Do companies need a knowledge graph?
Knowledge graphs deliver value when domain complexity, AI dependency, and system fragmentation exceed specific thresholds
Direct answer
Not every company needs a knowledge graph. The architecture delivers value when domain complexity, AI dependency, and system fragmentation create problems that simpler structures cannot solve efficiently.
Companies operating below specific thresholds for these factors gain marginal value from knowledge graph investment. Those above these thresholds face competitive disadvantage without them.
Domain complexity threshold
Knowledge graphs become necessary when a business domain involves interconnected entity types where relationships carry semantic meaning that changes frequently, and where relationship logic cannot be maintained efficiently through database schema design.
Indicators you have crossed this threshold:
Your organisation tracks more than 50 distinct entity types (products, materials, applications, certifications, customers, suppliers, regulations) where the connections between them define business logic. IT teams spend significant time modifying database joins whenever business relationships change. Documentation explaining how entities relate to each other exists separately from the systems that store entity data.
Indicators you have not crossed this threshold:
Your domain involves straightforward entities with stable relationships that map cleanly to database tables. Relationship logic rarely changes. Query patterns are predictable and well-served by existing database joins.
AI application dependency
Companies building or deploying AI systems that need to reason over domain knowledge—procurement agents, recommendation engines, automated research assistants, supplier evaluation tools—require machine-readable semantic structures.
Indicators you need knowledge graph architecture:
Your organisation runs 3 or more AI systems that need relationship reasoning (procurement agents, recommendation engines, automated research, supplier evaluation). AI systems must answer questions requiring entity connections, not just document mentions. AI-generated recommendations must be explainable through relationship chains. Hallucination risk creates business liability in your domain (regulated industries, safety-critical applications, supplier qualification).
Indicators you do not need knowledge graph architecture:
You operate fewer than 3 AI systems, or existing AI applications focus on classification, summarisation, or text generation where relationship reasoning is not required. Humans remain the primary consumers of your data. Statistical approximation is acceptable for your use cases.
System fragmentation
Organisations operating multiple systems where consistent entity resolution and relationship mapping matter face exponential integration complexity with point-to-point connections. Knowledge graphs provide semantic integration layers that scale linearly rather than geometrically.
Indicators you need semantic integration:
Your organisation operates 5 or more systems (CRM, ERP, PLM, documentation management, marketing automation) that reference overlapping entity sets. The same customer, product, or supplier appears differently across systems. Integration teams spend substantial time maintaining mappings between systems. Data quality issues stem from inconsistent entity references rather than data entry errors.
Indicators standard integration suffices:
Your systems operate independently with minimal overlap. Entity references remain consistent across the few systems that share data. Integration requirements are simple and stable.
Rate of change considerations
Knowledge graphs deliver higher value in domains where relationship logic evolves faster than IT can maintain hardcoded integration logic.
If your business relationships change quarterly or more frequently—new material applications, shifting supplier networks, evolving certification requirements, changing regulatory contexts—maintaining database schemas and application code becomes a bottleneck. Knowledge graphs allow business teams to update relationship logic without developer intervention.
If your domain experiences fewer than 4 significant relationship changes per year and those changes follow predictable patterns, the flexibility advantage diminishes.
When knowledge graphs are unnecessary
Companies should not invest in knowledge graph architecture when:
Information is simple and static A product catalogue with stable hierarchical categories and infrequent updates does not justify knowledge graph complexity. Standard database structures serve these requirements efficiently.
Accuracy is not critical Marketing content, general research, or applications where statistical approximation is acceptable do not require the precision knowledge graphs provide.
AI involvement is minimal If humans mediate all research, evaluation, and decision-making, machine-readable semantic structures provide limited value. Knowledge graphs optimise for machine reasoning, not human reading.
Resources are constrained Knowledge graphs require upfront investment in entity identification, relationship modelling, and ontology design. Organisations without capacity for this foundational work should defer until resources align with requirements.
What usually happens next
Organisations that meet the thresholds typically begin with:
Entity inventory across existing systems to identify overlap and inconsistency. Relationship mapping for high-value domains where semantic clarity creates immediate impact. Pilot implementations focused on specific AI applications or integration problems rather than enterprise-wide deployment.
Organisations below the thresholds typically continue with existing database and content management architectures until domain complexity or AI requirements force reevaluation.