Agentic analytics explained: AI that acts on your data

You’ve invested in dashboards, reports, and modern BI tools. You’ve built data pipelines, governed metrics, and trained teams to be data-driven. But turning your data into real, impactful action? That’s still a struggle. 

Even the best dashboards and BI reports stop short of what you really need—clear, timely action. They show you what happened, but leave you to interpret the results and figure out what to do next.

That’s where agentic analytics comes in. 

This new approach moves beyond simply presenting insights and instead uses AI-powered agents that can analyze, decide, and even act on your behalf. 

In this guide, you’ll learn what agentic analytics is, why it matters, and how you can start applying it in your organization.

Table of contents:

Agentic analytics is an approach to data analytics where AI agents move beyond simply surfacing insights; they sense, analyze, decide, and act on your behalf. These agents are goal-oriented systems that operate autonomously: they can monitor data streams, detect changes or anomalies, determine what actions to take based on your business objectives, and execute those actions without needing manual intervention.

Unlike traditional analytics tools that wait for you to ask a question or explore a dashboard, agentic analytics tools anticipate what needs to happen and take initiative, whether that’s adjusting a campaign budget, rerouting supply chain logistics, or flagging a risk in real time.

In practice, agentic analytics doesn’t replace human oversight—it augments your decision-making and operations by handling high-volume or routine decisions that would otherwise slow your team down. 

The result is a more agile, responsive, and consistent approach to using your data to drive real business results.

At the core of agentic analytics is the AI agent. Unlike traditional BI tools that deliver static reports, an AI agent is designed to be an intelligent component of your operations that’s always on.

These agents continuously watch your data environment for relevant signals and changes. 

Instead of waiting for you to spot an issue or ask a question, your AI agent can identify opportunities or risks, trigger workflows, update business systems, or communicate with other software in real-time. 

That’s the key distinction between agentic AI and GenAI, with GenAI often more focused on content generation than operational execution.

Let's take a look at the AI agent in action

ThoughtSpot Spotter, your AI analyst, lets you search your data in natural language, surface insights, and take action directly through agent-powered workflows. For example, a retail AI agent might reorder stock the moment inventory runs low, while a financial agent could block a suspicious transaction in seconds.

It’s the difference between knowing there’s a problem and automatically fixing it before it impacts your bottom line. But it’s not about handing over full control: it’s about designing systems that can execute your well-defined playbooks reliably and at scale. 

You might wonder how an AI agent differs from other AI-powered tools you’re familiar with, like chatbots or large language model (LLM) copilots. While all three can support analytics workflows, they operate at different levels of sophistication and impact. 

The table below breaks down how each contributes to analytics tasks, from asking questions to making decisions and taking action.

In other words, while chatbots and copilots can make you more productive, AI agents help your business by acting on your strategies directly. 

💡 Relevant read: What is an AI copilot? How is it different from an AI agent?

So why should you care about adopting agentic analytics? Because it solves one of the most persistent problems in business intelligence: the gap between knowing and doing.

Even with the best BI tools, you can still face delays, inconsistencies, and bottlenecks when it comes to acting on insights. Your people get busy. Your data can get stale. Opportunities are missed while teams debate what to do next.

By automating routine and time-sensitive decisions, AI agents free your team to focus on higher-value tasks. This means faster responses to opportunities, more consistent policies, and fewer errors. In a landscape where speed and precision matter, agentic analytics turns your raw data into a real, continuous business advantage.

• Faster time to action: Act on opportunities immediately instead of waiting for manual reviews or approvals. With real-time responsiveness as a standard capability, you have a decisive edge in competitive markets.

• Consistent decision-making: Apply your strategy uniformly across teams, markets, and systems without drift. You can reduce the variability that comes from manual interpretation and maintain alignment with corporate goals, even as conditions change.

• Adaptability: Markets shift, conditions change, and customer behavior evolves. Agentic systems are designed to handle dynamic environments. They adapt their behavior based on the latest data, whether that’s adjusting supply chain orders, tweaking pricing, or responding to anomalies in fraud patterns. 

