Authored by: Raahul Seshadri, Director – AI & Tech, WebEngage

Engaging users with a comprehensive omni-channel strategy is no longer a luxury, but absolutely essential. It’s absolutely essential to engage with the users at the right time, at the right place, with the right content and intelligence. Without being either fast or relevant, the user quickly loses interest and might choose a more prepared competitor instead. Despite the importance of omni-channel communication, the greatest barrier that every organization faces is the presence of data silos. Due to data silos, it’s impossible to get the right content, or even disambiguate identities across those silos, and engage the user in all the possible channels.

What is needed for omnichannel CX

In an ideal world, you’d want all the data systems to be integrated, such that the data in each system just becomes an attribute of global entities. The information gained from one area is immediately put to use in another. Behaviour, predictions, etc., come together.

Problems with silos?

Departments like marketing, sales, customer service, maintain different specialized systems that don’t naturally integrate. The ticketing system for support is completely isolated from the CRM that the sales team might be looking at for upsell opportunities. Such problems could also happen at a geographical level, where each geography has its own sets of systems, processes, and tools. Silos also exist across tools within the same geography. Tools within the same departments that don’t integrate well. Or even if they do, the formats are incompatible. For example, it’s not always convenient to reconcile emails with a customer with the other structured data for the same customer in the CRM in any meaningful way. Even if you do find a way to integrate all these systems, the way to disambiguate a particular user is not always obvious.

Why have they not been resolved?

Resolving systems to work together is inherently a time-consuming task. It requires an integration path between the two systems, and also a translation layer that makes sense of it in a unified fashion. Previously, this would mean commissioning a team to write and maintain an ETL layer that would create a centralized store or warehouse for all this content. But that’s just half the battle. The other half is to make sure that this data can then be used. And used meaningfully. The feedback from all of this should also be functional.

How AI solves silos?

AI helps solve this problem at many levels. Systems that don’t integrate with each other directly can still get integrated with a central system, run by AI, which automatically adapts itself to the different APIs across systems. And it can selectively pull and orchestrate data as it needs. It does not need to be explicitly programmed for each system. It can also convert between different formats seamlessly.

In addition to that, it can also parse formats that were not previously computer readable, like natural language. It can extract insights from emails, texts of support tickets, etc. It’s not even required to ask the question if they were satisfied with the response, you can infer it from the interaction. Retention is a big thing, and this helps you do timely intervention, the instant it happens, not after the user is already dissatisfied.

Once the battle of connecting systems is won, they still are not integrated. Because immediately, you won’t know how to correlate people and interactions at one place. The customer interaction on emails might be about current usage of the product, while the conversations in the CRM are about upsell. So it’s not just important to integrate data, but also interpret them in the right context. This is where AI can not only help intelligently index such data, but also pull up the right context when it’s most needed, when communicating with the customer.

Deduplication

One crucial aspect of this integration is tying people’s identities together. Systems use different identifiers, and don’t always have all the fields cleanly filled. What set of combinations of fields (from the many), are guaranteed to suggest that the users are the same? Which combinations have high likelihood? And which ones are contradictions?

Discovering and writing these rules by hand is a tedious task, and also very hard to test, because there are just too many combinations. Most of the time, the result is to never integrate the systems rather than wrongly integrating them.

AI allows automatically inferring what kind of rules should be applied to deduplicate records across systems where only a few fields are filled up. And also what kind of comparison should be done. It can derive such logic at runtime and then merge records.

What possibilities get opened up?

When we think about an omni-channel experience, we usually talk about channels like SMS, Email, Push, etc. But that’s a very narrow definition. You could be talking to a team that’s working on using your current product. Whereas you have another email thread open with the leadership to do upsells. Both are with the same customers. Both are through emails. But those are still “two different channels” of communication with the same entity, with different expectations. The upsell depends greatly on how successful the current integration is.

As customer expectations continue to rise, delivering seamless, context-aware engagement across channels is no longer optional, it’s foundational. While data silos have long stood in the way of true omnichannel experiences, AI is now enabling organizations to not just connect systems, but intelligently orchestrate and interpret data in real time. From identity resolution to contextual engagement, AI is transforming how brands understand and serve their customers.

“At WebEngage, we believe the future of CX isn’t just about being present on every channel, it’s more about being meaningfully present. AI is what makes that possible at scale.”

The future of customer experience lies in moving from fragmented interactions to fluid journeys and AI is the engine making that future possible.