80% of Enterprise AI Projects Fail After Pilot Stage Despite Growing Adoption

As AI moves beyond pilots, Dell Technologies sees India entering a new phase of enterprise adoption

Agentic AI, infrastructure modernisation, and growing boardroom interest are driving Indian enterprises from experimentation to execution. India's artificial intelligence (AI) journey is reaching a pivotal moment. After nearly two years of pilots, proofs of concept, and experimentation, enterprises are increasingly looking beyond AI as a technology showcase and towards deploying it at scale across business operations. According to Venkat Sitaram, Senior Director and Country Head, Infrastructure Solutions Group, Dell Technologies India, the clearest indicator of this shift is that organisations are beginning to move AI initiatives into production environments. "When you talk about inflection points, the first thing that comes to my mind is most pilots moving into production. And there are clear proof points. And that's when you can really say that it has reached an inflection point because the adoption rate has gone up," Sitaram said in an interview with Dhruv Mohan of The Economic Times. At the centre of this transition is the rapid emergence of agentic AI, which Sitaram believes is fundamentally changing how enterprises think about productivity, automation and business workflows. The Real AI Challenge Isn't Adoption. It's Scaling It. India's enterprises are no longer asking whether AI works. They're asking how quickly they can scale it.In an exclusive conversation with The Economic Times, Venkat Sitaram, Senior Director and Country Head, Infrastructure Solutions Group, Dell Technologies India, said organisations are increasingly moving AI projects from pilots into production. He explained why agentic AI, infrastructure modernisation, and growing boardroom interest are reshaping enterprise adoption, and why the next challenge is execution, not experimentation.Watch the full podcast. "The biggest catalyst for this is agentic AI because the cognitive work of agentic AI when it starts running into your business workflows has changed dramatically. So it's made analytics easy, it's made coding easy, right? And you don't need so many human interventions," Sitaram said. For Indian enterprises, the challenge is no longer whether AI has potential, but how quickly they can turn that potential into measurable business outcomes. Budgets, skills and prioritisation remain hurdlesDespite growing enthusiasm, enterprises continue to face familiar obstacles in scaling AI deployments. "Budgets are not growing," Sitaram said, describing cost pressures as a universal concern. Alongside funding constraints, he identified skills shortages and poor prioritisation of use cases as the two other major barriers slowing adoption. "Number two, skills. Number three, lack of right prioritisation of use cases sometimes leads to longer experimentation cycles. And that's where we see many of them have not progressed," he explained. However, he believes awareness around these challenges has improved significantly over the past year. Organisations are becoming more disciplined about identifying use cases with measurable outcomes rather than pursuing AI initiatives simply because of market hype. At the same time, the nature of AI deployment itself is changing. Increasingly, inferencing is occurring at the edge, bringing intelligence closer to business users rather than relying entirely on centralised infrastructure. "More and more inferencing is happening on the edge and that has become the business layer. What that would mean is you can have a simple edge device, an agentic edge device, and then start giving the power to that edge user." This shift is accelerating adoption across industries and reinforcing the importance of infrastructure planning. Infrastructure becomes the foundation of AI strategyAs organisations scale AI initiatives, infrastructure is emerging as a critical differentiator. "The adoption of AI is linked to technology infrastructure planning. And the right one size fits all approach will not work. You got to have rightly sized infrastructure," Sitaram said. Dell's strategy reflects this view. The company recently launched PowerStore Elite, which Sitaram described as more than a conventional storage upgrade. "It's not yet another storage array. It's a data platform. It's a modern data platform." The platform incorporates AI-driven operational capabilities designed to automate workload management, optimise performance and streamline recovery processes. According to Sitaram, this allows enterprise IT teams to redirect resources away from routine operational tasks and towards higher-value initiatives. "In PowerStore Elite with the AI ops integration, the IT operations work otherwise in managing workloads, placing the right workloads on performance and the recovery restore operations are all automated, made predictive." He also argued that AI is fundamentally reshaping the economics of enterprise storage as organisations seek to manage larger volumes of data and support increasingly sophisticated workloads. "The economics of storage is changing in