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Most organisations approach AI as a technology experiment rather than an operational transformation. Scaling requires more than building accurate models
In real-world settings, engineers must navigate cost constraints, compliance requirements, infrastructure dependencies, and stakeholder alignment, said an expert.
From banking and retail to healthcare, companies are pouring resources into AI pilots, chatbots and predictive tools to streamline operations, enhance customer engagement and sharpen internal decision-making. Yet, despite the enthusiasm and investment, a critical question persists: why do so many AI successes struggle to move beyond the pilot stage?
Globally, industry surveys consistently show that a majority of AI initiatives stall before full-scale deployment. The issue is rarely that the algorithms fail to deliver results in controlled settings. Instead, organisations often find themselves grappling with the far more complex challenge of maintaining, updating and integrating these systems into real-world business environments.
“Most organisations approach AI as a technology experiment rather than an operational transformation. Scaling requires more than building accurate models. It demands robust data pipelines, infrastructure readiness, governance frameworks, and clear cross-functional ownership. Many enterprises succeed at proof-of-concept stages but encounter friction when models must integrate with legacy systems and real workflows. The constraint is rarely intelligence. It is the absence of disciplined deployment practices and sustained accountability once systems move into production,” says Raghav Gupta, Founder & CEO, Futurense – an AI skilling company.
What Is The Skills Gap That Nobody Is Talking About?
India produces close to 1.5 million engineering graduates every year, one of the highest in the world. Yet the availability of engineers does not automatically translate into readiness for AI deployment. Building a machine-learning model and running it in a production system requires fundamentally different skill sets.
AI deployment means embedding models into live enterprise systems where they operate reliably and deliver measurable value. It involves integrating models with data infrastructure, ensuring performance under real traffic conditions, implementing monitoring frameworks, and maintaining governance standards, explains Gupta. “Deployment also requires clarity of ownership and defined accountability. A model in isolation is experimentation. A model embedded within workflows, influencing decisions and sustaining operational impact, represents true deployment.”
When asked about what skills are lacking in graduates, Gupta pointed to “exposure to live production environments”. “In real-world settings, engineers must navigate cost constraints, compliance requirements, infrastructure dependencies, and stakeholder alignment. What is often missing is lifecycle fluency. Enterprises require professionals who understand how early design decisions influence scalability, reliability, and measurable business outcomes. The gap is not technical potential. It is a structured experience operating within business-critical systems after launch,” he said.
Model building is often academic and experimental. Deployment demands expertise in data engineering, cloud infrastructure, cybersecurity, software architecture, performance monitoring and governance frameworks. It involves designing pipelines that continuously feed data, updating models without disrupting services, and ensuring systems remain secure and compliant. These are not skills that emerge from theory-heavy curricula alone.
Another, Krishna Khandelwal, Founder & CEO of Hunar.AI – an HR-tech start-up based out of Gurugram and Bengaluru – said, “Graduates understand theory, but struggle with workflows. They don’t grasp well how businesses actually operate day to day, what processes drive revenue, what tasks are repetitive, and where AI can truly augment output. They rarely think in terms of KPIs. Knowing which metrics to track and how to measure incremental gains from AI adoption is the missing muscle today.”
What Are Enterprise Blind Spots?
The responsibility for stalled AI projects does not rest solely with the workforce. Many enterprises approach AI as a plug-and-play solution rather than a long-term organisational capability. The expectation of quick returns often collides with the reality that AI deployment is iterative, resource-intensive, and dependent on cross-departmental collaboration.
“Fear and uncertainty are the biggest blind spots. The employee asked to ‘implement AI’ often does not know if they are augmenting their performance or automating themselves out. Without clarity, adoption becomes defensive. Enterprises must clearly communicate intent and ringfence teams driving AI transformation. AI should feel like leverage, not a threat. When employees understand the strategic impact and their role in it, adoption will accelerate,” stresses Khandelwal.
One of the most common blind spots is unclear business objectives. Companies frequently initiate AI pilots driven by competitive pressure or fear of missing out rather than by clearly defined problems. Without a strong link to measurable outcomes such as cost reduction, efficiency gains or revenue growth projects lose momentum once the initial excitement fades. Another persistent issue is data quality. AI systems are only as reliable as the information they consume, yet many organisations underestimate the effort required to clean, standardise, and secure their data.
Gupta highlights that companies “assume” that successful pilots will naturally scale. “Organisations often underinvest in data quality, monitoring mechanisms, governance processes, and post-deployment optimisation. Another gap is unclear ownership once AI systems go live. Without defined accountability, performance deteriorates, and adoption slows. Enterprises must treat AI as infrastructure rather than innovation theatre. That shift requires disciplined execution, deployment-ready talent, and long-term commitment to continuous improvement,” he said.
Cultural resistance within organisations also plays a role. Employees may view AI tools as threats rather than enablers, slowing adoption and integration. Leadership, meanwhile, may underestimate the change-management aspect, assuming technology alone will drive transformation. In reality, successful deployment demands continuous training, communication, and alignment across technical and non-technical teams.
