On this interview, we communicate with Srinivas Sandiri, a seasoned expertise chief in digital transformation, whose expertise spans over twenty years in IT and AI technique. Drawing from his background in enterprise-scale innovation, Srinivas explores how AI is reshaping buyer expertise, the place to stability automation with human contact, and what moral issues are crucial as AI methods acquire affect. He additionally shares sensible insights on aligning cross-functional groups and mentoring the subsequent era of tech professionals.
Discover extra interviews right here: Interview with Radhika Arora, Director – Autonomous Autos, Clever Sensing Division, ON Semiconductor
You’ve spent twenty years in IT, specializing in AI-driven digital transformation. How have you ever seen the evolution of AI impression buyer expertise over time, and what key traits do you foresee shaping the subsequent decade?
AI has developed over the previous twenty years from a assist operate to a strategic power that shapes the shopper expertise. Within the early levels, AI was primarily reactive and targeted on automating routine duties, corresponding to case routing and fundamental self-service. It improved effectivity however lacked personalization and depth. As we speak, AI performs a much more built-in function. We now see clever methods that anticipate intent, detect sentiment, and ship context-aware interactions in real-time. At this stage, AI isn’t simply fixing issues quick; it’s serving to companies perceive clients extra deeply and reply extra intelligently throughout channels.
A number of traits will redefine AI’s function in the way forward for CX:
- Actual-Time Personalization at ScaleAI will shift from segment-based personalization to moment-based engagement by tailoring interactions dynamically based mostly on context, conduct, and emotional tone.
- AI-Human Collaboration The long run isn’t full automation—it’s orchestration. AI will information brokers with the most effective actions, automate routine steps, and free people for complicated, high-empathy interactions.
- Moral, Explainable AI. As AI more and more drives choices, clients and regulators will demand higher transparency. Organizations might want to design AI methods that aren’t solely clever but in addition accountable, honest, and trusted.
- Expertise-Led Information StrategyClean, unified, customer-centric knowledge will change into the inspiration for all AI innovation. Organizations that deal with knowledge as an expertise enabler, not simply an enter, will lead the subsequent wave of transformation.
Along with your experience in designing scalable options, what are the largest challenges enterprises face when implementing AI-driven buyer expertise initiatives, and the way do you navigate them?
The most typical problem in AI-driven buyer expertise initiatives isn’t the expertise—it’s alignment. Many organizations pursue automation and personalization with out establishing a powerful basis of unified knowledge, clear enterprise targets, and cross-functional collaboration. Disconnected methods, siloed groups, and unclear possession usually result in fragmented buyer journeys, stalled implementations, or inconsistent experiences. AI options applied in isolation can battle to ship lasting worth with out the right knowledge context or course of integration.
Organizations should deal with AI each as a software and as an enterprise functionality that hyperlinks enterprise technique with system operation and group efficiency. Attaining success requires organizations to transform enterprise and buyer targets into measurable efficiency indicators whereas integrating AI into operational processes and establishing dependable stakeholder relationships. When thoughtfully applied, AI turns into greater than an optimization engine—it turns into a strategic enabler that improves decision-making, enhances buyer engagement, and drives long-term expertise transformation.
Automation has been instrumental in lowering deal with occasions and bettering operational effectivity. The place do you draw the road between automation and human interplay to make sure a seamless but customized buyer expertise?
Automation performs an important function in optimizing service supply; nonetheless, the important thing to a wonderful buyer expertise lies in figuring out when to automate and when to humanize. The perfect outcomes come from a hybrid method the place automation removes friction, and human interplay provides empathy.
Right here’s how that stability will be thoughtfully designed:
- Automate repetitive, predictable, and time-sensitive duties, corresponding to identification verification, standing updates, or doc retrieval.
- Route nuanced, high-emotion, or exception-based points to human brokers, higher outfitted to reply with empathy and judgment.
- Use AI to reinforce—not substitute—human decision-making, by providing guided actions, real-time insights, and context-aware suggestions.
- Design seamless handoffs between bots and brokers, guaranteeing clients don’t really feel like they’re beginning over when escalation happens.
The primary objective is to extend the standard of human interplay moderately than cut back it. Organizations obtain environment friendly and customized experiences when automation supplies clients with quick service whereas providing brokers detailed context.
You’ve led cross-functional groups in aligning expertise with enterprise objectives. What methods do you employ to bridge the hole between technical groups and enterprise stakeholders to make sure profitable AI implementations?
Bridging the hole between enterprise objectives and technical execution is without doubt one of the most important success components in any AI initiative. Expertise groups give attention to what’s doable; enterprise leaders give attention to what’s useful. Establishing alignment requires mutual settlement between events on an equivalent end result.
To shut the hole and drive scalable outcomes, I counsel the next:
- End result-first pondering: Outline success in enterprise phrases, corresponding to diminished churn or improved decision time, earlier than exploring options.
- Translating complexity into readability: Use visible storytelling, consumer journeys, and KPIs to make technical concepts accessible and aligned with stakeholder priorities.
- Inclusive collaboration fashions: Embed enterprise stakeholders in agile sprints or design classes to co-create options and speed up adoption.
Finally, AI succeeds not when it’s deployed, however when it’s understood, trusted, and operationalized throughout groups. True transformation requires not simply extra modern methods but in addition stronger collaboration.
AI-powered decision-making is remodeling enterprise operations. What moral issues ought to leaders be mindful when deploying AI-driven options to make sure transparency and equity?
As AI turns into more and more integral to decision-making, leaders should prioritize ethics all through your complete design and deployment course of. Bias in coaching knowledge can reinforce inequalities; subsequently, various and consultant datasets, together with common audits, are important. Explainability is equally crucial; clients and stakeholders ought to perceive how AI choices are made, particularly in high-impact areas corresponding to customer support or monetary eligibility.
Past technical equity, organizations should set up clear accountability frameworks. AI ought to assist human decision-making, not substitute it totally. Guaranteeing human oversight, escalation paths, and clear communication builds belief between inside customers and clients. Moral AI isn’t only a compliance requirement—it’s a management duty and a basis for constructing long-term credibility in digital transformation.
As a mentor within the tech house, how do you put together the subsequent era of pros for the challenges and alternatives introduced by AI-driven digital transformation?
I mentor professionals to suppose past instruments and give attention to the broader implications of expertise. As AI continues to form the evolution of industries, I encourage rising expertise to stay curious, domesticate cross-functional consciousness, and learn to apply expertise thoughtfully. I emphasize the significance of adaptability and moral data, and hyperlink modern options to significant enterprise objectives or buyer wants, for every group member working in knowledge, design, or growth. It’s not nearly studying new applied sciences—it’s about fixing actual issues with objective and duty.
The subsequent era will decide how digital transformation and AI evolve. I goal to develop professionals who will preserve technical competence and management qualities to information others via complicated conditions and generate helpful results in our clever future.