an environment. Who is changing the economics of the environment? Storage economics is AI." Future-proofing investments is another concern for enterprises making long-term infrastructure decisions. Sitaram highlighted lifecycle upgrade capabilities that allow organisations to adopt new generations of infrastructure without disruptive migrations. "We're telling customers that start with PowerStore Elite Gen 3 now and later if there is a Gen 4 that comes in after a few years, that can seamlessly coexist with this and you don't need to have any disruption." Agentic AI is raising infrastructure demandsThe rise of agentic AI is also increasing demands on enterprise infrastructure. "AI demands a lot of compute power, highly optimised storage and last but not the least, high bandwidth network links," Sitaram said. In his view, all three foundational layers of enterprise technology, compute, storage, and networking, must evolve simultaneously to support the next wave of AI adoption. Dell's approach combines infrastructure with advisory services and pre-validated architectures intended to help organisations shorten deployment cycles. "Pilot to production is the execution that you talk about. If you want pace, you need something that is qualified, ready, ready to use." The company engages customers through what it calls accelerated workshops, where business leaders and technology teams evaluate use cases, define measurable outcomes and develop implementation roadmaps. "We work with customers by engaging them with what we call an accelerated workshop. And in that workshop, we clearly discuss how and where they could start and what could be the outcomes, measurable outcomes, and use cases that need to be prioritised." These engagements are often followed by visits to Dell briefing centres, where customers can examine real-world deployments, simulations, and reference architectures before committing to large-scale investments. Making the economics workFor many organisations, the business case remains the deciding factor in AI adoption. Sitaram believes enterprises are increasingly evaluating AI through the lens of productivity gains and operational outcomes rather than focusing solely on upfront costs. "The cost of investment therefore becomes something that you can always invest and then take returns which are multi X in a certain period of time." While initial spending can appear substantial, he argues the returns can be transformative. "You're delivering outcomes which are 100X. So on the 100X, a 2X investment in the initial stage may look maybe a little more, but once you see the cycle, then the results are phenomenal." To ease adoption, Dell is also promoting alternative financing models through Dell Apex, including consumption-based and pay-as-you-grow structures. "We sometimes help customers do an Opex modeling of this, consume and pay as you grow models. And with the hand holding, so that is helping them get a quick fast start." Implementation timelines vary depending on architecture and workload requirements, but Sitaram said organisations are often able to deploy AI infrastructure within months rather than years. "We've seen best cases varying anywhere a smaller implementation from 60 days and going up to 180 days." Private cloud and cyber resilience gain importanceAs enterprises modernise their infrastructure, private cloud deployments are becoming increasingly relevant, particularly as AI workloads rely more heavily on containers and distributed architectures. According to Sitaram, enterprises are looking for environments that offer flexibility, control and scalability while avoiding large upfront commitments. "You're not offloading too much in one go. You can buy as you grow." At the same time, cybersecurity concerns are intensifying as AI systems become more sophisticated. "AI adoption has increased the sophistication of cyber attacks. Newer and newer forms of attacks are emerging." For that reason, Sitaram believes cyber resilience must underpin every AI strategy. "While AI is at the core, cyber resiliency should be the foundation layer." AI moves into the boardroomLooking ahead, Dell expects AI adoption to expand well beyond large enterprises and regulated sectors. "We're seeing this catching up with even small, medium enterprises, AI startups." More importantly, AI is increasingly becoming a board-level priority rather than a purely technology-led initiative. "Anywhere and everywhere you see there is a talk and buzz about how I can do and what I can do with AI, it's become board conversations, it's become management meetings." Those conversations increasingly centre on competitive advantage. "Are we leveraging enough? And are we seeing that as a competitive differentiator? Yes." For Dell, which has operated in India for nearly three decades, that shift signals a long-term opportunity. As enterprises move from experimentation to execution, the focus is increasingly turning towards infrastructure readiness, operational outcomes and the ability to scale AI responsibly. The companies that succeed, Sitaram suggests, will be those that can bridge the gap between ambition and deployment, and do so quickly.

