In 2024, even non-tech companies began exploring AI, with generative tools flooding the market. But in 2025, AI moved from plan to reality in Indian organisations. The question shifted from whether companies would adopt AI to how fast and how deeply they could integrate it.

From major conglomerates to IT services giants and startups, AI tools, copilots and autonomous agents entered everyday workflows—drafting emails, analysing data, handling customer interactions, supporting developers, reviewing contracts, and predicting demand.

According to an EY report, 47% of organisations now have multiple AI deployments in operation, 10% are scaling use cases at an enterprise level, and close to 50% say that a good portion of their GenAI proof-of-concepts have moved into production.

2025 marked the shift from curiosity to conviction, industry experts told Inc42. Indian businesses moved beyond trials to serious commitment, seeing tangible gains in customer engagement, faster turnarounds, fewer operational errors, optimised supply chains and shorter innovation cycles.

Industries including banking, retail, healthcare, manufacturing and IT services began to fundamentally rethink how work was done—reimagining decision-making, redesigning internal processes, and rebuilding workflows around AI, industry experts told Inc42.

“AI is no longer an experiment but an execution imperative. In 2025, global and Indian enterprises turned intent into impact by moving from pilots to production and from scattered initiatives to integrated systems that deliver measurable business outcomes. What began as experimentation has matured into enterprise-wide transformation,” Khadim Batti, cofounder and CEO of SaaS giant Whatfix, said.

Despite widespread experimentation through pilot programmes, proof-of-concepts, innovation labs and internal AI task forces, few organisations unlocked true enterprise-level transformation, industry experts said.

AI existed almost everywhere, but wasn’t yet fully embedded in core processes that drive efficiency, scale and long-term competitiveness. Teams were often using AI to accelerate old workflows, not redesign them. 2025 was the year AI moved from hype to habit, but largely remained in the pilot phase.

“Today, AI lives in pockets like marketing, customer experience, supply chain and finance, but rarely connects into the organisation’s core decision architecture. It’s like installing high-performance engines in cars that still run on broken, old roads. The value gets trapped at the use-case level,” Shub Bhowmick, cofounder and CEO of data science startup Tredence, said.

A clear shift inIndia’s AI journey in 2025 has been localisation. After years of relying on broad, globally-trained models, organisations now prioritise systems that understand India—how people speak, how businesses operate, and how data is shaped by local behaviour.

As Ganesh Gopalan, founder and CTO of Gnani.ai, puts it: “In India, AI can’t just be intelligent, it has to be relevant. If it doesn’t reflect local language, infrastructure limitations, and business realities, it simply won’t scale. Local context is what separates experiments from real adoption.”

Global tech giants are following suit. Earlier this month, Google announced an expansion of its AI infrastructure in India. The move will let organisations train and deploy advanced Gemini models entirely within the country, addressing critical data residency and sovereignty requirements.

Other global players are following a similar path. Earlier this year, German software giant SAP launched sovereign cloud capabilities in India, enabling enterprises to store and process data locally.

Meanwhile, homegrown startups are also adding to the momentum. Hyderabad-based Cyient Semiconductors, in partnership with Azimuth AI, recently introduced ARKA GKT-1, a custom system-on-chip built specifically for edge intelligence in the energy and utilities sector.

Multilingual AI, once treated as a nice-to-have feature, is now non-negotiable. From Tier I cities to small towns, organisations want systems that work seamlessly in Hindi, Tamil, Telugu, Marathi, Bengali and other languages. Solutions that fail to grasp cultural and linguistic nuance are increasingly seen as incomplete.

“If your AI doesn’t understand local language, local context and local behaviour, it’s simply not useful. We’ve seen adoption jump almost overnight once we localised our models,” according to Aman Goel, founder of agentic AI startup GreyLabs AI.

Beyond language, organisations are innovating for infrastructure. Demand is growing for AI systems that function with limited or no internet connectivity, particularly in rural and semi-urban areas where network access remains inconsistent, Goel added.

