IA para além dos chatbots – Cinco formas de expandir a IA na sua Contact Center

Deploying proven approaches to benefit customers, agents, and the wider business

Often organizations start their contact center AI automation journey with customer-facing online chatbots. However, chatbots are just one use case for AI in contact center operations and only the tip of the iceberg when it comes to the technology’s potential. By exploring the five applications covered in this article, contact center leaders can select their best next step for successfully scaling AI.

For further practical guidance on AI use cases beyond chatbots read our guide to applying artificial intelligence in customer service.

Understanding the limitations of basic chatbots

Half of contact centers globally have deployed basic AI chatbots, according to analyst firm Valoir ^[1]. However, while this can deliver some benefits, they don’t scratch the surface of what is possible with AI. We’ve all experienced chatbots that can only respond to basic questions, don’t integrate with human agents or simply provide unclear or even incorrect answers.

As contact centers mature on their AI journey, they need to embrace AI use cases beyond chatbots with practical applications that engage customers, better support agents, increase efficiency and improve the customer experience.

Once you’ve completed a maturity assessment to understand where you are now and drawn up a list of contact center AI automation opportunities based on business and customer need, the next step is to investigate the AI technologies that can deliver real value to the organization.

In this article I’m going to cover these applications, including how to plan for successful deployments and integrate them into your wider contact center infrastructure.

Self-service via virtual agents

AI-powered virtual agents hold natural conversations across voice and digital channels, using connected knowledge and systems to understand requests and support customer self-service.

Key benefits of virtual agents:

• Improve customer access by providing immediate, 24/7 intelligent support across voice and digital channels.
• Reduce waiting times by responding immediately and absorbing peaks in contact volumes.
• Increase self-service completion by understanding intent, retaining context and completing routine requests from start to finish.
• Deliver more consistent service by drawing answers from the same accurate, up-to-date knowledge source across all channels.
• Make better use of agent expertise by routing sensitive or especially complex inquiries to live humans, along with the full conversation context.

Best practices to achieve success with virtual agents:

• Prioritize an initial set of use cases; the process and ranking outlined in o meu blog anterior help you to work with the right internal teams to identify use cases with the best combination of low risk and high value. Start with well-understood requests where the virtual agent can deliver a clear outcome, then expand only after performance meets agreed targets.
• Establish clear knowledge governance rather than relying on one individual. Assign an accountable owner, subject-matter contributors, approval rules and review schedules so information remains accurate, compliant and available as products, policies and customer needs change.
• Securely connect virtual agents to the systems needed to complete each approved customer request. Apply least-privilege access, identity verification, transaction limits, audit trails and human approval for higher-risk actions, and test failure and recovery paths before launch.
• Involve agents from use-case selection through testing and optimization. Use their insight to identify common failure points, shape escalation rules and test responses, while explaining how virtual agents will change roles, workloads and development opportunities rather than simply presenting the technology as a way to remove repetitive work.
• Design escalation rules around customer intent, risk, sentiment and repeated failure. Pass the full conversation history, verified customer details and actions already attempted to the human agent, then test handovers across channels so customers do not have to repeat themselves.
• Manage virtual agents as a continuously improving service. Review unsuccessful journeys, escalations, customer feedback, answer accuracy and completed transactions; compare performance across customer groups and channels; and assign clear owners and review cycles for correcting issues and expanding scope.
• Apply the same assessment criteria across all interactions, whether handled by virtual or human agents. Monitor both against shared customer outcomes, such as resolution, accuracy and satisfaction.
• Define success measures before launch, covering customer outcomes, operational performance and risk. Track indicators such as successful task completion, containment, repeat contact, escalation reasons, customer satisfaction, answer accuracy and cost per resolved enquiry, with baseline figures and targets for each use case.
• Don’t just plan the happy path: Plan how the service will respond when confidence is low, a connected system is unavailable or a request falls outside the approved scope. Provide a clear fallback route, prevent unsupported answers or actions, and make it easy for operational teams to pause affected journeys without disrupting the wider service.

Assisting agents through knowledge

AI agent assist supports employees during customer interactions by interpreting the conversation and surfacing relevant guidance from connected knowledge and systems.

Key benefits of agent assist:

• Reduce handle time and interruptions by surfacing relevant, verified information to agents in real time during interactions, without the need for hold or consultation.
• Improve answer accuracy and consistency by giving agents access to the same governed knowledge, across all channels.
• Increase first-contact resolution by helping agents resolve complex or unfamiliar inquiries without transfers or callbacks.
• Improve agent confidence and reduce cognitive load by guiding agents through policies, processes and next-best actions.
• Shorten onboarding and time to proficiency by supporting new agents while they build knowledge and experience.
• Improve compliance by prompting agents with required wording, disclosures and process steps.
• Deliver more personalized responses at scale by adapting suggested content to the customer’s context and enquiry.