7 Reasons Enterprise AI Projects Fail After the Pilot Stage

Enterprise AI has moved beyond experimentation. Over the past two years, organisations have invested heavily in AI pilots across customer service, operations, software development, finance, and supply chains. Yet despite this momentum, very few initiatives successfully make the transition from pilot to enterprise-wide deployment. The challenge is no longer whether AI works. In most cases, it does. The bigger question is whether organisations are prepared to integrate AI into the realities of day-to-day business operations. Recent industry research puts a hard number on the scale of the problem: roughly 80% of enterprise AI initiatives fail to deliver their intended business value. Nearly a third are abandoned before they ever reach production. This is not a model performance issue. It is an operationalisation issue. Having spent over 25 years across banking, financial services, and enterprise technology, and now working with enterprises deploying AI in regulated environments, I have seen the same seven failure patterns repeat with striking consistency. * Pilots are built on ideal conditions Most pilots are developed using clean, structured, and carefully prepared datasets. Production environments are very different. Data is fragmented, inconsistent, and spread across multiple systems. As projects move into real-world environments, data quality quickly becomes one of the biggest obstacles to scaling AI successfully. * Success is never clearly defined Many AI initiatives begin with enthusiasm but without clearly agreed business outcomes. Teams focus on building a working model instead of defining measurable objectives such as cost reduction, productivity improvement, turnaround time, or customer experience. Without clear success metrics, it becomes difficult to justify further investment once the pilot ends. * Legacy technology slows enterprise AI Most organisations continue to operate on technology that was never designed for AI-driven workflows. Legacy applications, disconnected systems, and lengthy deployment cycles make it difficult to integrate AI into everyday business processes. The technology may be ready, but the underlying enterprise architecture often is not. This is precisely where I see the most enterprises stall. The instinct is to treat legacy modernisation and AI adoption as sequential projects: rebuild the core first, add AI later. That sequencing is expensive and slow. The organisations that move faster are the ones that build a configuration and orchestration layer over what already exists, rather than waiting to replace it. * Organisations underestimate the change required Technology alone rarely determines the success of an AI initiative. Scaling AI requires collaboration between business teams, technology teams, compliance, security, and operations. A pilot may succeed with a small project team, but enterprise-wide adoption demands organisational alignment that many businesses underestimate. * Operational readiness receives little attention Building an AI model is only one part of the journey. Production environments require governance, monitoring, security, model updates, and performance management. Without these operational capabilities, many promising pilots fail to deliver sustainable business value after deployment. This is where architecture matters more than ambition. The enterprises that succeed treat governance, auditability, and human approval as design principles built into the system from day one, not compliance work added after a pilot succeeds. AI should be confined to design-time configuration, with deterministic, auditable systems handling execution in production. That separation is what makes operational readiness achievable rather than aspirational. * Expectations are higher than reality Many organisations expect AI to deliver transformational results within a single planning cycle. In reality, enterprise AI creates value over time through continuous improvement and refinement. Unrealistic expectations often lead organisations to abandon initiatives before meaningful outcomes are achieved. The most effective way I have seen organisations manage this is by starting small and shipping fast. A single, well-defined workflow or dashboard delivered in days builds more organisational confidence than a twelve-month transformation roadmap. Speed early in the journey buys the patience needed for the larger initiatives that follow. * Executive sponsorship does not last long enough Enterprise AI programmes often span multiple budget cycles. When leadership priorities shift, projects lose momentum, funding, and organisational focus. Sustained executive sponsorship is often the difference between isolated pilots and enterprise-wide transformation. Sponsorship rarely fades because leaders stop believing in AI. It fades because the initiative stops producing visible wins. Programmes that deliver measurable outcomes every few weeks, rather than promising one large outcome a year from now, keep sponsorship alive because the business case renews itself continuously. The next phase of AI is execution The conversation around enterprise AI is changing. For the last few years, organisations have focused on adopting AI. The next phase will focus on delivering measurable business outcomes. Competitive advantage will not come from simply deploying more AI models. It will come from integrating AI into business processes, modernising enterprise technology without ripping it out, strengthening governance as a design principle, and enabling faster execution across the organisation. The organisations that succeed will not necessarily be the ones investing the most in AI. They will be the ones with the right architecture: AI used responsibly at the design stage, deterministic and auditable systems in execution, and the discipline to measure business outcomes rather than pilot activity. That is what separates enterprises still explaining their AI strategy from those already running on it. Shrish Anand Lal is the Executive Director and Chief Business Officer at New Street, the company behind MiFiX.ai. With over 25 years of experience across banking, financial services, and enterprise technology, he leads enterprise growth, business development, and strategic partnerships globally, and the views expressed in this article are his own

