Most customer 360s are incomplete. Few are AI ready.

Most organisations do not have a complete, accurate, real time view of their customers.

Customer data is distributed across e-commerce platforms, POS systems, ERP, CRM, loyalty and supply chain applications. The same customer often exists multiple times across systems with different identifiers and inconsistent attributes. Product hierarchies vary by channel and region. Inventory updates at different speeds. Margin is calculated separately. Data quality is typically addressed after issues appear rather than prevented at source.

The result is fragmentation.

The consequences are measurable. Research shows that 67% of AI projects fail due to data readiness issues (Gartner), and only 14% of organisations possess the data maturity required to fully exploit AI’s potential (McKinsey). The barrier is not ambition. It is the foundation.

Many organisations attempt to solve this by building a Customer 360 inside a CRM or warehouse. Records are aggregated. Attributes are stitched together. Dashboards appear unified.

But stitching profiles is not the same as creating operational truth.

A traditional Customer 360 reflects what a single system knows about the customer, typically refreshed in batches and only loosely synchronised with other platforms. It creates visibility, but it does not guarantee that customer, product, sales and inventory data are accurate, complete, and unified in real time.

AI does not struggle because algorithms are weak. It struggles because the data foundation beneath them is inconsistent, fragmented and ungoverned.

What an AI ready data foundation requires

To be AI ready, organisations need more than dashboards. They need an AI ready, continuous data foundation.

That foundation must operate at event level. Every transaction, product view, stock movement and behavioural interaction must be captured as it occurs. Customer identities across systems must be matched and deduplicated into a governed golden record. Product, sales, inventory and location data must sit within a consistent canonical model that standardises definitions, hierarchies and relationships across the organisation.

Without a canonical structure, each system interprets data differently. With one, the organisation operates from shared meaning.

But unifying and governing data is only part of the answer. An AI ready foundation must also orchestrate how data drives action.

Orchestration coordinates decisions, workflows and automation across systems in real time. When an event occurs, the right processes must trigger instantly across ecommerce, ERP, CRM and supply chain platforms. Pricing, promotion, replenishment and personalisation must remain aligned.

This orchestration layer is essential for Agentic AI.

Agentic AI does not simply analyse data. It acts on it. It makes decisions, initiates workflows and coordinates execution across systems. Without orchestration, AI remains advisory. With orchestration, AI becomes operational.

Data must be transformed, standardised and enriched as it flows. Governance must be embedded into the architecture. Real time observability must provide transparency into data movement and performance. Automated alerting must surface anomalies before they distort reporting, automation or AI execution.

Crucially, this cannot be achieved by assembling disconnected tools. An AI ready data foundation must be delivered end to end, integrating systems, managing and governing data, orchestrating real time flows, powering live reporting and advanced analytics, and enabling AI execution from the same unified platform.

This is the approach taken by Xiatech through its Xfuze platform, unifying integration, data management, orchestration, real time analytics and AI into a single composable foundation for modern business.

From customer insights to AI execution

Consider a multinational retailer selling luxury goods across online, in store and wholesale channels.

A customer browsing online selects a pair of luxury shoes, checks availability in two stores, adds the item to their basket and abandons checkout.

These events, product view, size selection, store lookup, basket addition and abandonment, must be captured instantly and attached to the correct customer record. That record may already exist in the loyalty system, POS and CRM, potentially with slight identity variations. Those identities must be resolved and merged into a single governed record aligned to the same canonical product and inventory model.

This is where a true 360 at event level becomes essential.

AI shopping and Agentic AI depend on behavioural precision. If the organisation only sees summarised transactions or overnight updates, intent is lost. Without event level visibility, AI cannot distinguish between casual browsing and high purchase intent. It cannot coordinate inventory, pricing and personalisation safely across systems.

With a real time, event driven 360 view of customers, every interaction becomes actionable, unlocking value for both your business and your customers. That is why capturing and orchestrating every event matters.

An AI shopping assistant can detect high intent from the abandonment pattern and automatically trigger personalised follow up communication. It can adjust recommended alternatives based on live inventory and margin. It can check regional stock levels before suggesting nearby store availability. If demand spikes for that SKU across regions, replenishment workflows can be orchestrated in real time. Pricing logic can adapt where margin pressure appears.

This is not simply insight.  It is AI execution, orchestrated across the organisation and powered by event level intelligence.

The retailer does not just understand customer behaviour. It aligns inventory, margin and experience through real time orchestration. In an era defined by AI, automation and margin pressure, dashboards are not enough.

Organisations need an AI ready data foundation that integrates, governs, orchestrates and activates data across the business because it defines business performance and success.

Assess your data capabilities

To help you get your business data AI ready, I have provided questions that will help you assess where you stand.

• Do you have duplicate customer records across systems, and are they resolved in real time?
• Are customer events captured instantly and attached to a governed golden record?
• Do all systems share a consistent canonical model for customer, product and inventory data?
• Can workflows be orchestrated automatically across systems when customer behaviour changes?
• Do your dashboards reflect live operational data rather than overnight refreshes?
• Can AI models act confidently without manual reconciliation of stock, product or margin data?
• Is data quality embedded into the flow with observability and alerting, or handled reactively?

If these questions are difficult to answer confidently, the issue may not be insights.  It may be the foundation.