Preparing Enterprises for the Next Wave of AI: An Interview with Puneet Ramaul

Puneet is a seasoned technology leader with deep expertise in observability, AIOps, and enterprise-scale AI adoption. With years of experience helping organisations modernise hybrid IT and operationalise emerging technologies, he focuses on bridging the gap between innovation pilots and scalable business strategies. His work emphasises full-stack observability, automation, and responsible deployment of GenAI and agentic AI to drive measurable business impact.

Que 1. Puneet, you've been working at the intersection of AI and enterprise technology for nearly a decade. How did you first become interested in this space?

My journey into AI wasn't planned. It evolved naturally from my career path. I've been in tech for the last 10 years, and 13 in total, I have 15 years of professional experience. What's been consistent throughout my roles is that they've always had an overlap between business and technology. That unique positioning gave me a front-row seat to watch how technology evolved and, more importantly, how businesses actually adopted it.

The real turning point came around 2015 when I took on the role of product manager for GenAI-based software products and platforms at HCLTech. I eventually led that team until 2021 as the head of product management for the same portfolio. During that time, we were focused on utilising AI to drive intelligent automation, and that's when the intersection became truly fascinating. The value that AI could generate for enterprises was enormous, but I also saw firsthand the challenges organisations faced in realising that value.

Que 2. What struck you most during those early years of enterprise AI adoption?

The gap between potential and reality was striking. We had incredible technology capabilities, but many organisations struggled to move beyond proof-of-concepts. I realised that the technical challenges weren't the biggest barrier. It was the organisational, strategic, and cultural aspects of AI adoption that determined success or failure.

Que 3. From your extensive experience, what are the most common misconceptions leaders have about AI adoption?

There are two major misconceptions I encounter repeatedly. First and foremost, there's this persistent fear that AI will eliminate human jobs wholesale. This is fundamentally wrong. AI doesn't eat up human jobs, it actually creates more space for humans to do something more creative and strategic by taking over redundant and repetitive tasks. When leaders frame it as humans versus machines, they miss the real opportunity for human-AI collaboration.

The second major misconception revolves around ROI concerns. Most leaders are worried about whether AI will generate sufficient return on investment, and I understand that concern. However, in my opinion, if the right amount of time is invested upfront in identifying the specific areas where AI should be utilised within an enterprise, driving ROI becomes much more straightforward.

The key is being strategic rather than reactive. Rather than implementing AI just because competitors are doing it, leaders need to invest time in clearly identifying what they want AI to solve for their organisation specifically. When you start with a clear problem definition and business impact assessment, the ROI question answers itself.

Que 4. That leads perfectly to a challenge many of our readers face. Many enterprises get stuck in what some call "pilot purgatory". They run successful AI pilots but struggle to scale to enterprise-wide adoption. What are the key steps to break out of this cycle?

This is probably one of the most critical challenges facing enterprises today, and I've seen it countless times. The solution starts way before you even begin your pilot. It's about asking and answering the right questions upfront.

First, invest time in identifying and crystallising the specific problems you want AI to solve. This isn't just about finding any problem AI could address, it's about finding the right problems. Then, assess the impact these problems have on your business and the intensity of that impact. Ask yourself: Is this a problem your business needs to solve right away? Why is it critical now?

Next, establish clear ownership and sponsorship. Who will drive the solution and its adoption across the organisation? Who is the executive sponsor, and why are they committed to seeing this through? These aren't just procedural questions. They determine whether your pilot will scale or die.

In my experience, if these fundamental questions are answered thoroughly and honestly right at the beginning, moving from POC to enterprise-wide adoption won't be a challenge. That's because the problem and its criticality for the business were established much earlier. The POC then becomes just a means to validate your approach toward a larger, predetermined adoption strategy.

The organisations that get stuck in pilot purgatory are usually those that started with the technology and tried to find problems to solve, rather than starting with clear business problems and finding the right AI solutions.

Que 5. Let's dive deeper into the technical side. Can you explain how observability and AIOps work together to ensure resilience and trust in hybrid IT environments that are deploying AI?