Beyond the AI Pilot Trap: Industrializing Enterprise AI for Measurable Value

Scaling enterprise AI is no longer a challenge of technology, but of strategy, engineering, and organizational readiness. While proofs of concept demonstrate potential in controlled environments, moving beyond the pilot trap requires a shift from curiosity-led experimentation to a value-realization mindset. True transformation happens when organizations treat AI not as a collection of isolated innovation projects, but as a core enterprise capability built on trusted data, scalable engineering, responsible governance, and clear business ownership. As agentic systems and autonomous workflows reshape the corporate landscape, the winners will be the organizations that modernize their underlying architectures and align cross-functional teams around shared outcomes. Moving from proof to production demands robust platforms, continuously governed data, and a workforce empowered to reimagine how the business creates value. Bridging this gap between experimentation and lasting impact is central to the strategy outlined by Mayank Verma, Global Head of Data and AI at Xebia. CXOToday: Why do enterprises continue to struggle with scaling AI beyond successful pilots? Mayank: Enterprises rarely struggle because the pilot failed; they struggle because the pilot was never designed for enterprise scale. Proof of concept can demonstrate potential in a controlled environment, but production AI must perform reliably across real users, real data, regulatory constraints, and changing business conditions. The common blockers are clear: unclear ownership, fragmented data, limited engineering discipline, and governance that arrives too late. Many pilots are also led by technology curiosity rather than measurable business value, which makes it difficult to justify investment once the excitement fades. AI also does not scale like traditional software. It requires continuous monitoring, model improvement, data quality management, security controls, and human oversight. Without these foundations, even a promising pilot becomes difficult to industrialize. At Xebia, we encourage clients to move from an experimentation mindset to a value-realization mindset. The question should not be, "Where can we use AI?" It should be, "Which business outcomes can AI improve, and what operating model will help us scale them responsibly?" CXOToday: What is the 'AI pilot trap,' and what are the biggest barriers to enterprise-wide AI adoption? Mayank: The AI pilot trap is the gap between proof and production. It happens when organizations celebrate a successful demonstration but do not build the enterprise capabilities needed to repeat, govern, secure, and scale that success across business functions. The barriers are usually structural, not technical: disconnected data estates, unclear decision rights, legacy systems, limited platform maturity, and weak linkage to commercial outcomes. Too often, organizations start with "How do we use Generative AI?" when the sharper question is "Where can AI create measurable value?" As AI becomes more autonomous through agentic systems, these challenges become even more pronounced because AI is only as reliable as the quality of the data and processes behind it. The organizations that are scaling successfully are treating AI as an enterprise capability not a collection of isolated innovation projects. They are investing equally in data, engineering, governance, and organizational change. CXOToday: What does an effective AI operating model look like? Mayank: An effective AI operating model starts with business outcomes, not technology choices. AI shouldn't sit in an innovation lab it should become part of how the business operates. The model rests on four pillars: business ownership, trusted data, AI engineering, and governance. Business teams define the outcomes, data provides the foundation, engineering industrializes AI through scalable platforms, and governance ensures security, compliance, and responsible AI. Equally important is collaboration. AI can no longer be owned solely by IT. It requires business leaders, data teams, engineers, and governance functions to work together from the outset. At Xebia, we see AI engineering as the bridge between experimentation and enterprise value. When organizations combine strong engineering discipline with business alignment and trusted data, AI moves beyond pilots to become a scalable business capability. CXOToday: How can organizations better align data, engineering, governance, and business teams? Mayank: The best alignment mechanism is a shared business outcome. When teams are aligned around revenue growth, customer experience, productivity, risk reduction, or operational resilience, AI stops being a handoff between departments and becomes a joint transformation agenda. Business teams should define the problem and