Q & A

Research suggests that while the largest majority of companies ask their customers for feedback, only a small minority close the loop and actually analyse the feedback provided to them, consider it and then set about acting on it.

An even smaller percentage complete all of these steps and then communicate to their customers what they have done with their feedback.

Oftentimes, providing feedback to a brand from a customer’s perspective can feel like it goes into a ‘black hole.’ Now, there could be numerous reasons behind a brand’s inaction, ranging from a gap between their rhetoric and reality to how they are organised and how feedback is collected, analysed,reported and shared.

Another problem with feedback is that many companies rely solely on survey data to help them understand how they are doing and also to identify areas for improvement.

Relying only on survey data, however, is a flawed strategy. Survey data provides only a partial view of what is going on and is becoming increasingly less reliable due response or selection bias and survey fatigue.

A better approach would be one that pulls in formal and informal feedback (structured and unstructured data) from numerous data sources, including surveys, emails, call transcripts, social reviews, ratings, messaging threads, etc., analyzes that data across customer groups, products and services, and different parts of their respective journeys, identifies areas for improvement, and then connects those insights to the people who can take action.

SearchUnify Lens:

Adrian’s point exposes a common CX failure: companies collect feedback but lack the mechanisms to act on it, leaving data trapped in a “black hole.”

SearchUnify solves this by transforming unstructured feedback into action through the AI Case Quality Auditor. Instead of waiting for lagging surveys, this agent continuously monitors live transcripts, emails, and messaging threads to detect recurring friction points, service gaps, and customer sentiment in real time.

This bypasses manual reporting silos and connects customer voices directly to operational workflows and quality monitoring. By automating this pipeline, enterprises turn passive feedback into a visible, continuous driver of CX optimization.

Too often have I seen organisations buy technology on a whim or because it’s in vogue or because ‘everyone else is doing it.’ They buy technology and then think about what they can do with it afterwards. This is the wrong way round. Always start with the end in mind i.e. Design the experience you want to achieve and then figure out what tech and data you need to fulfil that. Not the other way around. You might be thinking that this should be self-evident. But you’d be surprised how often it is not.

When you do things this way then you are likely to make better technological procurement decisions, have a clearer insight on what data you need to help deliver on your experience vision and, as a result, are likely to drive better customer, employee and business outcomes.

SearchUnify Lens:

Adrian’s point reinforces a critical pitfall in modern digital transformation: many organizations rush to adopt AI tools before defining the specific customer and agent experience they actually want to create. When technology precedes design, the result is a fragmented ecosystem of disconnected point solutions that complicate the journey instead of simplifying it.

SearchUnify approaches enterprise AI differently by enabling experience-led orchestration across support ecosystems. Through a Model Context Protocol (MCP) driven architecture, we connect knowledge, workflows, live customer signals, and autonomous agents into a single operational context. Instead of forcing your teams to adapt to rigid tools, this framework ensures that data and context remain completely portable across the entire journey.

Despite the investment and expected improvements in self-service tools and technology, there will be times when a customer needs to reach out for live support from another human being, especially when their issue is urgent, concerning, or overly complex.

These interactions often carry heightened expectations of a swift and positive resolution, especially if a customer has tried and failed to self-serve or has had to wait for any length of time to speak to someone.

Moreover, these interactions can make or break a customer’s relationship with a brand if handled poorly. Therefore, agents are increasingly becoming specialised problem-solvers and relationship managers and skills such as empathy and the ability to provide personalised experiences are becoming more important.

SearchUnify Lens:

As routine volume fades, the “human core” of support is redefined by high-consequence interactions requiring deep empathy. The biggest barrier here is the cognitive friction of agents fighting siloed systems for information. The role must evolve from a manual “searcher” to a Specialized Problem Solver.

SearchUnify solves this by turning the workspace into a collaborative environment. When a customer reaches a human agent after failed self-service, expectations are high and the margin for error is thin. Agent Helper ensures agents are instantly prepared by automatically surfacing relevant knowledge and recommended next steps within their workflow, so attention stays on the customer, not the search. AI Agent Partner then reads live sentiment signals throughout the interaction, giving agents the awareness to adjust their approach before frustration escalates. When risk is detected, the AI Escalation Manager alerts supervisors proactively, turning a potential relationship-breaking moment into a recoverable one.

The design and delivery of outstanding service and a great customer experience is not just about technology, process and systems; it’s also about people and the whole network of people required to deliver the service and the experience that our customers desire. Therefore leaders should spend as much time designing their agent/employee experience as they do their customer experience.

SearchUnify Lens:

The “More” technology often creates more cognitive fatigue. When rigid, tiered escalations leave agents isolated with complex issues, burnout follows.

Moving away from these silos requires an Intelligent Swarming framework. Instead of routing a customer through multiple hands, it uses AI to instantly connect the front-line agent with a dynamic cross-functional network of experts.

This is operationalized through SearchUnify Agent Helper, which unifies case history and collaborative swarming directly within CRMs like Salesforce or Zendesk. By automating data retrieval and eliminating exhausting tab-switching, SearchUnify transforms fragmented tools into a genuinely supportive, people-first system.

We need to remember that while AI can generate content, humans are essential for providing clarity, intent, judgment, empathy, and leadership.

Therefore, when utilising AI in knowledge management, it should be used in partnership, augmenting rather than replacing human capabilities.

SearchUnify Lens:

Adrian is right, AI shouldn’t replace human judgment; it should liberate it. SearchUnify Knowbler bridges this gap by acting as a collaborative co-pilot that enhances, rather than replaces, the human element. It works by monitoring live support interactions and using machine learning to automatically capture the unique troubleshooting steps, context, and customer sentiment in real time. It then auto-drafts a structured, template-ready knowledge article right inside the agent’s CRM workspace (like Salesforce or Zendesk).

By automating the tedious formatting and drafting, Knowbler eliminates administrative overhead while leaving the agent in full control. The human provides the final review—validating the accuracy and injecting the essential empathy, intent, and clarity that AI lacks.