Рубрика: AI

Enterprise software firm Zoho has launched Zoho ERP, a comprehensive, AI-native enterprise resource planning platform built in India, positioning it as a homegrown alternative to global ERP solutions.

The company said Zoho ERP is designed to help fast-growing Indian businesses scale locally and globally without the rigid architectures, high costs, and consultant-heavy implementations associated with legacy ERP platforms.

Unlike conventional ERP systems that add artificial intelligence as an external layer, Zoho ERP embeds AI across the platform. The solution includes AI-driven customisation, automation, predictive insights, anomaly detection and voice-based assistance through Ask Zia, along with end-to-end visibility across finance and operations.

“With Zoho ERP, we have built a powerful, compliance-ready platform that serves as a strong homegrown alternative to global ERP solutions,” said Shailesh Davey, CEO and co-founder of Zoho Corp, in a statement. He added that the product was developed with talent from Kumbakonam, Tamil Nadu and that Zoho plans to drive future innovation from the region.

“By creating opportunities for local youth, we are helping reverse talent drain and strengthen the regional economy while building swadeshi technology from rural India,” he said.

Sivaramakrishnan Iswaran, the global head of Finance and Operations Business Unit at Zoho and CEO of Zoho Payment Technologies, said the platform reflects how modern businesses operate. “Zoho ERP connects fintech, banking and business software, offering built-in local compliance, an intuitive role-based experience, and improved operational efficiency,” he said.

Zoho ERP integrates financial management, billing, spend management, supply chain, payroll and omnichannel commerce into a single platform. It also offers asset management, budgeting and continuous financial close, along with strong audit trails and financial controls. The system is compliant with GST and e-invoicing norms, supports revenue recognition under IFRS 15 and ASC 606, and includes payroll features tailored to Indian regulations, including EPF, ESI, TDS, Professional Tax, and Labour Welfare Fund requirements.

The initial release includes industry-specific capabilities for manufacturing, distribution, retail, and non-profit organisations, with more vertical-focused features planned. Manufacturing firms can manage the full production lifecycle, while distribution businesses can streamline inventory, dealer management, and field sales.

The post Zoho Launches AI-native ERP Platform From Rural Tamil Nadu appeared first on Analytics India Magazine.

Intel on January 22 reported fourth-quarter 2025 revenue of $13.7 billion, exceeding its forecast, even as revenue fell 4% from a year earlier. The company also shipped its first products built on the Intel 18A manufacturing process.

On earnings, Intel reported non-GAAP profit of $0.15 per share, above its guidance of $0.08. On a GAAP basis, the company posted a loss of $0.12 per share for the quarter.

For the full year, Intel reported revenue of $52.9 billion, roughly flat year over year.

Intel CEO Lip-Bu Tan said shipping the first 18A chips was a major step forward. But Intel struggled to supply enough AI data-centre chips and predicted lower-than-expected revenue and profit, causing its stock to fall 13% in late trading.

“The introduction of our first products on Intel 18A—the most advanced process technology developed and manufactured in the United States—marks an important milestone,” Tan said. “We’re working aggressively to grow supply to meet strong customer demand.”

Intel confirmed it is now shipping revenue products built on Intel 18A, making it the first manufacturer to deliver gate-all-around transistors with backside power for commercial products. The process underpins the new Core Ultra Series 3 client processors, formerly known as Panther Lake.

However, the early ramp of 18A weighed on profitability. Intel Foundry reported an operating loss of $2.5 billion in the fourth quarter, $188 million worse sequentially, primarily driven by higher costs associated with scaling 18A production.

Tam said yields are improving but remain below internal targets. “I am disappointed that we are not able to fully meet the demand in our markets. My team and I are working tirelessly to drive efficiency and more output from our fabs, and while yields are in line with our internal plans, they are still below where I want them to be,” he added.

Intel products’ revenue totaled $12.9 billion in the quarter, up 2% sequentially. Data Centre and AI (DCAI) revenue rose 15% quarter over quarter to $4.7 billion, marking the fastest sequential growth in the segment in more than a decade, according to the company.