success metrics; data teams should ensure trusted, contextual information; engineering teams should build scalable and reusable AI platforms; and governance teams should shape the guardrails from day one rather than reviewing risk after deployment. Shared accountability is critical. Cross-functional AI product teams with common KPIs consistently outperform siloed approaches because AI systems continue to evolve after launch and require continuous business, data, engineering, and risk input. Ultimately, trusted data, engineering excellence, and governance are not independent functions they are interconnected capabilities that determine whether AI creates sustainable business value. CXOToday: How is AI reshaping infrastructure and cloud investment decisions? Mayank: AI is fundamentally changing how enterprises think about infrastructure. The conversation is no longer cloud-first, it's outcome-first. Enterprises are now evaluating hybrid cloud, sovereign AI, edge computing, GPU infrastructure, and modular architectures through the lens of workload sensitivity, latency, regulatory requirements, cost, and business criticality. As infrastructure decisions should follow the value case not the other way around. Generative AI and agentic AI also demand greater flexibility. Enterprises need architecture that allow workloads to move seamlessly across cloud, on-premises, and edge environments while maintaining governance and security. The winners will be organizations that avoid lock-in, design for portability, and build cloud-agnostic foundations that can adapt as models, regulatory expectations, and AI workload patterns continue to change. CXOToday: What infrastructure is required to support Generative AI and Agentic AI workloads? Mayank: Generative AI and Agentic AI require more than GPU capacity. They require an AI-ready enterprise fabric that connects compute, data, security, orchestration, observability, governance, and engineering discipline. For Generative AI, enterprises need scalable compute, modern data platforms, vector databases, orchestration frameworks, prompt and model management, and robust security. Agentic AI raises the bar because autonomous agents interact with multiple systems, trigger actions, and make decisions that require stronger control, traceability, and human oversight. The right infrastructure depends on the business use case. A customer-facing AI assistant, an internal knowledge system, a regulated decisioning workflow, and an edge-based industrial use case will each demand different choices across public cloud, private cloud, sovereign environments, and hybrid deployments. Equally important is designing modular architectures that can evolve as AI technologies mature. The pace of innovation is unprecedented, and enterprises need platforms that allow them to adopt new models, tools, and deployment strategies without rebuilding their entire technology landscape. CXOToday: How are enterprise data architectures evolving in the age of AI? Mayank: AI is fundamentally changing what enterprises consider valuable data. Traditionally, organizations focus on structured information stored in data warehouses and transactional systems. Today, much of an organization's intelligence resides in unstructured assets such as documents, policies, emails, technical manuals, and knowledge repositories. The challenge is no longer the volume of data but its quality, accessibility, and context. Many enterprises have abundant data but lack the trusted, governed information that AI systems need to generate reliable outcomes. As agentic AI becomes more prevalent, this becomes even more critical because autonomous systems rely on contextual enterprise knowledge to make informed decisions. Modern data architecture therefore needs to unify structured and unstructured information through strong metadata management, semantic layers, knowledge graphs, vector search, governance, and AI-ready pipelines. Organizations that can turn enterprise knowledge into trusted, contextual intelligence will have a clear advantage in scaling AI. CXOToday: Is workforce readiness becoming more important than technology investments? Mayank: Workforce readiness is becoming the decisive factor. Technology is increasingly accessible, but the ability to redesign processes, make informed decisions, and adopt AI responsibly depends on people. AI transformation requires employees to understand where AI can support their work, where human judgement remains essential, and how processes should change when intelligence becomes embedded into everyday workflows. That requires continuous learning, experimentation, and leadership commitment. Equally important is creating multidisciplinary teams where domain experts, engineers, data scientists, and business leaders work together to solve real business problems. AI delivers its greatest value when technical expertise is combined with deep industry knowledge. Ultimately, AI doesn't replace human expertise, it amplifies it. Organizations that invest in both technology and talent will create a far more sustainable competitive advantage than those focusing on technology alone. CXOToday: What AI skills will enterprises need over the next two to three years? Mayank: The demand is shifting from isolated AI expertise to integrated AI engineering capabilities. While data science remains important, enterprises increasingly need professionals who can build, deploy, govern, and continuously improve AI systems at scale. Skills such as AI engineering, platform engineering, MLOps, LLMOps, data engineering, AI security, and responsible AI will become foundational. At the same