Agentic DI: A modern Agentic AI approach to enterprise decision making

A Real Story

An executive asked for a new portfolio management dashboard to track project and portfolio performance and resource allocation. The data team got to work, pulling reports from multiple disconnected tools - PMO systems, timesheets, portfolio management systems, finance, and capacity planners.

But when the first version landed, it lacked key insights. Definitions were unclear. Important metrics were missing.

Weeks went by as the data team struggled to keep up with evolving demands, rewriting queries, manually aligning inconsistent definitions across systems, and rebuilding visuals repeatedly. Meanwhile, critical business decisions were put on hold, waiting for clarity that never came quickly enough.

The disconnect between business needs and the analysts' understanding of those needs created costly delays and frustration on both sides.

Takeaways

This real-world example reveals a fundamental challenge: traditional BI processes and tools are not designed for fast, aligned, and adaptive decision-making. There has to be a better way.

Today's enterprises face a paradox: we have more data than ever, but decisions are slower than ever.

Long review cycles and manual handoffs in legacy BI pipelines create decision latency - the gap between when data is available and when action occurs. Studies show that delays caused by fragmented data and manual reporting erode the value of analytics investments. In practice, teams spend hours each week reconciling sales, inventory, and supplier data; 73% of organizations report decision delays due to this fragmentation.

By the time a dashboard reaches an executive, the insight is often too late to matter. Decision Latency: Gartner reports that over 60% of data leaders now rank "reducing time to action" as a top priority. Static dashboards and scheduled reports don't meet today's demands for agility. Siloed Data: With data living in disconnected systems (ERP, CRM, data lakes), 67% of businesses use five or more analytics platforms. This leads to 12-15 hours of manual stitching per analyst per week. Analyst Bottlenecks: Ad-hoc questions still rely on scarce analyst cycles. Most BI tools weren't built for instant answers or autonomous workflows.

These problems create drag. They undermine strategic agility.

And they point to the need for a new intelligence layer that's fast, connected, and adaptive.

The Rise of Agentic Decision Intelligence

For decision making using a modern Agentic AI approach, I introduce the concept of Agentic Decision Intelligence (ADI).

Agentic Decision Intelligence (ADI) is an AI-native approach to enterprise decision-making that integrates real-time data analysis, predictive modeling, and autonomous agents to guide actions. Unlike traditional BI, which focuses on retrospective reporting, ADI continuously interprets data, anticipates outcomes, and recommends or initiates optimal next steps. It transforms analytics from a passive resource into an active participant in business execution - enabling faster, context-aware, and adaptive decisions at scale.

An Agentic DI platform ingests and analyzes data and applies AI agents to simulate outcomes, optimize recommendations, and automate responses. These agents don't just interpret data-they can act on it. They can be configured to be autonomous, real-time, and continuously learning.

The output isn't a static chart. It can be an intelligent suggestion or autonomous enough to execute actions on the system on behalf of the user. Delivered on time, in context.

Agentic Decision Intelligence transforms how decisions are made:

From reports to recommendations From questions to guided action From reaction to preemptive strategy

IDC reports that by 2026, 50% of Asia/Pacific-based Top 2000 organizations (A2000) will adopt AI-driven headless BI and analytics with chat, Q&A, and proactive notification functionality. This is the rise of composable and integrated, analytics-driven decision making. Forward-thinking firms are experimenting with adopting agentic AI systems that adapt continuously, detecting shifts before humans even realize something changed.

Overcoming Challenges in Implementing Control-Tower Dashboards

While the benefits are significant, businesses may face some challenges in implementing such systems:

1. Data Integration: Aggregating data from multiple sources into one dashboard can be complex, especially if different departments use distinct systems.
   • Solution: Leverage modern integration platforms and APIs to facilitate seamless data connections.

2. User Adoption: Getting employees and managers to consistently use the dashboards may require a cultural shift.
   • Solution: Provide training and demonstrate the value through quick wins and easy-to-understand visualizations.

3. Customization Needs: A one-size-fits-all dashboard may not meet every department’s unique needs.
   • Solution: Design modular dashboards that allow departments to view the KPIs and metrics most relevant to their functions.

A Layered Architecture for Agentic DI

Control-tower-style dashboards are game-changers for businesses looking to thrive in a complex environment. They empower companies to stay ahead of disruptions, make data-driven decisions, and continuously improve their operations. As technology evolves, more businesses are embracing these dashboards not just as a monitoring tool but as a strategic enabler, offering the visibility and agility required to succeed.

Organizations that invest in such tools position themselves to grow faster, adapt more easily, and delight customers in ways that competitors with limited visibility cannot. Whether you’re optimizing logistics, managing manufacturing, or overseeing cross-functional operations, a control-tower-style dashboard is no longer a luxury—it’s a necessity for sustainable success.

