd AI's Contextual AI Agents Layer powers trustworthy, explainable GenAI analytics.