Intel said demand for traditional server CPUs remains strong as AI workloads increase the importance of CPUs for orchestration, inference, and data movement. Revenue growth was constrained by limited internal wafer supply.

Client Computing Group revenue was $8.2 billion, down 4% sequentially, even as AI PC units grew 16% year over year. Intel said it shipped three Core Ultra Series 3 SKUs in the quarter, ahead of its original plan to deliver one by year-end.

Intel Foundry revenue increased 6.4% sequentially to $4.5 billion, driven by a higher mix of EUV wafers, which accounted for more than 10% of wafer output in 2025, up from less than 1% in 2023.

External foundry revenue was $222 million, primarily from US government projects. Intel said it continues to engage with potential external customers on its upcoming 14A process, with supplier decisions expected to begin in the second half of 2026.

The company’s custom ASIC business grew more than 50% in 2025 and exited the year with an annualised revenue run rate above $1 billion, supported by demand tied to AI infrastructure buildouts.

Intel forecast first-quarter 2026 revenue of $11.7 billion to $12.7 billion, reflecting what it described as the most acute period of supply constraints.

For 2026, Intel said it expects supply conditions to improve each quarter, positive adjusted free cash flow, and operating expenses of about $16 billion. The company plans to retire $2.5 billion in debt maturities during the year.

The post Intel Q4 Beats Estimates as 18A Ships, Supply Constraints Hit Early 2026 appeared first on Analytics India Magazine.

Inferact, an AI startup founded by the creators of the open-source vLLM, has secured $150 million in seed funding, valuing the company at $800 million.

This funding round was spearheaded by venture capital firms Andreessen Horowitz (a16z) and Lightspeed, with support from Sequoia Capital, Altimeter Capital, Redpoint Ventures, and ZhenFund, the company announced on January 22.

According to the company, vLLM is a key player at the intersection of models and hardware, collaborating with vendors to provide immediate support for new architectures and silicon. Used by various teams, it supports over 500 model architectures and 200 accelerator types, with a strong ecosystem of over 2,000 contributors.

The company aims to support the growth of vLLM by providing financial and developer resources to handle increasing model complexity, hardware diversity and deployment scale.

“We see a future where serving AI becomes effortless. Today, deploying a frontier model at scale requires a dedicated infrastructure team. Tomorrow, it should be as simple as spinning up a serverless database. The complexity doesn’t disappear; it gets absorbed into the infrastructure we’re building,” Woosuk Kwon, co-founder of Inferact, posted on X.

The startup also plans to develop a next-generation commercial inference engine that works with existing providers to improve software performance and flexibility.

Inferact is led by the maintainers of the vLLM project, including Simon Mo, Kwon, Kaichao You, and Roger Wang. vLLM is the leading open-source inference engine and one of the largest open-source projects of any kind, used in production by companies like Meta, Google, Character AI, and many others.

The team plans to further enhance vLLM’s performance, deepen support for emerging model architectures, and expand coverage across advanced hardware. They believe the AI industry requires inference infrastructure that is not confined within proprietary limitations.

“For a16z infra, investing in the vLLM community is an explicit bet that the future will bring incredible diversity of AI apps, agents, and workloads running on a variety of hardware platforms,” a16z said on X.
Inferact is also hiring engineers and researchers to work at the frontier of inference, “where models meet hardware at scale,” Kwon said.

The post Andreessen-Backed Inferact Raises $150 Mn to Develop Next-Gen Commercial Inference Engine appeared first on Analytics India Magazine.

With India just a month away from announcing its first homegrown large language model (LLM), as confirmed recently by IndiaAI Mission CEO Abhishek Singh in an exclusive interview with AIM, there’s an urgent need for a legal framework that balances the interests of AI developers and content owners.

Global disputes over AI using copyrighted content for training are intensifying. Publishers Hachette and Cengage have sought to intervene in a class action against Google, alleging mass copying of books and textbooks to train AI systems. In December 2025, journalist John Carreyrou sued major AI firms for unauthorised use of copyrighted books. Earlier the same year, Disney and Universal sued the AI image generator Midjourney over the use of their intellectual property.

