In an exclusive Q&A with VMblog, Mukesh Bansal, founder and CEO of Nurix AI, shares his vision for how artificial intelligence is poised to revolutionize enterprise operations in the coming years. Bansal predicts that AI will evolve from a supporting tool to an essential component that takes full ownership of repetitive workflows while enabling companies to scale without proportional cost increases.

With insights on overcoming implementation challenges, balancing human and AI decision-making, and practical strategies for businesses just beginning their AI journey, Bansal offers a compelling roadmap for organizations looking to harness AI's transformative potential across customer support, sales operations, recruitment, and beyond.

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VMblog:  How do you foresee AI transforming enterprise workflows in the next 2-3 years?

Mukesh Bansal:  We stand at a pivotal moment, with the next wave of enterprise transformation being driven by AI. What was once a supporting tool is rapidly becoming essential, particularly in areas like sales and customer support, where speed, context, and precision are critical. Currently, AI is automating routine tasks such as lead scoring, ticket management, and follow-ups. Over the next 2-3 years, this shift will deepen: these processes will become more autonomous, intelligent, and adaptable. AI will go beyond assisting; it will take full ownership of repetitive workflows, make real-time decisions, learn from interactions, and continuously enhance results. More importantly, this will enable scaling workflows without adding additional cost - The ROI is a no-brainer. We're already witnessing early indicators of this with agentic AI: systems capable of understanding context, acting proactively, and integrating seamlessly with human oversight.

VMblog:  What are some specific areas within enterprise operations that you believe will benefit the most from AI integration?

Bansal:  AI is set to have the most significant impact in areas that are repetitive, data-intensive, and customer-facing. Examples include customer support, sales operations, recruitment, and claims processing. At Nurix, we've observed AI agents dramatically reduce resolution times in support, automate follow-ups in sales, and optimize hiring processes from screening to onboarding. Even internal resources like accessing company policies or addressing compliance questions can be managed instantly by intelligent assistants. However, what truly excites me is when AI agents evolve beyond task execution to actively influencing outcomes-such as boosting revenue, lowering churn, or speeding up time-to-market. That's where the greatest enterprise value truly resides.

VMblog:  In your experience, what are the common hurdles organizations face when implementing AI agents into their workflows?

Bansal:  One of the major obstacles organizations run into is system fragmentation. Many enterprises operate with a complex array of legacy tools, CRMs, ERPs, and other systems that don't always communicate seamlessly. Incorporating AI agents into this landscape and ensuring they can access and respond to the right data in real time is a significant challenge. Another common issue is the absence of well-defined workflows. AI performs best when processes are structured and repetitive, but many organizations haven't documented their workflows sufficiently for automation to be effective. This often leads to AI being underused or producing inconsistent results. Lastly, trust remains a concern. Companies are understandably cautious about granting AI too much autonomy, especially in critical workflows. They require mechanisms like approval steps, transparency, and audit trails to feel secure deploying AI at scale. At Nurix, we've tackled these challenges, designing it for smooth integration, flexibility, and control so AI can improve operations without disrupting existing processes.

VMblog:  What is your take on the future of AI deployment and how it will impact human workers and their job security?

Bansal:  I believe AI won't eliminate jobs, but rather automate repetitive tasks. The future is about collaboration, not replacement. AI will take care of routine activities like data entry, coordination, and reporting, allowing humans to concentrate on higher-level tasks such as strategy, creativity, and building relationships. However, this shift will require reskilling; roles will change, and organizations must invest in supporting their teams through the transition. At Nurix, we view AI agents as amplifiers, not job eliminators. They help increase efficiency, enhance accuracy, and create opportunities for people to engage in more meaningful work. Ultimately, the most successful companies will be those that blend AI's precision with human judgment and empathy.

VMblog:  What would you say to so many enterprises that are still sorting through what generative AI means for their organizations and how to get value out of utilizing various language models and sorting through AI governance?

Bansal:  It's completely fair that many enterprises are still in the exploration phase. Generative AI is a powerful new layer, but it's far from plug-and-play. My advice is to start small, but start now. Identify one or two high-impact workflows and pilot AI agents there to get immediate feedback on ROI, integration challenges, and operational fit. When it comes to language models, it's not about choosing the biggest or most advanced model-it's about orchestrating the right models for the right tasks, ensuring they align with your data, workflows, and compliance needs. Success comes from setting up strong guardrails, integrating seamlessly with existing systems, and embedding human-in-the-loop flows for critical decision points.

VMblog:  How do you see the balance between human intelligence and AI when it comes to decision-making in businesses?

Bansal:  The balance is shifting from humans making every decision to humans designing the frameworks within which AI operates. AI today is capable of driving a significant portion of operational decision-making, especially where speed, scale, and data-driven consistency matter, think pricing adjustments, lead routing, customer query resolution, or even risk assessments. The role of human intelligence is no longer about intervening in every decision but about setting the rules, guardrails, and objectives defining what "good" looks like, where AI has autonomy, and where escalation is necessary. It's about governance, not micromanagement. In this model, AI handles dynamic, repeatable decisions within clear parameters, continuously learning and optimizing. Humans focus on overseeing outcomes, refining strategies, and addressing exceptions that require ethical judgment, creativity, or cross-domain thinking. The future isn't about AI replacing human decision-making, it's about humans architecting intelligent systems that can make thousands of decisions per second, aligned with business goals and values.

VMblog:  How can business leaders start preparing their teams today for future collaboration with AI agents?    

Bansal:  You don't need to overhaul your entire organization on day one. My advice is simple: start slow, but start smart. Identify a contained, high-impact workflow where AI can deliver quick wins-whether that's automating support queries, streamlining internal approvals, or accelerating lead qualification. Once you see measurable success and more importantly, that security and control are intact, it builds confidence across the organization. The next step is cultural, not technical: get your teams actively using AI, embedding it into their day-to-day routines. The real value of AI comes when it's not just a tool, but a core part of how your people work like augmenting decision-making, improving efficiency, and freeing up time for higher-value tasks. Treat AI adoption as iterative, prove it in small pockets, learn, and then scale with intention.

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