time, organizations will need business leaders who understand how to translate AI capabilities into measurable commercial outcomes. Perhaps the most valuable skill will be the ability to combine domain expertise with AI. Technology alone doesn't create competitive advantage it's the ability to apply AI to solve industry-specific challenges that drives real business value. The future workforce will therefore be defined less by job titles and more by the ability to collaborate across technology, business, and engineering disciplines. CXOToday: How should organizations approach AI governance, responsible AI, and risk management? Mayank: Responsible AI should not be viewed as a compliance exercise it should be treated as a business capability. As AI becomes embedded in critical enterprise decisions, governance becomes essential for building trust, ensuring regulatory compliance, and managing operational risk. Governance needs to be built into every stage of the AI lifecycle from data quality and model development to deployment, monitoring, and continuous improvement. Waiting until after deployment to address governance often creates unnecessary risk and slows adoption. As organizations adopt autonomous AI systems, strong governance becomes even more important. Trusted data, Zero Trust architectures, explainability, human oversight, and clear accountability are all essential for ensuring AI operates safely and responsibly. As autonomous systems rely entirely on trusted data and trusted pipelines, making governance a non-negotiable foundation for enterprise AI. The organizations that will lead in AI are not those taking the greatest risks they are the ones building innovation on a foundation of trust, transparency, and responsible engineering. CXOToday: Which industries are leading AI industrialization, and what can others learn from them? Mayank: The strongest AI industrialization is happening in sectors where AI is tied directly to measurable business outcomes. Financial services, manufacturing, healthcare, retail, and telecommunications are leading because they are using AI for fraud prevention, predictive maintenance, intelligent customer engagement, clinical and operational decision support, and supply chain optimization. What differentiates these organizations is that they don't treat AI as a standalone technology initiative. Instead, they integrate AI into core business processes and build the engineering, governance, and data capabilities required to sustain it at scale. Another common characteristic is their willingness to modernize legacy systems rather than simply layering AI on top of existing infrastructure. They understand that AI delivers its greatest value when supported by modern data platforms, scalable engineering practices, and cross-functional collaboration. The lesson for every enterprise is clear successful AI transformation is not about deploying the most advanced model. It is about selecting high-impact use cases, modernizing the underlying data and technology foundation, building responsible guardrails, and scaling what works with discipline. CXOToday: What differentiates organizations that are successfully scaling AI across the enterprise? Mayank: Organizations that scale AI successfully share one mindset: they treat AI as a business transformation capability, not a series of disconnected technology experiments. First, they begin with a clear business objective. Every AI initiative is linked to measurable outcomes, whether that's improving customer experience, increasing productivity, reducing costs, or accelerating innovation. Second, they invest in the fundamentals. Trusted data, AI engineering, governance, and modern platforms are treated as strategic capabilities rather than project-level requirements. Third, they foster collaboration across business, technology, and governance teams. AI doesn't succeed in silos. It requires shared accountability, continuous iteration, and executive sponsorship. Most importantly, they build for change. The AI landscape is evolving quickly, so leading organizations design flexible, modular ecosystems that allow them to adopt new models, tools, and deployment patterns without disrupting existing operations. Ultimately, organizations that scale AI successfully don't chase every new technology trend -- they build the organizational capabilities needed to continuously innovate. CXOToday: What is the growing role of AI engineering in enterprise transformation? Mayank: AI engineering is becoming the foundation of enterprise AI transformation. While Generative AI has made AI more accessible, enterprise success depends on the ability to operationalize it reliably, securely, and at scale. A model is only one component of enterprise AI. Organizations also need robust data pipelines, platform engineering, model orchestration, observability, evaluation frameworks, security, and continuous monitoring. AI engineering brings these disciplines together, so AI systems perform consistently in real business environments. As AI evolves toward autonomous and agentic systems, engineering becomes even more critical. Enterprises need platforms that can manage multiple models, integrate with business applications, enforce policy controls, capture feedback, and detect drift or unintended behavior. At Xebia, we see AI engineering as the bridge between innovation and business value. It enables organizations to move beyond isolated pilots and build AI solutions that are scalable, resilient, and aligned with enterprise objectives. In the years ahead, AI engineering will become as fundamental to business transformation as software