Inside Priya Vijayarajendran’s AI Playbook: Efficiency with Empathy at Enterprise Scale

AI that mimics people is old news. The AI that scales business is the real revolution.

In this conversation, Priya Vijayarajendran, CEO at ASAPP, dives into how she’s pushing past cost-cutting narratives in AI — toward growth, scale, and business intelligence. A future where human-quality service becomes a machine-level capability.

You’ve built AI systems at some of the world’s biggest tech companies. How does that experience shape your approach as a CEO now leading an AI-native enterprise?

At Microsoft and IBM, I learned the value of governance, trust, and accountability in AI systems. But I also learned the limitations of legacy infrastructure. Those environments taught me that true progress comes when you can connect models to the operational core of a business. At ASAPP, we’re not building isolated AI tools but architecting systems that become part of how a contact center thinks, learns, and improves instantly. That enterprise-grade discipline, paired with startup-level velocity, defines how we innovate responsibly and deploy AI that endures time.

Your mission for ASAPP to bring “human-quality service at machine scale” is visionary. How do you translate that vision into practical AI design and deployment?

It starts with defining “human quality” in measurable terms like accuracy, empathy, and resolution. Then we reverse-engineer the system architecture to deliver those outcomes at scale. Every ASAPP deployment is built on a world-class transcription layer for data fidelity, an agentic reasoning layer for context retention, and a human-in-the-loop mechanism for continuous learning. That balance allows enterprises to automate responsibly without sacrificing the nuance that defines great customer experience.

Many enterprises still treat AI as a cost-cutting tool. What mindset shift do you think leaders need to make to see AI as a growth-driving asset?

AI should be viewed as a capacity expansion and not cost compression. The most forward-thinking enterprises are using AI to handle surges in demand, shorten their customer resolution cycles, and unlock new service models. It’s not just to trim headcount. Once leaders start measuring AI’s contribution in revenue growth, CLV, and efficiency, the conversation moves from “savings” to “scale.” And that’s the point where AI becomes a business multiplier.

ASAPP’s platform tackles some of the most complex human interactions—customer conversations. What’s been the hardest technical challenge your team has had to solve so far?

The hardest challenge has been building a platform that manages thousands of simultaneous conversations while maintaining context, safety, and compliance across each one. It’s a deeply multi-dimensional problem involving orchestration, reasoning, and human escalation. We’ve solved it by building a platform for real-time recognition to secure workflow integration so the AI operates with the same context awareness a skilled human would.

AI-native companies like ASAPP are moving beyond just prediction toward agentic systems—AI that acts, reasons, and collaborates. How close are we to that reality in the enterprise context?

Closer than many realize. Prediction was the first chapter of AI. Agentic systems are the next, where models can reason across multi-step workflows and collaborate with humans. In enterprise deployments, the key isn’t just autonomy but accountability. Our platform already executes actions within defined safety boundaries and escalates when the confidence threshold caps. The future isn’t “human” or “machine” but a synchronized system where each amplifies the other’s strengths.

Generative AI is transforming CX, but also raising new questions about authenticity and accuracy. How is ASAPP ensuring its models remain both human-aligned and business-safe?

Safety begins with grounding. Each model’s output at ASAPP is tied to verified enterprise data, and every deployment has human oversight for transparency. We’ve built layered safeguards (scoring, redaction, bias checks) to prevent hallucinations and maintain factual integrity that brands need to uphold. Being “human-aligned” means more than being polite. It means the AI understands, respects intent, tone, and generates a positive outcome. Business-safe AI must meet the same bar of accuracy and reliability as your best human agent.

Having led large teams at Microsoft and IBM, how has your leadership evolved now that you’re steering a leaner, innovation-focused AI company?

In large organizations, you lead through systems. In a lean company, you lead through proximity. I’m close to the work that we do. I listen to calls, talk to customers regularly, debate model architecture, and review our product demos regularly. That proximity keeps me grounded in execution and has also sharpened my bias towards velocity and clarity. When you’re innovating in AI, ambiguity compounds, but precision resonates. I’ve learned to favor fewer, faster decisions anchored in data.

You’ve spoken about empathy being the missing ingredient in enterprise AI. What does “empathetic AI” mean in a world dominated by efficiency metrics?

Empathy in AI isn’t really about emotion. Instead, it’s about understanding context and consequence. A truly empathetic system recognizes why a customer is reaching out and not just what they said. In business terms, empathy translates to lower escalation rates, higher customer satisfaction levels, and more effective automation. Efficiency without empathy is very brittle. The future belongs to systems that can achieve efficiency and empathy simultaneously.

What’s one belief about AI’s future that you hold strongly, even if the rest of the industry isn’t ready to agree yet?

I believe that the winning AI systems won’t be the most human-like. They’ll be the most business-intelligent. The race isn’t to sound more human, but it’s to deliver the best customer experiences that people trust and rave about. That’s where AI moves from novelty in the business to a necessity for growth.