When a supervisor must select a call, listen to it, complete an evaluation, and document observations, they can review only a portion of interactions. This workload can limit teams’ ability to detect certain trends and to provide feedback quickly.
The arrival of artificial intelligence makes it possible to reconsider this way of working. With NiCE Inform AI, analysis goes beyond transcription, search, and categorization to include automated interaction evaluation, coaching suggestions, and performance dashboards.
In a traditional quality assurance process, supervisors can generally listen to only a portion of the interactions.
NiCE Inform AI now makes it possible to evaluate all calls against defined protocols, score interactions, and highlight those that require particular attention. This makes it possible to analyze 100% of calls rather than a manually selected sample, while still maintaining human validation.
This approach can notably help identify:
This difference matters because the objective is no longer only to select enough calls to get an overview of quality. It becomes possible to rely on a much larger volume of data to identify deviations, repetitions, or trends.
The supervisor’s role also evolves. Less time is devoted to identifying interactions that need review, and more time is devoted to understanding what the results reveal.
This evolution relies in part on transcription.
NiCE Inform AI automatically converts calls into timestamped and searchable text. The transcription engine is designed to account for realities specific to emergency communications, such as rapid exchanges, overlapping voices, background noise, or speech under stress.
However, the value isn’t only in obtaining a transcription. Supervisors can search for information directly within the content and metadata of the interactions.
They can notably:
A recorded call therefore becomes much easier to use in day-to-day operations, whether to reconstruct an incident, conduct an internal investigation, review a complaint, retrieve interactions with similar characteristics, or support audit, training, and quality assurance activities.
The benefit therefore does not come only from automated analysis, but also from the speed of access to relevant information.
Identifying an issue is a first step. Next, it is necessary to understand what can be improved.
NiCE Inform AI adds coaching suggestions to the analysis results. Supervisors can review summaries, areas for improvement, and tasks to assign to telecommunicators.
The objective is to shorten the path from the interaction to its evaluation, issue identification, and coaching.
The dashboards also make it possible to observe the results at different levels:
This view makes it easier to identify trends that are harder to detect by reviewing calls individually.
Quality assurance then becomes less occasional and more integrated into a continuous improvement approach.
In an environment as sensitive as emergency communications, human validation remains essential.
NiCE Inform AI is an assistive technology. AI can analyze large volumes of data, perform part of the processing and evaluation, detect certain trends, and generate suggestions, while the results can be submitted for human review and approval.
The issue is therefore not to transfer judgment to the algorithm. AI can process volume and highlight key elements, but judgment, context, and decisions remain the responsibility of professionals.
People responsible for quality can therefore work with a more complete view of the interactions and focus their expertise on situations that truly require human analysis.
The evolution of NiCE Inform AI reflects a broader trend in public safety: making better use of the data already available rather than collecting more.
With NiCE Inform AI, transcription, search, quality assurance, coaching, and trend analysis are centralized within the same environment.
For emergency communications centers, the issue becomes less about knowing whether they have the information and more about quickly transforming it into useful information to improve quality, support teams, and better understand operations.
AI now makes it possible to approach this issue differently: analyze interactions more systematically, highlight relevant information, and leave teams to interpret it and take action.
This balance between automation and human judgment is where this type of tool adds value for emergency communications centers.