• Stronger competitive positioning: Move from reactive reporting to proactive, automated action that compounds over time. By building institutional muscle memory for decision-making, you’re not just keeping pace with competitors; you’re setting the pace.

⚡ For a deeper dive into agentic AI benefits, download the Gartner Agentic Analytics report.

1. Automate routine, rules-based decisions

Think of all the repetitive “if X, then Y” decisions that happen daily in your business. Agentic systems can monitor these scenarios and trigger the appropriate response automatically, saving you time and reducing the risk of human error.

2. Monitor for anomalies and disruptions

Whether it’s a sudden drop in conversion rate or a spike in supply chain costs, agentic systems can catch what you might miss. But more importantly, they can react. Anomaly detected? A mitigation plan is already in motion.

3. Optimize in real time

From pricing adjustments to ad budget reallocation, agentic analytics can continuously optimize based on performance signals. With a system that adjusts as your conditions shift, you no longer have to wait for weekly reviews or manual approvals.

4. Orchestrate complex workflows

Some decisions require multiple steps across systems. For example, changing a delivery schedule may involve logistics, customer communication, and backend updates. Agentic systems can coordinate those actions in sequence, without needing a project manager to hold it all together.

5. Trigger alerts and escalate intelligently

Not everything should be fully automated. In higher-stakes scenarios, the agent can flag issues and recommend an action paired with human oversight. It becomes your partner, not just a robot, helping you make better calls, faster.

6. Act directly in operational systems

Agentic analytics is deeply integrated. It doesn’t just suggest actions in isolation; it pushes changes directly into tools like your ERP, CRM, warehouse system, or ad platform. The result: data that actually drives outcomes.

💡 Watch our webinar: Talking Business to Your Data to explore how generative AI, conversational interfaces, and AI agents are shaping the future of BI

Agentic analytics is already driving real-world outcomes in sectors where speed and precision matter most, like:

Retail

Retailers often struggle with striking the right balance between overstocking and running out of stock. Using agentic analytics, a retailer can monitor inventory levels, sales trends, and supplier lead times to anticipate future demand.

Instead of waiting for a store manager to notice low inventory and manually reorder, the system does it automatically. If demand surges unexpectedly, it adjusts the plan. That means better margins, happier customers, and less firefighting on the floor.

For example, in an episode of The Data Chief, Dr. Katia Walsh, Chief Global Strategy and AI Officer at Levi Strauss & Co., shared how AI-augmented analytics personalized every touchpoint in their customer journey, guided by what Walsh calls the three C’s: consumer connections, commerce, and creation.

“No two Levi consumers are the same—why should the rewards that we offer in our loyalty program be the same? We use more data than we have ever had in the past and apply machine learning to that data to ensure that we provide exclusive, fully individualized benefits for each of our individual loyalty program members.” 

Financial Services

In banking, timing is everything, and when fraud happens, even minutes matter. Agentic systems constantly scan transactions, customer behavior, and risk signals.

If something seems suspicious, like an unusual withdrawal pattern, the agent can instantly freeze the account, flag the transaction, or send a high-priority alert to a fraud team. This kind of immediate action helps minimize losses and protect customers.

Take Neobank Northmill, for example. By leveraging customer segmentation, they personalized banking experiences, which resulted in a 30% increase in conversions.

“What moves the needle is turning insight into actions. To run a business, the ability to produce nice graphs and monitor interesting data is not even half the story—it's what you do with it that's important.”

Manufacturing

Unplanned downtime is one of the costliest problems in manufacturing. For example, imagine a global manufacturer that builds heavy machinery. With hundreds of machines and dozens of suppliers, delays used to go unnoticed until it’s too late, triggering missed deadlines, costly overtime, even full line stoppages. 

Now, AI agents track everything in real time with modern manufacturing analytics software. These agents aren’t just predictive, they’re proactive. They can reroute production, trigger maintenance, and suggest alternate suppliers before problems spiral, all without requiring your intervention. 