What Skills Are Companies Seeking Today
“Enterprises increasingly seek professionals who combine technical depth with execution maturity. This includes systems integration, data engineering, cloud infrastructure awareness, monitoring frameworks, and security compliance, along with the ability to collaborate across product and business teams. Building accurate models is no longer sufficient. Organisations need engineers who can take ownership once systems move into production and remain accountable for performance and measurable outcomes. In many AI-native companies, these responsibilities are formalised under roles such as Forward Deployed Engineers, who bridge experimentation and sustained enterprise impact,” explains Gupta.
Every function, whether it is sales, marketing, HR, or finance, now requires subject-matter expertise and fluency in AI, highlights Khandelwal. “Employees should use tools like Zapier and n8n to create lightweight workflows, set up basic agents, and write effective prompts. We are seeing the rise of hybrid roles: Sales Engineers, Marketing Engineers, and HR Engineers. The gap is not in intelligence; it is about applied capability. Talent today finds it difficult to operationalize AI into day-to-day tasks,” he said.
He further said AI projects are frequently viewed as tactical trials. “When leaders portray AI as an afterthought, teams follow suit. It takes a backseat to their real work. A clear top-down strategic mandate is necessary for scaling AI. AI must be proclaimed a directional lever rather than a pilot by leadership. What transforms experiments into infrastructure is ownership, ringfenced teams, and clearly defined outcomes.”
Beyond skills and strategy lies another layer of complexity: infrastructure and regulation. Deploying AI at scale requires robust computing resources, cloud integration, and secure networks capable of handling vast volumes of data. For many Indian enterprises, especially mid-sized firms, the cost and technical complexity of building such infrastructure can be daunting.
The Economic Stakes For India
The implications of stalled AI projects extend beyond individual companies. India may be seen as a nation that excels at experimentation but struggles with execution.
Conversely, bridging the deployment gap presents a significant opportunity. The demand for professionals skilled in AI operations, cloud engineering, and enterprise technology management is growing rapidly. These roles command higher value than entry-level coding jobs and have the potential to create a new tier of specialised employment. Productivity gains from successful AI deployment can also enhance competitiveness across industries, from manufacturing and agriculture to finance and public services.
On a macroeconomic level, widespread adoption of production-ready AI systems can improve efficiency, reduce operational costs, and stimulate innovation. The challenge lies not in access to technology but in the readiness to integrate and sustain it. India’s digital ambitions depend as much on operational maturity as on technological invention.
At the ongoing AI Summit in New Delhi, Prime Minister Narendra Modi highlighted on February 18 India’s transformative potential and the role the country can play in the AI revolution. “We are not just nurturing talent, but we are building the infrastructure, policy ecosystem, and skills base required for India to move from participating in the AI revolution to shaping it.”
“My vision for AI in Aatmanirbhar Bharat rests on three pillars: sovereignty, inclusivity, and innovation. My vision is that India should be among the top three AI superpowers globally, not just in the consumption of AI but in the creation of models,” he added.
February 18, 2026, 11:47 IST
“Most organisations approach AI as a technology experiment rather than an operational transformation. Scaling requires more than building accurate models. It demands robust data pipelines, infrastructure readiness, governance frameworks, and clear cross-functional ownership. Many enterprises succeed at proof-of-concept stages but encounter friction when models must integrate with legacy systems and real workflows. The constraint is rarely intelligence. It is the absence of disciplined deployment practices and sustained accountability once systems move into production,” says Raghav Gupta, Founder & CEO, Futurense – an AI skilling company.
What Is The Skills Gap That Nobody Is Talking About?
India produces close to 1.5 million engineering graduates every year, one of the highest in the world. Yet the availability of engineers does not automatically translate into readiness for AI deployment. Building a machine-learning model and running it in a production system requires fundamentally different skill sets.
AI deployment means embedding models into live enterprise systems where they operate reliably and deliver measurable value. It involves integrating models with data infrastructure, ensuring performance under real traffic conditions, implementing monitoring frameworks, and maintaining governance standards, explains Gupta. “Deployment also requires clarity of ownership and defined accountability. A model in isolation is experimentation. A model embedded within workflows, influencing decisions and sustaining operational impact, represents true deployment.”
When asked about what skills are lacking in graduates, Gupta pointed to “exposure to live production environments”. “In real-world settings, engineers must navigate cost constraints, compliance requirements, infrastructure dependencies, and stakeholder alignment. What is often missing is lifecycle fluency. Enterprises require professionals who understand how early design decisions influence scalability, reliability, and measurable business outcomes. The gap is not technical potential. It is a structured experience operating within business-critical systems after launch,” he said.
Model building is often academic and experimental. Deployment demands expertise in data engineering, cloud infrastructure, cybersecurity, software architecture, performance monitoring and governance frameworks. It involves designing pipelines that continuously feed data, updating models without disrupting services, and ensuring systems remain secure and compliant. These are not skills that emerge from theory-heavy curricula alone.