Copyright concerns extend beyond publishers and authors. Last month, US recording artist Jerry Anders filed a lawsuit against Stability AI and AudioSparx, alleging his music was used to train the Stable Audio AI model without permission, despite his requests to opt out. He is seeking statutory damages and an injunction to prevent further use. Meanwhile, Oscar-winning actor Matthew McConaughey has trademarked his image and voice to protect them from unauthorised AI use—the first such case by an actor.

India is grappling with similar disputes. In 2024, news agency ANI sued OpenAI, alleging ChatGPT illegally scraped both freely available and paywalled copyrighted content from its website. Last year, the Federation of Indian Publishers filed a lawsuit against OpenAI, claiming its copyrighted books were used for training and summarisation, mirroring ANI’s concerns.

Indian actors Salman Khan, Abhishek Bachchan, Aishwarya Rai, and Nagarjuna have all filed cases to protect their personality rights in light of AI companies using their characters and voices to develop their models.

These cases mark a pivotal moment in efforts by artists, publishers, and content creators to prevent AI firms from using their work without consent or compensation.

Training LLMs and Copyright

LLMs are AI systems designed to understand and generate human-like text. They are trained on vast datasets, including books, websites, articles, and other written material. This breadth of data allows models to learn grammar, facts, context, and some reasoning.

During training, text is tokenised and converted into numerical representations. The model learns by predicting the next token in billions of examples. Essentially, LLMs learn patterns, not documents.

Abhishek Upperwal, CEO of artificial general intelligence-focused Soket.AI, explains how AI datasets are typically built. “We get data from the internet, from wherever it is available. We also extract content from copyright-free books and audio-visual materials.”

This common industry practice puts AI companies on a collision course with creators and publishers intent on protecting their materials. In August 2025, Anthropic reached the first major settlement in an AI copyright dispute, agreeing to pay $1.5 billion to a class of authors who alleged the company had pirated millions of books.

Experts note that AI developers cannot guarantee all content is copyright-free. “AI systems are powerful because they learn from the world, and the world is copyrighted. Developers need governed data supply chains that combine licensed content, structured publisher partnerships, public-domain material, and carefully applied synthetic data,” Sanchit Vir Gogia, founder and CEO of Greyhound Research, tells AIM. He adds that reducing memorisation and output leakage is crucial, as these are the sources of most lawsuits.

Nikhil Pahwa, founder of MediaNama, also highlights how AI training on copyrighted content affects revenue. “Retrieval-augmented generation (RAG) models scrape copyrighted content and reproduce it in other forms, often shrinking the monetisation window that news publishers have. It is theft. Copyright must be respected, or creators’ business models will collapse, leaving AI as the only beneficiary.”

This ongoing conflict underscores the need for AI-specific copyright laws.

India vs the World

India does not yet have a binding AI law, but has issued guidelines, advisories, and policy frameworks shaping AI governance. Other countries have also moved to address AI copyright issues: the EU, UK, Japan, and Singapore introduced text and data mining exemptions to support AI training. While the EU combines risk-based AI rules with copyright opt-outs, Japan permits broad AI training without licences, and China uses layered AI and content controls. South Korea has passed landmark AI laws requiring human oversight for “high-impact” uses, including healthcare, transport, nuclear safety and financial services such as credit and loan decisions.

The US, despite witnessing the most AI copyright lawsuits, has no specific law and relies on courts and fair use.

In December 2025, DPIIT (Department for Promotion of Industry and Internal Trade) released a working paper proposing a dedicated legal framework for copyrighted content in AI training. The proposal recommends a mandatory blanket licensing system, allowing developers to use any lawfully accessed copyrighted material for training without seeking individual permissions. Creators would receive statutory royalties once the AI system is commercialised. A government-designated non-profit, the Copyright Royalties Collective for AI Training (CRCAT), would handle collection and distribution.

