AI is changing how we work with data. One big shift is the rise of agentic analytics. This means using smart AI tools that can do many tasks a data analyst normally would—like exploring data, finding trends, and even building reports. These tools, called AI agents, are becoming more common in business intelligence systems.
This raises an important question: Will AI replace data analysts?
As a strategic modern data consultancy, Biztory is the home of many talented data analysts, engineers and enthusiasts. So we’re very aware that this question is on the minds of many people who are working in data roles.
The answer isn't simple. While AI agents are powerful, they also create new types of jobs and skills. This blog will explain what agentic analytics is, how it works, and what it means for the future of data analysts.
We’ll explore the changes happening in the analytics world and explain how AI agents in business intelligence are transforming the way teams use data to make decisions.
Let’s begin by understanding what agentic analytics really means.
1. What is Agentic Analytics?
Understanding AI Agents in Business Intelligence
Agentic analytics is a new way of doing business intelligence. It uses smart AI agents that can think, act, and make decisions on their own. These agents can run entire analytics tasks without waiting for humans to tell them what to do.
In the past, people had to write queries, clean data, and build charts by hand. Now, AI agents can do all of this by themselves. For example, they can connect to data sources, find useful patterns, create reports, and even send alerts when something important happens. They don’t just answer questions—they ask them too.
That’s what makes agentic analytics different from older BI tools. Traditional tools are passive. They wait for users to ask for data or create dashboards. AI agents are active. They take initiative and help users before they even know what to ask.
This shift is part of a bigger change in AI agents in business intelligence. Businesses are starting to use AI not just to speed up analysis, but to let AI lead parts of the thinking process. Instead of helping humans work faster, these agents work with humans to find answers together.
For example, Tableau is now adding agentic features with Tableau Next. This tool watches key business data and sends smart updates to users when something changes. It doesn’t just tell you what happened—it tells you what matters and what to do next.
As these AI agents get smarter, they’re becoming true partners in data work. They act more like junior analysts than simple software tools.
2. The Traditional Role of Data Analysts
and the Future of Data Analysts
Before we look at how AI might change jobs, we need to understand what a data analyst does today.
The Role of Data Analysts Today
A data analyst works with data to help businesses make better decisions. They start by collecting and cleaning raw data. They check for errors, combine data from different places, and get it ready for analysis. Then, they use tools like SQL, Excel, or BI platforms to explore the data, find patterns, and answer business questions.
They also create reports, charts, and dashboards so that teams can understand the numbers. Often, they explain their findings in meetings or write summaries for decision-makers. Many analysts also help spot risks, understand customer behavior, and improve products or services.
These jobs require both technical and communication skills. Analysts need to know how to code, use analytics tools, and also explain data in plain language.
But this role is changing. Tasks like cleaning data, writing simple queries, or creating standard dashboards are now being done by AI tools. These are the very areas where AI is starting to take over.
The Future Role of Data Analysts
So what does that mean for the future of data analysts? Some parts of their work will be automated. But other parts—like solving complex problems, using business knowledge, and making sense of tricky situations—still need human thinking.
The fear of being replaced is real, but the shift is more about changing roles, not removing them. Analysts need to move from just producing charts to helping companies ask better questions and use AI tools wisely.
3. How AI Is Changing Analytics with AI Agents in Business Intelligence
AI is changing how we do analytics at every level. Today, we’re seeing a shift from manual, human-driven analysis to automated, AI-assisted processes. These changes are happening because of AI agents in business intelligence—systems that don’t just support analysis but can actually run the analysis themselves.
Here’s a short breakdown of the big changes we currently see happening today:
Asking questions in natural language
One big change is natural language querying. Tools like Tableau Next and ThoughtSpot Spotter let users ask questions in plain English, such as “What were our best-selling products last month?” The AI understands the question, pulls the data, and shows the answer—often with a chart or a short explanation.
Automated, proactive insights
Another key shift is automated insights. AI agents can scan datasets to find trends, patterns, or problems—without being told exactly what to look for. For example, they might find that sales are dropping in a certain region or that a new product is performing better than expected.
Predictive analytics
AI also brings predictive modeling to the table. These models can forecast future results based on past data. In the past, you needed a data scientist to build these models. Now, AI can build, test, and run models automatically, with little or no code.
Insights delivered where you work
One of the most helpful changes is automated storytelling with data. AI doesn’t just give charts—it can explain them and push them to relevant key stakeholders wherever they work. Embedded insights in Slack, Microsoft teams or other platforms like your CRM make it easier for non-experts to understand insights and take action directly from their flow of work.
These AI agents are no longer just helpers—they’re beginning to act like junior analysts. They are fast, tireless, and always watching the data for changes. This is what makes agentic analytics so different: it's active, smart, and always on.
4. The Future of Data Analysts in an AI-Driven World
The question many professionals are asking is: what is the future of data analysts in a world where AI can do so much of the work?
The short answer is that the job of a data analyst is not disappearing—but it is changing. Tasks that are routine, like cleaning data or generating simple reports, are already being handled by AI agents. These tools are faster, consistent, and available 24/7. That means analysts will no longer spend time on low-level tasks.
