Год: 2025

When Satya Nadella declared that “SaaS is dead,” it sounded like provocation. But in the unfolding age of agentic automation, the idea might not be that far-fetched. Software, as enterprises have known it for two decades, is morphing into something fundamentally different. Something that learns, acts, and reasons in ways that break the boundaries of traditional apps.

In this transformation, it is not just technology that is changing, people too, are undergoing a shift. As automation systems evolve into intelligent agents, the nature of IT work, the definition of skills, and even the way humans communicate with machines are all being rewritten.

The future of software will belong to those who can think in systems, not syntax, said Sanjay Koppikar, chief product officer at EvoluteIQ, in an exclusive conversation with AIM. Koppikar has watched automation evolve from rule-based scripts to autonomous digital agents.

When Software Becomes Service

For decades, enterprise software was defined by silos. One app managed HR, another handled finance, and a third took care of customers. The best any company could do was integrate them, a patchwork of applications trying to behave as one.

But that construct is now under question. “When the transformation is happening with an agentic approach, the construct of one app trying to serve you for one particular construct actually goes out of the window,” Koppikar said.

He believes that was the real meaning behind Satya Nadella’s statement. “It’s not that something like Zoho or Salesforce or some of these SaaS products are going to just die off.”

“The idea was that the software, as we saw it traditionally… everything became app-oriented.”

He explained that the idea is fading fast. An agent can comprehend natural language requests and independently execute multi-step tasks.

For example, to provide data on the top 10 customers and their pending invoices, an agent can automatically access and retrieve necessary information from systems like Salesforce (for customer data) and ERP (for outstanding invoices), and present the findings.

This capability eliminates the traditional, time-consuming software development cycle, where a software engineer is needed to understand requirements, create a detailed requirement document, receive approval, and then spend months on development before delivering a final dashboard. This entire manual process is now obsolete.

For him, this isn’t just about faster software; it’s about architecture replacing applications. Agentic systems, he said, are moving enterprises “from software as a service to service as software.”

In a previous AIM report, it seems evident that AI is transforming SaaS into “Service-as-a-Software,” where AI agents deliver outcomes instead of tools. Indian IT firms like Infosys and TCS are leading this shift, building AI agents that automate client operations. As AI replaces human roles, SaaS evolves into agentic systems powering a trillion-dollar services economy.

The New Language of Machines

The invisible software layer also has implications for those who build and maintain it. For decades, programming languages such as Java, .NET, or Python have acted as the intermediary between humans and machines.

“What is Java, .NET, C-Sharp, Python, all of these?” Koppikar answered next. “Computer languages. Now, are they actually computer languages? See, computers only understand zeros and ones.”

“They were the intermediary languages invented by humans so that they could communicate better.”

The change, he argued, is that natural language is becoming the interface. “If English becomes that language, do you need these intermediaries? No. Now what you need is a better communication skill.”

He highlighted the rise of “prompt engineers,” saying that the creation of jargon appears to be an attempt by some trainers to significantly overcharge for their services.

But the core idea, he said, is that people who can explain what they want clearly will get better results.

Still, he made it clear that fundamentals cannot be ignored. Relying solely on surface-level skills, like good English, is not enough to excel in software development.

While some progress is possible, achieving significant milestones still requires the expertise and deep understanding of those who specialise in the subject matter. Ignorance, in this context, serves as a barrier to genuine progress.

Jobs Won’t Disappear, They’ll Multiply in Meaning

Every era of automation has been shadowed by fears of job loss. The agentic era is no exception. But Koppikar believes history tells a different story. “Since the automation journey started, since Charles Babbage came out with the first computing machine, automation means that I need speed and I need less dependency on humans so that things can be faster,” he said.

He recalled Jensen Huang’s famous remark when asked about AI’s impact on jobs: “People are not going to lose jobs. People with AI are going to take jobs away from people without the knowledge of AI.”

That, Koppikar said, sums up the transition well, as “the nature of work starts getting transformed.”

“So the same five people you employed to manage, say, network and all of those things possibly, who were able to cater to only two customers earlier, now will be able to cater to 10 customers.”

