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Arun Hiremath, Chief Business Officer and Co-Founder of EvoluteIQ, is a technology executive and entrepreneur with nearly three decades of experience spanning enterprise automation, product strategy, business development, and telecommunications. At EvoluteIQ, he is responsible for driving revenue growth and strengthening customer relationships, particularly through the company’s eiq360 business. Before joining EvoluteIQ, Hiremath co-founded and led low-code platform provider AmperAXP, which was integrated into EvoluteIQ in 2021. His earlier career included senior product management positions at Qualcomm (QCOM ), Ikanos Communications, Conexant Systems, and Globespan, as well as responsibility for Asia-Pacific business development at Tata Elxsi (TATAELXSI.BO ).
EvoluteIQ is an enterprise software company developing an AI-native platform for automating and orchestrating complex, end-to-end business processes. Its EIQ platform combines agentic AI, generative AI, process and decision automation, robotic process automation, intelligent data and event processing, enterprise integrations, analytics, and application development within a low-code/no-code environment. Designed to replace fragmented automation tools with a unified architecture, the platform helps organizations build adaptive workflows that connect legacy and modern systems while maintaining enterprise-level governance, security, and scalability. EvoluteIQ serves organizations across industries including banking, insurance, healthcare, telecommunications, manufacturing, energy, and retail.
In our previous interview with Sameet Gupte, CEO of EvoluteIQ, we discussed EvoluteIQ’s broader vision for AI-native enterprise automation. From your vantage point working closely with customers, what has changed most in how enterprises are thinking about agentic AI since that conversation?
The biggest shift is that enterprises have stopped asking, “Can AI do this” and started asking, “Can I trust AI to do this?”
A year ago, most conversations focused on which model had which capabilities. Customers are much more pragmatic now. They want to know whether AI can coordinate people, applications, policies, approvals, compliance and other agents around a measurable business outcome.
Claims processing is a good example. The AI itself is not necessarily the difficult part. The challenge is designing how autonomous agents work alongside people and within IT governance across multiple systems, teams and exceptions.
Many companies still seem to treat AI as another software layer added on top of existing workflows. Why is that approach limiting, and what does it look like when an enterprise redesigns a process around autonomous execution from the start?
Putting AI on top of a broken process simply allows you to reach the wrong outcome faster.
Many enterprise workflows were designed 20 years ago around manual decision-making. Adding AI as another step may optimize the process, but it does not transform it. The better question is which decisions genuinely require a human in the loop, and which can be safely delegated to autonomous agents.
In expense management, for example, AI can evaluate policies, spending patterns, employee history and supporting documents. Routine claims can be approved in minutes, while only unusual cases are escalated to managers.
You’ve argued that many customers do not have a vendor problem as much as an operating model problem. What are the most common organizational issues that prevent enterprise AI from scaling beyond pilots?
Most enterprises do not have an AI shortage; they have an ownership shortage.
IT owns the infrastructure. The business owns the process. Security owns governance. Data teams own the models. Operations owns execution. But no one necessarily owns how all those pieces come together to deliver an autonomous business process. What is missing is a unified way to put the technology to work.
That is why pilots often become isolated successes. Organizations that scale automation successfully are changing their operating models, not simply buying more technology. They are creating cross-functional teams that jointly own business outcomes rather than individual systems. The important question is: Who owns the outcome, and who is responsible for delivering it?
Where do you draw the line between useful automation, intelligent automation, and true agentic automation?
I look at them as three stages of maturity. Automation follows instructions. Intelligent automation understands information that’s provided. Agentic automation understands the business objective and works toward the outcome.
In invoice processing, traditional automation copies information between systems. Intelligent automation reads invoices, extracts data and flags inconsistencies. Then, agentic automation can identify missing information, communicate with suppliers, apply business policies, handle exceptions, obtain approvals and complete the process.
The difference is not simply that the AI is smarter. The difference is that it owns the outcome rather than one task.
Enterprise leaders are under pressure to show AI ROI quickly, but complex process transformation takes time. How should companies balance speed, governance, and long-term operating model change?
Speed without governance creates risk. Governance without speed creates frustration. Enterprises have to balance both.
Companies should start with a business process that matters—something visible that can demonstrate measurable value—but build it on a foundation that supports governance, security, observability and compliance. The initial solution should not become another silo.
The mistake is believing that governance slows innovation. Good governance actually accelerates adoption because it creates trust. Once the right controls are in place, organizations can move faster and scale with greater confidence.
EvoluteIQ has emphasized end-to-end process automation rather than automating isolated tasks. Why does that distinction matter so much when moving from AI experimentation to production deployment?
Businesses do not experience work as individual tasks, they experience outcomes. Nobody celebrates because document extraction became 60% faster. They celebrate because customer onboarding now takes a few hours instead of two weeks. Improving isolated tasks often just shifts the bottleneck somewhere else.
When intake, validation, approvals, decision-making, system integrations and customer communication are orchestrated as one AI-native workflow, the organization begins to see meaningful business results. Optimizing a task improves efficiency but optimizing the process changes the overall business performance and can impact the P&L.
Cloud marketplaces are increasingly becoming distribution channels for enterprise AI. How do you see platforms like Google Cloud changing how large organizations discover, evaluate, procure, and deploy AI-native automation?
Cloud marketplaces are doing for enterprise software what app stores did for consumers, but with enterprise governance built in.
They remove friction across procurement, security reviews and commercial agreements. Organizations can also leverage existing cloud commitments, allowing teams to focus less on how to purchase software and more on the business outcomes.
A customer should not have to wait through a six-month procurement cycle simply to evaluate an AI-native platform. Marketplaces allow them to begin solving business problems much faster while remaining within established enterprise governance.
EvoluteIQ recently highlighted eiq360 as a Google Cloud and Gemini-based platform that can turn business intent into enterprise-ready workflows using natural language, while maintaining governance and control. What does that shift mean for non-technical business users?
We are moving from a world where business users describe requirements to engineering teams and wait for results, to one where users describe outcomes.
With eiq360, users can now describe the process they want to improve in natural language. The platform can then help generate enterprise-ready workflows while automatically applying governance, security, compliance and operational controls.
This does not eliminate IT; it elevates IT. Technology teams can now focus on defining enterprise guardrails, reusable services and governance standards instead of manually building every workflow. That’s one of the most important changes AI is bringing to the enterprise software as we see it.
The phrase “enterprise AI should feel boring” runs counter to much of the current hype. What should feel boring, predictable, or repeatable about AI when it is being used for mission-critical business execution?
Nobody wants payroll to be exciting. Nobody wants an insurance claim to be surprising. Nobody wants a banking transaction to be creative.
Enterprise AI should absolutely be innovative during experimentation. Once it enters production, it should become wonderfully predictable. It should produce consistent outcomes, explain its decisions, recognize when confidence is low and request human intervention when necessary. The best enterprise AI is not the AI everyone wants to talk about. It is the AI nobody notices because the business simply works better.
Looking ahead, what will separate enterprises that successfully operationalize agentic AI from those that remain stuck in pilot mode?’
The winners will not necessarily have the most advanced AI models. They will be the ones with the best operating models for AI. They will redesign processes instead of digitizing inefficiencies, build governance before scale and create reusable enterprise capabilities rather than isolated proofs of concept.
Most importantly, they will stop thinking of AI as another application and begin treating it as a new workforce that collaborates with people, systems and business policies. The next decade will be about optimizing how people, AI agents and enterprise systems work together.