A quiet revolution is taking place in the global race to harness artificial intelligence (AI). While headlines celebrate breakthrough AI models and their consumer applications, the real transformation requires a purposeful and methodical adoption of AI in the enterprise.
Today, the AI hype cycle is crowded with proof-of-concepts, many of which remain confined to experiments in COEs rather than reaching enterprise-wide deployment. The journey from sandbox to scale represents one of the most significant opportunities of our time.
Pilots Alone Don’t Win Markets
Consider the difference: a GenAI-based customer service pilot may work well to showcase conversational AI capability, but if it is not integrated with the knowledge graph, the tribal knowledge, of the organization and the decision rules specific to that enterprise, the accuracy and outcomes will be far from desired.
AI’s value as a productivity multiplier comes from enterprise-wide application: automating repetitive and, more importantly, complex tasks; enhancing decision-making; improving forecasting; and creating new revenue models. But this can only happen when we move AI out of the sandbox and into the operational core.
Multiple examples help us visualize the art of the possible with AI. Imagine an AI that not only detects a supply chain risk but also takes action to check for alternate suppliers, reroute shipments, and keep managers informed in real time. With Agentic AI, this is no longer a futuristic vision.
Moving Past HesitationDespite rapid advances in generative and agentic AI, hesitation persists. Enterprises are carefully weighing disruption, uncertainty, and security risks. Yet the greater risk may lie in delaying operational integration.
Organizations that operationalize AI effectively improve efficiency and redefine customer expectations and industry benchmarks. CIOs are beginning to address concerns through robust guardrails, governance frameworks, and security controls. The message is clear: the time to scale is now.
The journey from sandbox to scale is not without obstacles, and several common barriers often stand in the way. One such challenge is unclear objectives, where projects begin with enthusiasm but lack a clear and measurable business-aligned outcome. Without this alignment, success measures can become subjective, making the business case for investment difficult.
Another major hurdle is security, where customers worry about access and authentication controls that the AI agent will have vis-vis a human agent. Enterprises also hesitate to send data to the cloud and are exploring options where core parts of the AI can remain sovereign and on-prem.
There is also a crucial non-technological hurdle, rolling out AI can trigger fears of large-scale job displacement. If change is not managed well, there will be resistance from within, slowing down organizations compared to peers. Employees must upskill themselves to augment their jobs with AI rather than resist it and risk being left behind.
Enterprises will have teams comprising humans and AI co-workers, and accepting this new normal will require careful and well-thought-out management change.
Building for Scale: The Leadership Imperative
Scaling AI is more than a budgetary decision; it demands leadership conviction and organizational readiness. We must drive foundational shifts:
- Robust data foundations: AI cannot scale without clean, accessible, well-governed data. Build enterprise-wide data architectures that allow AI models to train and operate at scale.
- Reskill at scale: AI literacy should not be confined to data scientists. Developers, managers, and frontline staff must understand how to leverage AI in their daily work.
- Governance: Ethical frameworks, decision rights, and data accountability are essential. Without proper guardrails, AI may undermine trust and brand equity.
- Measure what matters: Move beyond technical metrics like accuracy rates. Success should be linked to business outcomes such as increased productivity, quicker time-to-market, and improved profit margins. Key performance indicators focused on customer experience, brand perception, and reputation are also crucial.
- Partnership ecosystem: Create a networked approach to scaling AI. Partnerships with AI startups and established players bring agility and domain-specific innovation, while academic collaborations keep enterprises connected to cutting-edge research and emerging techniques.
- Platform-based approach: Unified platforms with reusable components, APIs, and end-to-end MLOps pipelines should replace siloed solutions. Pre-built accelerators for NLP, forecasting, and personalization improve deployment speed, ensure compliance, and enable consistency. Building AI as a system—not isolated projects—drives faster operationalization, standardization, and measurable business value.
- AI and cybersecurity: As AI scales, cybersecurity integration is critical. AI systems can proactively detect anomalies, identify threats, and automate responses faster than traditional methods. Securing pipelines, protecting model integrity, and safeguarding endpoints ensures trust. Converging AI with cybersecurity not only protects digital assets but also enables resilient, responsible adoption at scale.
- Culture and change management: Communicate transparently and highlight success stories to build momentum. This is especially important as we head into a future where humans and AI will become allies in continuous enterprise evolution.
India’s Opportunity
India is home to one of the largest pools of global tech talent, complemented by thriving, data-rich sectors like Banking, Financial Services, Insurance (BFSI), and telecommunications. This unique position enables India to leapfrog traditional adoption challenges and directly implement AI on a large scale.
For example, the country could use AI for fraud detection in banking and network optimization in telecommunications. This proactive approach allows India to achieve advancements at a speed that many mature markets might find difficult to match, fostering innovation and improved service delivery.
The scale and diversity of India’s markets make it an ideal proving ground for enterprise AI — if we have the ambition to seize it.
This isn’t just about adoption at home. Just as India has become the global IT hub over the last three decades, it now has the opportunity to export enterprise-grade AI capabilities worldwide. By leveraging its depth of talent and sectoral diversity, India is uniquely positioned to demonstrate how global enterprises can adopt and scale AI effectively.
With the India AI Mission, we also have a unique opportunity to build sovereign AI models that help our communities realize the value of AI by leveraging our own compute infrastructure, data ecosystems, and startup networks.
Unlocking Potential: The Critical Importance of AI Scaling
Scaling AI is not about chasing the latest tool, it is about embedding intelligence into the enterprise systems and decision-making processes that define an organization’s business and operations.
This requires us to champion AI as an operational necessity, not an optional innovation accessory. The leadership mandate is clear: move beyond treating AI as a standalone experiment and embed it into core operations.
Scale with purpose, govern with discipline, and measure with clarity.
The future belongs to those who can transform AI from a promising trial into a productivity engine that drives their entire enterprise.
The author is Kunal Purohit, President – Next Gen Services, Tech Mahindra,
Disclaimer: The views expressed are solely of the author and ETCIO does not necessarily subscribe to it. ETCIO shall not be responsible for any damage caused to any person/organization directly or indirectly.