He added that the AI wave is similar to the advent of mobile phones that led to job displacement for those involved with pagers.

A Quiet Revolution

SaaS to agentic architecture isn’t just a technological shift, but a turn for humans too. As software fades into the background, people come to the forefront — not as coders or operators, but as designers of intent.

The future of automation, as Koppikar sees it, isn’t about building machines that replace humans. It’s about building systems that make humans more consequential.

With a storyteller’s flourish, he concluded: “Stories come in all forms. Technology stories become products. Non-technology stories become novels that can be printed and spoken about.”

The post The SaaS and Human Shift in the Age of Agentic Automation appeared first on Analytics India Magazine.

China is breathing down OpenAI’s neck. Alibaba-backed Moonshot AI has launched a new model, Kimi K2 Thinking, which outperforms OpenAI’s GPT-5 and Anthropic’s Claude Sonnet 4.5 on several key benchmarks.

Moonshot said the model’s architecture activates 32 billion parameters per inference out of a total of one trillion, and supports context windows of up to 2,56,000 tokens. It can also perform 200 to 300 sequential tool calls without human input.

“The AI frontier is open-source!” wrote Hugging Face CEO Clement Delangue after the launch of Kimi K2 Thinking.

At the same time, NVIDIA CEO Jensen Huang warned that China could soon lead the global AI race. He said that China’s lower energy costs, lighter regulations, and government subsidies make building and running AI infrastructure far cheaper than in the West.

However, he later softened his stance, clarifying on social media that “China is nanoseconds behind America in AI,” adding that it is vital for the US to maintain and extend its lead globally by attracting more developers to American technology platforms.

As researcher Yuchen Jin aptly put it, “If you ever wonder how Chinese frontier models like Kimi, DeepSeek, and Qwen are trained on far fewer (and nerfed) Nvidia GPUs than US models — remember that NASA landed people on the moon in 1969 with just 4KB of RAM.”

It’s a reminder, Jin says, that creativity often thrives under constraints.

The estimated cost of developing the Kimi K2 Thinking AI model was around $4.6 million, according to reports from outlets such as CNBC. That price tag is strikingly modest when compared with the billions of dollars usually invested in training top-tier AI systems in the United States.

Side effect of blocking Chinese firms from buying the best NVIDIA cards: top models are now explicitly being trained to work well on older/cheaper GPUs.
The new SoTA model from @Kimi_Moonshot uses plain old BF16 ops (after dequant from INT4); no need for expensive FP4 support. https://t.co/KI1quF6iiQ pic.twitter.com/910XgbK3rR

— Jeremy Howard (@jeremyphoward) November 9, 2025

That efficiency is now translating into global influence. Alibaba Group’s Qwen AI models are finding eager adopters among major Western firms like Airbnb — a sign of the growing international appeal of China’s open-source AI ecosystem.

According to a Bloomberg report, Airbnb co-founder and CEO Brian Chesky said the company relies heavily on Alibaba’s Qwen models to power its AI-driven customer support agent.

Chesky, a longtime friend of OpenAI’s Sam Altman, added that ChatGPT’s integration tools weren’t “quite ready” for Airbnb’s needs, while Qwen proved “very good” as well as “fast and cheap.”

NVIDIA’s Struggles in China

Most recently, the US government has moved to bar NVIDIA from selling its newest scaled-down AI processor, the B30A, to Chinese customers. According to reports, the White House has notified federal agencies that exporting this chip to China violates existing trade rules, as Washington continues to tighten its technology restrictions.

Meanwhile, China has introduced new subsidies that halve energy bills for primary data centres using domestic chips, aiming to strengthen its semiconductor industry and reduce reliance on US technology, the Financial Times reported, citing sources.

Local governments in provinces such as Gansu, Guizhou and Inner Mongolia are offering discounts of up to 50% on electricity for facilities that adopt chips made by Chinese firms, including Huawei and Cambricon Technologies.

Also, the top Chinese tech firms, including Alibaba and ByteDance, are ramping up spending on AI and cloud infrastructure. Alibaba has pledged more than $50 billion over the next three years to boost its cloud and AI capabilities, while ByteDance plans to invest about $20 billion in GPUs and data centres to power AI development.