The proposal has drawn mixed reactions. “The framework is extremely balanced for both AI developers and content creators. Revenue sharing is ideal,” IndiaAI Mission CEO had earlier told AIM. “My concern is implementation—deciding who gets how much will be challenging. That said, since this is still a proposal, there is room for refinement.”

The IT Ministry also released guidelines requiring AI platforms to label AI-generated content, prevent bias, and ensure accountability. It noted that copyright laws may need to be amended to enable large-scale AI training while protecting copyright holders.

A recent EY report, AI Governance Guidelines: A Bet on Innovation, stresses: “How existing regulations—from consumer protection to penal codes—are interpreted in the context of rapidly evolving AI applications will be the true test. Expert-informed, interdisciplinary evaluation is essential to provide remedies and develop robust Indian AI jurisprudence.”

The need is for a law that can do the balancing act—let innovation thrive alongside fair practice. “It should cover pre-training, fine-tuning, retrieval systems and continuous learning, leaving no grey areas. Disclosure must be meaningful, focusing on source categories, licensing approaches and safeguards—not long dataset lists. Crucially, training rules must be separated from output disputes,” Gogia says.

As global courts, regulators, and creators grapple with the disruptive implications of generative AI, the battle over training data has become a defining fault line in AI governance.

Criticising tech giants lobbying for exemptions in copyright laws, Pahwa reacts, “Big Tech AI firms are currently lobbying extensively for a text and data mining exemption. Why should it be an opt-out anyway? The fact is that many of them have trained their models on pirated content off Sci-Hub and Libgen. The DPIIT Committee’s suggestion to introduce a compulsory license with retrospective applicability is a mechanism for legitimising theft.”

He warns of widespread damage to millions of people in music, movies, news, and publishing. “A voluntary contract regime and enforcement of copyright seems to be the only way forward,” he says.

Bragadish Sureshkumar, Chief Technology Officer, Zopper, agrees, “A statutory model would ensure fair compensation, prevent unfair licensing practices, and create a level playing field between global AI giants and India’s vast base of creators. But effective policy requires more than good intentions.”

Copyright is no longer a peripheral issue in AI policy. It sits at the core of how nations balance innovation, economic competitiveness, and the rights of creators in an AI-driven future.

The post AI’s Copyright Reckoning: Can India Balance Creators And Code? appeared first on Analytics India Magazine.

As finance minister Nirmala Sitharaman prepares to present the Union Budget on February 1, there’s an unusual mix of urgency and maturity bubbling in the Indian startup ecosystem.

The conversation has moved decisively beyond tax holidays or symbolic incentives. Instead, founders and investors are calling for structural interventions that determine whether India retains ownership of innovation or continues to lose value as startups scale.

Entrepreneurs and investors tell AIM that while personal tax relief and consumption-led growth will dominate popular attention, the real test of Budget 2026 lies in how it treats technology, manufacturing, and innovation as long-term economic infrastructure.

From Innovation to Infrastructure

Several founders argue that India must rethink how it categorises emerging technologies, especially AI. “As India stands at the cusp of an AI revolution, the upcoming Union Budget must pivot from viewing AI as a mere software vertical to treating it as strategic national infrastructure, akin to power or telecom,” says Amit Kumar Tyagi, co-founder and CEO of business intelligence solutions company TrueReach AI.

Tyagi points to a fundamental mismatch: while Indian startups are expected to compete globally, the country spends only about 0.7% of GDP on R&D, far below the global average of nearly 2%. He urges the restoration of the 200% weighted tax deduction for R&D, particularly for deep tech startups that operate on long development cycles and delayed revenue realisation. Without this, he argues, India risks remaining an AI consumer rather than a creator.

The weighted deductions were phased out in 2021 under Section 25 of the Income Tax Act. Currently, startups can qualify for a 100% tax deduction for both in-house and outsourced research, capital and revenue costs combined.

