Bridging the AI production gap: Over 100,000 Indian engineers trained to build with Gemini
India is seeing a significant shift in its technology workforce towards enterprise AI integration and solutions. The Build with Gemini initiative has trained over 100,000 developers and architects across several major organizations. This transitio...
By ET Spotlight | Updated:
ET Spotlight
As businesses move from exploring artificial intelligence to integrating it into everyday operations, the demand for professionals who can build, deploy and scale AI solutions is growing. This shift is putting greater emphasis on technical skills, system architecture and the ability to translate AI capabilities into practical applications at an enterprise level.
As India’s technology workforce prepares for the next phase of enterprise AI, the Build withGemini initiative on Google Cloud has crossed a milestone of more than 100,000 trained developers and architects across seven leading global system integrators (GSIs) in September. The scale signals a broader shift from AI experimentation and application support towards the engineering, architecture and intellectual property required to put AI into production.
India’s next technology advantage may depend less on the size of its IT talent base and more on how quickly that talent can be retooled for enterprise AI.
As organisations worldwide move from generative AI experimentation to production-grade deployments, the skills required are changing rapidly. Enterprises no longer need teams that can simply prompt a model or build a proof of concept. They need engineers who can ground AI in proprietary data, integrate it with business applications, orchestrate agentic and multiagent systems, establish security and governance controls, and scale workloads through cloud-native infrastructure.
That shift presents a significant opportunity for India.
The country already has one of the world’s largest pools of software engineers and enterprise technology professionals. The next phase is about building the depth required to move that workforce further upstream, from application implementation, maintenance and support towards enterprise AI architecture, reusable frameworks, industry-specific solutions and intellectual property.
ADVERTISEMENT
Against this backdrop, the Build with Gemini initiative on Google Cloud has crossed the milestone of more than 100,000 trained developers and architects across Cognizant, HCLTech, IBM, Infosys, Tata Consultancy Services, Tech Mahindra and Wipro.
The significance lies not only in the number, but in the capability being built around it.
The first phase of generative AI adoption created familiarity with models, prompting and early use cases. The next phase requires something more demanding: the ability to engineer AI into existing enterprise technology environments.
That means understanding enterprise data grounding, APIs, application integration, evaluation, multi-agent orchestration, identity, security, observability and governance. It also means designing AI systems that can operate reliably across complex cloud environments and business workflows.
ADVERTISEMENT
In other words, the challenge is shifting from using AI to engineering with AI.
Build with Gemini: INDIA AI talent at scale
100,000+ trained professionals
Developers and architects trained through hands-on work with the platform, enterprise AI agents and agent orchestration.
The difference between an AI pilot and a production deployment becomes clear when the technology is placed inside a real business process.
Consider an AI agent handling a customer request.
Understanding the query may be the simplest part of the task. The system may also need to authenticate the user, retrieve information from multiple databases, interpret policy, interact with operational applications, recommend or execute an action and determine when human intervention is necessary.
That creates a harder set of questions.
What data can the agent access? What actions can it perform? How are permissions controlled? How is output quality evaluated? What happens when confidence is low? How are decisions monitored and audited?
These are not predominantly model questions. They are architecture, integration, security and governance questions.
For CIOs and technology leaders, this changes the centre of gravity of enterprise AI. Model capability remains important, but the ability to engineer reliable systems around those models increasingly determines whether an initiative progresses beyond the PoC stage.
Why GSIs become the multiplier
Global System Integrators are particularly important to this transition because their teams already operate deep inside enterprise technology environments.
They work across application modernisation, cloud transformation, data platforms, infrastructure, cybersecurity and business-critical systems. Building advanced AI engineering capabilities across this workforce can therefore create a multiplier effect.
Instead of AI expertise remaining concentrated within small innovation teams, enterprises can draw on a larger pool of developers and architects who understand both emerging AI technologies and the environments into which they must be integrated.
The scale of that effort is already visible at Cognizant.
“Build with Gemini is what a technology partnership looks like when both sides commit at scale. More than 55,000 Cognizant associates, nearly one in six of our workforce, went hands-on with Gemini models on Google Cloud across enterprise AI agents and agentic workflows. This is the AI-ready foundation we are building together with Google Cloud, and our clients will see it in every engagement.”
— Annadurai Elango, President Core Technologies and Insights, Cognizant
The enterprise value of this capability will increasingly be industry-specific.
A banking deployment may need to operate within risk, compliance and transaction controls. A manufacturing system could connect engineering, plant and supply-chain data. A retail agent may span customer service, inventory, commerce and fulfillment.