But while AI is good at automating steps, it still lacks something important: human context. AI doesn’t understand business goals, political dynamics within teams, or why one insight matters more than another. That’s where analysts come in.
In this new world, analysts will act more like data strategists. They’ll work with AI, not against it. Their job will shift toward guiding the AI, checking its outputs, and making sure results align with business needs. This includes:
- Prompting AI tools correctly to ask better questions
- Interpreting and validating AI-generated insights
- Communicating insights to teams clearly and with impact
- Watching for bias in AI outputs and making ethical decisions
Analysts will also help build trust in AI. We’ve said it many times before, great AI starts with great data, so just because a model says something doesn’t mean it’s right. Humans are needed to judge whether the AI’s answer makes sense in real life.
5. Will AI Replace Data Analysts? The Impact of Agentic Analytics on Jobs
Let’s answer the big question directly: Will AI replace data analysts?
The clear answer is no—agentic analytics will not fully replace analysts. But it will change the job. AI agents are already handling many tasks that used to take analysts hours or even days. These include things like generating standard reports, detecting anomalies, and answering routine business questions.
That said, AI agents still have limitations. They don’t understand the full business context, and they can’t navigate messy real-world situations. They can’t explain why the same data matters more to one department than another, or how to resolve conflicting goals across teams.
According to GeeksforGeeks, AI can handle repetitive, logic-based tasks. But it struggles with tasks that require judgment, empathy, and strategic thinking. That’s why human analysts are still essential.
Agentic analytics tools act like junior team members. They can do the legwork, but they still need human supervision. Analysts must now focus more on designing questions, guiding AI agents, and turning outputs into real-world actions.
In fact, some analysts will grow into AI orchestrators—people who manage how AI agents are trained, prompted, and used. Others will become storytellers, helping leaders understand what the data means and how to act on it.
For those willing to evolve, AI is not a threat. It’s an opportunity to take on higher-value work and have a bigger impact on the business.
6. What Businesses Need to Know About AI Agents in Business Intelligence
For companies, the rise of AI agents in business intelligence presents both opportunity and responsibility. These AI tools offer speed, scalability, and insight—but they also demand new ways of working. If you want to benefit from agentic analytics, you have to make smart choices about how to adopt and manage these technologies.
The first step is to start small: Companies can pilot AI-powered analytics tools like Tableau Next or ThoughtSpot Spotter on specific teams or business units. This allows them to test AI in a controlled way and learn what works before scaling it across the organization.
Next, businesses need to upskill their people: The most valuable analysts will be those who can work closely with AI—guiding it, checking its work, and turning AI outputs into business action. Training in areas like prompt engineering, data ethics, and model validation is key.
Governance is also essential: AI tools should never run without oversight. Businesses need clear rules about how AI agents are used, who checks their work, and how to manage risks like bias or misinterpretation. Establishing a human-in-the-loop model, where analysts validate insights before action is taken, is a good starting point.
Most importantly, businesses should not view AI as a way to cut staff: Instead, you should see it as a way to amplify the work of their analysts. With AI agents handling repetitive tasks, analysts can spend more time on strategy, innovation, and value creation.
Leaders should rethink the analyst role—not eliminate it. AI agents are best used when paired with skilled humans who bring context, creativity, and judgment to the table.
This isn’t just a technology shift. It’s a people shift too.
Conclusion – Embracing the Future of Agentic Analytics
Agentic analytics is not a trend—it’s the next phase of how we work with data. AI agents are already changing the game in business intelligence, helping teams move faster, uncover more insights, and act on them with less manual effort.
But with this shift comes change. The role of the data analyst is evolving. Analysts will spend less time pulling reports and more time steering strategy, asking smart questions, and ensuring AI tools produce useful, fair, and accurate results.
So, will AI replace data analysts? No. But it will replace the old way analysts worked. Analysts who lean into this change, learn to guide AI, and bring human context to machine insights will become even more valuable.
For businesses, the key is not to fear automation but to prepare for it—by investing in the right tools, training teams, and building systems that combine AI’s power with human judgment.
AI agents in business intelligence aren't just tools. They’re teammates. And together with skilled analysts, they can help organizations make faster, better, and smarter decisions.
FAQ: Will AI Replace Analysts?
What is agentic analytics?
Agentic analytics is a form of business intelligence where AI agents take autonomous action—like analyzing data, generating insights, and making decisions—without needing constant human direction.
Will AI replace data analysts?
No, AI won’t replace data analysts. It will automate repetitive tasks, but analysts will still be needed for strategic thinking, decision-making, and guiding AI tools effectively.
How are AI agents used in business intelligence?
AI agents in business intelligence help automate querying, data cleaning, trend detection, reporting, and even generating insights in natural language—making analytics faster and more accessible.
What skills will future data analysts need?
Future data analysts will need skills in AI prompting, data storytelling, ethical oversight, and business strategy, in addition to traditional data handling abilities.
Should businesses worry about AI replacing entire teams?
No. Businesses should see AI as a tool to enhance teams, not replace them. Human analysts paired with AI agents can deliver faster and more strategic insights than either could alone.
