An AI Strategy engagement delivers:
AI Readiness Assessment
Analysis of your current state across people, process, data, and technology dimensions. This assessment identifies capabilities you have and gaps that need to be addressed.
Use Case Identification
3-5 high-value AI use cases grounded in your business metrics. Each use case articulates the business problem, how AI solves it, expected outcomes, and implementation requirements.
Business Case Development
Financial analysis showing expected ROI for each use case, including implementation cost, ongoing operating cost, and expected benefit.
Data Requirements Analysis
Assessment of data quality, availability, and governance required to execute each use case. Identifies data gaps and remediation needs.
Implementation Roadmap
A 12-18 month roadmap showing which use cases to implement first, timeline, resource requirements, and success metrics.
Capability Gap Analysis
Identification of skills, tools, and platform capabilities you'll need to build or acquire to implement the roadmap.
The BPR-AI Connection
AI is most powerful when it's augmenting or automating well-designed processes. If you implement AI on top of broken processes, you're amplifying those problems.
"Conduct BPR across your core processes (8 weeks), identify where AI can amplify the value of reengineered processes (4 weeks), and develop AI implementation roadmap (4 weeks)."
This sequence ensures that AI is solving real business problems in the context of optimized processes. We often recommend business process reengineering as a foundation for AI to ensure long-term scalability and maximum ROI.
Retail Chain Demand Forecasting:
From Spreadsheets to AI
The Challenge
A retail chain with 120 stores and A$500M annual revenue struggled with demand forecasting accuracy. Manual methods were missing patterns, resulting in 2-3% annual margin loss through excess inventory and forced discounting.
Current State
Spreadsheet-based forecasting using only historical sales. Forecast accuracy at 75%, leading to A$15-20M in annual excess inventory and A$10-15M in excess discounting.
What DSV Did
Identified demand forecasting as the primary ROI opportunity. Developed a ML model trained on historical sales plus external data (weather, competitor activity). Designed a 12-week implementation roadmap.
The Results
Engagement Timeline
AI Strategy development, use case identification, and business case validation.
Data preparation, model training, pilot testing, and production deployment.
What We Don't Do
Clarity is the foundation of a successful engagement. To ensure we deliver maximum value, we are clear about where our expertise ends.
We are not a software firm
We don't build custom AI models or write production code. We identify where AI creates business value and guide you toward proven, scalable solutions.
We are not an AI research lab
We focus on practical, proven applications that deliver business outcomes in 12-18 months, not experimental or academic research.
We are not a GenAI hype vendor
We don't recommend Generative AI because it's trendy; we recommend it only when it solves a specific, quantified business problem.
We aren't an 'AI Transformation' firm
We don't promise to turn your business into an AI company; we promise to identify where AI creates measurable business value and guide you toward outcomes.
Frequently Asked Questions
Strategic clarity on the AI landscape.
Stop Experimenting.
Start Competing.
Don't build an AI demo. Build a strategic advantage. Let's discuss your AI readiness.
Talk to Our AI Strategists