Kore.ai is gearing up for its next growth phase, and CX leaders should pay attention.
In a CX Today interview, CEO and Co-Founder Raj Koneru said the company secured a “significant growth investment” from AllianceBernstein. He also claims Kore.ai’s business has “more than doubled” since its last fundraising round, just over two years ago.”
The bet is clear: enterprise buyers want AI that ships faster, proves ROI, and scales safely in regulated environments.
As someone who hears daily from CX teams stuck between AI ambition and operational reality, I see why this matters. The market is moving from pilots and proof-of-concepts to production-grade deployments, and vendors are now being judged on governance, reliability, and measurable outcomes as much as model performance.
Why Kore.ai’s Investment Matters For CX Leaders
Koneru pushed back on the idea that investors are simply chasing hype, framing the opportunity as shifting toward applications and outcomes.
For CX leaders, the implication is practical. If investment continues flowing toward enterprise AI applications, your roadmap decisions will increasingly be evaluated against hard metrics: cost-to-serve, cycle-time reduction, containment rates, agent productivity, and customer effort.
Koneru described Kore.ai as “an AI platform company for over a decade,” and said it is focused on delivering “real value.” He outlined two main pillars: AI for service and AI for work, supported by a platform layer for customization.
In customer service, he said Kore.ai offers prebuilt applications “for banking, for retail, for healthcare,” and also provides “an underlying platform which can be used to configure and customize those applications.”
For employee experience, he said it supports IT, HR, and recruiting workflows, alongside enterprise search and tools to build employee agents.
“We also provide use cases for employee experience, but importantly, with applications that are pre built so that those customers can get time to value quickly.”
What Kore.ai’s Scale Claims Signal About Enterprise Readiness
Koneru emphasized scale, including in regulated industries where security and resilience requirements are high:
“We handle up to like, 800 million calls and chats and emails a year for one customer.”
He also highlighted scale on the employee side, “We provide employee experience with AI agents for like, 180,000 employees.”
AI can no longer be evaluated as a novelty layer. It needs to behave like enterprise infrastructure, with security, uptime, observability, and controls that satisfy governance teams.
Koneru described a shift from app-first interactions to agent-led experiences, where conversational AI becomes the primary interface to services and workflows. “I talk to an agent, which is the application now.”
In CX terms, that points toward a future where channel strategy becomes less about which interface customers use, and more about whether your enterprise can deliver consistent outcomes across channels with the same policies, identity checks, and workflow orchestration behind the scenes.
Why Regulated CX Will Blend Deterministic Rules And Autonomy
Koneru said enterprise AI is evolving from a split model, probabilistic NLP plus deterministic workflows, toward systems that can handle both language and workflow steps. But he also stressed regulated reality. “The reality is within enterprises, especially regulated enterprises, it’s somewhere in between.”
That matters for CX leaders because the next phase of ‘agentic’ CX will not be a flip-the-switch moment. It will be a controlled progression, with guardrails, validation steps, escalation paths, and measurable risk management baked into the journey design.
Koneru tied that to business speed, using a lending example:
“If it’s taking, you know, 10 days to process as a loan, maybe you can do that in one hour.”
Real-World Use Cases And Designing For Governance
Koneru pointed to employee onboarding as a cross-functional workflow across HR, IT, managers, and collaboration tools. He positioned an onboarding agent as a coordinator that can reduce manual overhead and accelerate time-to-productivity.
He also described a loan journey where an agent connects across CRM, loan management, asset management, and risk systems, collects documents, routes tasks to approvers, and reduces decision cycle time.
The biggest ROI tends to come from end-to-end orchestration, and not from narrow chatbot deployments.
Koneru’s guidance focused on selecting measurable use cases and designing from day one for security and regulation.
“Pick the right use case that has clear ROI, clear, measurable ROI, and design it in such a way that will adhere to your security regulations.”
He also warned that many teams rush into execution: “I think people are jumping too fast at execution without putting in thought, into the what, why and how.”
For CX leaders, that’s the future signal. As agent-first CX becomes achievable, success is defined less by experimenting with AI and more by operationalizing it with governance, discipline, and outcome-based measurement.
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