Compute access has emerged as another pressure point. In its Budget expectations, Deloitte noted that high infrastructure costs, especially for GPUs and advanced hardware, are constraining domestic AI development even as global investment in the sector accelerates.

Tyagi proposes a national compute credit scheme and a 3–5 year customs duty holiday on critical AI hardware such as GPUs and TPUs, warning that without affordable access to the physical backbone of AI, Indian startups will remain structurally disadvantaged.

That concern resonates with other founders. Ankush Sabharwal, founder and CEO of conversational AI startup CoRover, says startups are watching closely for targeted AI R&D funding and infrastructure investments that align India with global trends.

“The technology sector is poised to contribute over $1 trillion to the global economy by 2030, but India faces a shortage of nearly two million AI professionals,” he says, adding that investments in skilling and infrastructure must move in tandem to build a sustainable ecosystem.

At the operating level, founders building from India say cost and access matter more than headline schemes. Paramdeep Singh, co-founder of Shorthills AI, notes that while government initiatives such as IndiaAI Mission and subsidised GPU access help, many mature startups still rely on hyperscalers like AWS and Azure for speed and scale.

“Quick access to technology would definitely be welcome,” Singh highlights, but adds that predictability and execution matter more than announcements when building enterprise-grade AI systems.

Scaling Without Losing Ownership

A deeper anxiety is plaguing India’s startup ecosystem: who owns innovation once companies begin to scale.

Prem Barthasarathy, founder and managing partner at UK-India VC fund Pontaq, frames this as a structural failure rather than an execution gap. As companies move from validation to deployment, founder ownership often falls sharply, from nearly 80% at inception to below 20% by the Series C round. For several large Indian technology platforms, foreign ownership has already crossed 70%, Barthasarathy stresses.

“This is not a failure of ambition,” he says. “It is a financing gap at the most value-accretive stage.” He argues that the Union Budget presents an opportunity to formally recognise mass commercial deployment as a distinct financing phase and enable regulated, non-dilutive instruments such as revenue-linked or cash-flow-based financing.

Coordinated regulatory pathways across SEBI, RBI, and IFSCA could unlock long-term capital from global pension funds and sovereign wealth funds seeking predictable yield, while allowing Indian startups to retain IP and strategic control.

Without such frameworks, he warns, India risks creating innovation domestically but exporting economic value abroad.

Founders building hardware and manufacturing-led startups echo this concern. Shishir Gupta, co-founder and CEO of IoT-focused Oakter, says the Budget must decisively move India beyond assembly-led production. “For companies building products from concept to scale in India, the priority must be design-linked incentives, deeper component localisation, and easier access to working capital,” he says.

Gupta adds that refining and expanding production-linked incentive support for original design manufacturer (ODM)-led manufacturing, alongside targeted incentives for batteries, power electronics, IoT hardware, and semiconductor-linked supply chains, could significantly improve India’s global competitiveness. He also points to operational bottlenecks that erode margins, calling for stable GST structures, faster input tax credit refunds, and infrastructure support for automated factories.

Data, Markets, And the Role of the State

Beyond capital and infrastructure, founders increasingly see the state as a potential market-maker rather than just a regulator. Priyanka Aeron, co-founder of Thrive Global AI, says AI startups face friction in accessing large customers.

“Reduced compliance frameworks and stronger public-private collaboration will help startups gain access to markets faster,” she says, adding that responsible AI governance must evolve alongside innovation to build trust and global credibility.

Agritech founders also have similar concerns about last-mile execution. Anand Chandra, co-founder and executive director of Arya.ag, points to near-farm infrastructure such as micro-warehousing and scientific storage as interventions that have already improved farmer incomes and reduced distress sales.

He hopes the Budget will continue supporting decentralised infrastructure, inclusive finance, and stronger farmer-producer organisations, while recognising the role startups play in deploying drones, AI-based grading, and climate advisories at scale.

For AI startups like Shorthills AI, access to data remains a sensitive but critical issue. Co-founder Pawan Prabhat argues that India’s data protection norms, while necessary, limit even anonymised data access for legitimate innovation. He suggests that a structured, anonymised data-sharing framework, similar in spirit to public digital infrastructure initiatives, could be a game-changer for domestic AI development.

