AI Sales Forecaster

The Challenge

Sales and commercial teams needed consistent, explainable demand forecasts to support planning and commercial decision-making. The gap was not the idea — it was data readiness, stakeholder alignment, and the absence of evaluation frameworks that could make model outputs trustworthy enough to act on.

My Approach

1. 01

   Stakeholder alignment

   Worked with Sales and Commercial stakeholders to define forecasting objectives, decision use cases, and success criteria before any modelling work began.

2. 02

   Data readiness audit

   Assessed historical Canon Australia sales data for quality, completeness, and gaps. Mapped governance constraints to ensure data usage was appropriate for downstream model inputs.

3. 03

   Scenario framework

   Defined scenario-based forecasting dimensions by product, retailer, and time period to give planning teams structured, actionable views of demand.

4. 04

   Evaluation framework

   Established confidence ranges, validation approaches, and guardrails to ensure forecasts are suitable for business decision-making before deployment.

5. 05

   MVP scoping

   Prioritised the minimal feature set required to launch safely and validate real-world utility with Sales and Commercial teams.

The Outcome

Established a clear MVP definition, evaluation framework with confidence ranges, and validation approaches that set the stage for a trustworthy and scalable sales forecasting launch.

Key Learnings

• Explainability is a prerequisite for AI adoption. Teams will not trust what they cannot interpret.

• Data readiness must be validated before committing to model timelines.

• Governance and guardrails are product features, not afterthoughts.