With more than 16 years of experience leading global engineering teams and architecting intelligent platforms, Sumit Sinha has built a career at the intersection of large-scale technology solutions, AI systems, and customer experience innovation. From startups to enterprise environments, he has driven impactful transformations that combine technical precision with a deep understanding of human interaction.
Passionate about conversational AI and next-generation user experiences, Sumit is a frequent speaker at major industry events, including CCW Vegas and the upcoming Genesys Xperience in September 2025. He has also contributed extensively to the tech community through peer reviews for top conferences such as AMCIS, DIS, and ICIS.
In this conversation with AllTech Magazine, Sumit shares why the mission of conversational AI should go beyond cost savings, the overlooked engineering challenges that make AI truly effective, and how companies can strike the right balance between automation and empathy. He also offers candid insights into the current AI hype cycle, the most common mistakes leaders make in deploying AI, and the real-world strategies that lead to resilient, scalable systems.
If a conversational AI could have a “mission statement,” what do you think it should be, and how would that contrast with most companies’ current goals for their AI systems?
Mission Statement: To enhance human interaction with digital technologies, which helps achieve appropriate and efficient outcomes for humans accurately and emphatically.
This would contract as the goal for most companies is to save cost by using AI, often by automating or streamlining tasks, which means a lot of times the focus is on volume and not on the accuracy or empathy of the interaction. Depending on which use case AI is used on, focus may not be on the best outcome for the customer but on the best outcome for the company.
We often hear about AI transforming customer experiences, but what’s one quiet, behind-the-scenes engineering challenge that a great AI system solves that the public would be surprised to learn about?
Reading through different scanned letters sent to a company and then either documenting if an action is required or not, and if an action is required, then pushing that task to a monitored queue. This saves a lot of man-hours and is way more efficient.
Having seen various tech trends rise and fall over 16 years, what’s a current ‘AI hype’ that you believe is genuinely foundational for the next decade, and conversely, one that’s currently overblown but might still hold niche value?
LLM and its move towards multi-modality is foundational as this has opened up doors for a broader audience in terms of automation and creative solutions for individuals and small businesses without much investment and as DIY.
Virtual Reality is a niche which applies to gaming, entertainment, education etc., but is overblown currently to think that it can create a virtual world where people will live their life virtual. In my opinion, there is a human need to meet people face to face, meeting virtually all the time doesn’t fulfill the need for social human interaction.
What are the most common mistakes enterprise leaders make when deploying conversational AI, and how can they avoid them?
Most common mistake I think is to leverage conversation AI for customer-facing complex use cases from the get-go, in my experience it is a much better use of conversational AI to automate repetitive tasks, triage tickets, and surface knowledge instantly while keeping humans in the loop for high-empathy, complex cases. Situations involving finances, relationships, or health often require not just a creative solution but also a compassionate touch to reassure the user. Hybrid experiences, where AI empowers agents in real time, consistently outperform fully automated approaches.
How can large companies balance cost-efficiency with the human touch in AI-enhanced customer experiences?
Leveraging AI as the first line of defense for self-service and then augmenting human touch with AI for information retrieval, summarization, forming structure for human agent responses etc., can help with achieving cost efficiency by pushing complex opportunities and issues to humans.
You’ve led global software teams for over a decade. What are your biggest takeaways on building scalable, resilient systems for real-time applications?
A few things that help in building a scalable real-time application:
- Microservices – This allows global teams to work on different services parallel without stepping on someone else’s toes. This also makes the overall system resilient, as if one service has issues, all other services continue to work usually.
- Event driven architecture – This makes the system asynchronous and does not block anything for any action that can be near real-time. In case of a spike in traffic, this will still keep working and processing events without causing failures.
- Design for failure and redundancy – Using multiple availability zones and regions will help protect against local outages. Load balancing will help distribute traffic to healthy instances.
- Observability – Logging and alerting for the issues happening in real time is necessary to make sure systems remain stable.
- Automating Testing and Deployment: It ensures rapid validation of code changes, reducing human error and accelerating releases. Continuous deployment pipelines enable seamless, reliable updates with minimal downtime, crucial for real-time systems.
In your view, why is AI often misunderstood as a replacement tool, rather than one that augments human capability?
Since AI excels at recognition and execution, it’s often mistaken for a full replacement. However, complex, high-empathy scenarios will always require human judgment—where AI serves best as an augmentation tool. In those scenarios, AI will be able to augment humans to get them the information they need to perform the task at hand more efficiently.
Many companies focus only on customer-facing AI. What internal use cases do you think are being overlooked, and why do they matter?
Internal applications—like AI copilots for agents, automated summarization, documentation review, and test simulations—are often overlooked yet boost productivity and quality. These use cases can increase productivity, reduce cost, and deliver with better quality.
What advice do you have for engineering leaders who want to innovate responsibly with generative AI?
My advice would be first to understand the problem or opportunity at hand, and then to think about the best way to approach it using generative AI. Also, to set a small target and achieve it before going big, as that will help in understanding if full automation is viable or if a human touch is required.
