Workshop: AI Analytics Use Cases

Why existing tools fall short?

You See the Numbers. Not the Answer.

Dashboards describe. They don't diagnose.
"Revenue is down 12% in Region A" is a dashboard answer. "Revenue is down because three key accounts shifted orders to Q3 due to a procurement freeze, and two competitors launched promotional pricing" is an AI agent answer. The difference is not technology, it's the playbook that tells the agent how to reason about your domain.
Every answer requires an analyst.
When a CFO asks a question, it flows to the analytics team, gets queued, gets answered 48 hours later in a slide. AI agents answer instantly, at any hour, with the same data and they take action based on the answer. This workshop installs that capability directly into your team.
Pilots don't reach production.
Most AI analytics projects die after the demo because the team doesn't know how to encode domain knowledge into a playbook that works reliably at scale. This workshop teaches exactly that, the playbook architecture that separates working agents from impressive demos.

Three use cases built on the day

Real Business Scenarios. Real Working Agents.

Let's talk data!

Schedule a meeting

Accounts Receivable Root Cause Analysis

A finance director notices overdue AR spiked 35% month-over-month. Instead of queuing a request to the analytics team, she asks the agent directly. Within minutes, it identifies the two clients driving 78% of the increase, checks their invoice history, flags an unauthorized payment term change, and drafts the follow-up emails, with invoice data already injected.

Decompose by dimension - agent breaks down overdue AR by client, region, aging bucket, and payment terms. Identifies which accounts drive the variance.
Drill into root cause - checks invoice history, payment patterns, credit limits, and order volumes. Flags unauthorized term changes and stalled invoices.
Benchmark - compares current DSO to 6-month average and industry benchmark. Quantifies the deviation.
Act - drafts personalized account manager emails with invoice data injected. User reviews, edits if needed, and sends from within the interface.

Revenue Variance with Market Context

Internal revenue growth looks strong, until you compare it to the market. The agent queries internal sales data, pulls external category benchmarks, and surfaces where the company is genuinely outperforming versus simply riding a market tailwind. It then identifies high-growth segments with low company presence and pushes a structured opportunity brief to the right channel automatically.

Internal analysis - agent queries revenue by category, region, and channel. Identifies top growing and declining segments YoY and QoQ.
Market enrichment - searches external sources for category-level market growth rates. Compares internal performance against market movement.
Opportunity scan - identifies high-growth market segments with low company presence. Applies competitive intensity scoring.
Act - generates a structured opportunity brief, pushes to Slack, drafts a calendar invite for the category review meeting.

Accounts Receivable Root Cause Analysis

Impact mapping - agent identifies all affected products, current inventory, production schedules, and stockout risk timelines.
Revenue exposure - calculates revenue at risk across committed orders and forecast. Flags key accounts above materiality threshold.
Scenario modeling - presents two scenarios: wait vs. emergency alternative sourcing. Includes cost delta and customer impact for each.
Act - on manager's decision, executes cascade: Slack alert to production, Jira PO ticket for procurement, personalized emails to key account managers.

The difference between a demo and a decision tool

An AI agent without a playbook is an LLM with a data connection. An AI agent with a well-designed playbook is a decision support system. This workshop teaches you to build the latter.

Decomposition rules

Which dimensions to drill first, in what order, and at what threshold. The logic that ensures the agent reaches the same root cause a senior analyst would

Benchmark lookups

What constitutes a deviation worth flagging. Historical averages, industry benchmarks, and internal targets embedded directly into the agent's reasoning chain.

Action triggers

When to send an email, create a Jira ticket, post to Slack, or draft a calendar invite. Actions are conditional, context-aware, and require human approval before execution

Hallucination prevention

Testing strategies, feedback loops, and monitoring frameworks that keep the agent within the boundaries of what it knows, and transparent about what it doesn't.

Who should attend?

For business and technical teams

Data analysts & BI developers
The practitioners who translate business questions into analytical workflows. They build and configure the agents, connect to the DWH, and design the playbooks.
Analytics managers & data product owners
Responsible for what the analytics function delivers to the business. They identify which use cases have the highest ROI and own the production deployment decisions.
CDOs, CFOs, and VP Commercial
Senior leaders who need to understand what's possible before committing budget. They observe the use cases live, including the AR and revenue variance agents running against realistic data.

Dashboards Tell You What.
AI Tells You Why.

You See the Numbers. Not the Answer.

Dashboards describe. They don't diagnose.

Every answer requires an analyst.

Pilots don't reach production.

Real Business Scenarios. Real Working Agents.

Let's talk data!

Accounts Receivable Root Cause Analysis

Revenue Variance with Market Context

Accounts Receivable Root Cause Analysis

The difference between a demo and a decision tool

Decomposition rules

Benchmark lookups

Action triggers

Hallucination prevention

For business and technical teams

Data analysts & BI developers

Analytics managers & data product owners

CDOs, CFOs, and VP Commercial

Three Use Cases Built.
One Day Invested.

We are here to answer your questions

Dashboards Tell You What.AI Tells You Why.

You See the Numbers. Not the Answer.

Dashboards describe. They don't diagnose.

Every answer requires an analyst.

Pilots don't reach production.

Real Business Scenarios. Real Working Agents.

Let's talk data!

Accounts Receivable Root Cause Analysis

Revenue Variance with Market Context

Accounts Receivable Root Cause Analysis

The difference between a demo and a decision tool

Decomposition rules

Benchmark lookups

Action triggers

Hallucination prevention

For business and technical teams

Data analysts & BI developers

Analytics managers & data product owners

CDOs, CFOs, and VP Commercial

Three Use Cases Built.One Day Invested.

We are here to answer your questions

Dashboards Tell You What.
AI Tells You Why.

Three Use Cases Built.
One Day Invested.