Prabhat also believes government departments could play a stronger role as early anchor customers. Simplified procurement mechanisms, pilot-friendly frameworks, and partnerships involving academic institutions could help startups demonstrate value without navigating prolonged tendering processes.

What sets Budget 2026 apart from earlier years is the sophistication of expectations for startups. Founders are calling for targeted interventions, R&D-linked tax incentives and affordable compute access.

The post Compute Credit, R&D, Capital: Inside Startups’ Budget 2026 Wishlist appeared first on Analytics India Magazine.

Cursor, an AI-powered coding tool, has revealed that over 20,000 engineers within SaaS giant Salesforce use its platform as a part of their daily software development workflow.

This accounts for more than 90% of the company’s engineers, resulting in a 30% increase in pull request (PR) velocity.

“I would say that it’s 0 to 1 in terms of how Cursor has transformed the way our developers use tools to improve the quality of the product,” said Shan Appajodu, SVP of engineering at Salesforce, in the blog post.

Earlier, Salesforce invested in its own internal AI tools and an open-source code-generation tool called ‘CodeGenie’. “But Salesforce wanted its engineers to have a range of options, so it made Cursor available,” Cursor stated. “Junior engineers were the first adopters. Many had started their careers during the pandemic, when remote work made standard ways of learning a codebase unavailable. Cursor helped them catch up.”

Appajodu added that these junior engineers didn’t have any “senior engineers sitting with them and explaining a lot of things”. According to them, Cursor took their spot instead, and helped them better understand existing code so they could contribute more effectively.

Furthermore, he stated that senior engineers initially used Cursor for tedious and repetitive tasks that were “inefficient to tackle manually”. Eventually, they expanded the use case quickly to higher-value tasks.

“Adoption followed the same pattern across teams: a small group would try Cursor, see the impact, and the rest would follow. Within a few months, Cursor went from a new tool at Salesforce to one that nearly every single engineer at the company was using,” Cursor added.

Last August, Salesforce revealed that a team within the company, which maintains the data infrastructure powering its sales AI agent, had integrated Cursor into its software development process.

This was aimed at tackling a company-wide 80% code coverage mandate and accelerating testing across a legacy codebase with less than 10% coverage spread across dozens of repositories

By using Cursor to analyse coverage gaps, generate unit tests, and iteratively improve test quality, the team reduced unit test development time from 26 engineer days per module to just four days, achieving an 85% productivity gain while scaling coverage across more than 70 repositories.

The post 90% of Salesforce’s Engineers Use Cursor Every Day appeared first on Analytics India Magazine.

The partnership will focus on exploring advanced satellite-based connectivity solutions to support digital inclusion, public infrastructure, coastal safety and emergency response systems, particularly in regions with limited terrestrial network access.

The Goa government’s information technology, electronics and communications department (DITE&C) has signed a memorandum of understanding (MoU) with Starlink Satellite Communications Private Limited to enhance digital connectivity, disaster resilience and smart governance across the state.

The collaboration is expected to improve connectivity for government schools, healthcare facilities and disaster management centres, while also strengthening Goa’s emergency preparedness and response capabilities.

Speaking at the event, CM Pramod Sawant said, “The Goa government is committed to harnessing technology to drive digital transformation and improve the lives of our citizens. This partnership with Starlink is a significant step towards achieving our vision of a digitally empowered Goa.”

Under the agreement, DITE&C and Starlink will collaborate on initiatives spanning satellite broadband connectivity, disaster resilience, smart governance, tourism infrastructure, and coastal development.

The partnership also includes plans to build local capacity through training programmes, explore affordable tariff structures for socially beneficial use cases, and support public infrastructure with reliable connectivity solutions.

Meanwhile, Maharashtra is set to become the first state in the country to roll out Elon Musk’s Starlink satellite internet service in March, expanding high-speed connectivity across the region.