Best practices to achieve success with agent assist:

• Prioritize use cases by value, complexity and risk, as outlined in o meu blog anterior. Start where there is the most value, e.g., high volume interactions where agents spend significant time searching for information, or queues handling important questions that are regularly answered inaccurately or inconsistently. Then expand after performance meets agreed targets.
• Use governed, trusted knowledge sources. Assign clear ownership, approval processes and review schedules so recommendations remain accurate, current and compliant.
• Keep agents in control. Present suggestions for review; for text interactions, rather than automatically sending responses on behalf of agents, or completing higher-risk actions, allow them to revise before sending.
• Design prompts around the agent workflow. Surface information at the right moment based on conversation context, without obscuring the customer conversation or overwhelming agents with unnecessary suggestions.
• Provide citations and explainability. Show where recommendations come from so agents can verify information before sharing it.
• Involve agents throughout agent assist deployment. Use their input to select use cases, test suggestions, identify gaps and refine the experience.
• Integrate securely with relevant systems. Apply identity controls, least-privilege access, audit trails and safeguards around sensitive customer information.
• Measure customer and agent outcomes. Track handling time, first-contact resolution, answer accuracy, compliance, agent adoption, customer satisfaction and repeat contact against clear baselines.
• Monitor for weak or inappropriate suggestions. Review rejected recommendations, incorrect answers, missing knowledge and differences in performance across channels or customer groups.
• Plan for continuous optimization. Assign owners and review cycles for improving knowledge, prompts, workflows and integrations as needs change.

AI quality management

AI quality management evaluates customer interactions across channels against tailored, consistent criteria, giving supervisors broader evidence for quality assurance, coaching and compliance.

Key benefits of AI quality management:

• Scale quality coverage by potentially evaluating up to 100% of interactions across all voice and digital channels, rather than relying on limited manual sampling.
• Improve consistency and fairness by applying the same transparent criteria across teams, queues and channels.
• Focus coaching where it matters most by identifying specific behaviors, knowledge gaps and recurring process issues.
• Strengthen compliance oversight by detecting missing disclosures, process deviations and higher-risk interactions for review.
• Free supervisors for higher-value work by reducing manual review and directing attention to exceptions and recognition or improvement opportunities.
• Track quality improvements over time by connecting evaluation results with coaching, training and operational changes.
• Empower agents to take ownership of their development by enabling them to evaluate their own interactions, recognize strengths and identify areas for improvement.

Best practices to achieve success with AI quality management:

• Define the purpose of each evaluation. Separate criteria for compliance, customer outcomes, process adherence and coaching so one score does not obscure important differences.
• Build a clear, representative framework. Assess relevant stages of the interaction and adapt out-of-box vendor templates to your channels, policies, customer needs and risk profile.
• Validate AI results before operational use. Compare automated evaluations with reviews by experienced assessors across different interaction types, teams, languages and outcomes.
• Keep human oversight for consequential decisions. Use AI to highlight possible issues, but require supervisors to review disputed, unusual or higher-risk interactions before escalating.
• Make evaluations transparent to agents. Share the criteria, evidence and reasoning behind results, and provide a clear route to query or appeal an assessment.
• Use findings to improve systems as well as engagement. Distinguish individual coaching needs from problems caused by knowledge, workflows, policies or technology.
• Monitor model performance over time. Recalibrate criteria when products, policies, channels or customer behavior change, and investigate unexpected shifts in scores.
• Protect customer and employee data. Limit access, retention and use of interaction data, with appropriate controls for sensitive information and regulatory requirements.

Customer analytics (Voice of the Customer, VoC)

AI-powered customer analytics turns conversations across channels into structured insight about customer needs, contact drivers and emerging issues, then recommends internal actions to address them.

Key benefits of customer analytics:

• Resolve root causes more effectively by identifying why customers make contact and exposing underlying needs, events and process failures.
• Prevent emerging issues from escalating by detecting changes in topics, sentiment, effort and contact volumes early enough for teams to intervene.
• Lower avoidable contact volumes (failure demand) and costs by exposing recurring problems in products, policies, digital journeys and third-party services.
• Improve customer journeys faster by linking customer themes with operational impact, value and urgency.
• Increase the impact of CX improvements by comparing customer conversations and outcomes before and after interventions.
• Make better product, service and process decisions by bringing direct customer evidence into business planning.
• Strengthen executive support for important decisions by using evidence from customer interactions to demonstrate the scale, urgency and business impact of issues.

