As we dive into the evolving landscape of enterprise IT and AI, I’m thrilled to sit down with Vernon Yai, a renowned expert in data protection, privacy, and governance. With a career dedicated to pioneering risk management strategies and cutting-edge techniques for safeguarding sensitive information, Vernon brings a unique perspective to the challenges and opportunities surrounding agentic AI—a transformative technology reshaping how businesses operate. In this conversation, we explore how agentic AI differs from traditional tools, the complexities of pricing such innovations, the balance between automation and human roles, and the critical need to demonstrate value to business leaders.
Can you explain what agentic AI represents in the context of modern enterprise platforms and how it stands apart from older tools like bots or FAQs?
Agentic AI is a game-changer because it goes beyond the static responses of traditional bots or FAQs. It’s about creating an intelligent, adaptive layer that can act autonomously to solve problems, anticipate needs, and integrate deeply with a company’s entire ecosystem. Unlike older tools that just answered basic queries, agentic AI can handle complex interactions—like managing sales calls or customer service issues—by learning from data and context in real time. It’s not just a helper; it’s a partner that works alongside humans to boost efficiency and outcomes.
How does this kind of AI technology fit into the broader strategy of a company focused on customer relationship management?
Within a customer relationship management framework, agentic AI acts as a force multiplier. It connects with various touchpoints—sales, service, marketing—to deliver a seamless experience. For instance, it can analyze customer data to prioritize leads for sales teams or resolve service tickets before they escalate, all while feeding insights back into the system. This integration ensures that every interaction is smarter and more personalized, aligning with the core goal of building stronger customer connections.
Developing a sophisticated AI solution often takes years of effort. Can you walk us through some of the major hurdles faced during such a long-term project?
Absolutely. Developing something as advanced as agentic AI over a multi-year timeline comes with significant challenges. Early on, there’s the hurdle of defining what ‘agentic’ really means in practical terms—how much autonomy should it have? Then, integrating it with existing systems without disrupting workflows is a huge technical lift. Data quality and security are constant concerns; the AI is only as good as the information it processes. And of course, there’s the iterative process of testing and refining based on real-world use, which often reveals unexpected gaps or user resistance that you have to address on the fly.
There’s a lot of buzz about AI handling massive workloads, like thousands of sales calls in a short time. What does that kind of capability mean for a company’s day-to-day operations?
When AI can manage something like 50,000 sales calls in a week, it fundamentally changes the pace and scale of operations. It frees up human employees from repetitive, high-volume tasks, letting them focus on strategic work—like building relationships or closing complex deals. It’s a massive efficiency boost, but it also means rethinking workflows. Teams need to adapt to this new dynamic, ensuring the AI handles routine stuff while humans tackle the nuanced, high-value interactions. The impact is profound, but it requires careful orchestration to avoid bottlenecks or over-reliance on tech.
With AI taking on more roles, there’s often concern about job displacement. How do you see the balance between automation and human workers evolving in organizations adopting this technology?
It’s a valid concern, and the balance is definitely shifting. Automation like agentic AI does reduce the need for certain roles, especially in repetitive areas like basic customer service. But it’s not just about replacement—it’s about redistribution. We’re seeing companies pivot resources toward areas like distribution, innovation, or specialized support where human judgment is irreplaceable. Plus, new opportunities emerge, like roles in AI oversight, data strategy, or training models. The workforce doesn’t shrink; it transforms, and the focus becomes upskilling people to thrive in this hybrid environment.
Pricing new AI technologies seems to be a sticking point for many vendors. Why is it so challenging to come up with a consistent pricing model for something like agentic AI?
Pricing agentic AI is tough because it’s not a one-size-fits-all product. Customers have wildly different expectations—some want to pay per user, others per action or outcome, and some based on overall consumption. Then there’s the issue of perceived value; it’s hard to quantify the benefits upfront, especially when the tech is so new. Unlike traditional software with clear licensing fees, agentic AI’s impact varies by use case, making standard models feel rigid or unfair. Vendors are caught between offering flexibility and maintaining a sustainable business model, which creates a lot of trial and error.
How can companies help business leaders, like CIOs, justify the investment in AI when the return isn’t always immediately clear?
Demonstrating ROI for AI requires a shift in how we measure success. It’s not just about productivity gains—though those are important—but about broader outcomes like customer satisfaction, reduced churn, or faster market response. Companies need to work hand-in-hand with CIOs to define specific metrics that matter to their business, whether it’s call resolution times or sales conversion rates. It’s also about storytelling with data—showing small wins early to build confidence. Transparency and partnership are key; no one has all the answers yet, so it’s a collaborative journey to prove value.
Looking ahead, what is your forecast for how agentic AI and its adoption will shape the future of enterprise technology over the next few years?
I see agentic AI becoming a cornerstone of enterprise tech, but its adoption will be a gradual evolution rather than a sudden revolution. Over the next few years, we’ll likely see tighter integration with existing platforms, making AI less of a standalone tool and more of an invisible enabler. Pricing models will mature as vendors and customers gain clarity on value, and I expect regulatory frameworks to emerge around data use and AI ethics, which will shape deployment. Most importantly, the human-AI partnership will deepen—organizations that balance automation with human insight will lead the pack, while those that over-automate risk losing the personal touch that still drives loyalty.
