In today’s rapidly evolving AI landscape, CIOs are no longer just technology overseers; they are strategic leaders driving enterprise-wide digital transformation. As every department fights for budget and AI resources, one area consistently delivers the highest ROI in the shortest timeframe: customer service and contact centers.
I recently sat down with Swapnil Jain, co-founder and CEO of Observe.ai, to explore why contact centers should be at the top of every CIO’s AI priority list. What emerged from our conversation was a compelling case for reimagining customer experience as the ultimate competitive differentiator.
Watch the full discussion here:
The Perfect Storm for AI Success
Contact centers represent what Jain calls “the perfect breeding ground” for AI implementation. The repetitive nature of customer service interactions, from package tracking inquiries to refund requests, creates an ideal environment for AI automation. Unlike other business functions that require complex reasoning or high emotional intelligence, many contact center tasks can be effectively handled by AI agents.
What makes this even more compelling is the data goldmine that contact centers have been quietly building for years. Those familiar words we’ve all heard: “This call may be recorded for quality and training purposes,” have created massive datasets of customer interactions that AI systems can learn from. This combination of repetitive tasks and rich training data sets the stage for rapid AI deployment and measurable results.
Navigating the Risk Landscape
However, implementing AI in customer-facing roles isn’t without challenges. Jain highlighted a critical shift that CIOs must embrace: moving from deterministic to non-deterministic systems. Traditional software follows predictable patterns—click here, fill out a form, proceed to the next step. AI agents, particularly large language models, operate differently.
“If you ask ChatGPT the same question twice, you might get different responses,” Jain explained. “The meaning stays the same, but the delivery varies.” This requires CIOs to thoughtfully select use cases where this variability is acceptable, like answering general questions about account status, while avoiding scenarios that demand exact scripting, such as reading insurance policy disclosures.
Breaking Free from Legacy Limitations
One of the most significant hurdles I see organizations facing is the burden of sorting what to do with legacy systems. While on-prem infrastructure works for some, in many other instances, CIOs are discovering that their on-premise infrastructure doesn’t support their vision for modern AI capabilities, requiring a fundamental architectural shift.
Beyond infrastructure, successful AI implementation demands centralized data strategies. When customer information is siloed across CRM systems, telephony platforms, and order management tools, AI cannot access the comprehensive view needed to deliver exceptional service. CIOs must prioritize breaking down these data barriers.
The Agility Imperative
Perhaps most importantly, the AI era demands a complete rethinking of planning cycles. While we have collectively shifted our thinking in the age of digital transformation and embraced the reality that super long term plans are no longer realistic, in the age of AI, those planning cycles are shortened even more. The traditional approach of five-year technology roadmaps simply doesn’t work when AI capabilities evolve monthly. As Jain pointed out, “If you create a two-year AI plan, it’s irrelevant in three months.”
Instead, successful CIOs are adopting an experimentation mindset, running four-to-six-week pilots that deliver measurable ROI quickly. This approach allows organizations to test, learn, and iterate without massive upfront investments.
The Build vs. Buy Decision
While many organizations have talented engineers who could theoretically build AI solutions in-house, Jain offers sage advice: getting to 80% functionality is easy, but reaching production-ready 99% is where most internal teams struggle. The last mile of AI implementation, handling interruptions, managing latency, dealing with background noise, requires specialized expertise that isn’t core to most businesses.
“You’re a healthcare company or financial services company,” Jain reminded me. “You should focus on improving those core experiences, not building contact center AI from scratch.”
The Customer Experience Differentiator
What resonates most deeply with me, in this conversation with Jain and with many of the other senior leaders I’ve had the opportunity to talk with, is how customer service has become the ultimate differentiator. In a world where everything is commoditized and when switching banks, telecom providers, or insurance companies is just a few clicks away, the quality of customer interactions and brand loyalty have never been more important.
We’ve all experienced frustrating customer service calls: being transferred multiple times, repeating our story to different agents, waiting on hold for extended periods. But we also remember the exceptional experiences where our problems were solved efficiently and professionally. In the age of AI, organizations that can consistently deliver those positive experiences will capture and retain customers.
The message for CIOs is clear: start with customer service, think experimentally, and partner strategically. The contact center isn’t just a cost center, it’s your gateway to transformative business impact.
Find and follow Swapnil Jain on LinkedIn here, and keep your eye on the team at Observe.ai. They are rapidly setting the bar high on the voice AI front.
This article was originally published on LinkedIn.
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