⚡ See how Fabuwood, a luxury cabinet manufacturer, achieved a 2.5x increase in BI tool adoption

Getting started with agentic analytics and delivering truly actionable analytics doesn’t have to mean overhauling everything. Here’s a practical, phased approach:

Step 1: Assess your data and tech landscape

Make sure your data is reliable, accessible, and well-governed. Agentic analytics relies on clean, real-time data to make data-driven decisions. Evaluate your current BI and operational systems for compatibility and integration potential.

Step 2: Identify your first use case

Look for a decision-making process that’s frequent, predictable, and rules-based, inventory restocking, fraud alerts, or pricing adjustments, for example. Starting with one strong use case helps demonstrate value quickly and build trust.

Step 3: Define goals, policies, and constraints

What do you want the agent to achieve? Are there boundaries it must stay within? Set clear business objectives (maximize margin, reduce downtime, etc.) and define what actions the system is allowed to take autonomously.

Step 4: Choose the right platform or partner

Evaluate tools that offer agentic capabilities, ideally those that integrate with your current workflows. Look for platforms that not only integrate with your workflows but also provide strong visualization capabilities. ThoughtSpot’s Muze, for example, lets teams create complex, interactive charts to better explore and explain their data.

Step 5: Launch a pilot

Run a small-scale pilot to validate the use case. Monitor performance, measure ROI, and gather feedback from teams. Treat this as both a test of the technology and a way to build internal confidence.

Step 6: Scale gradually

Once the pilot proves successful, expand to additional use cases. Document learnings, refine processes, and involve more teams. Treat agentic analytics as a capability you build over time, not a one-time project.

While agentic analytics offers real upside, it’s not without its challenges. Here are a few to keep in mind:

• Data quality issues: Garbage in, garbage out. If your data is inconsistent, outdated, or incomplete, agentic systems will struggle or, worse, act on flawed information.

• Over-automation without oversight: Autonomous systems need boundaries. Failing to implement approval steps or monitoring mechanisms can lead to unintended consequences if something goes wrong.

• Transparency and explainability: Choose systems that can explain how a decision was made, and make sure that the explanation is understandable to your team and stakeholders.

• Integration complexity: Legacy systems and siloed data make implementation harder. Building connectors and automation may require upfront investment and technical expertise.

• User trust and adoption: If your people don’t trust the system, they’ll fight its adoption or simply won’t use it. Communication, training, and transparency are critical to long-term success.

• Security and compliance risks: Any system that can take action, especially one that touches financial or customer data, must be tightly secured and audited. You’ll need strong governance to stay compliant.

💡Learn more about handling hallucination risks in AI systems to support trust and reliability

Agentic analytics is just getting started, and it’s evolving quickly. Here’s what you can expect on the horizon:

Multimodal agents

Future agents won’t just process structured data, they’ll interpret text, images, sensor data, and more to make richer, more nuanced decisions.

Better collaboration

More mature human-in-the-loop frameworks will let agents escalate edge cases, learn from human input, and build trust through clear, explainable behavior.

Industry-specific agents

Expect more pre-trained, purpose-built agents tailored to your domain, whether you’re in retail, logistics, finance, or healthcare.

Stronger regulatory frameworks

As autonomous decision-making becomes more common, governments and industries will push for clearer standards around fairness, ethics, and accountability.

Tighter integration with AI ecosystems

Agents don’t operate in isolation; they collaborate with LLM copilots, forecasting models, and other automation tools to drive end-to-end workflows across the enterprise.

Agentic analytics is more than a technology upgrade; it’s how you shift from asking what happened to making the best actions happen automatically. It reimagines decision-making by embedding intelligent, goal-aligned action directly into your operations.

ThoughtSpot is the Agentic Analytics Platform that closes the gap between insight and action. By embedding AI agents into your data and workflows, you can respond instantly and maintain strategic consistency at scale.

If you’re ready to make your data work for your business, book a demo today.