Customer service has become one of the most decisive factors in an organization’s reputation and profitability. However, ensuring a consistent, secure experience that complies with international quality standards remains a challenge for many contact centers. In this context, the ISO 18295 standard has established itself as the global benchmark for standardizing the relationship between companies, service providers, and consumers.
Today, Artificial Intelligence (AI) is emerging as the tool that allows for the automation, scaling, and objective auditing of compliance with this standard, transforming quality control management and continuous improvement.
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What is ISO 18295 and why has it become essential?
ISO 18295 is the international standard that establishes quality and management requirements for contact centers, both internal and outsourced. Its main objective is to ensure a consistent, effective, and ethical customer experience, regardless of the channel or provider involved in the interaction.
This standard is particularly relevant in sectors where telephone or multichannel customer service has a direct impact on trust and regulatory compliance: finance, insurance, telecommunications, energy, healthcare, and public administration, among others.
Two complementary parts
The standard is divided into two sections that address different responsibilities:
- ISO 18295-1:2017 – Requirements for contact centers. It establishes the operational obligations of the service provider: agent management, quality control, training, resolution processes, response times, customer satisfaction, etc.
- ISO 18295-2:2017 – Requirements for contact center customers. Defines the responsibilities of the organization contracting the service (e.g., an insurance company outsourcing a BPO). Requires service level agreements (SLAs), performance metrics, audit mechanisms, etc.
Main requirements of ISO 18295
The standard covers all elements that influence the perceived quality of service and the responsible management of interactions.
Among the most notable requirements are:
1. Quality of interactions and agent behavior
- Calls or contacts must be handled with professionalism, empathy, accuracy, and consistency.
- There must be documented protocols for opening, identification, handling objections, resolution, and closing.
- The organization must periodically evaluate the quality of interactions and provide structured feedback to agents.
2. Customer protection and regulatory compliance
- It must be ensured that all information provided is accurate, understandable, and compliant with current legislation (data protection, consumer protection, telemarketing, etc.).
- A traceable record of interactions is required, both to resolve complaints and to demonstrate compliance during audits.
3. Skills and training
- Staff must receive initial and ongoing training tailored to the characteristics of the service, products, and customer expectations.
- Supervisors must maintain evaluation and continuous improvement systems based on objective metrics.
4. Operational and technological management
- The organization must have documented processes for routing, recording, monitoring, and managing data, ensuring the security, integrity, and confidentiality of information.
- Operational incidents must be recorded and improvement or corrective measures applied.
5. Measurement and continuous improvement
- The standard requires a system of indicators that measures customer satisfaction, operational efficiency, and compliance with service levels.
- Improvement decisions must be based on verifiable data, not subjective perceptions.
The AI quality control revolution
Historically, quality control in contact centers was based on manual sampling: a supervisor listened to a small percentage of calls and evaluated the agent’s performance. This approach, while useful, is subjective, costly, and has limited coverage.
AI applied to quality control has completely changed that model. Thanks to technologies such as speech analytics, natural language processing (NLP), and machine learning, it is now possible to analyze 100% of interactions using consistent, measurable, and auditable criteria.
How automation works
- Automatic recording and transcription of calls or chats.
- Semantic and emotional analysis of content (tone, courtesy, compliance, interruptions).
- Automatic evaluation according to defined quality criteria (greeting, empathy, solution, legality, closure).
- Real-time alerts for deviations or possible non-compliance.
- Reports and dashboards with consolidated metrics by agent, team, or campaign.
From manual auditing to continuous intelligence
The introduction of AI into quality control processes not only facilitates compliance with ISO 18295 but also raises its scope to levels impossible with traditional methods.
Some specific transformations include:
1. Full coverage and complete traceability
AI enables 100% of calls to be analyzed, ensuring that every interaction is auditable and verifiable. This directly satisfies the principles of fairness, consistency, and documentation required by the standard.
2. Objective and transparent evaluation
The algorithms apply the same criteria to all agents and campaigns, eliminating bias and ensuring impartiality in evaluations, a key principle of this standard.
3. Alerts and proactive actions
Unlike the reactive models of the past, AI detects deviations in tone, compliance, or satisfaction in real time, allowing action to be taken before they turn into complaints.
4. Cost reduction and increased productivity
Automating quality control can reduce the operational effort of supervisory teams by up to 60% and free up resources for coaching, analysis, or innovation tasks.
5. Integration with business indicators
Voice analytics platforms such as Recordia, for example, allow quality results to be linked to commercial or retention metrics, identifying correlations between agent behavior, satisfaction, and business results.
The new vision of ISO compliance: data, evidence, and continuous improvement
Compliance with ISO 18295 no longer means simply obtaining a certificate but demonstrating an evidence-based culture of quality. AI enables organizations to move from measuring quality on an ad hoc basis to doing so continuously and predictively, aligning the standard with technological innovation.
Supervisors no longer need to rely solely on sampling or perceptions. AI systems offer detailed reports by agent, comparative analyses, risk indicators, and early detection of patterns of dissatisfaction or non-compliance. This facilitates simpler audits and continuous, documented, and auditable improvement, as required by the standard.
In addition, the combined use of AI and human supervision reinforces transparency: technology provides objectivity, and people provide context and judgment. This combination—adopted by platforms such as Recordia and other conversational analytics solutions—is becoming the gold standard for quality in modern contact centers.
Learn more about automated quality control using AI solutions by clicking here.