engineering has been over the last two decades. CXOToday: Based on Xebia's experience, what are some practical best practices for enterprises looking to scale AI? Mayank: The first best practice is to anchor every AI initiative in a clear business outcome. Organizations that define the problem, success metric, owner, and path to adoption upfront are far more likely to move from experimentation to measurable impact. Second, prioritize data readiness. AI can only generate reliable outcomes when it is built on trusted, well-governed, and context-rich data. In many enterprises, preparing the right data is more challenging and more valuable than choosing the model itself. Third, invest early in AI engineering and governance. Scalable platforms, reusable components, evaluation mechanisms, security controls, and responsible AI frameworks create the consistency needed to expand AI across business functions. Finally, organizations should adopt an iterative approach. AI transformation is not a one-time implementation but a continuous journey of learning, refinement, and improvement. Those that build flexibility into their architecture and operating model are far better positioned to adapt as AI technologies continue to evolve. CXOToday: What is one key piece of advice for CIOs and business leaders looking to move beyond AI pilots in 2026? Mayank: My advice would be simple: don't think incrementally think transformational. The conversation should no longer be about launching another AI pilot. It should be about reimagining how the organization will operate in an AI-first world, how it serves customers, empowers employees, improves decisions, and creates new sources of value. That means asking harder questions: Is the data trusted? Is the architecture flexible? Are governance and security built in? Do teams have the skills to adopt AI? And, most importantly, is every AI investment connected to a business outcome leaders are willing to own? Technology will continue to evolve rapidly, but organizations that build strong foundations trusted data, modern engineering, responsible governance, and a business-first AI strategy will be best positioned to capture long-term value. The goal is not simply to automate existing processes. It is to reimagine how the enterprise creates value in an AI-driven world. Organizations that build that foundation now will be better positioned to define the next decade of digital transformation.

Navigating Enterprise AI: Insights on Governance, Agentic AI, and Top-Line Growth

The rush to deploy enterprise AI has left many organizations trapped in an expensive cycle of experimentation, delivering impressive laboratory demos but negligible P&L impact. Crossing the divide from speculative pilots to scalable, production-grade systems requires a fundamental shift in priorities: establishing unified, well-governed data foundations long before chasing ambitious use cases. From navigating the guardrails of Agentic AI to redefining marketing ROI through rapid experimentation, the true value of artificial intelligence lies not in automating legacy workflows, but in committing to top-line growth and systemic process redesign. In this comprehensive Q&A, industry leaders dissect the real-world mechanics of scaling AI responsibly, drawing vital governance lessons from heavily regulated sectors like BFSI to establish lineage, auditability, and trust from day one. Crucially, the discussion shifts the strategic focus for CXOs away from simple cost reduction toward unlocking entirely new revenue opportunities and accelerating execution speed across the enterprise. Offering a pragmatic roadmap for executive leadership, these critical insights on bridging the gap between technological potential and measurable business value are delivered by Raghvendra Kushwah, Co-founder of Eucloid Data Solutions. CXOToday: Is enterprise AI moving beyond experimentation to measurable business outcomes? Raghvendra: Selectively, yes, but the honest picture is that most enterprises are still stuck between the two. Industry research suggests the vast majority of generative AI pilots deliver no measurable P&L impact, and that matches what we see on the ground. The organizations that are crossing over share a pattern. They invested in their data foundation before chasing use cases, they picked problems with genuine scale, and they changed their processes to absorb AI rather than bolting it onto workflows designed for a different era. One of our healthcare clients in the US spent close to a year consolidating more than 65 source systems onto a unified platform before pursuing AI seriously. Their first AI deployments followed almost immediately after, at a fraction of traditional build costs. The outcomes are measurable when the sequencing is right. Experimentation without that groundwork stays experimentation. CXOToday: What role does data quality and governance play in scaling AI successfully? Raghvendra: It is the difference between a demo and a production system. AI is only as good as the data layer beneath it, and fragmented, inconsistent data produces confident, wrong answers faster than any human analyst could. But I would push the point further: governance is where promising pilots most often die. A pilot can impress everyone in the lab and still fail at the production gate, when security and compliance teams ask entirely reasonable questions about how the system handles sensitive data, what the model saw, and who can trace it. If lineage, access controls, and auditability are built into the foundation from day one, those questions have ready answers. Bolted on afterwards, they become the