Data Friction & AI Readiness Scan

Expose hidden data friction, quantify manual rework costs, and build a clear roadmap for AI deployment.

Quantify manual rework costs, resolve source-of-truth conflicts, and allocate proven automation methods across high-yield use cases.

Best For

Organizations seeking an objective evaluation of data readiness and friction before committing capital to AI or automation initiatives.

Pricing

Executive Diagnostic Sprint: $5,000* (Remote / $5,500–$6,500 data dependent)

In Person Deeper Sprint: $8,500* (On-Site Visit)

*50% credit toward Solution Blueprint within 30 days

Resource & Time Investment

Timeline: 2 to 3 Weeks (Fixed Sprint)

Interventions: Data Systems Inventory, Rework Dollarization & 4-Tier Method Allocation

Allocation: 12-18h Technical Scan + 12-18h Principal Consultant + 4h Admin

Ongoing Tech: 2-3 Flat File Exports (Zero Production Write Access)

Phase 01

Phase 02

Phase 03

Days 1 – 5

// Objective

Catalog all active applications, data stores, and spreadsheets while conducting focused stakeholder interviews to map data flows.

// Action Steps

Conduct 60 to 90 minute structured interviews with primary process and data owners.
Catalog core operational platforms, peripheral tools, and shadow IT spreadsheets into a unified systems register.
Collect 2 to 3 representative data exports to examine structural integrity, missing values, and formatting drift.

// Linked Models

// Key Deliverable

Enterprise Systems Inventory Register and Stakeholder Interview Findings Memo.

Days 6 – 10

// Objective

Quantify the financial burden of manual re-keying and identify conflicting records across software silos.

// Action Steps

Audit data overlap across systems to highlight conflicting sources of truth and redundant entry points.
Convert manual data manipulation, CSV formatting, and rework hours into annual labor costs.
Score data cleanliness, schema stability, and accessibility across potential automation vectors.

// Linked Models

// Key Deliverable

Manual Rework Cost Ledger & Comprehensive Data Readiness Scorecard.

Days 11 – 15

// Objective

Rank viable automation opportunities and assign the mathematically appropriate technology method to each.

// Action Steps

Rank identified use cases by feasibility, required data readiness, and expected business yield.
Assign strict Method Allocations (Deterministic, Traditional ML, Generative AI, or Process Change) to prevent over-engineering.
Deliver an executive synthesis outlining high-ROI quick wins versus prerequisites for full-scale AI integration.

// Linked Models

// Key Deliverable

Ranked Use-Case Portfolio & Formal Method Allocation Blueprint.