Picture this: you’re a merchandiser approaching the end of a crucial sales period, with targets slipping just out of reach. Traditional BI tools hand you yesterday’s numbers, forcing you to guess which promotions or markdowns might spark a turnaround. But what if, instead of sifting through static dashboards and stale reports, you could just ask a digital assistant to identify your top-selling products at noon on Saturdays, highlight emerging inventory issues, and suggest immediate steps to keep customers happy?
Our data and AI trends report shows that agentic AI is now a game-changer for the data and analytics industry, making information more actionable and intuitive than ever before. This is the promise of AI agents: going beyond passive analysis to deliver proactive, context-rich guidance at exactly the right moment—while still keeping you firmly in the loop. In this article, we’ll explore how agentic AI turns scattered data into timely, practical insights that help you make confident business moves that matter.
Table of contents:
Agentic AI refers to systems or programs that can autonomously perform specific tasks based on a goal, with input or context. These agents learn from data, process real-time information, and execute tasks with varying degrees of human intervention and oversight. They’re fast, efficient, and capable of bridging the gap between thought and action in record time.
Traditionally, extracting insights and value from data required two things—highly trained data specialists and lots of time. Legacy BI tools have only added to this challenge by following a “dashboard-ticket drudgery” methodology, where business users have to ‘file a ticket’ with data teams every time they want to explore a specific data point. What follows is a nightmarish cycle where data experts spend days, even weeks digging through datasets, building dashboards, and validating results. Meanwhile, business leaders and frontline teams are left waiting, unable to access real-time insights or make decisions when they matter most.
Imagine simply asking a question and getting the insights you need instantly, with no more tickets or bottlenecks. Take your inventory management process, for instance. Traditional AI may help you track stock and send reorder alerts, but agentic AI goes further. It predicts demand, places orders, and detects real-time anomalies in data, reducing operational overheads and boosting efficiency.
By adopting an agentic solution like Spotter, ThoughtSpot’s AI Agent for Analytics, you can immediately gain AI insights and real-time visualizations to identify the places where you can make the most impact.
With these detailed insights, you can transfer inventory between locations, adjust pricing strategies, or even offer tailored promotions to specific customer segments. This kind of instant access to data changes how individuals make decisions—creating a measurable impact across the organization.
Gartner research shows that by 2028, 90% of enterprise software engineers will use AI code assistants, which is far higher than 14% in early 2024. By leveraging agentic AI assistants, developers can gain the support they need to solve difficult problems like understanding unfamiliar code, generating test cases, and scaling deployment. They can focus on more strategic, creative work.
And it doesn’t stop there—businesses are embedding conversation AI interfaces to help decision-makers find insights wherever they work, making it an interesting agentic AI use case. Users can now also receive real-time, actionable insights through proactive alerts and smart AI agents, ensuring insights are delivered at the point of decision-making.
Consider a GTM (Go-to-market) team preparing to launch a new product. Instead of manually logging into different tools to analyze customer feedback, market trends, and sales data, they can embed an agentic solution like Spotter within their modern data stack.
With Spotter, as their AI Analyst, teams can gain immediate access to real-time data—such as emerging customer preferences, competitor activities, and sales trends—directly within their workflow. This streamlined approach ensures teams have all the relevant data to make fast, informed decisions. As a result, they can optimize their product launch strategy, improve campaign performance, and drive higher sales for a successful product launch.
See in action how embedding Spotter into your applications can help you create new value streams and generate returns from their data products:
Let’s take one agentic AI example to see how it will help you improve customer engagement.
According to PwC, 73% of customers view their experience with a company as a crucial factor in their purchasing decisions. This clearly shows customers today expect personal, meaningful interactions with the brands they choose. They want tailored experiences that meet their unique preferences, backed by quick, thoughtful responses. However, analyzing complex customer journeys and providing prompt responses across a large, diverse customer base is no easy feat.
Agentic AI can help you meet these needs in impactful ways. These specialized agents can be trained as intelligent chatbots to deliver personalized, real-time responses to customer queries. By analyzing past interactions, purchase history, and behavioral patterns, these chatbots provide tailored responses that make customers feel understood and valued while streamlining support. AI agents can also be leveraged as analysts, empowering your customer support teams to effortlessly query data in natural language and instantly find the answers they need.
Picture this scenario at an e-commerce outlet: a customer wants to return an item but isn’t sure about the return policy. Instead of navigating complex pages, an agentic AI chatbot quickly pulls up the customer’s purchase history, checks eligibility, and provides a personalized reply. It can even suggest the best return time or offer a discount on future purchases as a gesture of goodwill.
While the customer gets instant help, the customer service team receives a concise summary of the interaction, enabling them to follow up if needed. This streamlined approach resolves issues faster while fostering long-term customer loyalty.
Did you know that 92% of data workers spend most of their time on tasks outside their actual roles? Legacy business analytics tools often require a lot of manual work—processing data, maintaining dashboards, and fixing errors—which leaves little room for focusing on bigger, strategic projects. It’s no wonder teams often feel stuck dealing with slow pipelines, delayed queries, and constant checks to make sure everything is accurate.
This is where AI agents come in. By building on your company’s data and advanced LLM training, these systems can take over the tedious stuff—gathering, cleaning, and formatting data—and even set up smart data structures for you. And they don’t stop there: AI agents can spot trends, uncover patterns, and help your team make more accurate predictions and decisions. By automating the heavy lifting, they free up your team to focus on high-impact projects, making it easier than ever for your organization to grow and innovate.
In a recent episode of The Data Chief, Scott Stevens, Executive Director of Global Technology at JPMorgan Chase, shared, ‘Business intelligence tends to have this notion of looking backward. It's not thinking about prescriptive or predictive analytics or live analytics. Luckily, powered by the new AI capabilities that we're seeing, I do think we're going to evolve to a new name.”
As the practical applications of agentic AI take shape across many industries, Stevens' words ring truer than ever. Rather than focusing on past data, AI agents give businesses the power to predict future trends and explore all possible outcomes before making a decision.
Imagine this agentic AI example: a sales rep describing their target audience, preferred outreach channels, and other key parameters in simple terms. The AI agent takes this input and combines it with historical sales data, customer behavior, and market trends to generate multiple lead-generation strategies. By testing different approaches, the sales team can fine-tune their tactics, focus on high-conversion channels, and optimize their outreach efforts. This ability to test and adapt in real-time not only increases efficiency but also improves the chances of closing more deals."
Of all the businesses using GenAI today, our MIT survey revealed that 56% of early adopters have already exceeded their business goals. But what truly sets them apart? It’s their ability to leverage the right technology to build a strong foundation for success.
To drive measurable results with Agentic AI—or any AI initiative, for that matter—you can't settle for just another chatbot or basic chart generator. You need an AI partner who can handle the complexity, security, and scale of your enterprise data and deliver actionable insights, right where it matters.
Spotter—ThoughtSpot’s AI Analyst uses agentic AI to deliver reliable, actionable insights to everyone. With decision-ready insights and enterprise-grade experiences, you have the ability to answer the most complex questions you can imagine on your massive, real-world datasets. Want to experience this yourself? Try Spotter today to see how it can bring your data to life.