The post Goa Signs MoU with Starlink to Boost Digital Connectivity, Disaster Resilience & Smart Governance appeared first on Analytics India Magazine.

ALCF Opens Call for AI-in-Science Collaboration Program, Deadline Feb. 27

Jan. 20, 2026 — The Argonne Leadership Computing Facility (ALCF) invites proposals for a new collaboration…

Nearly 60% of Indian businesses confident about scaling artificial intelligence responsibly already have mature Responsible AI (RAI) frameworks, but persistent gaps around high-quality data, regulatory clarity and emerging AI risks threaten to slow safe adoption, according to a Nasscom report released on Wednesday.

RAI frameworks guide the ethical, safe and accountable design, development and deployment of AI systems.

The State of Responsible AI in India 2025 survey flagged hallucinations as the most frequently experienced risk, cited by 56% of respondents, followed by privacy violations (36%), lack of explainability (35%), and unintended bias or discrimination (29%).
On implementation barriers, lack of high-quality data tops the list at 43%, while regulatory uncertainty (20%) and shortage of skilled personnel (15%) continue to weigh on organisations.
Regulatory ambiguity is a key concern for large enterprises and startups, whereas small and medium enterprises (SMEs) cite high implementation costs as their second-biggest challenge.

The report was released at Nasscom’s Responsible Intelligence Confluence in New Delhi. It is based on a survey conducted between October and November 2025 of 574 senior executives from large enterprises, SMEs, and startups involved in the commercial development and use of AI in India.

Despite these risks, the survey shows steady progress since 2023. About 30% of Indian businesses have established mature RAI practices, while 45% are actively implementing formal frameworks, indicating a shift from basic awareness to structured strategies and policies.
Nasscom noted a direct correlation between AI maturity and responsible practices, with stronger AI capabilities translating into more robust RAI frameworks.

“Nearly 60% of businesses confident in scaling AI responsibly have mature practices in place,” the report said.

Large enterprises lead RAI maturity at 46%, while SMEs and startups stand at 20% and 16%, respectively. Sector-wise, BFSI is the most mature at 35%, followed by technology, media and telecom at 31%, and healthcare at 18%, with nearly half of firms in these sectors actively strengthening their frameworks.

Workforce readiness is emerging as a priority, with nearly nine in 10 organisations investing in sensitisation and training.
Business leaders expressed the highest confidence in meeting data protection obligations, reflecting relatively mature privacy frameworks, although monitoring-related compliances remain a concern.

Accountability for Responsible AI remains largely top-down, with 48% of organisations placing responsibility with the C-suite or board.
However, 26% now assign it to departmental heads, and AI ethics boards are gaining traction. Among mature organisations, 65% have constituted AI ethics boards or committees, though some companies remain cautious about their effectiveness.

Sangeeta Gupta, senior VP and chief strategy officer at Nasscom, said in a statement that responsible AI has become foundational as AI gets embedded into critical decisions.

“The real measure of India’s AI leadership will not just be in the scale of adoption, but in how responsibly and inclusively these systems are designed and deployed,” she said.

She added that businesses must move beyond compliance-led approaches. “With the right investments in governance, talent, and transparent frameworks, India has the opportunity to set global benchmarks for trustworthy AI that serves society at large.”

The post 60% AI-ready Firms Mature on Responsible AI, Gaps Persist: Nasscom Report appeared first on Analytics India Magazine.

India’s top IT services companies are showing a visible disconnect between revenue movement and workforce expansion. December-quarter disclosures from Tata Consultancy Services (TCS), Infosys, HCLTech, Wipro, and Tech Mahindra indicate that changes in revenue are no longer accompanied by proportional shifts in employee count, utilisation or attrition.

Executives across the companies repeatedly pointed to productivity gains, margin stability, and capacity built ahead of demand as the primary drivers of performance, rather than workforce expansion.