wall the project never gets over. This is why we advise clients to treat governance as an enabler of scale, not a tax on it. CXOToday: How are enterprises approaching Agentic AI, and where are they seeing the biggest impact? Raghvendra: With more enthusiasm than discipline, in many cases. The appeal of agents is obvious, with systems that plan and execute multi-step work rather than answering single questions. But the fundamental constraint has not changed. AI today is not at a stage where it runs autonomously, and agents amplify both the capability and the risk. The impact we see is real but bounded: agents accelerating data engineering workflows, handling multi-step analytics requests, and compressing work that used to take days into hours, always with a human in the loop for decisions that matter. The organizations getting value are the ones that deploy agents where the cost of an error is low or recoverable, and keep humans on the approval path where it is not. The ones getting burned expected autonomy and planned no oversight. My advice is to treat agentic AI as a force multiplier for your teams, not a replacement for them. CXOToday: How can organisations improve marketing ROI using AI-driven analytics and customer insights? Raghvendra: The starting point is the data foundation. Marketing is one of the most fragmented functions in the enterprise, with customer information spread across CRM, web analytics, campaign platforms, and commerce tools, each holding a different view of the same customer. Without unifying that, AI-driven insights are little more than better-presented guesswork. Once that foundation is trusted, the familiar gains follow, be it sharper segmentation and propensity modelling, or attribution that goes beyond last-click. The bigger shift, though, is pace. The constraint has rarely been a shortage of ideas, it has been the wait between asking a question and getting an answer. When analytics sits on a governed data layer that can be queried conversationally, the marketer with the question can answer it in minutes rather than waiting days for an analyst. That compression changes behaviour more than any single model does. Teams test more variants and correct underperforming spend while a campaign is still running, instead of discovering it in a quarterly review. The discipline that decides whether any of it counts is measurement. Organisations need to define what improved ROI actually means, and instrument for it, before deploying AI. Marketing has a long history of adopting new tools without changing the processes around them, and AI will deliver the same disappointing results unless the workflows and decision-making practices evolve with it. CXOToday: What lessons from the BFSI sector can help other industries adopt AI more responsibly? Raghvendra: BFSI offers a useful lesson precisely because it has always had to innovate within tighter constraints than most industries. Banks and financial institutions already operate within established governance, security and risk frameworks, shaped by requirements such as RBI's IT and cybersecurity directions in India, Basel Committee standards, PCI DSS for payment data, and applicable data-protection laws. AI needs a similar level of restraint. Organizations need clear rules around what data can be used, who can access it, how decisions are explained, where human oversight is required, and what happens when a system gets something wrong. The lesson from BFSI is that governance should not be introduced after AI has been deployed. It should be built into the system from the beginning. Strong guardrails do not necessarily slow innovation down. They give organizations the confidence to deploy AI at scale because the risks, responsibilities, and boundaries are clearly understood. CXOToday: What are the biggest AI priorities CXOs should focus on over the next few years? Raghvendra: Three things. First, aim at the top line. Most AI budgets are still justified on cost reduction, and that is the smaller half of the opportunity. The use cases worth funding are the ones that help you sell more: faster product and content generation, new offerings that were never economical to build manually, better conversion across the customer journey. Growth use cases are also more forgiving of imperfection, which makes them the better place to build organisational confidence. Second, be realistic about what technology actually does to cost. It rarely takes cost out. What it does is make a capability cheap enough that people use far more of it than they did before, and that wider adoption is where the return sits. If a marketing team could afford one campaign variant and can now afford ten, the outcome is more revenue, not a smaller team. Leaders who go in expecting the cost line to fall usually end up disappointed on both counts, because they neither reduce spend nor pursue the growth that was available. Third, and this is the most underestimated: take the leap of faith on process change. Enterprises typically run a pilot inside existing workflows, wait for it to demonstrate value, and plan to redesign the process only once the results are convincing. The difficulty is that the value usually depends on those very changes. Tested in an environment that was never adapted for it, the pilot underperforms and the technology takes the blame, even though the experiment was constrained from the start. That commitment has to come from the top, and it has to reach the middle of the organisation, where transformation actually stalls. The technology is ready enough. The appetite to redesign how work gets done is usually what is missing.