For years, organizations have invested in business intelligence (BI) platforms that promised self-service analytics. The reality? Most business users are still waiting on data teams to build dashboards or run queries. The result is slow insights, mounting backlogs, and leaders making decisions on stale data.
We've reached a turning point — one powered in large part by the broad availability of generative AI (GenAI). The way we interact with data is changing — from prebuilt dashboards and filters to conversational, natural language experiences. But here's the catch: unless that conversation is powered by real business context, it's just another way to get wrong answers faster.
Why Traditional BI Falls Short
Manual setup is a bottleneck. Every BI tool requires data engineers to build and maintain semantic layers, mapping technical data into business terms.
The burden is on the user. Analysts must know which fields, joins, and filters to select just to translate the business question into the appropriate logic for the BI tool to work.
Insights lag behind the business. Dashboards reflect yesterday's questions, not today's decisions.
Where this gets really challenging is that all of this has to be pre-determined. I.e. you have to have the final answer in mind before you start, which in a fast paced world is pretty difficult to achieve. Which of course is not the way humans make decisions. More than often it is an iterative process of weighing up insights, looking at the data from a different point of view, and then coming to a conclusion.
The Work-Around: Investing In Data Literacy Training
The way many organizations have attempted to deal with this is via training the users to work in this environment. Unfortunately, it means we train users to think more like data engineers and analysts, which really does not solve the problem in the mainstream. What is really needed is for the machines to think a little more like a business savvy analyst.
Ultimately, the traditional model is expensive, slow, and falls short of achieving the objectives of the very people BI was meant to empower.
The Shift: Natural Language as the New Interface
While I don't propose the need and usefulness of BI with its reports and dashboards will go away any time soon, it seems clear that new styles of interaction with data are emerging. Generative AI (GenAI) has changed user expectations. If people can ask ChatGPT to draft a business plan or summarize an article, why shouldn't they be able to ask:
- "What were last quarter's top-performing products?"
- "How much of that was from my platinum customers?"
- "How is customer churn trending in the northeast?"
Even more powerful is when the agent is making suggestions and providing additional insights. Conversational interfaces remove barriers. Business users don't want to hunt through reports or learn SQL — they just want answers.
But here's the rub: GenAI without context is dangerous. We discussed this in our blog last week, but in short, to get GenAI to act like a smart analyst we will need to have it understand both the data and the business logic and rules. It doesn't know your business logic, rules, or definitions. It will happily hallucinate numbers.
From Analysts to Everyone, Everywhere: Democratizing Insight
"Democratizing Insights" have been a rallying cry for BI for decades and I guess is that BI insights are regularly consumed by less than 30% of people in any organization. Getting to the rest will require the insights to be embedded wherever folks are and be it to be surfaced in a much more natural conversational manner.
And rather than training people to think like analysts, we train the agent to think like a data engineer, analyst and reason like a business person with full context of all the data.
This is where Codd AI changes the game.
Codd AI automates the generation of a comprehensive semantic layer for your analytical domain that combines:
- Technical metadata: the joins, hierarchies, and relationships between your data.
- Business knowledge: your rules, KPIs, and definitions that make data meaningful.
With this foundation, our intelligent query agent can power natural language interactions that deliver trusted, context-rich insights. On top of that the agent intelligently analyzes all results and continuously makes recommendations and suggestions as to potential insights in the data or follow-up questions.
It's not just about talking to your data. It's about finally getting accurate answers that reflect how your business actually runs.
With Codd AI, the heavy lifting is handled by the semantic layer + query agent.
That means:
- Analysts spend less time wrangling data and more time driving strategy.
- Business leaders can ask questions directly and get meaningful, consistent answers.
- Data engineers are freed from endless semantic modeling requests.
And the same semantic layer and agent can be used in a conversational style app, your BI tools or embedded into other apps like Slack.
In short: data finally works for the business — not the other way around.
The Business Impact
- Speed: Insights in minutes, not weeks.
- Accuracy: Answers that reflect your business context.
- Scale: Every user, from the CEO to frontline managers, can access insights naturally.
- Cost savings: Less reliance on armies of data engineers just to keep dashboards running.
Closing: The Future of Analytics is Conversational + Contextual
Dashboards aren't going away overnight. But the future is clear: analytics will become more conversational, contextual, and deeply embedded into everyday decision-making.
With Codd AI, you don't just get another reporting tool. You get a business insights co-pilot — one that finally delivers on the promise of self-service analytics.
👉 Curious to see it in action? Reach out at [email protected] or book a demo today.