SAP’s new Engagement Index research finds a widening gap between what UK consumers expect and what enterprises can deliver across channels.
The study points to three root causes: weak real-time data access, uneven cross-team coordination, and AI that stays stuck in ‘strategy’ instead of daily execution.
The headline tension is simple. Brands believe they are creating strong relationships, seamless journeys, and meaningful personalization. Consumers mostly do not agree. That mismatch matters because it drives churn risk, rising cost-to-serve, and weaker brand differentiation in categories where products look increasingly similar.
The Engagement Divide Is Now a CX Problem
The report frames an engagement divide between modern customer expectations and enterprise capability. Consumers compare experiences instantly and switch quickly. Many also rely on AI assistants to filter information and recommendations. That reduces the power of traditional messaging and increases the importance of being contextually useful in the moment.
It also explains why better campaigns rarely fix the issue. If the organization cannot recognize a customer, share context across teams, and act on signals in real time, then personalization becomes cosmetic. Customers feel that, especially when they get bounced between departments or repeat themselves.
From a service and CX lens, the report’s most practical insight is that engagement maturity is less about having more channels. It is about whether those channels behave like one connected experience, with consistent context and next-best actions.
Customers Feel the Cost of Silos
A major consumer frustration in the report is operational disorganization. Customers describe brands as slow, inconsistent, and impersonal when teams cannot share context.
In an assessment, Balaji Balasubramanian, Chief Product Officer at SAP warned:
“Winning brands treat engagement as an enterprise capability, not a marketing layer. AI only drives growth when it is connected to real operational systems and customer context.”
That framing lands for CX teams because it shifts the conversation away from surface-level touchpoints and toward the back-end conditions that shape them. If marketing, sales, and service hold different “truths” about the customer, the experience cannot feel coordinated. It will feel procedural.
The report’s cross-functional alignment data reinforces that. Coordination levels between departments are not where enterprises think they are. If fully coordinated, is only a minority view across key pairings, customers will keep experiencing handoffs as friction, not help.
Real-Time Data Is the Bottleneck Nobody Wants to Admit
The research highlights what many CX leaders already suspect. A large share of enterprises struggle to access and use real-time data. Many also sit on ‘dark data’ and unstructured information that cannot easily power journeys, routing, or personalization.
For contact centers, this shows up in predictable ways. Agents cannot see what a customer did online five minutes ago. Self-service cannot adapt based on intent. Proactive service triggers come too late. And customers get generic interactions that feel disconnected from their history.
The report also undercuts a popular assumption. The problem is not that enterprises do not collect data. It is that they cannot reliably operationalize it across systems, teams, and moments that matter.
AI Investment Is Rising, but Execution Is the Real Gap
Most enterprises plan to increase AI investment. Yet many still cannot operationalize AI in day-to-day engagement work. That matters because the customer benchmark is no longer “better than our competitors.” The benchmark is the best experience a customer had last week, in any category.
Asked what changes now, Liat Ben-Zur, CEO at LBZ Advisory emphasized:
“The shift is from marketing as a megaphone to marketing as a steering wheel. The opportunity is building a growth intelligence engine from the data enterprises already have.”
For CX leaders, the takeaway is less about adopting another AI tool and more about designing the decision loops AI needs. If the organization cannot connect identity, consent, context, and orchestration, then AI will mainly automate noise. It will not lift outcomes like retention or loyalty.
The report also flags a trust and value gap with consumers. Many do not understand why sharing data helps them. Some do not believe brands use data responsibly or effectively. That puts more pressure on transparent consent models, clear value exchange, and experiences that visibly improve when customers share preferences.
What High Maturity Actually Looks Like in Practice
The report groups organizations into maturity bands. High maturity is defined by integrated data across functions and real-time, AI-driven personalization at scale.
In plain operational terms, that means a few non-negotiables: A unified customer profile that is usable across marketing, commerce, and service. Event-driven journeys that adapt to behavior and context. Governance that makes consent a built-in feature, not a legal afterthought. And cross-functional ownership of outcomes, not channel-by-channel optimization.
Looking ahead, Mark Ritson, Professor & Founder at MiniMBA argued:
“Engagement only works when teams share a single understanding of the customer. AI can execute at speed, but only if the organization agrees what it is optimizing for.”
That statement matters for enterprise buyers because engagement often dies inside politics. If teams optimize different metrics, the customer gets a fragmented experience. If teams share a lifecycle goal, orchestration becomes realistic.
A Practical Path Forward for CX Teams
The report’s strongest advice is also the least glamorous. Start with one journey where friction is visible and value is provable. Then connect the data and decisioning needed to make it feel seamless.
For many CX orgs, good candidates include cart abandonment support, post-purchase onboarding, delivery issues, returns, or high-friction billing moments. These journeys already create contacts. They already create cost. And they already shape loyalty.
If enterprises can prove impact in one journey, they create the internal momentum to fix the foundational issues. Data access. System integration. Governance. And cross-team operating models. That is how AI strategy becomes AI execution.
The bigger point is that engagement maturity is not a brand campaign. It is a business capability. Enterprises that treat it that way will reduce avoidable contacts, improve resolution quality, and build the kind of trust that consumers now reserve for only a few brands.
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