Relational artificial intelligence (AI) represents an evolution in systems that understand the interconnected nature of enterprise data and leverage those relationships to power intelligent applications. By modeling knowledge as a graph database rather than rigid code or statistics alone, both humans and machines can better collaborate to uncover insights.
The Accelerating Need for Connected Intelligence
Imagine an average enterprise today – it likely stores customer data in CRM systems, financial records in ERP software, supply chain transactions in logistics platforms and so on. These data lakes keep growing, but lie isolated in silos, much like islands disconnected from one another.
- Valuable customer insights get buried between marketing metrics and service call logs.
- Market indicators that can significantly impact financial forecasts go unseen.
- Weak signals insentiment analysis that can alert product teams to changing tastes are ignored.
This is where the connective power of relational AI comes in – to harness these dispersed data sources into a flexible fabric that links context, derives meaning and triggers actions.
Like the human brain which draws intelligence from the connections between neurons – the fundamental units of knowledge storage and processing – relational AI systems encode facts and rules as a network of data points and their interactions, mimicking the associative nature of cognition.
The Relational Knowledge Graph Approach
Relational knowledge graphs are structured repositories of concepts and relationships around business entities like customers, products, assets etc. that create a foundation for automated reasoning.
These graphs can ingest data from multiple sources, align metadata meaningfully and apply analytics to derive connections. Much like a database schema captures the structure of data, these graphs capture functional business knowledge and application logic declaratively.
Now, what can linking such enterprise knowledge together achieve?
| Business Area | Benefits of Interconnectivity |
|---|---|
| Customer Intelligence | Holistic customer profiles by connecting data across engagements, journeys, segmented behaviors etc. |
| Financial Forecasting | Informed projections by relating economic indicators, market dynamics to internal budgets. |
| Supply Chain Optimization | Align supply-demand by connecting inventories, production schedules, logistics flows to expected demand. |
| Personalization | Tailored recommendations by relating customer affinities to real-time contextual signals. |
| Predictive Maintenance | Early identification of asset issues by correlating sensor data, failure patterns and usage models to prescribe interventions. |
"Linked information networks mirror the networked nature of real-world interactions and lead to higher quality AI applications," says Dr. Michelle Zhou, Principal Data Scientist at RetailRelAI and author of ‘Knowledge Graphs for Enterprise AI‘.
Relational models adapt better to change across unlike rule or ML-based systems – as new data gets ingested, associations and behaviors automatically get updated without losing integrity. Zhou confirms that "continuous evolution comes intrinsically to graphs."
In 2022, knowledge graphs held an ~40% share of data fabric projects – showing rising adoption as data volumes are estimated to hit 79ZB globally by 2025 (source: IDC).
Bringing the Power of Graphs to Applications
Relational AI platforms utilize cloud-based graph databases to integrate data, create knowledge representations, apply automated reasoning, and serve insights through applications – accelerating development life cycles by over 10X.
Let‘s see two examples of industry applications powered by relational AI and knowledge graphs:
Financial Portfolio Optimization
WealthRelAI leverages customer portfolio data, profiles of available securities, market indicators etc. to create an ‘Investment Knowledge Graph‘ relating concepts like asset types, sectors, geographies and their real-time performance statistics. Declarative rules then enable automated monitoring, alerting and optimization of portfolio balances driven by market dynamics.
Healthcare Patient Journey Orchestration
PatientJourneysRelAI maps the end-to-end experiences of patient cohorts across different care facilities to connect diagnostics data, treatment plans, clinical workflows, insurance platforms and more. Customized analytics identify gaps in care continuity and maximize outcomes. Inventory systems also get real-time signals for resource allocation.
Such solutions showcase how connected intelligence amplifies value across the enterprise data stack – from ingestion to storage to analytics and visualization.
The Future with Living Business Models
As AI continues maturing across verticals like financial services, healthcare, retail, communication and more – achieving business success increasingly relies on building knowledge connectivity across processes, customers, products and partners.
Relational models create living maps that capture changing landscapes in near real-time – learning continuously like the brain through new entities, relationships and behaviors while retaining robustness. They tame data chaos and drive responsiveness simultaneously.
Much like GPS navigation derives power from the connections between destinations, routes and signals – helping chart best paths; knowledge graphs become the GPS for enterprises aiming to navigate mounting data complexity.
As Zhou puts it – "In linking isolated data islands, we weave a tapestry of insights greater than the sum of its parts." There lies the true potency of relational intelligence.