Just the Beginning

Deedy Das of Menlo Ventures told AIM that China’s Kimi K2 Thinking model shows that open-source systems can now rival, and in some cases even outperform, top-tier proprietary models. “They’re definitely bringing the heat to American labs that rely on far more resources — better chips, larger compute budgets, and higher R&D costs,” he noted.

Das added that while this doesn’t mean revenue will immediately shift toward Chinese models, given that US labs have strong user bases and many enterprises are reluctant to adopt Chinese systems, their lower pricing and open approach will help them steadily gain market share.

But not everyone agrees that openness alone gives China an edge. Anthropic CEO Dario Amodei said the idea of open source in AI is often misunderstood. “I don’t think open source works the same way in AI that it has worked in other areas,” he said, explaining that while traditional open source allows anyone to inspect and build on source code, AI models only make their weights public, not their inner workings.

Amodei called the open-source debate a “red herring,” arguing that what really matters is model quality, not openness. “When I see a new model come out, I don’t care whether it’s open source or not. I ask, is it good? Is it better than us at the things that matter?”

OpenAI is Self Sufficient

While the Chinese government continues to support its tech companies, OpenAI CEO Sam Altman has clarified that the company is not seeking any government guarantees for its data centres. His statement comes after recent comments from OpenAI’s chief financial officer, Sarah Friar, sparked speculation about possible state backing.

“We do not have or want government guarantees for OpenAI data centres,” Altman said. “Governments should not pick winners or losers, and taxpayers should not bail out companies that make bad business decisions.”

Altman also revealed that the company expects to surpass an annualised revenue run rate of $20 billion by the end of 2025 and is planning infrastructure commitments of around $1.4 trillion over the next eight years, as it scales up computing power to meet growing demand for AI systems.

Responding to concerns about whether OpenAI could become “too big to fail”, Altman said, “If we screw up and can’t fix it, we should fail, and other companies will continue on doing good work. That’s how capitalism works.”

Meanwhile, David Sacks, in a post on X, said, “There will be no federal bailout for AI. The US has at least five major frontier model companies. If one fails, others will take its place.”

Notably, Microsoft is now also forming its own AI team called the MAI Superintelligence Team, led by Mustafa Suleyman, the head of Microsoft AI and co-founder of DeepMind.

Sacks said the focus should be on making power generation and permitting easier to enable faster infrastructure growth, without raising electricity costs for consumers. He also clarified that the discussion around government support for AI firms had been misunderstood. “I don’t think anyone was actually asking for a bailout — that would be ridiculous,” he wrote on X.

The AI race won’t end with a single winner, but it will showcase who’s been running the smarter marathon. OpenAI may lead in compute, yet China’s mastery of constraint suggests the next frontier of intelligence might not be born in abundance, but in adversity.

The post Capitalism Built OpenAI, Constraints Helped China Defeat It appeared first on Analytics India Magazine.

Not long ago, the idea of data centres orbiting Earth sounded like pure science fiction. But in 2025, that fantasy has started to turn real. As AI systems demand more power and Earth struggles with energy and cooling limits, a new wave of companies is looking to space for answers.

From giant computing stations circling the planet to solar-powered systems that run nonstop and even backup servers on the Moon, the race to build data centres in space is taking off fast.

Here’s a look at the top companies building data centres in space.

Starcloud

Starcloud, formerly known as Lumen Orbit, is a startup headquartered in Redmond, Washington, that is building the next generation of data centres in orbit. The company is preparing to launch the first-ever AI-equipped data centre into orbit this November. The mission will mark the NVIDIA H100 GPU’s debut in space—and, possibly, the start of a new era where AI literally runs above our heads.

Their vision is to use the advantages of space, such as constant sunlight, natural cooling and the absence of Earth-based permits, to build large computing systems for AI and other heavy data tasks.

Axiom Space

Axiom Space, a Houston-based commercial space infrastructure company, is also venturing into the orbital data centre market. Their Orbital Data Centre programme will deploy data-processing nodes in low Earth orbit, offering services not only to terrestrial customers but also to space-based users.

