I’m thrilled to sit down with Matilda Bailey, a renowned networking specialist whose expertise in cellular, wireless, and next-gen solutions has made her a leading voice in the evolving landscape of enterprise technology. With a deep understanding of cutting-edge innovations, Matilda offers unique insights into how technologies like agentic AI are transforming unified communications (UC). In this conversation, we dive into the tangible business value of agentic AI, the challenges of integrating it into existing systems, its impact on employees, and the ethical considerations that come with adoption. Join us as we explore how companies can navigate this promising yet complex terrain.
How do you see agentic AI shaping the financial performance of companies using unified communications?
Agentic AI has the potential to significantly boost a company’s bottom line by automating complex, multi-step tasks in unified communications. This means reducing operational costs through streamlined customer service processes and minimizing human intervention in routine interactions. For instance, with AI handling a large volume of customer inquiries independently, companies can cut down on staffing expenses while maintaining or even improving service quality. It’s about efficiency—freeing up resources to focus on higher-value tasks and directly impacting profitability through cost savings and faster response times.
What key business outcomes should IT leaders prioritize when making a case for agentic AI?
IT leaders need to focus on concrete financial and operational metrics that resonate with the C-suite. This includes looking at reductions in operational costs, improvements in customer service efficiency, and enhancements in revenue generation. Metrics like profit and loss impact, cost per interaction, and sales cycle length are critical. It’s not about showcasing AI for the sake of innovation but demonstrating how it moves the needle on outcomes that already matter to the business, like increasing deal velocity for sales teams or cutting down on service request times.
Can you share an example of how agentic AI could enhance customer experience metrics in a real-world scenario?
Absolutely. Take something like first contact resolution rate, which is a key indicator of customer satisfaction. With agentic AI in unified communications, a customer reaching out about a billing issue could have their query resolved instantly through an AI agent that accesses account data, identifies the problem, and processes a correction—all without escalating to a human agent. This not only boosts the resolution rate but also improves customer satisfaction scores because the interaction is quick and seamless. It’s a direct win for both the customer and the company.
What are some of the major obstacles companies face when adopting agentic AI in unified communications?
One of the biggest hurdles is the complexity of integration with existing systems. Traditional IT infrastructure isn’t built for the kind of autonomous reasoning agentic AI requires, so there’s often a steep learning curve and significant upfront investment. Beyond that, there’s the issue of unclear business benefits—many companies struggle to define exactly how AI will deliver value. And let’s not forget risk management; without proper strategies, costs can spiral, and projects can fail. It’s a challenging landscape that requires careful planning and realistic expectations.
How significant are data privacy concerns with agentic AI, and what measures can mitigate these risks?
Data privacy is a massive concern, especially since unified communications often involve sensitive customer information. Agentic AI systems process vast amounts of data, and if not handled correctly, there’s a risk of breaches or misuse. Companies can mitigate this by engineering systems to mask sensitive data before it’s processed by AI models and ensuring data is stored securely, like on private customer instances. Adhering to privacy regulations is non-negotiable, and building in safeguards to prevent data retention or learning from customer interactions adds another layer of protection.
How does ethical decision-making factor into the deployment of agentic AI, and what protections should be in place?
Ethical decision-making is critical because agentic AI often operates with a degree of autonomy, making choices that can impact customers and employees. For instance, if an AI prioritizes certain customer queries over others, it needs to do so fairly and transparently. Companies should establish a clear delegation of authority framework, defining the AI’s role and limits. Safeguards like regular audits, transparent decision logs, and human oversight for critical actions are essential to ensure the technology aligns with ethical standards and doesn’t inadvertently cause harm.
What challenges arise when integrating agentic AI with traditional IT infrastructure?
The primary challenge is that most IT systems are designed for human interaction or simple automations, not for the complex, multi-step reasoning agentic AI demands. This mismatch can lead to compatibility issues, requiring significant reengineering of workflows and protocols. Additionally, ensuring seamless communication between AI agents and existing systems often means adopting new standards or protocols, which can be resource-intensive. It’s a fundamental shift from human-centric to AI-centric design, and that transition isn’t easy for most organizations.
How do you think agentic AI will influence the roles of employees in customer service and other UC-related positions?
Agentic AI will likely redefine roles rather than eliminate them outright, though the concern about job displacement is real. In customer service, for example, AI can handle routine inquiries, allowing human agents to focus on more complex, empathetic interactions that require a personal touch. This shift can elevate the role of employees to more strategic or creative tasks, but it also means they’ll need to adapt to working alongside AI. The key is reskilling—ensuring employees are trained to leverage AI tools effectively while addressing their concerns about job security.
What strategies can IT leaders use to ease employee fears about job security when introducing agentic AI?
IT leaders need to be transparent and proactive. Communicating that AI is a tool to enhance, not replace, human work is crucial. They should highlight how AI can reduce mundane tasks, allowing employees to focus on more meaningful work. Offering training programs to help staff adapt to new technologies shows a commitment to their growth. Additionally, involving employees in the implementation process—gathering their input and addressing concerns—can build trust and demonstrate that their roles are evolving, not disappearing.
What is your forecast for the future of agentic AI in unified communications over the next five years?
I believe agentic AI will become a cornerstone of unified communications within the next five years, with adoption rates soaring as the technology matures and costs become more manageable. We’ll likely see it resolving the majority of customer interactions autonomously, driving significant efficiency gains. However, the success will hinge on overcoming current challenges like integration and ethical concerns. Companies that invest in robust frameworks and prioritize transparency will lead the way, while those who rush in without a clear strategy may struggle. It’s an exciting, transformative period, but it will require careful navigation.
