Contact centers have always been obsessive about numbers. Average handle time (AHT), first call resolution (FCR), customer satisfaction (CSAT), and so many more. These KPIs have steered staffing, coaching, and technology investments for decades.
Yet for all that measurement, the view on the overall operation is limited. As leaders we have been restricted to what we can measure rather than what is most meaningful.
Generative AI is shattering those limitations. It is turning the vast, messy universe of unstructured data across call recordings, chat transcripts, bot interactions, emails, social posts, internal communications, case management systems, CRM software, and more into instantly searchable, quantifiable insights.
AI is enabling us to gain an unprecedented, panoramic view of our customers and our operations. The breakthrough isn’t merely that machines can “read” text and audio at scale. The real leap is the ability to convert that chaos into structured, action-ready intelligence and to trigger action across the rest of the enterprise.
Now you can see and size customer issues as they happen. You can measure and quantify their impact. You can advocate with data for the resolutions that need to happen.
And where you cannot rapidly resolve those broken processes that led to customers contacting you about their problems, you can identify areas for automation that could help you with them. Both in terms of topics, but also parts of the interaction that are open to it.
For example, if 18% of your AHT is taken up by verification and 23% is post-call wrap, then you have a clearly defined ROI for automation of these tasks. This allows the contact center to shift resources towards proactive engagement and revenue generation.
Getting There is the REAL Challenge
The technology exists to analyze your full corpus of unstructured data. The hard part is doing it well. It’s unfortunately not as easy as just throwing the data at a large language model (LLM), asking it to find a needle in the haystack, and getting a great result.
The data is in disparate systems, in different formats, and in vast quantities. LLMs are also most prone to generating hallucinations with the above approach. Furthermore, if you are throwing a million call records at OpenAI for each query, the cost becomes prohibitive very rapidly.
To achieve strong results, the data must be collated, organized, and digested by the system, and then presented to the appropriate LLMs in a carefully structured way. It’s a multi-step process to get a strong, data-oriented result, especially when drawing data from multiple solutions.
This is not a project that can be quickly vibe-coded in a hackathon. Generating a full and useful set of outputs from the corpus of data requires a carefully crafted multistage process. One that involves a thorough understanding of AI capabilities and how data is stored and structured, and of how to extract insight from it efficiently, cleanly, and safely.
But this is a specialist skillset that’s generally not present in most organizations’ IT departments.
You Don’t Have to “Walk” Alone
Many vendors are already putting generative AI features and analytics into their products (often at an add-on cost). While the overall level of quality is very variable, there are some excellent solutions out there.
Yet even when a product comes with a strong reporting solution, the solution is often limited to only accessing the data within the vendor’s own system.
Getting data from multiple systems is a challenge without specialist tools. While those like Power BI and Tableau are powerful, they are often a little too broad and general to really zoom in on specific customer service needs.
AI is enabling us to gain an unprecedented, panoramic view of our customers and our operations.
The most capable options to uncover data for contact center leaders are often CX-industry-specific analytics products. Standalone providers like Salted.cx, Medallia, or Qualtrics are often able to provide a more comprehensive overview than is provided by digital, contact center-as-a-service (CCaaS), or CRM vendors alone.
Before deciding on investment choices and approaches, CX leaders need to carefully identify all the sources of useful data within their organizations. Depending on the level of complexity in the customer service organization, an existing vendor solution may be sufficient to capture the full detail or a more specialist solution may be needed.
This is a decision that should be made before engaging with the vendors. Salespeople are skilled at shaping your needs to fit what they sell. The strongest process starts with a clear problem statement that asks vendors to show how they will solve it. If you skip this step, they’ll define the problem for you, on their terms.
Identify Where the Data Lies First
Before you go to your existing vendors, or build your RFPs, you need to identify all the locations that you need to be able to extract data from. Once you know what you need to have processed and generated into useful structured data, then you can find the vendor that can best do so for you.
While customer service interactions are a huge trove of data, many contact centers will leverage CRM or third-party solutions for digital interactions.
Internal discussions often have data relevant to why processes may be failing and causing customers to turn to the contact center. Ticketing systems either within the CRM or as third-party options will also often come up. Then industry-specific solutions will often hold further data sets.
Once you have the data you need to gather, add in any final requirements, such as data governance and residency requirements, privacy and data regulations that need to be adhered to, security accreditations, etc.
Do it Safely
This last point is critical. In all of this, there are safeguards that you cannot skip, both for compliance and for brand reputation, including avoiding devoting resources to handling contacts from angry customers.
Privacy must be built into the system with personal identifiable information (PII) redaction, data minimization, and clear retention policies. You also must put in role-based access controls and audits.
Any AI conclusions must be backed by referenced data, and that data should be looked over by a human. Bias testing and regular model reviews are also a necessary part of the process to ensure fairness and quality in data evaluation.
Going Forward With Your Data
Unstructured data is no longer an unruly archive. With the right pipeline and controls, it becomes a live feed for finding breaks, sizing impact, proving ROI, and triggering fixes or automation. Do this well and the contact center stops being a cost of failure and starts steering change across the business.
Once you have access to the insight in the data, make it a regular cadence, not a one-off project. It’s about understanding your business better and leveraging that vast treasure trove of data to drive operational discipline and continuous improvement.