Last week, Enterprise Connect 2024 took place in Orlando, bringing together a wide range of companies that specialize in solutions for customer contact centers and employee collaboration. One theme at this year’s conference was unmistakable: the omnipresence of artificial intelligence (AI), particularly large language models (LLMs) and Generative AI, across every session and discussion.

There’s a palpable race among solution providers to assert their longstanding commitment to AI innovation or to announce their latest upgrades, underscoring a commitment to remain at the cutting edge. However, this rush towards AI sophistication brings to light a discernible gap between the promises of AI and the practical realities businesses face when deploying these technologies. Stated concisely, the high-profile offerings of leading CCaaS and UCaaS providers challenge their customers’ ability to absorb them into employee and customer workflows.

While the potential of AI to transform customer contact centers and employee collaboration is widely acknowledged, there’s a growing dialogue about the tangible applications and measurable benefits of these advancements. Take call summarization using AI as an example. It is well understood and appreciated for its straightforward utility. Yet, the proposition of leveraging generative AI for customer-facing chatbots has sparked caution among experts. Concerns about the maturity of the technology were echoed throughout the conference, with warnings about the risk of AI delivering incorrect or inappropriate responses.

Nevertheless, amidst this atmosphere of caution and curiosity, there’s a clear indication that vendors are not just experimenting with AI but are integrating it into their solutions with a focus on delivering immediate, recognizable value. This approach reflects a pragmatic strategy to harness AI’s potential, focusing on areas where it can add measurable value and improve operational efficiencies, even as the broader conversation about its application continues to evolve.

Selective Roundup of AI and Analytics Announcements

Here’s a summary of just a few of the announcements from vendors regarding the AI enhancements they have made to their products. These updates highlight the industry’s ongoing efforts to refine and expand the role of AI in business communications, providing a glimpse into the future possibilities that these technologies may unlock.

Salesforce
Salesforce announced new AI-powered features for Service Cloud aimed at transforming call centers into revenue generators by increasing customer satisfaction and loyalty. Powered by Salesforce’s Einstein AI platform, the new capabilities provide agents and supervisors with insights, content generation, and automation to resolve cases faster.

Key innovations include Einstein Conversation Mining to analyze interaction data across channels and identify recurring issues, Generative AI Survey Summarization to surface root causes of low satisfaction scores, and Knowledge-Powered AI to suggest relevant knowledge articles during live conversations. Additionally, Einstein AI can now automatically generate new knowledge articles based on customer interaction data.

CallMiner
The latest enhancements to CallMiner’s platform include AI Classifiers and Semantic Search, alongside the already existing features like AI-based contact summarization and AI redaction. The new AI classifiers leverage LLMs to automatically label context within customer interactions, streamlining the process of extracting valuable insights and aiding strategic decision-making. Semantic search, now generally available, allows users to conduct searches based on the meaning of queries rather than just keywords, improving the ease of uncovering relevant business insights across various strategic initiatives.

Moreover, CallMiner has continued to refine its CallMiner GPT, an in-platform conversational assistant help bot that supports users in navigating the platform’s features more effectively. With ongoing updates to the help bot’s knowledge base, CallMiner aims to facilitate immediate, accurate responses to user queries, thereby enhancing the overall value users gain from the platform.

Google CCAI
Google announced a number of new or enhanced features for its CCAI product suite. CCAI Quality AI is a new AI-powered solution for automated quality scoring and performance management in contact centers. It leverages large language models (LLMs) and Google’s AI technology to automatically evaluate 100% of customer conversations against key service metrics.

By analyzing the full breadth of interactions with AI rather than just sampling, it promises to provide more comprehensive and actionable insights. This includes pinpointing areas for agent coaching, uncovering emerging topics raised by customers, and surfacing performance trends over time. The solution also offers holistic performance dashboards that visualize important contact center metrics with claimed greater accuracy driven by the AI analysis.

Verint
Verint has launched the TimeFlex Bot, powered by AI models on the Verint Open Platform, to redefine agent scheduling flexibility in contact centers. Leveraging workforce management forecasts, the TimeFlex Bot enables a frictionless process for agents to swap shifts, split schedules, and make changes that improve work/life balance. It uses a gamified “FlexCoin” system where agents earn coins for schedule changes that benefit the company, which they can then spend on preferred schedules.

The TimeFlex Bot aims to dramatically elevate employee experience and engagement by giving agents more control, while reducing manual scheduling efforts for supervisors. Early adopters saw an average of 10 schedule changes per agent monthly. By bridging the gap between business needs and agent preferences through AI, Verint claims the bot can drive ROI, reduce costs, and improve employee retention alongside customer experience. Like other Verint bots, TimeFlex can integrate with existing ecosystems for rapid deployment.

NICE
NICE announced the next generation of its Enlighten Copilot solution, powered by purpose-built AI models trained on CX interactions. It includes Copilot for Supervisors that provides real-time analysis of an organization’s CX data, enabling supervisors to get a 360-degree view of agent performance, identify coaching opportunities, and receive alerts to take action. Copilot for Agents enhances agent-assisted interactions by providing relevant content, generating conversational responses, and recommending compliance and upsell opportunities.

The release also highlighted NICE Enlighten Actions, which gives CX leaders visibility into operations using a conversational interface. It can analyze unstructured data, integrate with applications to initiate workflows and staffing adjustments, and generate comparisons against industry benchmarks.

Uniphore
Uniphore announced a significant update to its U-Analyze analytics solution, enhancing its comprehensive multimodal enterprise AI platform. This upgrade integrates generative AI, enabling businesses to harness the power of AI alongside their own data for more informed, timely decision-making. By analyzing customer interactions across text, voice, and video, U-Analyze aims to transform customer experiences and agent performance.

Following the release of U-Capture, a solution for capturing and applying multimodal data, this advancement underscores Uniphore’s commitment to data ownership and accessibility for its clients. U-Analyze, supported by the robust X-Platform, works in tandem with U-Capture to provide real-time conversational data analysis and generate tailored AI insights. These insights, coupled with coaching capabilities, aim to enhance customer engagement and anticipate market demands.

The Stage is Set for a Bright, AI-Infused Future

As we reflect on the insights and advancements shared at Enterprise Connect 2024, it’s clear that the intersection of AI, communication and contact center technologies is not just a fleeting trend but a fundamental shift in how businesses engage with their customers and employees. Adoption is imminent thanks to continuing development and delivery of solutions that address known business requirements.

Growth is tempered by lack of measured business benefits, skepticism about the reliability and trustworthiness of underlying models, misunderstanding of the total costs associated with AI-infused deployments and, perhaps most of all, a dearth of use cases and business cases that justify wholesale abandonment of current solutions and personnel that perform existing tasks with well-understood, predictable results. The enthusiasm for AI, from LLMs to Generative AI, underscores the industry’s ambition to innovate, but it also brings to light the critical balance between harnessing cutting-edge technology and meeting the practical needs of users.

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