Healthcare workers, government officers and small business owners need tools that work offline, sync periodically, and deliver insight even when connectivity is weak.

This has accelerated the rise of edge AI and on-device intelligence. From diagnostic tools in rural clinics and AI-powered education platforms in government schools, to offline-first compliance systems in small manufacturing units, AI in India is being rebuilt for real-world conditions rather than lab-perfect environments.

As AI adoption moved from experimentation to everyday enterprise use, the use of gen AI tools have matured as well. If 2023 and 2024 were about exploration and proof-of-concepts, 2025 has been about integration, according to industry analysts.

Gen AI has moved out of creative corners like writing social posts or designing visuals, into the operational backbone of organisations. Today, it is being used across revenue teams, risk departments, compliance functions, customer operations and finance but in a more mature way.

What’s changing in 2025 is not just the level of AI adoption, but the way organisations are thinking about it. Instead of force-fitting GenAI into every process, companies are becoming more thoughtful about where it truly adds value and where classic NLP works better, Ankush Sabharwal, founder of Bengaluru-based Corover AI, said.

“GenAI is powerful, but it’s expensive and it can hallucinate. You don’t need it for everything. The real shift is happening towards what we call a composite or layered AI approach where classic AI handles structured tasks, grounded GenAI handles contextual queries, and large GenAI models are only used when open-ended intelligence is actually needed. The smartest organisations are not using more AI , they are using the right AI in the right place, with strong guardrails,” he added.

This is where India’s enterprise approach is beginning to stand out. Instead of forcing a one-model-fits-all strategy, organisations are designing systems that combine traditional AI, domain-specific models and GenAI into a controlled, layered architecture and always with a human in the loop.

GreyLabs AI’s Goel also added that once teams are properly trained and workflows are redesigned around AI, adoption accelerates rapidly. The difference between marginal gains and real transformation is not the model itself but the discipline with which it is implemented.

GenAI, then, is no longer just a productivity booster in India. It is slowly becoming an operating layer, rather one that supports decisions, compresses time, and reshapes how work is initiated, reviewed and completed.

While gen AI use has matured in 2025, agentic AI has emerged as the next buzzword, where interest is high, but adoption is still uneven. Most organisations have started early adoption, but has remained confined to experimentation or tightly controlled pilots, typically within functions like customer support, IT operations or internal analytics. Only a small percentage have moved toward real scale, and even then, usually in silos rather than across core business lines.

According to Gnani.ai’s Gopalan, this hesitation is not technological, but psychological and structural. “In theory, agentic AI can run entire operations. In reality, most companies are not emotionally or legally ready to let go of that much control yet,” he said.

What is beginning to change, however, is the nature of the question enterprises are asking. Earlier, agentic AI was seen largely as a productivity layer, a way to improve cross-sell, upsell, onboarding or collections through agentic voice and automation platforms. Now, organisations are starting to explore where agentic AI can change their fundamental business processes.

Gopalan added that a growing shift in enquiries from peripheral use cases to core operational functions. Companies are no longer just asking how agentic systems can support existing workflows such as they are asking whether they can underwrite more loans in the same time, accelerate product launches, manage higher volumes with the same teams, or increase processing capacity without proportional increases in headcount.

The enterprises have started fetching real value from agentic AI, not by bolting it on” to traditional processes, but by re-engineering how work itself is designed and distributed. Startups, with fewer legacy constraints, are already rebuilding workflows around agent-driven models and seeing measurable efficiency gains.

Larger, more established organisations, by contrast, tend to layer agents onto outdated systems, which often leads to underperformance and reinforces internal skepticism.

Over the next 12 months, the key trend to watch will be how quickly agentic AI moves from support functions into core business architecture. As clarity around governance improves and leadership comfort grows, more Indian enterprises are likely to shift from controlled experimentation to structural integration, not because agentic AI is fashionable, but because it directly impacts scale, speed, and operating efficiency.

