AI-Powered CX Key Findings:
AI is rapidly transforming how brands interact with customers, but implementation often outpaces intention.
For some time now, companies have been trying to address customer needs through personalization, according to a 2025 McKinsey report, using data and analytics to craft more relevant consumer experiences.
But even as the tools evolve, many still struggle to translate AI investments into meaningful engagement.
Sriram PH, CEO and Co-founder of DaveAI, says the most critical decisions about AI happen before a single model is deployed.
I spoke with Sriram about the most common mistakes enterprises make with AI, the rise of multimodal customer journeys, and what it really takes to deliver digital experiences that convert.
Who is Sriram PH?
Sriram PH is the CEO and co-founder of DaveAI, a patented cutting-edge AI Conversation Experience Cloud designed to transform everyday interactions into engaging, intelligent experiences for Mobility Enterprises. With a background in enterprise sales, digital transformation, and AI innovation, Sriram has led DaveAI’s growth across industries like retail and automotive by blending conversational design with business outcomes. Prior to founding DaveAI, he held roles at HCL, Wipro, and IDC, and co-founded an AI-driven food tech startup.
Why AI Success Still Starts With Strategy
According to Sriram, one of the biggest reasons AI initiatives underperform is a lack of strategic clarity from the start.
“Many enterprises adopt AI tools without defining the KPIs they want to influence; whether it's lead conversion, engagement time, or CSAT. This lack of clarity leads to underwhelming results,” he explains.
Without clear goals, it’s impossible to design meaningful customer journeys, measure performance, or refine what’s not working.
Teams may build impressive tools, but if those tools aren’t tied to a business outcome, they fail to create value.
That’s why strategy must drive every AI implementation decision. When the “why” is missing, the results usually are too.
Your AI Is Only as Good as Your Data
Even the smartest AI will struggle if it’s trained on outdated or disorganized information.
Sriram identifies data readiness as a frequent blind spot for companies eager to deploy AI in customer experience.
“AI’s success hinges on good data. Enterprises often overlook data silos, outdated CRMs, or inconsistent taxonomies — all of which affect model accuracy and experience quality.” he says.
In other words, poor data hygiene doesn’t just slow down implementation. It actively degrades the experience.
A virtual assistant can’t recommend relevant products if your inventory data is outdated, and it can’t personalize conversations if your customer records are fragmented or incomplete.
Before layering on AI, brands must ensure they’ve audited and structured their data for accessibility and consistency across platforms.
If the input is flawed, the output, no matter how advanced the model, won’t meet expectations.
Don’t Automate Away the Human Element
AI is often deployed with the promise of speed and scale, but Sriram warns that brands lose something critical when they forget the role of empathy in customer experience.
“AI isn’t meant to replace human interaction; it should augment it,” he says. “When brands use AI purely for automation without empathy or design thinking, the result is cold, transactional engagement.”
This insight gets to the heart of why some AI implementations underperform.
When customer journeys are stripped of emotion or contextual understanding, brands risk alienating users rather than converting them.
The most successful implementations, Sriram says, blend automation with human-centered design: tools that guide and platforms that feel intuitive, not robotic.
Driving Real Results: From NexaVerse to Industry Innovation
Sriram points to the NexaVerse, a virtual showroom for Maruti Suzuki’s Nexa brand, as a powerful example of how DaveAI combines conversational and spatial intelligence to deliver business outcomes.
“We created an immersive 3D environment powered by a Virtual Sales Avatar… Users spent 2.5x more time in the virtual showroom, and the experience led to higher lead capture and better engagement rates,” he explains.
The platform delivered commercial results. Within the first month, NexaVerse generated over 30,000 user interactions and more than 700 leads per day.
In the first three months, 20% of all car bookings came directly through the virtual experience.
Maruti Suzuki now plans to launch all future models in the Nexaverse, with a target of over 500,000 virtual visitors per month.
This approach, merging visual storytelling with AI-driven guidance, has found traction in the automotive sector.
Sriram also notes that the car industry has been “incredibly forward-thinking” in leveraging AI for not just automation, but assisted selling and personalized discovery at scale.
Building for Agility: Why DaveAI Invested in Its Own GenAI Stack
Early in DaveAI’s journey, the team made a pivotal decision to build its own hybrid GenAI architecture, GRYD.
“Instead of being dependent on any single large language model or provider, we built a system that allows enterprises to plug in their data, customize small and large models, and deploy them with full control,” Sriram shares.
That move, he adds, has made their platform more adaptable, secure, and future-proof.
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If he were starting the company today, Sriram says he'd double down on vertical depth and channel partnerships even earlier.
“Our product was always ahead of its time, but we initially underestimated how crucial the right distribution channels and strategic alliances would be.”
That early gap meant slower adoption in key industries, even when the tech was enterprise-ready.
Stronger go-to-market motions and deeper partnerships could have accelerated proof of value and driven faster traction across verticals.
Breaking the Myths and Building the Future of CX
Despite the surge in AI-powered tools, Sriram sees several persistent myths holding companies back.
One is the idea that massive data sets are a prerequisite.
With domain-specific models and contextual workflows, even emerging brands can launch effective AI initiatives.
He also challenges the fear that AI will replace human agents entirely.
“The goal should be augmentation, not elimination.”
In fact, the human-AI handoff remains vital for complex or sensitive customer interactions.
Looking ahead, Sriram believes multimodal experiences will drive the biggest shift in customer engagement.
“Today’s consumers expect fluidity: they may start on chat, shift to voice, then interact with a product demo,” he says.
Platforms that integrate voice, text, video, and interaction into one coherent journey will define the next chapter in AI-powered CX.
Engineer Empathy at Scale
As AI capabilities evolve, Sriram remains focused on a core principle: customer experience is not just about automation, it’s about empathy at scale.
Whether through virtual showrooms, conversational agents, or multimodal interfaces, brands should use AI to design solutions that bridge the gap between digital efficiency and human nuance.
“In the end, AI should help customers feel understood — not processed,” he says.
That belief continues to shape their work across sectors, as more enterprises reimagine how intelligent systems can listen, adapt, and guide — just like the best human salespeople have always done.
