The future of decision-making: Agentic business intelligence (BI) in action

Introduction

Consider a scenario: A fortune 500 CFO confessed that they've 500+ dashboards, but we can't explain why our revenue dropped by 8% last quarter. They have a lot of unused or underutilized dashboards and most of their time went into preparing data rather than analysing it. The result? More dashboards are making them less informed.

For years, organizations have been trying to democratize analytics, i.e., to make analytics more accessible to business users. Traditional Business Intelligence has served organizations well, providing critical insights via structured reporting and analytics. But, as data volumes explode (181 zettabytes by 2025) and business questions grow more complex, the limitations of this approach have become increasingly significant. Legacy dashboards excel at answering predefined questions, but each answer often come up with another ten new queries— It's a vicious cycle of dashboard sprawl, not actual intelligence. But something is shifting. A new type of AI is emerging — one that not just wait for any directions but sets objectives, plans actions, and learns from outputs.

The next phase of AI is here - Agentic AI.

This shift presents moving from reactive analytics to proactive intelligence. Organizations are now deploying autonomous systems to replace traditional dashboards and manual analysis, enabling continuous data monitoring, insight generation, and decision execution. With market projections reaching $196.6 billion by 2034, we're witnessing the transition from experimental pilots to production deployments that transform entire business processes.

This blog explores the hidden inefficiencies of traditional dashboards and demonstrating how Agentic AI/BI is transforming the way businesses interact with data. What you'll discover may change the way you think about BI forever.

Defining Agentic Business Intelligence (BI)

Agentic AI presents a momentous shift from traditional data visualization to active, intelligent data analysis. Unlike conventional BI tools that show charts and need users to interpret results manually, Agentic BI works as an autonomous data analyst that can:

• Locate insights proactively once given business context, without requiring manual queries for every question.
• Interpret data contextually rather than presenting raw numbers in isolation.
• Impart results in natural language, explaining not just what happened but why it matters.
• Act on findings by generating recommendations and even executing decisions.

The main innovation lies in embedding AI agents directly into the data processing pipeline, eradicating the outdated approach that has afflicted traditional BI for years. To see the difference more transparently, let's compare between traditional BI and Agentic BI processes.

But WHY these traditional dashboards are making business users inefficient?

Companies invest millions in BI infrastructure, but still business users export data to Excel for actual analysis. Why? Dashboards track metrics, but they rarely answer deeper questions. If a filter is missing or you need a new metric, users have to export data to Excel or wait for your data team to create a new report. This is called the "last-mile problem" in analytics. Let’s us break down why this keeps happening:

Rigidity issues: Traditional BI tools rely on fixed semantic models, which can only answer questions that were anticipated and “pre-modeled” in advance. These rigid structures leave organizations with outdated insights limiting their adaptability, leading to inconsistent results.

Expert bottlenecks: Business users can't access insights independently—they stuck waiting for BI teams to build dashboards or answer ad-hoc questions. When a sales manager needs custom analysis, they submit requests and wait weeks for delivery. This dependency creates cascading roadblocks- insights arrive too late to act on, decision-making slows immensely, and opportunities goes away while requests queue up. In such situations,businesses unable to respond at market speed and teams invest more of their time waiting for reports while actually making data-driven decisions tedious.

Dashboard Overload: Organizations accumulate thousands of dashboards over time, each built for specific teams or use cases. The problem is not just volume—it's fragmentation and discoverability. Finance has one version of customer revenue metrics, sales and marketing has another. Business users waste valuable time searching through endless dashboards trying to find the right KPI or the "correct" version of the truth. Even when they locate the right dashboard, it only shows what happened—a static snapshot of past performance. There's no context explaining why metrics changed, no proactive alerts on anomalies, and no guidance on what actions to take. Users end up exporting data to excel to do their own analysis anyway, hindering the entire purpose of the BI investment.

Here are four ways Agentic AI for Business Intelligence changes the way you interact with data

Agentic AI embedded to your existing BI workflows has the potential to reshape how your org activates data and turns it into actionable intelligence. Have a look:

1. Democratized data analysis

Conventionally, deep data analysis was limited to skilled data analysts who understands how to build reports and query complex databases. Agentic AI democratize data analysis by making it more accessible to wide audience within an org.

