Collaboration platforms have become central to working life, with Microsoft Teams alone serving more than 320 million monthly active users worldwide. Everywhere you look, they are now part of workplace culture and have been a driving force for change in the use of voice services.
Part of their ongoing appeal is that they are extremely simple to deploy and their familiarity flattens the learning curve for many users almost entirely. For IT teams, one of the key benefits is that they sit within an established supplier environment and, as a result, can help make procurement more straightforward.
Sounds great, but communication infrastructure must also be able to adapt over time – for example, when a business expands or changes how it serves customers. Depending on the platform, any functional limitations may only become clear when a business needs to connect communications with other systems or adapt services for new teams and locations.
The point is that inflexibility can create complications that significantly increase the initial cost of a collaboration solution; something that businesses can easily overlook.
The hidden cost of inflexibility
But what do we mean by complications? Well, any given collaboration platform may work perfectly well for everyday internal calling, yet they all have the potential to become restrictive when communications need to support more specialized processes.
To break this down, everyday internal calls have relatively simple requirements because employees already know who to contact and how to reach them. However, in use cases such as customer-facing communications, employees may require calls to be directed based on factors such as location or the nature of the inquiry.
They may also need access to relevant customer information while the conversation is taking place, whether it is held in a CRM platform or another business application, including a bespoke system where technology integration is more challenging.
This is where the level and quality of the integration becomes extremely important, as any limitations may force employees to switch between applications or search manually for information that should be readily available.
Get this wrong, and users may require a customer to repeat information, which can extend the interaction and cause frustration. Some businesses may also need more detailed insight into call activity when they are trying to identify problems or improve customer service. For many, this is a central objective when building these systems and processes.
Adding workarounds, such as asking staff to look up customer records manually or using a separate connector to pass data between systems, is one option. However, these can make it harder for IT teams to manage the service consistently as requirements change.
Why AI changes the equation (again)
The issues are becoming even more consequential as businesses look to use AI to handle a greater share of customer communications, because these systems need access to the right information to respond usefully.
For an assistant to deal with an inquiry properly, it needs to draw on the same customer information that a human employee would use. But when an inquiry cannot be resolved automatically, the conversation needs to reach the appropriate person without losing its context.
This is where any disconnect between the communications environment and core business systems can really limit what an AI assistant can do well. AI that cannot access the right information can create an additional layer of friction for customers and employees.
Ideally, these compromises should be avoided entirely. A business should be able to retain the collaboration tools employees already know while adding communication and integration capabilities where needed.
In fact, seamless connectivity and interaction between the user, the AI tools being used, and the relevant customer and operational data should be a core objective rather than a nice-to-have. This means that the technology should be able to accommodate changing requirements without creating a dependence on manual workarounds, so organizations have full control over the pace at which they adopt new technologies and processes.
Businesses planning to expand their use of AI, which is likely to include a great many of them, should consider whether their current communications infrastructure can support those connections. Granted, it is usually possible to retrofit systems to bridge functionality and service gaps, but if the objective is to use AI effectively across a growing number of customer interactions, flexibility needs to be built into the foundation from the outset.
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