How enterprises are operationalising GenAI beyond pilots and proof points

For most enterprises, the GenAI conversation has moved past from curiosity to exploration to real adoption stage. It is no longer about demos, copilots, and proofs of concept. Today they are asking hard questions. Where is AI actually delivering value? How does it scale? And what changes when intelligence becomes part of daily work, not a side project?

Across industries, CIOs and digital leaders say the shift is clear. Enterprises are no longer experimenting with AI at the edges. They are embedding it into operating models, workflows, and decision-making processes. The focus has moved from tools to outcomes, and from pilots to execution.

This transition marks a defining moment for GenAI in the enterprise. What matters now is not whether organisations are using AI, but how deeply it is woven into the way work gets done.

From experimentation to AI-first operating models

For many organisations, digital transformation once meant modernising platforms or migrating to the cloud. That phase is largely complete. Today, enterprises are rethinking how intelligence itself flows through the organisation.

“Digital transformation has evolved from platform upgrades to systemic reinvention,” says Annadurai Elango, President, Core Technologies and Insights at Cognizant. “In this new paradigm, transformation is continuous, contextual, and deeply embedded across business functions. Enterprises are now architecting AI-first operating models where AI agents augment human workflows.”

This shift reflects a broader reality. GenAI is no longer treated as a bolt-on capability. CIOs increasingly see it as a core enterprise function that must operate with governance, scale, and accountability.

At Bosch Global Software Technologies, this thinking has reshaped how technology decisions are made. “We’ve moved beyond treating technology as a project; it’s now a living capability that continuously adapts to the market and customer needs,” says Ganesh Mahadevan V, CIO and CDO. “Our goal has been to adopt digital as a cultural mindset, not just a technology upgrade.”

The emphasis on culture matters. Enterprises that succeed with GenAI do not isolate it within innovation labs. They embed it into everyday processes, enabling teams to work faster, make better decisions, and focus on higher-value tasks.

Where GenAI is actually being used

Despite the hype, CIOs remain pragmatic about where GenAI delivers impact today. The strongest use cases sit close to the core of the business.

Many enterprises now use AI to augment decision-making, especially where data volumes overwhelm human capacity. Predictive insights, anomaly detection, and intelligent automation increasingly support operations, engineering, and customer-facing teams.

“Leading organisations no longer collect information; they operationalise it,” says Kelvin Cheema, Global CIO and Managing Director, Global Transformation and Change at Acuity Knowledge Partners. “AI and data models trained on proprietary insights are transforming how decisions are made and how problems are solved.”

In IT operations, AI-driven observability has emerged as a practical application. Instead of reacting to incidents, teams now rely on intelligent systems that surface risks before they escalate. At Datadog, this shift has changed how enterprises measure success.

“Enterprises are integrating AI-powered observability, security, and automation to enable real-time adaptability,” says Yadi Narayana, Field CTO, APJ. “The goal is to make technology ecosystems self-aware and self-optimising.”

Engineering productivity is another area seeing tangible gains. AI assists developers by reducing repetitive work, improving code quality, and accelerating testing cycles. These benefits may not always show up as direct revenue, but they compound over time through faster delivery and reduced operational friction.

The real challenge is not adoption, it is value

As GenAI moves deeper into enterprise systems, CIOs face a familiar challenge. Proving value remains harder than deploying technology.

“Proving ROI is the most elusive,” says Elango. “AI-led initiatives often yield nonlinear returns, making traditional metrics inadequate.”

Several leaders echo this concern. Traditional budgeting models struggle to account for productivity gains, improved decision quality, or reduced risk. Yet these outcomes define whether GenAI truly matters.

At ZEISS India Global Capability Center, the focus has shifted toward precision. “Technology investments must now deliver clear ROI while also enabling future readiness,” says Prakash Kumar, Head of Corporate IT. “Balancing cost optimisation with innovation requires strong governance and strategic clarity.”

For many enterprises, this has led to a more disciplined approach. AI initiatives now start with defined business outcomes, measurable owners, and clear timelines. Pilots without a path to scale no longer survive.

“Every transformation must begin with value and be engineered for agility,” says Cheema. “Without clarity of purpose, transformation risks becoming technology for its own sake.”

GenAI, talent, and the changing nature of work

The rise of GenAI has also reshaped conversations around talent. While concerns about displacement persist, CIOs focus more on augmentation than replacement.

Automation and AI increasingly handle repetitive tasks, allowing teams to concentrate on creative and strategic work. This shift demands new skills, but it also reduces burnout and operational overload.

“Talent remains the living core of transformation,” says Cheema. “Developing that human layer of digital capability is as critical as any system investment.”

At Mindsprint, the impact of GenAI extends into how productivity itself is measured. “GenAI and automation are changing productivity dynamics,” says Sagar PV, CTO. “Outcomes are delivered faster, often with fewer full-time equivalents, making traditional headcount-based planning outdated.”

This reality forces organisations to rethink workforce planning, funding models, and performance metrics. Enterprises that invest in upskilling and responsible AI adoption position themselves to scale GenAI sustainably.

From vendors to co-architects

As GenAI becomes enterprise-critical, the CIO partner ecosystem is also evolving. Transactional relationships no longer suffice.

“Technology partners must evolve from solution providers to co-architects of transformation,” says Elango. “The future belongs to partners who bring not just platforms, but foresight.”

CIOs now expect partners to understand business context, share accountability for outcomes, and support governance frameworks. This includes helping enterprises navigate AI ethics, data sovereignty, and operational risk.

At Bosch, this shift is explicit. “The era of transactional partnerships is over,” says Mahadevan. “What CIOs need today are co-creation partners who share accountability for outcomes.”

Open architectures and flexibility play a critical role here. Bhanu Jamwal, Head of India Business at TiDB, highlights why. “Every technology decision should preserve future choices rather than lock you in,” he says. “The best CIOs treat their tech stack like a portfolio.”

The GenAI enterprise takes shape

Across these conversations, a consistent pattern emerges. GenAI succeeds when it integrates into the fabric of the enterprise. It fails when treated as a standalone initiative.

CIOs increasingly view transformation as a continuous discipline, not a milestone. They embed intelligence into workflows, align AI investments with business outcomes, and build governance alongside scale.

“Transformation isn’t a one-time project; it’s a continuous loop of learning, experimenting, and scaling responsibly,” says Mahadevan.

For enterprises, the GenAI era is no longer about speed alone. It is about precision, accountability, and trust. Those who get it right will not just automate tasks. They will redesign how decisions are made, how work flows, and how value is created.

In that shift lies the real promise of GenAI.