When vendors of business communications platforms look at adding new technology, the motivation must be scrutinised. Is what they are doing in response to the evolving requirements from customers? Or are they just adding provider-driven features that are designed to increase profit or cut operational costs, or introducing “bells and whistles” that most are unlikely to use?
Contact centres are shifting from being traditional call centres into modern CX Hubs as businesses look to deliver exceptional customer experiences. For the solution providers who enable this evolution, a fundamental challenge is translating customer desires into a stable, efficient, and future-proof roadmap – and this requires approaching the incorporation of customer feedback in a pragmatic manner.
Collecting this feedback can be a technically complex endeavour, however, especially if the data resides in numerous disparate systems, and a unified software development interface will allow for the homogenisation of these data sources for seamless integration into a CRM, providing a complete view of the customer journey.
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A classic example is a customer asking for the caller’s name to pop up on screen. After digging deeper, the real need proved to be proper CRM integration, which drove a more complete solution with automation. The same thing happens often: a request for “faster recording downloads” turns out to be a compliance workflow issue, and a call for “colour-coded agent statuses” exposes a lack of real-time visibility. What is asked for isn’t always what is actually needed, and real value comes from interrogating the problem before building the solution.
Ultimately, while detailed feedback loops exist through support requests and engagement with developers, the core success metric for feature adoption often simplifies to seeing an increase in the overall number of users.
The rise of human-machine collaboration to drive customer experiences
While the rise of artificial intelligence (AI) is undeniable – with adoption rates highlighting its growing significance across sectors – organisations should avoid the temptation of over-automation, especially in the customer service environment, given that the technology is still in its early stages and can make mistakes.
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One must also consider the following: if an organisation uses AI to handle 80% of routine interactions, eliminating the need for extensive human training, how will they train human experts to handle the 20% of (likely to be complex) issues? Taking an approach that is overly reliant on AI and automation risks the loss of depth of experience within the workforce.
In addition, delivering successful CX in this omnichannel age requires that organisations invest in upskilling their workforce: both the technical skills necessary for channel ‘orchestration’ and driving seamless human-machine collaboration, and the emotional skills required for complex problem-solving.
Governance and investment risk
The biggest operational challenges for CIOs and CTOs today are not technical capability, but governance and strategic investment risk. The security landscape is demanding robust governance practices to align the use of Generative AI with core data privacy regulations like GDPR and POPIA. The danger lies in data leakage, where company or customer interaction data is inadvertently used to train external models, potentially disclosing private or incorrect information later.
This governance necessity drives explicit operational policy, and organisations have to implement internal guardrails, such as preventing employees from uploading sensitive spreadsheets to external AI platforms like Chat GPT or Claude until thorough validation is complete. Furthermore, integrating AI requires multiple layers of verification – a good practice is to use one AI to generate a response, a second to check if the answer makes sense, and a third to confirm if it addresses the customer query.
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The rapid adoption of AI has also created a highly volatile market, leaving technology leaders with the burden of choosing the “winning team” and navigating the potential “AI bubble”. We are seeing a complex relationship developing between major technology players who are swapping funds for computing power and hardware. This hyper-investment carries the significant risk of failure, forcing CIOs/CTOs to be wary of committing large financial resources to solutions that may rapidly fail or reverse course, as seen when major companies first laid off and then subsequently rehired staff due to AI limitations.
The ultimate strategic focus must therefore be on building adaptable ecosystems that can blend proprietary, off-the-shelf, and open-source models, while adhering to the highest standards of security and transparency. Organisations must choose safety over profit, ensuring that technology advancements enable better quality conversations, reduce friction, and build lasting loyalty.
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By Antony Russell, Chief Technology Officer, Telviva.