From AI Curiosity to AI in Production
AI creates value only when it is operationalized. Embedded in real workflows. Governed responsibly. Aligned to the outcomes your business is actually measured on.
Where Does Your Business Stand on the AI Readiness Curve?
What Separates AI Pilots From AI That Scales
Most B2B AI initiatives produce promising pilots and limited production impact. The patterns are consistent across organizations:
- AI use cases are selected for novelty rather than business impact
- Data foundations are not structured to support reliable AI consumption
- Ownership, operating models, and success metrics are undefined before the build starts
- ROI measurement frameworks are not established alongside the AI investment
- Governance and explainability frameworks are added after the fact, not built in from the start
The organizations that move past this pattern are not the ones with access to better models. They are the ones with better operating discipline, clearer ownership, and a partner who has built AI into real production environments before.
From Experimentation to Execution
AI enablement is the discipline of making AI work inside real business environments, not in a sandbox or a demo. It ensures models, prompts, and workflows operate reliably at the intersection of your ERP, your commerce platform, and your data architecture. That requires more than data science. It requires architecture, governance, and cross-functional alignment.
The organizations that succeed with AI aren't the ones with the best models, they're the ones with the best operating discipline.
Our AI Offerings
Named Services You Can Engage Today
These are the specific AI services McFadyen delivers. Each is scoped for real engagement, deployable against your current environment, and connected to measurable business outcomes, not theoretical ones.
Five-dimension gap analysis: SEO, performance, AEO/GEO readiness, content quality, accessibility
Instant prioritized report — free at audit.mcfadyen.ai
Identifies the specific data, content, and technical gaps and gives you a prioritized sequence for closing them
Self-serve benchmarking quiz — assessment.mcfadyen.ai
Benchmarks readiness across data, infrastructure, governance, and use cases
Outputs a personalized roadmap snapshot for leadership
Under 10 minutes, no sales call required
Strategic evaluation of data infrastructure and team readiness
AI use case identification and ROI prioritization
AI adoption roadmap with phased milestones
Governance framework design with clear ownership and accountability built in before deployment begins
AI chatbots for product discovery, order management, and support
Contract-aware pricing responses — returns customer-specific prices, not list prices
Integration with ERP, OMS, and PIM for real-time accuracy
Escalation paths to rep-assisted selling for complex deals
AI-generated product descriptions calibrated to your brand voice
Attribute enrichment across thousands of SKUs automatically
Semantic tagging for AI search and faceted navigation
Multi-language translation with commerce-specific accuracy
MSDS, safety, and regulatory attribute population
Answer Engine Optimization — structured content AI systems cite in responses
Generative Engine Optimization — content architecture for LLM-based discovery
JSON-LD FAQ schema implementation and validation
Authority signal building for AI-cited source status
Content gap analysis vs. AI-cited competitors
Our AI Delivery Capabilities
How We Deliver AI That Works
Use case prioritization by ROI and feasibility
Data and infrastructure gap assessment
AI adoption roadmap and operating model definition
Build vs. buy guidance for AI capabilities
Pilot-to-production sequencing with defined milestones and clear go/no-go criteria at each stage
Prompt engineering and fine-tuning for your commerce vocabulary
AI workflow design and integration — ERP, OMS, CRM, commerce platforms
Human-in-the-loop architecture — escalation triggers and override mechanisms
Partner AI feature implementation (Adobe, Mirakl, commercetools)
Chain-of-thought structuring for complex B2B decision workflows
Output monitoring and explainability reporting
Bias detection and quality controls
Escalation and override frameworks
Audit logging and regulatory compliance alignment
AI operating model definition — ownership, review, success metrics
We apply AI where it creates measurable impact in commerce and operations. Every use case listed here has been deployed in a real B2B environment, not staged for a demo.
- Search and Product Discovery — AI-powered relevance, guided selling, and personalization
- Personalization and Recommendations — behavioral, contextual, and account-based
- Merchandising and Content Generation — AI-assisted catalog enrichment and copy
- Conversational Commerce — AI chatbots for support, product search, and order management
- Experimentation and Optimization — A/B testing velocity and intelligent decisioning
- Operations and Support Automation — order routing, exception handling, ticket deflection
Built for Complex Ecosystems
Commerce & Operations Leaders
IT & Engineering Leaders
Digital Transformation Leaders
