Thought Leader – Bhavin Vyas
Understanding what happened in your business is no longer enough. Organizations today are asking a more strategic question:
Why did it happen, and what should we do next?
The answer lies in a powerful combination: Conversational BI and Causal AI. Together, they enable users to move from reactive dashboards to proactive decisions, simply by asking natural language questions. Whether you’re using Microsoft Power BI, SAP Analytics Cloud, Tableau, Qlik, or Snowflake, this evolution is already underway.
Let’s first understand both terms.
What is Conversational BI
Conversational BI represents a new paradigm in enterprise analytics. Instead of navigating filters and visuals, users can now interact with governed datasets using voice or text prompts.
Examples across the BI ecosystem:
- Microsoft Power BI integrates Copilot, letting users create visuals, measures, and reports with plain-language prompts.
- SAP Analytics Cloud, powered by Joule, features natural language query (NLQ), search-to-insight capabilities, and context-aware GenAI assistance directly embedded into dashboards and planning workflows.
- Snowflake, through Snowsight and Cortex AI, enables users to interact with their data using GenAI-powered SQL generation, contextual insights, and soon, prompt-driven agents.
- Tableau and Qlik are embedding AI assistants and NLQ into their platforms to simplify access for non-technical users.
This allows business users from sales to finance to quickly ask:
“Which product had the highest margin drop in Q1, and what drove it?”
And receive:
- Visual answers
- Automated narratives
- Suggested follow-up queries.
- Root-cause insights from connected data models
What is Causal AI and What-If Analysis
Where Generative BI answers “what happened?”, Causal AI dives into “why it happened” and “what might happen if we act”.
Across BI platforms, Causal AI is starting to surface in various forms:
- Power BI supports integration with Azure ML for causal modeling using tools like EconML and Downy.
- SAP Business Technology Platform enables advanced simulations and scenario planning based on interventions.
- Snowflake supports causal and predictive analysis via Cortex AI, with support for UDFs, Python ML models, and integrations with libraries like H2O.ai and custom causal inference frameworks.
This enables decision-makers to simulate interventions like:
- “What if we restore our earlier discount strategy?”
- “How would logistics costs change if we switch vendors?”
- “What is the most influential factor affecting churn?”
Causal models use counterfactual logic and uplift modeling to explain relationships, thus making analytics more actionable and strategic.
Conversational BI + Causal AI
Imagine the following experience inside your enterprise BI tool:
1. A marketing lead asks:
“Why did conversions fall on the website last month?”
2. The system responds with:
-
- A time-series chart showing a traffic drop.
- A summary attributed to the decline in reduced ad spending.
- A causal analysis showing a 60% influence from reduced retargeting.
3. The system suggests:
“Restoring campaign budget may regain ~15% conversion rate in 2 weeks.”
4. This recommendation is shared via Microsoft Teams or SAP Build Work Zone for cross-functional review and action.
Key Benefits Across Enterprise
Self-service analytics for every role
Anyone in the organization, whether from marketing, finance, or operations, can now explore data without needing to write code or wait for analysts. Conversational BI lets users ask questions in everyday language and get instant answers. It makes working with data feel as easy as chatting with a colleague.
No More Static Charts
Static dashboards often leave you guessing. With automated narratives and interactive visuals, insights come to life. You don’t just see numbers, you understand the story behind them. This makes it easier for non-technical teams to grasp what’s happening and what it means.
Faster Decision Making
Insights don’t have to wait; decision-makers can react quickly. You ask a question, the system gives you context, and you can act on it at the same time. That kind of speed makes a real difference.
More Accountability
When everyone can see not just what happened but why it happened, it’s easier to take ownership. Causal AI gives clear reasoning behind outcomes, which helps teams understand impact and align on the next steps. It brings transparency to decisions and encourages better collaboration.
Multi-tech support
Whether your team is using Power BI with Azure, SAP Analytics Cloud on HANA, Tableau, or Qlik with Snowflake, the foundation is already in place. These platforms are built to support natural language queries and AI-driven insights, so it’s more about enabling the experience than rebuilding your tools.
Enterprise Readiness: Importance of Governance and Trust
Scaling Conversational BI and Causal AI across the enterprise is about doing it in a way that builds trust, protects data, and keeps everything accountable.
- Keep prompts and results transparent
Every insight should be traceable. You should always know what question was asked, what data was used, and how the system got the answer. That kind of transparency helps avoid confusion and builds confidence in what the system delivers.
- Make AI explainable
It’s not enough for the system to say what to do. People need to understand why. Explainable AI ensures users can see how a conclusion was reached, which variables mattered, and whether the reasoning holds up. Especially in regulated industries, this matters a lot.
- Control access based on roles
Different teams need different levels of access. By tying insights into user roles and protecting data through semantic models, businesses can make sure the right people see the right information without risking exposure.
Autonomous Decision Intelligence
The future of BI is shifting from dashboards to decisions. We’re moving into a space where systems start doing some heavy lifting.
- Dashboards build themselves
You won’t need to build dashboards anymore. Ask a question like “Why did our Q2 sales dip in the West region?” and the system can generate a complete view with visuals, narratives, and suggested next steps, all in real time.
- Proactive Recommendations
Instead of just showing trends, systems will suggest actions. If churn is rising, it might be recommended to adjust your onboarding flow. If the margin is shrinking, it may suggest a price review. You’ll go from reacting to shaping outcomes in advance.
- Smart answers before you even ask
Eventually, the system will anticipate what you’re about to ask. It will surface insights before the question even forms in your head. Imagine getting a morning briefing that says, “Customer complaints are trending upward this week, mostly due to delays in shipping from vendor B.”
- Transition
It’s not a far-off idea. Companies in manufacturing, BFSI, retail, and healthcare are already adopting these capabilities. They’re not waiting for perfect conditions. They’re using AI to close the gap between data and action.
Final Thought: Ask Better, Act Smarter
Conversational BI and Causal AI are turning analytics into a dialogue. They help teams move faster, collaborate better, and make decisions backed by real insight, not just assumptions. As this shift continues, the organizations that adapt early will be the ones that lead with confidence and clarity.
At Stridely, we help businesses embrace this new way of working. We bring the strategy, technology, and expertise needed to move you from reactive reporting to intelligent, action-led insights. Contact us today.