A new survey by CCW Europe Digital (The Path to Omnichannel Customer Engagement, full release May 2026) highlights a clear divide in how businesses are adopting AI.
For ecommerce agencies and retailers, the question is no longer whether to implement AI, but where and how deeply.
Market reality: most businesses are still at the starting line
Key stats from the survey show:
- 48% of organisations still rely on basic AI (chatbots, scripted automation)
- 46% use AI primarily for reactive tasks
- Only 4% have implemented proactive, cross-channel AI
- 49% offer only basic cross-channel experiences
- Just 10% report true omnichannel maturity
- Only 4% can preserve full customer history across interactions
- 21% still operate in silos
Despite the hype, most businesses are not using AI in a way that meaningfully transforms performance.
The core problem: more data, less clarity
As ecommerce operations scale, so does complexity.
More channels mean more data. But without integration, that data becomes fragmented, inconsistent, and difficult to act on.
Layering AI on top of disconnected systems doesn’t solve the problem. It amplifies it.
Progress requires restructuring how data, teams, and systems work together, not just adding new tools.
What actually drives progress
The research points to four consistent factors behind successful AI adoption:
- Unified customer identity
- Predictive and proactive AI
- Cross-functional execution
- Real-time performance measurement
Without these, AI remains surface-level.
A single source of truth for customer data
Unified customer identity means consolidating data from every touchpoint — online and offline — into a single, reliable profile.
This creates a true “single source of truth”, enabling:
- Consistent personalisation across channels
- Reduced friction in the customer journey
- Stronger security and compliance
In practice, this requires integrating Customer Data Platforms with identity and access management systems to keep data accurate, verified, and privacy-compliant.
The result is not just better marketing, but better decision-making across the entire business.
Predictive analytics: from insight to action
Predictive AI turns historical and real-time data into forecasts.
Proactive AI turns those forecasts into action.
This shift is where real commercial impact happens.
Examples include:
- Demand forecasting using seasonality, trends, and external signals
- Identifying at-risk customers and triggering retention strategies
- Dynamic pricing based on demand, competition, and stock levels
At this stage, AI is no longer supporting decisions. It’s shaping them.
Proactive AI: closing gaps before they appear
The next step is automation that acts without waiting for user input.
This is already taking shape across ecommerce:
- Triggering offers when users stall at checkout
- Flagging delivery issues before customers raise complaints
- Adapting on-site experiences in real time based on behaviour
In more advanced use cases, AI is powering conversational commerce, acting as a continuous, context-aware shopping assistant across sessions.
From tools to systems
The direction of travel is clear.
AI is moving away from isolated tools towards connected, agent-driven systems that operate across the entire ecommerce stack, from merchandising and pricing to inventory and logistics.
We’re also seeing early signs of:
- Fully personalised storefronts generated in real time
- AI agents acting on behalf of consumers to search, compare, and purchase
- Conversational interfaces replacing static dashboards for decision-making
This isn’t speculative. It’s already emerging.
What this means for retailers and agencies
The gap between basic and advanced AI adoption is still wide, but it’s narrowing.
Businesses that continue to rely on reactive, siloed systems will struggle to keep up with those building integrated, data-driven operations.
The difference won’t be marginal. It will be structural.
Conclusion
AI in ecommerce is moving from experimentation to infrastructure.
The businesses seeing real impact are not those using more tools, but those building the foundations to use AI properly:
- Unified data
- Connected systems
- Real-time decision-making
The question is no longer whether to adopt AI. It’s whether your platform can support it at scale.