Unified Communications as a Service (UCaaS) has transformed how enterprises collaborate by combining various channels—such as voice, video conferencing, and messaging—into a single platform. Just as UCaaS has revolutionized communications, integrating artificial intelligence (AI) is driving further innovation in communication tools.
The first wave of AI improvements in UCaaS included automatic recording, transcription, and real-time translation on the front end. Back-end enhancements included noise suppression technology and improved video quality. The current wave of innovation in communications moves past automating individual tasks to fully automating workflows. AI can now automate transcripts, generate meeting summaries, and create a list of action items for absent team members (and automatically share those tasks with teammates).
Change in this sector is rapid, with the dawn of the increasingly autonomous “agentic” AI that requires less human oversight.
How can your organization stay current with AI integration in UCaaS in this fast-paced environment? Let’s examine nine best practices that allow your organization to embrace the benefits of AI in unified communications while sidestepping the potential risks.
Learn more: Integrating AI and automation into modern CCaaS platforms
The challenges of AI integration in UCaaS
Like any AI deployment, AI-embedded solutions present certain risks. IT teams should strive for the most consistent and reliable output from AI assistants to generate efficiencies and cost savings. Common hurdles to this goal include:
- Accuracy: AI hallucinations are well-documented issues, and numerous strategies have emerged to minimize them. However, AI must address challenges like accents, poor microphone placement, and verbal quirks in use cases like transcription.
- Nuance: Unified communications workflows can become complex and involve multiple tools. More than simply recording and creating a transcript is required. AI must enhance the transcript by providing more valuable outputs, such as meeting summaries and action items based on the employees mentioned during the call.
- Bias: The training of large language models (LLMs) can quickly become biased if their inputs are not closely monitored.
- Security: AI introduces a new avenue for malicious actors. They can use AI to create sophisticated attacks, produce realistic deepfakes, and discover ways to manipulate public GPT systems.
- Employee and stakeholder engagement: Achieving organizational alignment is a significant challenge. One department might be eager to implement fast AI workflows, while another may be uncertain about the benefits.
Best practices for AI integration in UC platforms
In some cases, a phased approach is advisable for AI deployment. However, working holistically in a communications setting is the better approach to ensure accuracy, enhance security, and guarantee the best user experience.
Choosing an AI partner like OnX saves your organization time and resources and prevents unhelpful tangents during implementation. The following best practices help your organization achieve the most value from AI integration into UCaaS, whether working with an AI implementation partner or not.
1. Assess your communications tools
Understanding the current portfolio of communications tools, applications, and workflows is the starting point for AI integration. This assessment aids in setting objectives and identifying obstacles and informs the discovery process of selecting appropriate AI tools. If your business uses a UCaaS solution such as Cisco Webex Calling, robust AI features are built into the platform, However, if your business heavily uses Microsoft Teams Voice, it makes sense to investigate Microsoft Copilot due to its advanced features and high compatibility with the Microsoft 365 suite.
2. Work toward buy-in
Achieving organizational alignment for AI implementation is a significant hurdle, as each team has its own AI needs (or misgivings).
One way to achieve buy-in is to form a committee with representatives from across departments. This committee should identify pain points and the best AI tools to meet the needs of multiple departments. Additionally, training sessions to demonstrate the effectiveness of AI integration are essential.
3. State objectives and deployments
Map out the objectives, strategies, use cases, and challenges your AI implementations will face. Be sure to identify the metrics that will help determine ROI.
4. Select the right tools
Once you identify the solutions that will benefit the organization, you can start to narrow down suitable AI tools. Consider the following factors:
- Native AI capabilities.
- Interoperability and ability to integrate with current UC platforms.
- Scalability.
- The AI tool’s reputation and customer reviews.
- Security of the AI platform.
5. Practice clean data principles
The fundamental principle of AI is “garbage in, garbage out.” Data management is essential for achieving accurate and reliable AI outcomes and ensuring security. It is important to note that data management for AI is not a one-time task. Implementing compliant data policies and regularly auditing data management processes is crucial for maintaining data quality and ensuring your AI tools operate at their best.
6. Cybersecurity
Ensuring the security of AI, particularly generative AI tools, is crucial for protecting sensitive and proprietary information. Secure AI models, such as private GPTs or specialized AI tools (such as legal AI applications), can help reduce risks and enhance your overall security stance. Always review the terms and conditions of any AI tools you plan to use to comprehensively understand how they handle your data and their security level.
Learn more: Boosting company impact through UCaaS security and compliance
7. POC and launch
Developing a proof-of-concept trial using your selected AI tools might be beneficial, particularly if you intend to implement them on a larger scale. However, there comes a time when the only step remaining is to launch. It’s essential to continuously foster employee support by communicating the forthcoming changes internally well beforehand.
8. Training and culture
Training is essential to support AI implementation post-launch. Keeping an open conversation about AI integration within communication tools helps to shift cultural stumbling points into buy-in. It ensures your organization gets the most out of your technology investments.
9. Track and iterate
Keep in mind the metrics you established in step three. Now is the time to monitor those metrics regularly. Developing dashboards to visualize these metrics and establishing a dedicated support team can help maintain and optimize your AI investment for the long term.
OnX is your AI support partner
While AI may be unlikely to render your organization obsolete, other companies that effectively leverage AI might surpass you. OnX recognizes the complexities of integrating AI within unified communications tools and across various business functions. We specialize in developing secure and customizable AI solutions that enhance efficiency. Our AI experts are here to guide you through the process outlined in this article, helping you adopt best practices that provide a significant competitive advantage through AI.
Contact us to discover how to integrate AI into your UC tools effectively. You can also learn more about our AI Accelerator for Microsoft 365 Copilot.
