Turn Data and AI into Confident Decisions

Move beyond experimentation to embed intelligence across commerce, operations, and the decisions that drive growth.
Let's Talk
View Solutions
Data, AI & Decision Intelligence image

Most organizations don’t struggle with access to AI—they struggle with applying it consistently, safely, and at scale.

McFadyen helps organizations transform data and AI into a reliable decision layer across commerce and operations. Our approach is grounded in proven adoption frameworks, governance models, and real-world use cases—ensuring intelligence delivers measurable business value.

The Problem

Why AI Initiatives Stall Before They Scale

Despite massive investment, many AI initiatives fail to move beyond pilots:

  • AI use cases are disconnected from business strategy

  • Data foundations are fragmented or ungoverned

  • Teams lack clarity on ownership and operating models

  • ROI is difficult to measure in non-linear systems

  • Trust erodes due to poor oversight and explainability

The result is AI fatigue, stalled adoption, and missed opportunity.

Our Point of View

Intelligence Is an Operating Model, Not a Toolset

Sustainable AI adoption requires more than technology. It requires an operating model that aligns people, data, governance, and economics.

McFadyen’s approach is built on years of research and implementation experience, including our reference work AI Best Practices for Commerce, which outlines how organizations move from vision to execution—and from experimentation to dependable intelligence.

Our Framework

A Practical Framework for AI at Scale

Insert copy about our five pillar framework for AI Success.

Strategic Alignment

  • Link AI initiatives directly to business outcomes

  • Prioritize use cases across commerce value streams

  • Balance experimentation with long-term scale

Data Foundations

  • Establish data readiness and accessibility

  • Design architectures that support real-time decisions

  • Maintain quality, lineage, and observability

Governance & Trust

  • Define ownership and accountability models

  • Implement responsible AI guardrails

  • Build trust through transparency and oversight

Capability Building

  • Enable cross-functional teams

  • Develop AI literacy beyond specialized roles

  • Embed prompt and model discipline into operations

Scaling & Economics

  • Measure ROI in compounding systems

  • Manage cost, performance, and risk

  • Transition AI from pilots to production infrastructure

Where Decision Intelligence Delivers Value

Embedded Across the Commerce Lifecycle

  • Intelligent product discovery and personalization

  • Pricing, promotion, and demand insights

  • Inventory and fulfillment optimization

  • Customer service and support intelligence

  • Experimentation and optimization prioritization

These capabilities work together as a system—continuously learning and improving decisions over time.

How We Engage

From Readiness to Production

  • AI readiness and maturity assessments

  • Use case identification and prioritization

  • Architecture and integration design

  • Governance and operating model definition

  • Scaled implementation and enablement
Image