Best practices to achieve success with customer analytics:

• Start with clear business questions. Define the decisions the analysis should support, such as reducing repeat contact, identifying failure demand or improving a specific journey.
• Develop a meaningful taxonomy. Go beyond broad labels such as “billing” to capture the issue, cause, journey stage, outcome and customer effort at a useful level of detail.
• Bring together relevant channels and data. Combine interaction insight with operational, journey and outcome data where appropriate, while avoiding unsupported conclusions about causation.
• Validate against known events. Test whether the analysis detects recognized incidents, campaigns or service changes, and manually review representative conversations before relying on the results.
• Track trends, not isolated snapshots. Monitor themes, volumes, sentiment and outcomes over time, using suitable baselines and thresholds to distinguish material change from normal variation.
• Create cross-functional ownership. Route findings to the teams that can fix root causes, agree actions and deadlines, and review whether changes reduce customer effort and contact demand.
• Include diverse customer journeys. Check performance across channels, products, languages and customer groups so dominant interaction types do not hide important issues.
• Apply strong privacy and security controls. Minimize personal data, restrict access and retention, and anonymize or pseudonymize information where appropriate.

Employee experience analytics (Voice of the Employee, VoE)

AI-powered voice of the employee analytics analyze structured feedback and free-text comments such as survey data, to reveal recurring themes in how contact center employees experience their work. For a broader employee analysis, this data can be combined with separate operational input.

Key benefits of employee experience analytics:

• Understand employee needs in greater depth by identifying recurring concerns, barriers and improvement ideas beyond headline survey scores.
• Identify operational causes of frustration by linking feedback with workload, schedules, processes, knowledge gaps and technology issues.
• Target improvements more effectively by showing which themes affect particular roles, teams or stages of the employee journey.
• Strengthen engagement and retention by responding to material concerns and demonstrating how employee input influences decisions.
• Improve customer outcomes by addressing the tools, processes and support issues that make it harder for employees to serve customers.
• Measure the impact of change by tracking employee feedback and operational indicators before and after improvement initiatives.

Best practices to achieve success with employee experience analytics:

• Define a clear and proportionate purpose. Explain what data will be analyzed, why it is needed and which decisions it will inform; avoid collecting information simply because it is available.
• Be transparent with employees. Communicate the sources, methods, safeguards and limits of the analysis in plain language before collection begins.
• Protect confidentiality and psychological safety. Aggregate results, restrict access, apply minimum reporting thresholds and avoid outputs that could expose individual employees.
• Do not use sentiment as an individual performance score. Treat automated interpretation as an indicator for organizational insight, not a definitive judgment about a person’s attitude, wellbeing or capability.
• Include employees in the design. Involve representative roles and employee groups in selecting questions, interpreting themes and testing whether findings reflect their experience.
• Combine insight carefully. Use employee, customer and operational data to explore patterns, while checking context and avoiding assumptions that correlation proves causation.
• Turn findings into accountable action. Assign owners, deadlines and success measures, then report back on changes made and what remains unresolved.
• Monitor participation and data quality. Check whether feedback represents the workforce and whether low response rates, language or channel access could distort conclusions.

The importance of connecting AI across the contact center

To achieve best outcomes, use the five use cases detailed in this article as parts of a connected AI strategy rather than isolated tools. Shared knowledge, data and integrations allow insight to flow between self-service, agent support, quality management and analytics, creating a more consistent experience and reducing duplicated effort.

This requires common foundations: clear program ownership, governed knowledge and data, secure integration, transparent communication, human oversight and shared measures of success. A phased roadmap can then prioritize lower-risk, higher-value opportunities while ensuring each deployment strengthens the capabilities that follow.

By connecting AI across the contact center, organizations can improve service quality, operational efficiency, decision-making and employee support while scaling automation responsibly.

Enghouse Interactive: Supporting contact centers on their AI journey

Enghouse Interactive helps contact centers turn AI into measurable improvements in customer experience, agent support, quality and decision-making. Drawing on deep contact center expertise, Enghouse AI focuses on practical business challenges and helps organizations prioritize the use cases most likely to deliver value.

Ready to identify the highest-value next step for AI in your contact center?

Wherever you are on your journey, Enghouse can help you assess and prioritize the AI use cases most likely to improve customer, agent and business outcomes.

Book an AI Roadmap Session

Sobre o autor

Steve Nattress Vice-presidente de Gestão de Produtos
Enghouse Interactive
LinkedIn

Steve’s unique background in Customer Support and AI development positions him perfectly to drive product and AI innovation at Enghouse. Steve is a creative and strategic leader, passionate about the potential of AI to transform customer experience, while staying sharply attuned to the critical need for secure and transparent industry practices.

Referências

1. Valoir: The State of AI in CX