Despite the visibility and excitement around AI, large-scale implementation across enterprises is still in its early stages. In India and even in more mature markets like the US, the reality on the ground is far less advanced than the hype suggests, the Corover founder said.

While individual employees and teams are actively using tools like ChatGPT and other AI-powered applications, true enterprise-wide integration remains limited “On the enterprise level, everyone is using ChatGPT. But that doesn’t mean AI adoption has really started in a serious, structural way,” Corover’s Sabharwal added.

In many Indian organisations, AI is now present across marketing, sales, operations, finance, HR, legal and customer success. At least half of all departments in large companies are experimenting with some form of AI in daily workflows. There is also a visible shift from purely reactive analysis to more predictive and proactive engagement. Businesses are using AI to anticipate customer behaviour, personalise journeys, flag risks earlier and resolve issues before they escalate. Seamless, relevant, hyper-personalised experiences are becoming the baseline expectation rather than a competitive advantage.

Unfortunately most of this usage remains surface-level. Very few companies have scaled AI deeply into their core processes or operating models. According to industry experts, the main problem is not access to technology, but approach and intent.

“Companies are not doing the hard work of value chain analysis. They’re not starting with a clear problem. They’re going shopping for solutions and then seeing what works, instead of defining the gap first and then applying AI to it,” senior analyst at a tech giant said.

Rather than breaking down their business into functions, sub-functions and specific problem areas, many organisations adopt a scattered, untargeted approach. Without a clearly defined problem statement, measurable outcomes or integration strategy, pilots struggle to move into production.

This is compounded by familiar barriers: fragmented data across silos, legacy IT systems that don’t integrate with modern AI stacks, internal resistance to change, limited AI literacy, and the absence of strong governance frameworks.

As AI adoption widens across Indian enterprises, risk exposure is expanding as well, often faster than governance frameworks can keep pace. More business-critical decisions are being influenced or made by algorithms, yet the structures required to control, audit and explain those decisions are still uneven and evolving.

Most organisations are now aware of the gap. They are engaging consultants, legal teams and technology partners to design Responsible AI policies, but maturity is inconsistent.

According to Gopalan, the real challenge is not the absence of policy documents but it is the lack of organisational internalisation. “It’s not about just having a document you can cut and paste. It’s about having internal conversations that say responsible AI is critical at the CXO level, at the board level, and across the organisation.”

While cybersecurity awareness has become deeply embedded through training, signage and protocols, responsible AI has yet to achieve the same level of visibility. “You need to take responsible AI to that same level,” Gopalan added.

Educating employees on internal AI policy is critical along with debates at the senior leadership level about how it should be applied. At the technical level, some organisations are moving deliberately away from fully open, general-purpose models in favour of domain-contained systems. Goel explained that one strategy being adopted is strict model confinement.

“The open models you use, like ChatGPT or Gemini, are designed for massive, global use cases. What we are doing is confining the model to financial services. If you ask our model how to fix a washing machine, it will refuse to answer because that is outside its domain,” he added.

Domain-specific training works like a built-in guardrail, helping prevent unexpected or inappropriate responses. This is strengthened through regular testing. At different stages, evaluator checks are used to see if the model is meeting the right standards. By keeping the model focused on a specific industry, using well-structured prompts, and reviewing its performance over time, companies can build much stronger AI governance, according to experts.

These measures are becoming essential as AI moves into higher-risk areas such as underwriting, fraud detection, compliance, legal analysis and healthcare decision-making. In many cases, businesses have already experienced incidents stemming from inaccurate outputs, misinterpretations or incomplete analysis.

In 2025, the companies that truly benefitted from AI are those that have combined bold experimentation with strong control, treating governance not as a hurdle but as a strategic advantage.

[Edited by Nikhil Subramaniam]

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