🎮 The Next Input — Issue #188

In partnership with

⚡ The Briefing — 60 sec

• Predictive text is showing a demonstrable decline after AI Autocorrect is very 2019, isn't it? We somehow upgraded from "ducking" to frontier models and managed to make parts of the experience worse. Technology remains undefeated at being weird.

• Anthropic's Dario Amodei has just one direct report One?! That's either the flattest org chart in Silicon Valley or the greatest delegation strategy ever conceived.

• Australian retailers lag AI ambitions as data gaps bite We have got to get on the ball, Australia. Every week the conversation is "we want AI" followed immediately by "our data is a complete dumpster fire."

🛠️ The Playbook — Data Readiness Accelerator

Mission
Build a practical foundation that allows AI projects to move from ambition to execution without being blocked by poor data quality.

Difficulty
Intermediate

Build time
3–4 hours

ROI
Unlocks future AI initiatives while improving reporting, operational visibility, and decision-making immediately.

0) Why This Matters

Most AI projects don't fail because the models are bad.

They fail because:

• data is fragmented

• systems don't talk to each other

• ownership is unclear

• reporting is inconsistent

• nobody trusts the numbers

The dirty secret of AI transformation is that the first step usually isn't AI.

It's fixing the plumbing.

1) Architecture

2) Workflow

1. Identify the systems containing critical business data.

2. Consolidate data into a central repository.

3. Establish ownership and validation rules.

4. Create dashboards that expose quality issues.

5. Connect retrieval and AI workflows to trusted data sources.

6. Continuously monitor and improve data quality metrics.

3) Example Prompts

Data Readiness Prompt

You are a data transformation consultant.

Review the following business systems.

Identify:
- data silos
- duplicate records
- reporting inconsistencies
- ownership gaps
- AI readiness blockers

Rank issues by business impact.

Data Quality Audit Prompt

Analyse the following dataset.

Identify:
- missing values
- duplicate records
- inconsistent formatting
- outdated information
- potential reporting risks

Provide remediation recommendations.

AI Readiness Assessment Prompt

Assess whether this organisation is ready for AI deployment.

Evaluate:
- data quality
- system integration
- governance maturity
- reporting accuracy
- operational ownership

Return a readiness score and action plan.

4) Guardrails

• Establish clear data ownership.

• Validate critical records before automation.

• Track data lineage and source systems.

• Monitor data quality continuously.

• Avoid automating bad processes.

• Ground AI outputs against trusted sources.

5) Pilot Rollout — 3 hours

1. Select one business-critical reporting process.

2. Map all contributing data sources.

3. Identify inconsistencies and duplicates.

4. Build a central reporting dataset.

5. Create visibility dashboards.

6. Measure quality improvements weekly.

6) Metrics

• Data completeness

• Duplicate record rate

• Reporting accuracy

• Dashboard adoption

• AI retrieval accuracy

• Time spent preparing reports

• System integration coverage

Pro Tip: Most organisations don't have an AI problem. They have a data problem wearing an AI costume.

🎯 The Arsenal — Tools & Platforms

• Pinecone Pinecone · retrieval and knowledge grounding · Link

• Microsoft Power BI · reporting and operational visibility · Link

• OpenAI GPT-5.5 · analysis and automation workflows · Link

• PostgreSQL PostgreSQL · centralised operational data store · Link

• Microsoft Fabric · data integration and analytics platform · Link

Copy-paste prompt block:

You are an enterprise data readiness consultant.

Assess my organisation's readiness for AI.

Evaluate:
- data quality
- system integration
- reporting maturity
- governance controls
- ownership structures
- AI deployment readiness

Return:
1. readiness score
2. critical blockers
3. quick wins
4. 90-day roadmap
5. governance recommendations
6. success metrics

💡 Free Office Hours

Nearly every AI conversation eventually arrives at the same destination: data quality. The good news is that fixing it usually creates value long before the first AI workflow goes live.

Book here: https://calendly.com

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🕹️ Game Over

Everyone wants the AI.

Nobody wants to clean the spreadsheet.

One of those things is usually required before the other.

— Aaron Automating the boring. Amplifying the brilliant.

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