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AI in communications improves productivity, but challenges lurk
AI services, combined with unified communications, can offer several benefits, including improved network monitoring capabilities and voice analytics in meeting rooms.
AI and machine learning provide new ways for organizations to improve and automate various aspects of their IT services. Unified communications is no exception. For organizations looking to improve UC performance and support employee productivity, AI can provide the necessary capabilities.
The first step to adopting AI in communications is determining the areas that would benefit the most from AI services. But organizations must also address potential challenges that can arise when implementing AI capabilities, such as speech recognition and chatbots.
How can AI improve UC performance?
AI in communications, coupled with machine learning, can help organizations monitor and improve the performance of their real-time communications applications. Organizations can use AI and machine learning to improve quality of service through end-to-end network visibility and dynamic routing.
AI and machine learning models augment centralized control planes for the network and can perform tasks such as identifying the purpose of a packet, simplifying real-time communications prioritization and rerouting high-priority traffic away from congested areas.
AI in communications can also provide additional monitoring and analytics capabilities. Algorithms can determine trends in collected communications data to identify outliers that could indicate issues, such as packet loss and session length. Organizations can also use AI to improve communications security by recognizing communications patterns to detect fraudulent activity, such as a local phone number suddenly dialing expensive international calls.
How can AI improve employee collaboration?
Meeting rooms are a key area where AI can improve employee productivity and collaboration. AI-driven voice analytics can provide speech-to-text capabilities for automatic transcriptions and meeting summaries. Organizations can also add virtual assistants or bots to replace remote controls and enable employees to use voice commands to conduct meetings.
Meeting room technology can use AI's computer vision capabilities to gain insight into how employees are using the room by tracking how often the rooms are used and how many people attend a meeting.
Organizations can also deploy cameras built with AI technology to support meeting room productivity and other video use cases. Video conferencing hardware providers offer smart cameras with AI-driven capabilities, such as counting people, automatic zoom and background blur. Some cloud providers, such as Microsoft and Amazon, offer general-purpose AI cameras that suit a variety of video use cases in verticals such as manufacturing and retail.
What are the challenges of rolling out AI in communications?
AI can be difficult to deploy if organizations don't have the right business case or talent to ensure a successful rollout. Organizations need to make sure the right data is collected and accurately processed by AI services to improve communications efficiency and UX.
AI also poses a potential security threat through virtual assistants and chatbots. Inaccurate speech recognition, for example, could result in collecting incorrect information or sending information to the wrong person. Virtual assistants and chatbots are also at risk of being hacked by someone outside the organization, who could then eavesdrop on conversations or steal information to commit identity theft.
However, many challenges of using AI can be addressed by training the technology. By nature, AI learns and improves the more it's used. By starting with recognizing speech patterns in small commands, AI's capabilities can expand from full meeting transcriptions to identifying and attributing information to individual speakers.
