For years, computing steadily moved toward centralisation. The cloud became the centre of gravity. Most of the early applications of AI too naturally evolved in the cloud because consumer services scale best there. Companies like Microsoft, Amazon and other hyperscalers built the massive infrastructure required to train and deploy AI at scale.

But what we are now witnessing is the next phase of AI,  the movement from testing and evaluating into production.

That is the real significance behind Dell Technologies adding one thousand new AI Factory clients in a single fiscal quarter and crossing five thousand total customers. Enterprise AI hardware demand is no longer restricted to hyperscalers alone. AI is now propagating across industries, countries, and enterprise environments.

This is not merely about more servers being sold. It reflects a deeper change in the approach to computing itself.

As was pointed out during the earnings discussion, intelligence has to be produced at the point of context. Wherever the action is, wherever the decision has to be taken, wherever the proprietary data exists, that is where intelligence increasingly needs to reside.

This becomes especially important in manufacturing, healthcare, finance, logistics, defence, pharmaceuticals and industrial systems where data is sensitive, proprietary, or operationally critical. Such industries may not always want their entire operational intelligence sitting in distant public cloud environments.

For many enterprises, the future therefore lies not only in cloud AI, but also in local AI factories, edge AI systems, and on-premise intelligence.

From Generative AI to Agentic AI

The first wave of generative AI demonstrated the extraordinary ability of machines to create content,  text, code, images and analysis. Systems like OpenAI’s ChatGPT changed public perception of what machines could do.

But making content is only the beginning. Doing work is transformative.

Agentic AI refers to systems capable of pursuing goals, planning multi-step actions, adapting dynamically to changing situations, coordinating tools, learning from outcomes, and refining their responses with minimal human intervention. Unlike simple AI assistants that merely answer questions or generate content, these systems can increasingly execute workflows.

That distinction matters enormously. A chatbot generating a paragraph is useful. An AI system managing procurement chains, optimising production lines, predicting equipment failure, coordinating logistics, monitoring cyber threats, or assisting medical workflows changes the economics of entire industries.

This is why companies are beginning to realise that AI may not merely deliver incremental improvements of five or ten percent. In some workflows, it may produce tenfold or even hundredfold gains in speed and efficiency. That is the speed businesses are now trying to capture. The race is no longer about experimenting with AI; it is about integrating AI into operational systems before competitors do.

Scaling Beyond the Hyperscalers

There is often a tendency to assume that all AI infrastructure is locked up inside the giant cloud companies because of GPU concentration. But the rapid addition of enterprise AI customers indicates that the supply chain itself is scaling up aggressively.

NVIDIA continues expanding GPU production and AI ecosystems. Networking, cooling systems, advanced packaging, memory systems, and AI server integration are all scaling in parallel. While demand still exceeds supply, the ecosystem is clearly moving toward broader industrial deployment.

An important distinction is now emerging.

Companies like NVIDIA create the core AI technologies. Hyperscalers operationalise those technologies at internet scale and convert them into cloud services. But there exists another massive layer in between. Large enterprises, manufacturers, hospitals, banks, telecom operators, governments, and regulated sectors all require AI systems adapted to their own workflows and environments.

That is where companies like Dell position themselves.

Dell is not merely selling servers. It is attempting to convert AI technology into deployable enterprise solutions capable of delivering operational impact. Factories may require AI integrated directly with robotics and industrial sensors. Hospitals may require secure AI systems operating within regulatory boundaries. Banks may need internal AI environments separated from public systems. Governments and defence organisations may increasingly seek sovereign AI capabilities operating within national infrastructure.

In such environments, intelligence cannot always remain dependent on distant cloud processing alone.

AI moves from software to infrastructure

This may gradually produce a hybrid AI world:

• hyperscale cloud AI for massive public services,
• edge AI for real-time industrial applications,
• enterprise AI factories for proprietary operational systems.

What makes this phase particularly important is that AI is moving beyond software into infrastructure.

AI increasingly resembles an industrial transformation requiring semiconductors, energy systems, advanced cooling, networking infrastructure, specialised hardware, and redesigned workflows operating together as a single ecosystem. Electricity transformed factories not because it produced light, but because it reorganised industrial production itself. Similarly, AI’s long-term impact may not come primarily from chatbots or content generation, but from the restructuring of operational systems across the economy.

India’s Opportunity in AI Industrial Wave

For India, this transition may represent a far bigger opportunity than the first internet revolution.

India may have entered late into semiconductor manufacturing, but the AI era is broader than chip fabrication alone. The next phase involves deployment, integration, services, workflow redesign, industrial AI applications, edge computing, and enterprise transformation, areas where India already possesses major strengths. India has one of the world’s largest pools of software engineers, IT service professionals, and system integrators. As AI moves into manufacturing, healthcare, logistics, finance, telecom and governance, India could emerge not merely as a consumer of AI, but as a major deployment and operational hub for enterprise AI systems.

India also has strategic reasons to build sovereign AI capabilities in sectors such as defence, finance, healthcare, governance, and telecommunications. This may create opportunities not only for software firms, but also for data centres, telecom operators, AI infrastructure providers, and manufacturing ecosystems.

The challenge, however, is speed. AI infrastructure and industrial deployment ecosystems are scaling rapidly worldwide. India therefore has a window of opportunity, but windows do not remain open indefinitely.

The beginning of AI’s industrial phase

The implications for employment and business structures are equally significant.

The real AI hiring wave may not emerge only from AI model companies themselves. It may come from enterprises redesigning their operations around machine-assisted intelligence.

Companies will increasingly require:

• AI workflow architects,
• automation specialists,
• cybersecurity professionals,
• industrial AI engineers,
• edge computing experts,
• robotics integration teams,
• and human-AI coordination roles.

The larger challenge may not be technological but organisational.

Most institutions today still operate at human speed,  human approvals, meetings, review cycles, and bottlenecks. Agentic AI compresses those cycles dramatically. Decisions that once took days may occur in minutes. Supply chains may self-adjust dynamically. Predictive systems may continuously optimise production and maintenance.

The companies that adapt structurally to this speed may gain enormous advantages. That is why Dell’s earnings call matters beyond quarterly financials. It signals that AI is leaving the demonstration phase and entering the operational phase.

The first era of AI created fascination. The next era may create industrial transformation.

And the companies positioning themselves today are not merely buying hardware. They are laying the foundations for a future in which intelligence itself becomes part of operational infrastructure.

(The author is an Indian Army veteran and a contemporary affairs commentator. Views expressed are personal. He can be reached at  kl.viswanathan@gmail.com )