Observability is absolutely fundamental. It's the foundation on which any modern enterprise should be built. With the rapid pace of technological change and the endless generation of data we see today, it's crucial to have observable systems in place that enable leaders to understand the impact of even the slightest tech disruption on their business operations.

Think about it this way: when you're deploying AI systems into production, you're adding new layers of complexity to your IT environment. Without proper observability, these AI systems become black boxes. You might get good results, but you won't understand why, and more importantly, you won't know when things start to go wrong until it's too late.

Once you have solid observable infrastructure in place, it becomes much easier to identify areas that can be optimised through automation. That's where the window opens to bring AI into the picture and utilise it effectively to automate processes, enable conversational interfaces, and analyse patterns that humans might miss.

AIOps takes this further by applying AI and machine learning to IT operations data. It can predict failures, automatically remediate issues, and provide insights that help optimise performance. When you combine comprehensive observability with intelligent AIOps, you get a system that's not just reactive but predictive and self-healing.

This combination leads to better decision-making and overall optimisation of your hybrid IT environment. More importantly, it builds trust because stakeholders can see what's happening, understand the impact, and have confidence that the system will alert them to issues before they become critical business problems.

Que 6. Trust and compliance are major concerns for enterprises deploying AI. What governance frameworks or internal structures have you found most effective in balancing AI speed-to-market with trust and compliance requirements?

This is where many organisations stumble because they treat governance as an afterthought or a barrier to speed. In reality, the right governance structure actually accelerates sustainable AI adoption.

The most important step is establishing a governance board that includes representatives from different departments: technology, finance, operations, HR, cybersecurity, marketing, legal, and business units. This isn't just a checkbox exercise; each of these perspectives is critical for successful AI deployment.

The board must be chaired by an executive sponsor who truly owns driving the change. This person needs to have the authority and commitment to make difficult decisions and drive adoption across organisational boundaries.

Critically, this board should be active from the very beginning: from the moment the need for AI-based transformation is identified, not after the first pilot is complete. Once you have this cross-functional governance in place early, it becomes much easier to chart out the steps for change and put the necessary guardrails in place.

Having stakeholders from different teams involved from the start ensures that all aspects including value creation, cost management, security, organisational impact, and adoption challenges are addressed upfront. This might include formulating new policies, updating job descriptions, creating new incentive structures, or establishing new training programs.

As the AI deployment progresses, the board can establish regular governance meetings with a specific charter to measure and monitor the areas that matter most to the organisation. This creates transparency and accountability while maintaining the agility needed for effective AI implementation.

I believe this is the most critical step in any AI transformation journey. It makes the entire process transparent, manageable, and ultimately more likely to yield real business value.

Que 7. Looking ahead, if you had to identify one game-changing AI capability that businesses should prepare for now, what would it be and why?

Without hesitation, I'd say it's the ability to utilise AI in any and all ways possible to make sense of the data that's generated within your enterprise and derive actionable insights from it. This might sound broad, but it's intentionally so because the implications are massive.

Every enterprise today is sitting on enormous amounts of data: structured and unstructured, from operations, customer interactions, market signals, internal processes, and more. Most of this data remains underutilised because traditional analytics approaches can't keep pace with the volume, variety, and velocity of modern data generation.

AI's capability to process, analyse, and extract meaningful patterns from this data at scale is transformative. But it's not just about analytics. It's about enabling leaders to make data-driven decisions that can transform every aspect of their business.

When you can effectively harness your enterprise data through AI, it enables transformation across multiple areas simultaneously: intelligent automation, enhanced user experience, improved observability, predictive maintenance, personalised customer interactions, optimised supply chains, and much more.

This is game-changing because it's not just about implementing AI in one area of your business, it's about creating an AI-driven nervous system for your entire organisation. Companies that master this capability will have a significant competitive advantage because they'll be able to respond more quickly to market changes, optimize operations continuously, and innovate based on deep insights rather than gut feelings.