AI Readiness Assessment - CPG Case Study

Overview

The Challenge

A mid-sized CPG company faced mounting pressure to adopt AI tools without a clear understanding of how, when, and why these technologies should be implemented. The organization needed a strategic framework to evaluate AI solutions based on actual business value rather than market hype.

Discovery Phase

Conducted comprehensive interviews with 12 C-level employees and department heads to understand process inefficiencies, technology debt, and operational gaps

Strategic Approach

Developed company-wide AI ethics and compliance protocol, comprehensive training for organizational alignment, and clear roadmap for tool adoption

Outcome

Prepared organization to make informed AI decisions and avoid blind tool adoption under pressure, creating sustainable competitive advantage

Discovery

Operational Gaps

Through extensive stakeholder interviews, we identified five critical operational gaps preventing the organization from effectively leveraging data and technology.

Data Analysis Paralysis

Teams unable to effectively extract insights from available data, leading to delayed decision-making

Manual Process Bottleneck

Heavy reliance on manual workflows creating inefficiency and increased error rates

Cross-Functional Misalignment

Only 10% overlap in competitive landscape identification across departments

Information Silos

Critical data trapped in departmental silos, preventing holistic business view

Document Management

Lack of centralized system for version control, retrieval, and collaboration on critical documents

Analysis

Departmental Pain Points

Pain points were consolidated into two primary categories: technology infrastructure challenges and process methodology issues.

Tech Debt

Data Infrastructure

Fragmented systems preventing integrated analysis and insights

Automation Gaps

Manual processes for budgeting, reporting, and monitoring

Master Data Quality

Inventory and supply chain data requiring cleanup

Process Debt

Strategic Alignment

Cross-functional misalignment on landscape and priorities

Planning Methodology

Tension between operational needs and strategic planning

Content Management

Manual workflows for compliance and brand consistency

Solution

Implementation Roadmap

A phased approach to sustainable AI integration, designed to build organizational capability and strategic alignment before technology deployment.

1

Foundation

Goal Alignment + Competitive Landscape Mapping

Established organizational consensus on strategic priorities and competitive positioning, creating baseline understanding of market forces and internal capabilities.

2

Structure

Data Centralization Strategy

Designed infrastructure roadmap to break down information silos and create unified data architecture supporting advanced analytics and AI applications.

3

Training

Internal FAQ Development

Created comprehensive knowledge base and training program to democratize AI understanding across all organizational levels and departments.

4

Execution

Inventory Clean Up + Strategic Projects

Launched pilot initiatives teaching AI fundamentals through hands-on projects, establishing clear evaluation criteria for future tool adoption.

Key Outcomes

By addressing foundational process and tech debt issues, many organizational challenges were immediately mitigated—removing the perceived and artificial reliance on AI as a catch-all solution
Strategic projects teaching AI fundamentals at individual level, building organizational capability from the ground up
Clear pathway for tool adoption based on actual business needs rather than vendor marketing or competitive pressure
Organization equipped to evaluate AI tools critically versus blindly, ensuring ROI and strategic alignment on all technology investments
Established AI ethics and compliance framework protecting the organization from regulatory and reputational risks
Cross-functional alignment on competitive landscape and strategic priorities, reducing organizational friction and accelerating decision-making