Prashant Singh discusses how generative and agentic AI can drive measurable revenue, improve customer engagement and ROI, while vertical AI and seamless integration help enterprises scale adoption.
As enterprises move from experimenting with AI to integrating it into core business operations, the focus is shifting from model performance to measurable business outcomes. CIOs and technology buyers are increasingly evaluating AI investments based on their ability to drive revenue, improve customer engagement, deliver faster ROI and integrate with existing enterprise systems without adding operational complexity. At the same time, tighter technology budgets are pushing organisations towards more targeted, outcome-driven deployments rather than broad platform investments.
In this interview, Prashant Singh, Co-founder & COO, LeadSquared, discusses how LeadSquared is using generative and agentic AI to move CRM from a system of record to an active participant in customer engagement. He also explores the company’s vertical AI approach, changing enterprise software buying criteria, the growing importance of measurable ROI and governance, and how API-led integration and configurable workflows can help regulated sectors such as banking and healthcare adopt AI without replacing their legacy systems.
CIO&Leader: The enterprise AI narrative is moving from raw model capabilities to operational execution. Where are you seeing generative and agentic AI deliver measurable revenue impact rather than just incremental efficiency?
Prashant Singh: The industry has moved past debating which model is better. The practical question now is whether AI can influence outcomes that matter: conversion rates, acquisition costs, retention, and how effectively teams engage customers.
The practical question now is whether AI can influence outcomes that matter: conversion rates, acquisition costs, retention, and how effectively teams engage customers.
The biggest opportunity is in execution. Sales organisations generate enormous volumes of customer signals across calls, emails, digital channels, and field interactions, yet much of that has historically gone unused. AI changes this by understanding intent, prioritising opportunities, recommending the next best action, and increasingly executing routine interactions itself.
At LeadSquared, we’re seeing AI shift CRM from a system that records activity to one that actively participates in engagement. Voice AI qualifies prospects, AI agents manage routine follow-ups, and conversational intelligence surfaces buying signals teams might otherwise miss. These influence revenue because they improve response quality and execution consistency across the lifecycle. AI creates value when it becomes part of the operating workflow rather than another layer above it.
CIO&Leader: Enterprise software buyers have shifted from aggressive expansion to rigorous cost rationalisation and fast ROI demands. How should enterprises adapt their pricing, bundling, and customer retention strategies to align with this shift toward profitable growth?
Prashant Singh: Enterprise technology spending has become far more disciplined. Budgets still exist, but every investment is expected to solve a defined problem and show value within a realistic timeframe.
This changes how software companies must think about commercial models. Buyers want solutions aligned to operational priorities, not broad platforms full of capabilities they may never adopt. Industry-specific offerings, modular deployment, and commercial flexibility let organisations start with a focused use case and expand as value becomes evident.
Retention is now a different conversation too. It’s no longer driven by contracts or incentives, but by whether customers consistently realise value after implementation. That places greater responsibility on vendors to drive adoption and improve outcomes. Enterprise software should adapt to how businesses operate, not the other way around.
CIO&Leader: How does LeadSquared’s vertical AI strategy differ from horizontal, one-size-fits-all CRM giants? What specific architectural or workflow advantages are winning over conservative enterprise buyers?
Prashant Singh: Every industry has its own customer journeys, regulations, and frontline processes. A lender evaluating loan applications has fundamentally different needs from a hospital managing patient engagement or an institution handling admissions. AI becomes far more valuable when it understands that context rather than operating as a generic assistant.
Conservative buyers aren’t looking for AI demonstrations. They want predictable implementation, governance, and measurable outcomes.
Our approach has always been to build around industry workflows, and AI follows the same principle. Rather than asking enterprises to assemble multiple technologies before seeing value, we embed intelligence directly into the workflows teams already use: lead qualification, admissions, onboarding, field sales, patient engagement, or distributor management. That means AI operates with a richer understanding of process stages, customer context, and industry-specific rules, so recommendations are more relevant.
Conservative buyers aren’t looking for AI demonstrations. They want predictable implementation, governance, and measurable outcomes. AI that fits naturally within established operating models reduces adoption risk and delivers value earlier.
CIO&Leader: How has the enterprise evaluation process changed over the last 12 to 18 months? Are you seeing procurement heads and CIOs demanding different metrics, tighter proof-of-concept timelines, or deeper integration capabilities before signing off?
Prashant Singh: Evaluations have become significantly more outcomes-driven. Earlier conversations centered on platform capabilities and long-term roadmaps. Today, CIOs, business leaders, and procurement teams start with a different question: how quickly can this improve a measurable business process without adding operational complexity?
Proofs of concept are narrower and more structured: a specific workflow, success criteria defined upfront, and compressed implementation timelines. Integration, governance, security, and user adoption now get as much attention as product functionality.
AI is no longer assessed as an innovation initiative but as business infrastructure and the standards applied are understandably higher.
Decisions are also increasingly cross-functional. Sales, operations, business owners, and IT evaluate platforms together, because AI affects engagement, workflow design, and compliance simultaneously. AI is no longer assessed as an innovation initiative but as business infrastructure and the standards applied are understandably higher.
CIO&Leader: As legacy IT infrastructure in large enterprises, especially in banking and healthcare, struggles to keep pace with modern AI agentic workflows, how is LeadSquared solving the integration barrier to ensure zero friction during deployment?
Prashant Singh: Banking and healthcare rely on core systems refined over years to support highly regulated operations. The goal shouldn’t be replacing those systems but enabling them to participate in more intelligent customer journeys.
Our platform is designed for that reality. Through APIs, configurable workflows, and integrations, organisations can introduce AI-driven engagement while continuing to run their existing core applications — making sales, onboarding, and servicing more intelligent without disruption. Configurability also lets enterprises reflect their own governance and approval models without extensive custom development, making deployment more predictable.
The broader lesson is that enterprise AI adoption won’t be determined by model sophistication alone. It will depend on how well new intelligence works alongside existing systems. Organizations that solve that integration challenge will be best positioned to scale AI across the business.