Another, Krishna Khandelwal, Founder & CEO of Hunar.AI – an HR-tech start-up based out of Gurugram and Bengaluru – said, “Graduates understand theory, but struggle with workflows. They don’t grasp well how businesses actually operate day to day, what processes drive revenue, what tasks are repetitive, and where AI can truly augment output. They rarely think in terms of KPIs. Knowing which metrics to track and how to measure incremental gains from AI adoption is the missing muscle today.”
What Are Enterprise Blind Spots?
The responsibility for stalled AI projects does not rest solely with the workforce. Many enterprises approach AI as a plug-and-play solution rather than a long-term organisational capability. The expectation of quick returns often collides with the reality that AI deployment is iterative, resource-intensive, and dependent on cross-departmental collaboration.
“Fear and uncertainty are the biggest blind spots. The employee asked to ‘implement AI’ often does not know if they are augmenting their performance or automating themselves out. Without clarity, adoption becomes defensive. Enterprises must clearly communicate intent and ringfence teams driving AI transformation. AI should feel like leverage, not a threat. When employees understand the strategic impact and their role in it, adoption will accelerate,” stresses Khandelwal.
One of the most common blind spots is unclear business objectives. Companies frequently initiate AI pilots driven by competitive pressure or fear of missing out rather than by clearly defined problems. Without a strong link to measurable outcomes such as cost reduction, efficiency gains or revenue growth projects lose momentum once the initial excitement fades. Another persistent issue is data quality. AI systems are only as reliable as the information they consume, yet many organisations underestimate the effort required to clean, standardise, and secure their data.
Gupta highlights that companies “assume” that successful pilots will naturally scale. “Organisations often underinvest in data quality, monitoring mechanisms, governance processes, and post-deployment optimisation. Another gap is unclear ownership once AI systems go live. Without defined accountability, performance deteriorates, and adoption slows. Enterprises must treat AI as infrastructure rather than innovation theatre. That shift requires disciplined execution, deployment-ready talent, and long-term commitment to continuous improvement,” he said.
Cultural resistance within organisations also plays a role. Employees may view AI tools as threats rather than enablers, slowing adoption and integration. Leadership, meanwhile, may underestimate the change-management aspect, assuming technology alone will drive transformation. In reality, successful deployment demands continuous training, communication, and alignment across technical and non-technical teams.
What Skills Are Companies Seeking Today
“Enterprises increasingly seek professionals who combine technical depth with execution maturity. This includes systems integration, data engineering, cloud infrastructure awareness, monitoring frameworks, and security compliance, along with the ability to collaborate across product and business teams. Building accurate models is no longer sufficient. Organisations need engineers who can take ownership once systems move into production and remain accountable for performance and measurable outcomes. In many AI-native companies, these responsibilities are formalised under roles such as Forward Deployed Engineers, who bridge experimentation and sustained enterprise impact,” explains Gupta.
Every function, whether it is sales, marketing, HR, or finance, now requires subject-matter expertise and fluency in AI, highlights Khandelwal. “Employees should use tools like Zapier and n8n to create lightweight workflows, set up basic agents, and write effective prompts. We are seeing the rise of hybrid roles: Sales Engineers, Marketing Engineers, and HR Engineers. The gap is not in intelligence; it is about applied capability. Talent today finds it difficult to operationalize AI into day-to-day tasks,” he said.
He further said AI projects are frequently viewed as tactical trials. “When leaders portray AI as an afterthought, teams follow suit. It takes a backseat to their real work. A clear top-down strategic mandate is necessary for scaling AI. AI must be proclaimed a directional lever rather than a pilot by leadership. What transforms experiments into infrastructure is ownership, ringfenced teams, and clearly defined outcomes.”
Beyond skills and strategy lies another layer of complexity: infrastructure and regulation. Deploying AI at scale requires robust computing resources, cloud integration, and secure networks capable of handling vast volumes of data. For many Indian enterprises, especially mid-sized firms, the cost and technical complexity of building such infrastructure can be daunting.
The Economic Stakes For India
The implications of stalled AI projects extend beyond individual companies. India may be seen as a nation that excels at experimentation but struggles with execution.
Conversely, bridging the deployment gap presents a significant opportunity. The demand for professionals skilled in AI operations, cloud engineering, and enterprise technology management is growing rapidly. These roles command higher value than entry-level coding jobs and have the potential to create a new tier of specialised employment. Productivity gains from successful AI deployment can also enhance competitiveness across industries, from manufacturing and agriculture to finance and public services.
On a macroeconomic level, widespread adoption of production-ready AI systems can improve efficiency, reduce operational costs, and stimulate innovation. The challenge lies not in access to technology but in the readiness to integrate and sustain it. India’s digital ambitions depend as much on operational maturity as on technological invention.
At the ongoing AI Summit in New Delhi, Prime Minister Narendra Modi highlighted on February 18 India’s transformative potential and the role the country can play in the AI revolution. “We are not just nurturing talent, but we are building the infrastructure, policy ecosystem, and skills base required for India to move from participating in the AI revolution to shaping it.”
“My vision for AI in Aatmanirbhar Bharat rests on three pillars: sovereignty, inclusivity, and innovation. My vision is that India should be among the top three AI superpowers globally, not just in the consumption of AI but in the creation of models,” he added.
