Generative AI (Gen AI) has dominated headlines since OpenAI debuted ChatGPT in late 2022. Since then most unified communications and collaboration (UC&C) as a service (UCaaS) and contact center as a service (UCaaS) vendors have included Gen AI capabilities into their cloud offerings. Right now, Gen AI use cases primarily involve providing assistance for a range of tasks – email writing, meeting summaries, customer interaction summaries, etc. The benefits of Gen AI primarily involve reducing the amount of time it takes to complete a task.
There are various concerns associated with using and implementing Gen AI, which range from data privacy and security, to ensuring the Gen AI tool produces output in accordance with corporate “guardrails,” as well as ethical and responsible AI principles.
Check out the following articles for insights on these Gen AI topics, as well as trends, best practices and recommendations.
There are many different large language models (LLMs), but they all work in the same way. We explain how models are trained and how they work so you can understand their power as well as their limitations, then examine how each of the major UCaaS platforms perform on two key use cases.
“Learning to Live With Your UCaaS LLM, Part 1” – “Before discussing the specific LLMs and how they are used in your UCaaS solution, a short discussion of how generative AI (Gen AI) works and how LLMs are trained might be useful so that you can understand their power as well as their limitations.”
"Testing Gen AI Use Cases in UCaaS Platforms, Part 2" -- In this article, we review the capabilities of the AI assistants offered by Zoom, Cisco, Microsoft, and Google. Our primary intent is to help users who have already selected a UCaaS platform (or are perhaps in the process of choosing) better understand and leverage the AI capabilities they can access.
Gen AI foundation models, the LLMs themselves – e.g., OpenAI GPT, Google Gemini, Nvidia Nemo, Meta Llama 2.0, etc. – are all trained on massive datasets. The current trend is to “ground” … sometimes referred to as “retrieval augmented generation” … those foundation models on a company’s proprietary data and then make sure, via guardrails, etc., that the grounded model can only access, and thus generate responses, based on what it has ‘learned’ from the company data.
"Enlightening Generative AI" -- This interview with NICE CEO Barak Eliam talks about how the company views generative AI and how it plans to use it to help customer service professionals.
"Microsoft 365 Copilot: How The AI-Powered Assistant Boosts Productivity" -- This article summarizes how Microsoft 365 Copilot assists in routine knowledge-based tasks commonly performed by people.
Around 85 percent of customer interactions begin with non-Gen AI-powered bots or intelligent virtual assistants (IVAs). Gen AI-powered bots can handle even more complex transactions, but not without human supervision – at least right now. Here are a few steps toward thinking critically about Gen AI in the contact center.
"Assessing Gen AI's Impact in the Contact Center, Part 1" -- In this installation of No Jitter’s Conversations in Collaboration, Forrester senior analyst Christina McAllister defines contact center productivity and how AI could change it for the better. For example, Gen AI-powered summarization can be used to summarize a customer interaction, which saves the agent time in their after-call work. So, if an agent can handle 50 calls in a day and with Gen AI summarization, they can now handle 53, and if you do that across 500 agents the savings becomes meaningful.
"How Generative AI Will Improve ROI in the Contact Center in 2024" -- Generative AI-based solutions will help reduce operational costs and reallocate operational resources within the customer service environment while promoting high-quality customer interactions.
"Understanding the Role of Generative AI in Modernizing Customer Experience" -- Generative AI is impacting the role of customer service agents, challenging the belief that AI might make these roles redundant. Despite significant, recent advancements there are situations where generative AI falls short – which, paradoxically, makes customer service agents both less and more important.
Companies are spending millions in technology to provide a great customer experience (CX) without thinking through everything the customer needs when they call. Here are the articles that address those dilemmas and how to solve them.
"Generative AI Already Embedded in Contact Centers" -- The biggest value of generative AI is to automate tasks—keeping in mind that the algorithms are trained on language, not truth, so the ability to put guardrails around data becomes vital to the success of generative AI in business. How can businesses and consumers use the technology?
"Planning to Transform your Customer Experience? Here are the Numbers You Need to Know" -- AI reduces need for new hires, boosts revenue, improves CSAT, and more…
"Using Generative AI to Improve Customer Experience" -- Microsoft and Boston Consulting Group provide some insights on how generative AI-powered contact center solutions can improve customer experience and help agents become more efficient. The key is starting small.
"The Good, the Bad and What We Can Do with Generative AI" -- Generative AI-based chatbots cannot think, reason, or show emotion. Be careful with all the latest technology and stay in control of its answers.
Sure, Gen AI vendors promise to make your workforce more productive – but how are we defining “productivity” here?
"Conversations in Collaboration: HFS Research’s David Cushman on How Gen AI May Help Unlock Unexpected Value" -- Ferreting around in productivity too much distracts from what the real value of Gen AI actually is – the emerging value propositions, the things you couldn't do before but you can now because of the magic of AI in your hands every day.
"Conversations in Collaboration: Slack’s Christina Janzer on How AI-powered Tools May Require Thinking Differently about Productivity" -- As we think about AI potentially replacing a lot of the visible busy work, or this “work of work,” we really need to think about productivity in a different way.
Many of No Jitter’s conversations with industry leaders touched on the need to exercise caution when implementing Gen AI.
"Generative AI and Data: Critical Elements for Future CX" -- Generative AI is only as good as the data it draws on. How can we optimize to integrate and maintain quality data?
"Conversations in Collaboration: Cognigy’s Philipp Heltewig on Orchestrating Generative AI’s Role in the Contact Center" -- Cognigy’s CEO and cofounder on the bearing generative AI has for Cognigy’s AI platform, and how his company’s approaching the incorporation and use of that new tech.
"Humans Remain a Vital Part of the AI Equation; Cloud Providers Are Using AI in Their Marketplace Battles" -- At he Voice & AI show one speaker stressed the importance of remembering that AI will be useless if it's not getting good data – and good directions – from the skilled human beings using it, while another pointed out that the advances in AI are part of cloud providers' bigger battles.
"Zoom’s AI Approach: Leading with Openness and Accountability" -- No UC&C vendor can guarantee a top-notch AI toolkit without refining their algorithms using extensive customer datasets. The key is communicating with customers.
"Where Does AI Go From Here?" -- The rapid, and accelerating advance of AI, has created challenges for IT and business leaders as they move from hype to adoption. These include measuring ROI, change management, and ensuring security, governance, and compliance.
"Considerations for AI Product Acquisition" -- Where does liability fall when reliance on an AI-powered system causes harm? The short answer is “everywhere,” but that’s not really a useful answer.
"What to Expect From AI in Business Communications in 2024" -- Despite AI’s promise, there’ll be a lot of mistakes made along the way as we navigate the early days of some of the new AI capabilities, particularly for generative AI.