With the right implementation, operational visibility becomes a catalyst for better outcomes across every layer of the business, making control-tower dashboards a vital part of the modern organization’s toolkit.

Scalable, real-time intelligence requires more than just smart algorithms. It requires a layered architecture that turns raw data into real-time action.

Here's how I envision an Agentic DI stack should be structured:

1. Data Layer - Unified Connectivity Ingests streaming, batch, and archived data from all enterprise systems. This layer harmonizes input from ERP, CRM, data lakes, APIs, and more - reducing integration latency and breaking silos. Cloud-native data platforms, data warehouses and lakehouses accelerate this unification.

2. Semantic Layer - Shared Understanding A business-centric model defines key metrics, dimensions, and logic. This "universal translator" ensures consistent meaning across systems and queries. It enables AI agents to reason with context and recommends role-based filters and privacy rules by default - limiting exposure of sensitive data.

3. Transformation & Action Layer - Orchestration Where logic meets execution. This layer transforms data, applies rules, and connects analytics to operational systems. It can trigger alerts, generate transactions, or adjust plans automatically based on, either user-defined rules, or instructions received from the AI Layer. For example, a predicted stockout triggers a restock order - either with or without a human in the loop.

4. AI Layer - Models and Agentic Intelligence This is the engine. It houses the core analytics piece to make sense of the data. It includes predictive models (e.g., forecasting, anomaly detection) and LLM-powered agents that understand natural language. Because it takes the semantic layer into consideration, this AI is context-aware, continuously updated, and self-improving. It replaces days of analysis with instant, intelligent insight. It also determines the right action to take and orchestrates agents in the Transformation & Action Layer accordingly.

5. Frontend Layer - Decision Surfaces Gone are static dashboards. This layer provides interactive interfaces where decision-makers ask questions, test scenarios, and take action. Users can prompt to create entire dashboards. They can also chat with charts. From a chart showing predicted sales dip, the user can get recommendations on next steps - if marketing plans need to change or inventory needs to be adjusted - and even execute them in a few clicks, from the dashboard itself.

Each layer resolves a traditional pain point. Combined, they form a real-time, autonomous loop that continuously adapts decisions to reality.

Scenario: Real-Time Inventory & Logistics

A global retailer managing thousands of SKUs relied on lagging dashboards.

• Reports took days to compile.
• Stock decisions were reactive, not proactive.
• Supply was not able to efficiently meet demand.

Now, with Agentic DI:

• Sales and shipment data stream in hourly.
• Predictive models forecast demand and detect anomalies.
• The semantic layer aligns data across warehouses and SKUs.
• The action layer suggests restocking orders and logistics adjustments.
• Users can freely create and update charts by chatting and take actions from the dashboard itself.

AI agents provide proactive insights - not just status updates.

The result? Fewer stockouts. Faster pivots. Smarter plans. Real value unlocked.

Teams became free to focus on other important things instead of waiting for the right insights.

From Concept to Capability: Tower as an Agentic DI Implementation

The architecture is clear. The value is proven. But most organizations don’t have the time or resources to custom-build a fully integrated Agentic DI stack. Generative AI and standalone AI agents are not enough - especially when consistency, security, and business alignment are at stake.

Enter Tower - AI Dashboards for Business Leaders by Codygon.

Tower is a purpose-built implementation of Agentic DI that integrates seamlessly with existing data and BI stacks. While Tower can be used standalone for AI dashboards, it doesn’t aim to replace your existing data stack completely. It acts as an intelligent control layer, stitching together data ingestion, semantics, data transformations and actions and AI, into a single decision system. It integrates with platforms like Spotfire, Power BI, Snowflake, Databricks, HRMS, ERP systems and more - augmenting them with context-aware agents that understand your business logic and act on it, on time.

Using the semantic model as a guide, Tower federates queries across systems and applies AI on the fly. It converts dashboards into decision surfaces. A sales chart becomes a control panel: approve discounts, route leads, adjust pricing - all in a few clicks, or autonomously.

Tower ensures governance too. By masking or aggregating sensitive data at the semantic layer, it enables privacy-first AI. End users and agents see only what they're allowed to see, often in anonymized form. Actions go through secure APIs, with full audit trails.

Tower gives business leaders the ability to freely create reports by chatting, while still having the guardrails defined by data managers and AI specialists in the organization This is decision-making, automated. Data-driven, agentic.

Tower isn't just enabling reports. It's enabling outcomes.

And it's doing it across every tool in your stack.

The future isn't just insight. It's intelligent action.