The race is on. Manufacturers and distributors everywhere are sprinting to adopt artificial intelligence (AI), automate processes, and deliver personalized digital experiences. Yet, even as they pump resources into ambitious AI initiatives, many find themselves tripping over the very first hurdle—their foundational data infrastructure.
Companies rush toward sophisticated AI solutions—assuming their data foundation is solid. But that assumption often proves disastrously optimistic. The reality for most organizations is stark: outdated taxonomies, disconnected data silos, inconsistent product information, and opaque governance practices create foundational cracks that no amount of fancy AI can patch. When you build your AI capabilities on a shaky foundation, the outcomes are predictable: poor search experiences, failed personalization efforts, frustrated customers, and digital initiatives that underperform or outright fail.
The Hidden Enemy: Foundational Data Problems
We've seen firsthand the costs companies incur when they overlook foundational data issues. These challenges typically lie hidden in plain sight—within tangled taxonomies, fragmented data repositories, and opaque product management processes.
One prospect, a major B2B enterprise, had identified over 600 data elements scattered across multiple departments that urgently needed structuring into a coherent ontology. Their manual data catalog was slow, error-prone, and unable to support scalable AI-driven applications. This isn't an isolated case; it's representative of a systemic issue across B2B commerce.
The hard truth? Without an effective data ontology, businesses cannot reliably expose product data or content via AI-driven channels such as SharePoint, MS Teams, or sophisticated knowledge graph applications.
AI and the Taxonomy Imperative
Advanced AI demands clarity and consistency. Yet organizations often overlook or underestimate the power of a well-crafted product taxonomy. Proper taxonomy structures your information, enabling precise search, enhancing content findability, and facilitating effective personalization and merchandising.
Consider this scenario: an industrial distributor invests significantly in AI-driven search optimization but fails to invest equally in taxonomy management. The result is a sophisticated search tool that still returns irrelevant, confusing, or redundant results. In contrast, our experience shows that optimized taxonomy structures can boost SEO performance by 20% and improve site search click-through rates by up to 40%.
The implications are profound: improved findability leads to fewer customer frustrations, better merchandising, and significant revenue growth. Yet, many businesses continue neglecting these foundational improvements.
Building the AI-Ready Architecture
Great AI systems are born from carefully cultivated data architectures—not from flashy AI tools alone. When we work with clients, our initial step is always an in-depth discovery process. We dive deep into their current state, analyzing existing taxonomies, product data workflows, search experiences, and underlying data architectures.
The critical discovery activities include stakeholder workshops, comprehensive technical assessments, benchmarking exercises, and rigorous user testing. This holistic approach reveals not just technology gaps but governance and process inefficiencies as well.
A best-in-class AI architecture means having clearly defined and governed data flows, streamlined product onboarding processes, consistent attribute labeling, and robust metadata management. This ensures AI applications have clean, structured data to work from—driving accurate, meaningful personalization and insights rather than generic, frustrating digital experiences.
Avoiding Costly AI Detours and Technology Dead-Ends
Rushing into AI without fixing your foundational data structures is like building a house on quicksand—it might look good initially but soon collapses under pressure. Time and again, we see organizations chasing after the latest AI trends—such as agentic architectures and advanced automation—without first considering how these technologies interact with their underlying data infrastructure.
We advocate a cautious, phased approach. Begin with proof-of-value (POV) projects in limited areas or product categories. These smaller initiatives reveal crucial insights, validate the impact of well-structured data, and avoid costly mistakes from premature scaling.
A Playbook for AI-Driven Transformation
Rather than contributing to the overhyped promises of AI, practical guidance grounded in reality is needed. Here’s the Earley playbook to fixing your foundation first:
Moving Beyond the AI Hype
The real transformation doesn’t come from jumping straight into the deep end of AI hype. It emerges from diligent groundwork—methodically building, refining, and governing the structures your AI depends on. This is the hard but essential work of digital transformation.
As we advise our clients at Earley Information Science, effective AI requires robust foundational data practices—not magic bullets. Only by investing in solid taxonomies, coherent data architectures, and rigorous governance can manufacturers and distributors truly unlock AI’s potential for growth, customer satisfaction, and lasting competitive advantage.
In short: fix your foundation first. Your future digital success—and the scalable AI benefits you seek—depend on it.