Building Decision Systems: From Data to Action

You have dashboards. You have reports. You have more data than you know what to do with. But data isn't decisions—and most organizations have built systems that produce data, not action.

01 // The Friction

The Dashboard Trap

Here's the uncomfortable truth: most business intelligence creates more work, not less. You've invested in data infrastructure, built beautiful dashboards, automated reports. But someone still has to read them. Interpret them. Decide what to do.

This is the dashboard trap. The final mile—from insight to action—remains manual. And that's where decisions stall.

The average manager spends 2+ hours per day processing information that should trigger automatic responses. Inventory restocks. Follow-up emails. Pricing adjustments. Scheduling changes. These aren't strategic decisions. They're pattern recognition—exactly what AI does best.

02 // The Framework

The Decision Stack

A proper decision system has three layers. Most organizations only build the first one.

Layer 1: Data Collection

Gathering data from multiple sources into a centralized location. Most organizations have this figured out.

// Output: Dashboards, reports, data warehouse

Layer 2: Pattern Recognition

AI that monitors data streams, identifies anomalies, recognizes patterns, and surfaces what matters. This is where AI adds the most value.

// This is where most organizations are missing

Layer 3: Action Execution

Automated responses to recognized patterns. Reorders triggered. Emails sent. Schedules adjusted. Humans only handle exceptions.

// Output: Actions, not reports

System Insight_

The goal isn't to automate decisions. It's to automate the obvious ones.

80% of operational decisions follow clear patterns: if X happens, do Y. These should never require human attention. Free your people to focus on the 20% that actually requires judgment.

// Prompt: "What decisions am I making today that follow predictable patterns?"

03 // The Toolkit

Picks & Shovels

Building decision systems doesn't require custom development. These tools, combined intelligently, handle most use cases.

Data Layer

Airtable / Notion

Structured data that's actually usable. The foundation of any decision system.

Intelligence Layer

Claude / GPT-4

Pattern recognition and natural language understanding. The brains of the operation.

Automation Layer

Zapier / Make

Connecting systems and triggering actions. The muscles that execute decisions.

Monitoring Layer

Custom Alerts

Slack, email, or SMS notifications for exceptions that need human judgment.

04 // The Signals

How to Know It's Working

A working decision system should produce measurable changes. Here are the signals we track:

→

Time-to-decision

How long from data availability to action taken? Should decrease by 70%+.

→

Manual touchpoints

How many human interventions per decision? Should approach zero for routine decisions.

→

Exception rate

What percentage of decisions require human override? Should stabilize at 10-20%.

→

Decision quality

Are automated decisions producing better outcomes than manual ones? Track and compare.

05 // The Actions

Where to Start

Don't try to automate everything at once. Start with one decision flow:

Map it. Measure it. Automate the obvious parts. Monitor the exceptions. Iterate.

The best candidates for decision automation share three traits: high frequency (happens multiple times per day), clear triggers (specific data conditions), and predictable responses (limited options for action).

Start there. Build confidence. Expand systematically.

Ready to stop drowning in dashboards?

We help businesses build decision systems that turn data into action. Let's map your highest-value automation opportunities.