What the Numbers Say

TCS reported constant-currency revenue growth of 0.8% quarter-on-quarter in Q3 FY26, even as its total workforce declined by over 11,000 employees sequentially. Despite the reduction in headcount and an attrition rate of 13.5% over the past year, the company maintained a healthy operating margin of 25.2% and generated cash flows equivalent to 130% of net profit. Management said these outcomes were driven by productivity gains and operational efficiencies rather than workforce expansion.

Infosys followed a similar trajectory, posting sequential constant-currency revenue growth of 0.6% in the December quarter. Employee numbers increased marginally to about 3.37 lakh, but utilisation excluding trainees fell by 100 basis points to 84.1% as the company continued to onboard freshers ahead of demand, framing the dip in utilisation as a strategic investment in future capacity.

HCLTech delivered the strongest revenue performance among peers, with constant-currency growth of 4.2% quarter-on-quarter. Total headcount stood at 2.26 lakh, largely unchanged sequentially, with a small net decline even as nearly 2,900 freshers were added. EBIT margin for the quarter—denoting core operational profitability—was 18.6%, excluding the one-time impact of the new labour code.

Wipro reported sequential constant-currency revenue growth of 1.4% in the December quarter, though year-on-year growth remained negative. Headcount rose to about 2.42 lakh, but utilisation excluding trainees declined to 83.1% from 86.4% in the previous quarter.

Tech Mahindra posted sequential constant-currency revenue growth of 1.7%, with total headcount at 1.50 lakh, down nearly 900 employees year-on-year. Operating margin, however, expanded to 13.1%, marking the ninth consecutive quarter of margin improvement, supported by higher productivity and improved delivery efficiency.

During the earnings call, Tech Mahindra’s chief executive and managing director, Mohit Joshi, said the company was using efficiency gains in fixed-price programmes to drive growth rather than replacing attrition.
“As we become a lot more efficient in our fixed-price programmes, we free up capacity for us,” he said. Joshi added that revenue per employee had continued to increase even as headcount had “more or less stayed stable” over the past few years, with higher productivity expectations across development and support roles.

Decoupling of Revenue Growth

Across companies, the quarterly results indicate that revenue performance is no longer aligned with workforce movement. This is a departure from earlier growth cycles, where hiring closely tracked revenue expansion.
According to Biswajeet Mahapatra, principal analyst at Forrester, the emerging decoupling of revenue growth and headcount reflects both structural and cyclical forces at play in Indian IT.

He tells AIM that the improvement in productivity and revenue per employee is “partly structural due to automation, large-scale AI adoption and tighter pyramid management,” but also “partly cyclical because muted deal ramp-ups and selective hiring temporarily boost per-employee metrics.”
The durability of this shift, he adds, will depend on how quickly discretionary spending and new transformation programmes recover.
However, Madhu Bandarapu, chief delivery officer at Hyniva, an IT services and consulting firm, asserts that a structural shift is now underway across the services industry, with clients increasingly being promised efficiency gains of 20–30% on contracts, driven by the adoption of enterprise AI tools.
“AI is not being positioned as a fully autonomous replacement, but clearly as a productivity enhancer,” he observes.

Developers are using tools such as coding copilots to improve output, while testing, documentation, and product support functions are also seeing automation gains, Bandarapu says. As a result, IT services firms are under pressure to deliver significant efficiency improvements using their existing workforce.
“The push over this year and next will be to extract 20–30% productivity gains from current teams and contracts,” he shares, adding that this is becoming especially critical during contract renewals.

“Companies that can blend AI-based solutions into their delivery models and demonstrate these efficiency gains will have a clear advantage.”
While this could moderate traditional hiring at mid- to senior-level roles, Bandarapu expects hiring at lower bands to continue. “Firms still need talent that can work alongside these tools, allowing productivity gains to be achieved at a lower cost.”

However, he notes that the trends point to a broader shift away from headcount-linked growth models. “We are clearly moving towards outcome-based, transaction-based and gain-share models. This trend is set to continue and will become more pronounced over time.”

The post Indian IT is Growing Without Expanding Workforce, And Why It May Be Irreversible appeared first on Analytics India Magazine.