Unified Communication as a Service (UCaaS) has revolutionized how enterprises collaborate. It is a streamlined method of uniting multiple channels (voice, video conferencing, messaging, etc.) into a single platform. And just as UCaaS transformed communications, AI integration drives innovation within communications tools.
The initial AI innovations involved front-end improvements like automatic transcription and real-time translation, while backend functionalities improved the user experience with advanced noise suppression and immersive video improvements. The emerging cases for AI in UCaaS go beyond automating tasks to fully automating workflows. Not only can AI automate transcripts and create summaries, but it can draw on cues to schedule action items or automatically send meeting summaries to absent roster members.
A third wave of AI innovation is right around the corner for UCaaS, with the imminent release of ChatGPT 5 and competing LLMs (large language models). Current AI features include “quick catchup” when you step away from a meeting and the ability to rephrase a response in a more diplomatic or professional tone among others, but the next generation of AI will likely be even more autonomous and require even less human interaction.
Considering the current landscape, how can your organization best integrate AI within your existing UC tools? This post outlines the best practices. But first, let us look at the challenges involved.
Learn more: AI and automation solutions in contact centers: The evolution of CCaaS
Challenges of AI integration within UC tools
The challenges with utilizing AI tools in communications are like any AI implementation. The primary objective is to get the most reliable and consistent results from your AI assistants in a way that saves time and money. The obstacles to this goal include:
- Accuracy – AI hallucinations are well-documented at this point. In voice-driven use cases like transcription, AI must cope with accents, poor microphone placement, and various verbal tics.
- Nuance – Unified Communications workflows can quickly become complex and work across multiple tools. In other words, recording and creating a transcript is not enough. AI must render the transcript into more valuable forms for users, such as meeting summaries and action items based on employees named during the call.
- Bias – LLM training can easily become biased if its inputs are not closely monitored.
- Security – AI represents a whole new line of attack for bad actors. They utilize AI to create sophisticated attacks and realistic deep fakes and find ways to manipulate public GPT systems.
- Employee and stakeholder buy-in – Creating organizational alignment is a crucial challenge. One department may be enthusiastic about implementing fast AI workflows, while another is unsure about the benefits.
Steps to integrating AI within UC tool sets
While taking a piecemeal approach to AI implementation is possible, working holistically to generate comprehensive AI solutions for your organization is better. Working with a provider with AI implementation experience like CBTS saves time and keeps you from unproductive rabbit holes. Whether you choose to work with an AI partner or not, we recommend taking the following steps to get the most out of your gen AI workflows within UC tools.
1. Audit the UC portfolio
A clear picture of your current communications platforms, applications, and workflows will help clarify objectives and create a starting point for investigating potential AI tools. For example, if your organization currently relies on Microsoft Teams Voice for Unified Communications, chances are that AI integrations with Microsoft Copilot will be an excellent choice. However, you will want to identify gaps or needs to help find the most relevant solutions.
2. Organizational alignment
Getting buy-in from multiple departments represents a significant challenge. Each team has unique needs for AI and automation and your organization’s needs as a whole.
One way to address this challenge is to create an AI-focused council with representation from departments across the enterprise. This approach can help identify key pain points and what tools can serve the needs of more than one department, thus creating more buy-in and even excitement.
3. Define objectives and implementations
At this point, you will want to create a road map that lays out objectives, strategies, AI use cases, and pain points and identify metrics you will want to monitor to define the project’s success or failure.
4. Choose appropriate tools
Once it is clear which use cases will benefit the greater part of the organization, you can begin narrowing down appropriate AI tools. You will want to consider:
- Integration within existing UC tools.
- The ability to scale.
- Reputation and reviews from other customers.
5. Implement clean data practices
The golden rule of AI is “garbage in, garbage out.” Data management is the key to more accurate and consistent AI and is vitally important for security. However, data management is not “one and done” for AI. Adopting compliant data policies and auditing data management processes helps keep your data clean and your AI tools firing on all cylinders.
6. Security
Keeping AI, especially GenAI, tools secure is essential for securing sensitive and proprietary information. Secure AI models like a private GPT or specialized AI tools built for purpose (such as legal AI platforms) can mitigate risk and increase overall defense posture. Be sure to read the terms and conditions of any AI tools you would like to adopt to fully understand how the tool uses your data and how secure it really is.
Learn more: Security and compliance enhancements for UCaaS solutions
7. Proof-of-concept and deployment
It may be worth creating a proof-of-concept trial with your chosen AI tools, especially if you plan to deploy them at scale. At a certain point, though, the only thing left to do is launch. Be sure to consistently build buy-in from employees by promoting the upcoming changes internally well in advance.
8. Employee training and culture shifts
Proper training and open conversation will support the transition to AI integration within UC tools, boost buy-in and help ensure your organization gets the most out of its AI investments.
9. Measure and iterate
Recall the metrics you defined in step three. Now is the time to measure those metrics regularly. Creating dashboards to track these metrics and setting up a dedicated help desk can ensure your AI investment stays optimized well into the future.
Finding an AI partner
While it is unlikely that AI will put your organization out of business, other enterprises that better utilize AI might. CBTS understands the nuances of AI integration within unified communications tools and every other area of business. We know what it takes to create secure and customizable AI toolsets that truly increase efficiency. Our AI experts advise you on the process discussed in this post and help you adopt the best practices that give you a competitive edge with AI.
Get in touch to learn more about incorporating AI into your UC tools. Or learn more about our AI Accelerator for Microsoft Copilot 365.