The use cases differ, but the engineering requirement remains consistent. AI has to work within the realities of the enterprise, not alongside them.
For Tata Consultancy Services, developing this engineering capability forms part of a larger strategy to transform its workforce for AI-led technology services.
“AI is transforming every industry and every role. At TCS, our priority is to build AI capabilities at scale that help customers move faster from experimentation to enterprise-wide adoption. Through Build with Gemini at TCS, we are executing talent transformation which is a core component of our 5-pillar strategy to become the world’s largest AI-led tech services company. The strength of our next generation AI capabilities ultimately depends on our people, which is why we continue to invest in developing one of the industry’s most AI-ready workforces to lead the next era of AI-driven growth.”
— Murali Ramanathan, Vice President and Chief Learning Officer, Tata Consultancy Services
The relevance of these skills extends beyond familiarity with AI models. What matters is whether technical teams can combine models, enterprise context and operating controls into systems that are usable, secure and dependable in production.
Moving India further up the AI value chain
This is where the 100,000+ milestone assumes a broader significance for India’s technology sector.
India’s IT services industry built its global position through software development, application management, infrastructure services, cloud transformation and large-scale technology delivery.
Enterprise AI creates an opportunity to build on that foundation.
Agentic systems require engineers to make decisions around data architecture, model interaction, orchestration, workflow design, security, evaluation and human oversight. They also create opportunities for technology services companies to develop reusable frameworks, accelerators, industry solutions and intellectual property around enterprise AI.
The shift is therefore not about moving away from India’s services strength. It is about using that strength to participate in higher-value layers of enterprise technology.
A deeper AI engineering bench can enable Indian teams to move from executing programmes designed elsewhere towards co-creating the architectures, platforms and intelligent systems on which those programmes depend.
At Infosys, the focus is similarly on combining hands-on experience with enterprise AI engineering and responsible deployment capabilities.
“The conversation around enterprise AI is rapidly shifting from what AI can do to how organizations can deploy it reliably, responsibly and at scale. This shift requires not just technology, but also hands-on experience and capabilities in areas such as data integration, multi-agent orchestration, and responsible AI governance. Through Google Cloud’s Build with Gemini initiative, we are enabling Infosys employees to gain hands-on experience with Gemini. Combined with Infosys Topaz, our AI-first set of services, solutions, and platforms, this investment in skills and practical adoption strengthens our ability to help organizations unlock AI value, scale responsibly, and accelerate business transformation.”
— Balakrishna D. R. (Bali), Executive Vice President, Global Services Head, AI and Industry Verticals, Infosys
That evolution could help strengthen India’s ambition to become a global engineering centre for enterprise AI, not merely through talent scale, but through deeper ownership of architecture, platforms, solutions and IP.
What this means for CIOs
For enterprise leaders planning the next two to three years of AI architecture, three implications stand out.
First, AI architecture will increasingly become enterprise architecture. AI systems will have to be designed alongside data platforms, APIs, identity, security, observability and application-modernisation strategies.
Second, agentic AI will raise the governance bar. As systems move from generating responses to initiating actions, organisations will need stronger controls around permissions, evaluation, escalation, auditability and human oversight.
Third, engineering capacity will become a strategic AI asset. Enterprises will need people who understand not only models, but also the data, systems, infrastructure and controls required to operate them reliably at scale.
For Google Cloud, the milestone reflects the importance of building an ecosystem capable of translating advanced AI models into real-world enterprise outcomes.
“The true promise of AI is realised only when it is deeply integrated into the fabric of enterprise systems. Our Global System Integrator partners are the critical bridge between advanced models and real-world business outcomes. By empowering over 100,000 professionals in India to build with Gemini, we are cultivating a massive ecosystem of engineering talent that will help CIOs worldwide move confidently from AI experimentation to secure, scalable production.”
— Kelly Rogan, VP System Integrator and AI Native Partnerships, Google Cloud
As access to advanced models becomes more widespread, competitive differentiation will increasingly move to what enterprises can build around those models.
That is what gives the Build with Gemini milestone its broader relevance.
The 100,000+ trained developer and architect base represents more than a workforce training milestone. It points to the creation of an engineering bench capable of helping enterprises close one of AI’s most persistent gaps, turning experimentation into secure, scalable production systems.
For India, it also signals the next stage of its technology evolution, from being a global centre for technology delivery to becoming a deeper source of enterprise AI architecture, engineering and intellectual property.
