Emarsys's 2026 Global Engagement Index exposes a critical disconnect between AI adoption and execution: whilst 78% of enterprises view AI as essential to customer engagement, 77% cannot operationalize it effectively. The core problem is data infrastructure, not technology. Fifty-four percent of organisations cannot access real-time data, 60% sit on "dark data" that remains unactivated, and 55% struggle with unstructured datasets. This creates a fundamental limitation for AI systems that lack visibility into operational realities—inventory levels, order status, fulfillment timelines, and service activity. Without these connections, AI-driven personalization becomes dangerously disconnected from business capability, resulting in customers receiving promotions for already-purchased items, recommendations for out-of-stock products, or offers that contradict recent transactions.
For CX teams, this research signals that the bottleneck isn't generative AI or agent technology itself, but the unglamorous work of data integration and governance. Support teams relying on platforms like Zendesk or Freshdesk already understand this tension: customer service data exists in silos, disconnected from commerce, inventory, and marketing systems. The implication is stark—AI agents and automation tools will underperform without operational context. A chatbot trained on customer service interactions alone cannot resolve issues efficiently if it cannot see order status or inventory. This raises a pressing question for teams already investing in AI-powered customer engagement: are you prioritising data unification before expanding AI capabilities, or are you deploying agents into fragmented systems and accepting degraded performance? The research suggests that teams treating data integration as secondary to AI implementation will continue to see disappointing ROI, regardless of how sophisticated their underlying models become.
The path forward requires CX leaders to audit their data architecture before scaling AI initiatives. Operational data—what customers can actually receive, when, and under what conditions—must flow into engagement systems. This is less about choosing between Salesforce, Genesys, or other platforms and more about ensuring those platforms can communicate with inventory, fulfillment, and service systems in real time. Teams that treat this as a prerequisite rather than a nice-to-have will extract genuine value from AI; those that don't will continue to deploy technology that looks intelligent but behaves recklessly.
Emarsys: Most brands struggle to operationalize AI Mass Market Retailers