Beyond the Hype Cycle: Why Practicality, Human Empathy, and Governance Define the Future of the CX Hub

Rather than simply chase shiny new features, companies that provide CX solutions should turn to customer feedback to drive roadmaps, enhance human-machine collaboration, and simplify governance in the age of AI and increased privacy regulation.

The ability to deliver exceptional customer experience (CX) is now a crucial pillar of business success, and this has seen traditional contact centres shift to being modern, proactive CX Hubs. For the solution providers who enable this evolution, a fundamental challenge is translating customer desires into a stable, efficient, and future-proof roadmap. It’s also about ensuring that the technology delivers real value, rather than simply chasing vendor hype or introducing expensive “bells and whistles”.

When organisations consider adding new technology, the motivation must be scrutinised: are they responding to an overwhelming request from customers, or are they adding provider-driven features aimed only at increasing profit or cutting operational costs?

Businesses should approach this incorporation of customer feedback in a pragmatic manner. Telviva, for instance, looks to the urgent requirements of key, trusted customers, using their operational needs to drive significant portions of application development. Features born from these specific customer requests are then applied broadly to benefit the entire customer base. 

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.

Harvesting meaningful feedback is technically complex, however, due primarily to three key factors:

• Homogenising disparate data: Customer interaction data often resides in numerous disparate systems – WhatsApp messages live here, emails there, and phone calls elsewhere. A unified software development interface allows for the homogenisation of these data sources for seamless integration into a customer’s CRM, providing a complete, unified view of the customer journey.
• Accurate measurement: In contact centre environments, where agents might handle multiple concurrent digital chats, accurate work measurement is critical. There is a need for tracking agent attention to correctly measure actual work output versus the potential hours implied by the end-to-end length of all parallel conversations.
• Prioritising growth: 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

The rise of artificial intelligence (AI) is undeniable, with adoption rates highlighting its growing significance across sectors. Despite this, organisations should avoid the temptation of over-automation, especially in the customer service environment, given the inherent instability of models that can ‘hallucinate’ or provide incorrect information.

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.

For the immediate future, the Agent Assist model, where AI supports human agents by providing them with real-time suggestions and prompts, has come to the fore. This approach is superior to a fully automated one; while the AI can be more efficient in gathering context and data by analysing the full customer journey across all touchpoints, humans are still crucial when it comes to applying the necessary empathy and judgement to resolve a difficult query. 

Given that AI models require explicitly written “guard rails” to prevent simple errors, having a human act as the final decision-maker further reduces operational risk significantly.

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.

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, whether through Agent Assist or through streamlined DevOps driven by customer feedback, ultimately enhance the conversation, reduce friction, and build lasting loyalty.