Genesys Cloud - Main

Hello, @Victor Soares

In my experience, the main AI capabilities I'm currently working with in Genesys are Agent Copilot and bots.

For Agent Copilot, one of the biggest challenges is not just enabling the feature, but first building a solid foundation behind it. A good Knowledge Base is essential, with well-structured articles, clear content, and good intent mapping. If the knowledge and intents are not properly designed, Copilot may not provide the best recommendations to agents. So, a big part of the work is organizing, tuning, and continuously improving the Knowledge Base and intents to make the assistance more accurate and useful.

For bots, I see a lot of value in journey design. It is important to understand the customer's real path and decide when the bot should answer with FAQ functionality, when it should guide the customer through a flow, and when it should transfer to an agent. Implementing FAQ features can help reduce simple and repetitive contacts, but the experience needs to be well designed so the customer does not feel stuck in the bot.

Going forward, I would like to see more native reports for Agent Copilot. Today, reporting is very important to show customers the real value of the solution, identify improvement opportunities, and make their lives easier when analyzing results. Having more built-in reports would help teams track performance, understand usage, and continuously improve the Copilot experience without depending so much on custom reports.

We are currently exploring and using AI capabilities in Genesys mainly to improve customer self-service, agent productivity, and operational visibility.

Some of the most valuable use cases include:

• Bots and digital flows to automate common requests and reduce repetitive interactions for agents.
• Intent recognition and routing to better understand customer needs and direct conversations to the right queue or specialist.
• Agent assist / knowledge suggestions to help agents find the right information faster during interactions.
• Conversation summaries to reduce after-call work and improve handoff quality.
• Sentiment and interaction analytics to identify pain points, recurring issues, and opportunities to improve the customer journey.

These capabilities have helped solve challenges such as long handling times, inconsistent responses, high transfer rates, and the difficulty of analyzing large volumes of interactions manually.

Going forward, I would like to see even stronger AI features in Genesys around:

• More flexible and accurate generative AI for bots
• Better native integration between AI, Architect flows, scripts, and Data Actions
• Improved multilingual support, especially for Portuguese
• Easier tools to train, test, and monitor AI models
• More advanced real-time agent guidance
• Better AI-driven insights for identifying automation opportunities

Overall, I see AI in Genesys as a way to make customer interactions faster, more personalized, and more consistent, while also giving agents better tools to focus on complex cases.

Hi Victor,

We are currently using several AI capabilities in Genesys Cloud, mainly around Agent Copilot, knowledge surfacing, summarisation, sentiment/topic analysis, and automation workflows.

Some of the biggest benefits we've seen are:

• faster agent onboarding and guidance through surfaced knowledge/articles
• reduced manual effort with AI-generated conversation summaries
• better visibility into customer sentiment and emerging issues
• identifying coaching/training opportunities through topic detection
• improved self-service experiences through bot and knowledge integrations

We've also implemented workflow-based notifications using Speech & Text Analytics topic and sentiment triggers combined with AI Insights summaries. This has been particularly useful for surfacing potential complaints, dissatisfaction trends, and possible knowledge gaps much faster to operational teams.

We will also soon be looking into AI-powered auto-evaluation use cases within QA to help further streamline quality and coaching processes.

One area I'd personally love to see continue improving is deeper cross-platform integration and orchestration, especially around:

• AI-driven workflow actions
• proactive recommendations
• richer Copilot APIs
• more configurable summarisation outputs
• tighter CRM/ITSM integration patterns
• broader automation capabilities from conversation insights

Would also love to see even more flexibility around AI-powered quality management and auto-evaluation scenarios in the future.