• Natural language queries: Agentic AI assists business users interact in regular English; they can ask tricky questions and receive concise and conversational answers without the need for anyone to know of SQL or other technical language.


• Personalized insights: AI agents can easily personalize insights they provide to a user's specific preferences, roles, and priorities. For example, a fintech executive can get a disparate briefing than sales executive, even they're analysing the same data.


• Embedded analytics: By embedding AI agents directly into daily workflow tools (like- ERPs and CRMs), analytics are delivered where they're most applicable, removing the need for context switching.

2. Automated data preparation

Before insights are generated, data must be cleaned, validated, and prepared — conventionally one of the most time-taking stages that precedes BI. Agentic BI platforms can automate this layer:

• Dynamic data harmonization: Agentic Business Intelligence platforms adapt and monitor new data fields in real time, schema changes, ensuring continuity across dashboards without manual intervention.


• Automated quality assurance: Embedded AI agents constantly scan for lineage breaks, data anomalies, or quality degradation, triggering automated rectification steps to preserve data reliability.


• Reduced ETL effort: Multiple Agentic BI platforms can connect directly to live SQL, APIs, and NoSQL environments, maintaining a continuous synchronized analytical layer.

3. Prescriptive and adaptive decisions

Beyond explaining what happened or predicting what might happen, Agentic Business Intelligence systems through tools like Insights Portal powered by 1Platform now recommend the next best action and even execute the best course of action. This capability is managed by the decision layer, Agenthood AI, which acts as the "heart or brain" of the system, converting base data and enriched data into suggested actions.

• End-to-End Automation: Agentic AI in BI eliminates the lag between identifying an issue and acting on it. For instance, when the system detects a sales dip, it doesn’t just flag the problem — it autonomously recommends the optimal next step and can even trigger targeted marketing campaigns to recover lost momentum. This seamless transition from diagnosis to decision is powered by Agenthood AI, the “decision layer” that serves as the system’s brain, converting both base and enriched data into executable actions.


• Strategic Experimentation: Decision-making becomes a continuously learning process. Business leaders can test, simulate, and optimize strategies directly within the Insights Portal using pre-trained functional plays and embedded AI/ML models. Once a performance issue is identified, Agentic AI automatically runs scenario explorations — comparing possible interventions and presenting the user with a prioritized list of responses and their projected impacts.


• Continuous Adaptation: Unlike traditional BI dashboards, Agentic BI systems constantly refine their models in real time. Developed on enriched data from ML Orion — which adds anticipatory signals, predictive context, and behavioral flags — the agents evolve with every decision. This provides the system to make context-aware suggestions such as avoiding assigning leads to a sales representative who’s on leave or reallocating marketing budgets dynamically based on campaign ROI.

4. Multi-agent collaboration and orchestration

Gartner points out in it research that by 2028, 70% of AI applications will utilize multi-agent systems. This will help business functions and ecosystems to provide holistic performance assessments, which will take business intelligence to the next level. In the current challenging business scenarios, numerous specialized AI agents can collaborate to resolve multifaceted issues across disparate systems, like -

• Supply chain optimization: A multi-agent ecosystem could've one agent negotiating with vendors, another forecasting demand and the third looking to optimize logistics routes, all agents basically collaborate to ensure supply chain resiliency. This orchestration needs multiple agents to run (one for base data, one for anticipated data, one for calculations, and one for conversion into a decision).


• Cross-functional workflows: Agents can collaborate for tasks across disparate departments. For instance, an agent can analyse the customer feedback and communicate further with both customer service and product dev teams to acknowledge the identified problem.


• Enhanced governance: In the multi-agent systems, orchestration ensures good collaboration while oversight and guardrail mechanism ensure that all autonomous actions remain auditable and compliant.

Some FAQs on Agentic Business Intelligence (BI)

Final Thoughts

Henceforth, it can be understood that Agentic AI in business intelligence marks a revolutionary change from dashboards to goal-centric intelligence. Despite mere reporting, it capitalizes businesses to anticipate, learn, and act in real-time.

The road ahead for business intelligence lies in systems that not just inform decisions but actively accelerate them. That’s where Agenthood AI comes into play. Built to operationalize Agentic BI, Agenthood AI understand teams and leaders with proactive, conversational intelligence aligned with your business goals.

Hence, if dashboards defined the last decade of BI, Agentic AI will redefine the next. Get in touch today!