Axiom has partnered with companies like Kepler Communications and Skyloom Global to provide optical inter-satellite links that allow high data-rate communications to and from the orbital data centre. Moreover, one pilot project, the Axiom Data Center Unit One (AxDCU-1), runs on the Red Hat Device Edge stack and is being sent to the International Space Station to demonstrate in-orbit computing and data storage.

Google Project Suncatcher

Google has announced a research initiative known as Project Suncatcher, which explores placing AI data-centres in space to harness the near-constant solar energy available in low Earth orbit or sun-synchronous orbit.

The plan involves satellites equipped with custom tensor processing units, linked via high-throughput free-space optical communications, forming a distributed compute cluster above Earth.

Google says solar panels in orbit can generate far more power than those on the ground, while also reducing cooling needs and environmental impact. Although still in the research stage, with prototype launches planned around 2027, Project Suncatcher reflects Google’s growing belief that the future of large-scale, sustainable AI computing lies beyond Earth.

Lonestar Data Holdings

Lonestar Data Holdings is a Florida-based startup with a bold ambition to deploy data-centre infrastructure on the lunar surface and in cislunar orbit. Their Freedom data centre payload focuses on disaster recovery, long-term data storage and secure national backups, instead of ultra-fast computing.

The idea comes from the fact that data centres on Earth face risks like natural disasters, political instability and limited resources. By building infrastructure on the Moon, Lonestar hopes to offer a safer and more reliable place to store critical data.

Sophia Space

Sophia Space is a relatively new startup, based in Seattle, focused on orbital compute modules and edge data centres in space. Their core product is a modular TILE architecture designed to process data in orbit—for satellites, defence and commercial users—and then relay indigenised results to Earth.

The startup raised $3.5 million pre-seed round in 2025 to develop its space-native compute platform, sourcing solar power, passive thermal management and radiation-hardened hardware to deliver compute closer to the data source.

Madari Space

Madari Space is a UAE-based venture that has announced a pilot programme to deploy data centres in orbit by 2026, targeting governments, enterprises and space operators in the Middle East region. Their announcements emphasise the global interest in off-Earth compute infrastructure, beyond the traditional US/Europe start-ups.

Madari aims to serve national security, communications and enterprise clients with orbital compute or storage modules leveraging solar power and space cooling.

SpaceX

SpaceX, while primarily known as a launch and satellite/space-transport company, is a key enabler for the entire ecosystem of data centres in space. Its launch vehicles—Falcon 9 and Starship—reduce access cost to orbit and enable deployment of compute or hardware modules in space, which many of the companies above rely upon.

Elon Musk said the company could transform its Starlink satellites into fully fledged data centres in orbit, bringing computing power closer to the stars. He revealed that SpaceX’s next-generation Starlink V3 satellites could be scaled up to form the backbone of orbital computing systems.

Built for gigabit internet speeds, they use laser-based inter-satellite links to transfer data directly across orbit, with no need for ground relays. Each satellite is larger, smarter and more powerful, creating a mesh of high-performance nodes circling the planet.

They’ll hitch a ride aboard Starship, SpaceX’s still-in-development super-heavy rocket, which is expected to deploy dozens of V3 satellites in a single launch. If timelines hold, the first batch could be in orbit by early 2026.

TakeMe2Space

Hyderabad-based TakeMe2Space is building what it calls the world’s first open low Earth orbit satellite infrastructure to make space computing accessible to everyone, not just deep-pocketed institutions.

The company’s Indian-built satellites act as miniature computers in orbit, offering an open, AI platform for developers and researchers to run real-time applications directly in space. By using proprietary radiation shielding, TakeMe2Space can integrate standard terrestrial hardware into satellites, significantly lowering costs.

The post 8 Futuristic Companies Building Data Centres in Space appeared first on Analytics India Magazine.

At 16, while most teenagers were cramming for board exams, Naman Pushp was sanding carbon-fibre drone parts on his study table.

Driven by his dream to turn drone delivery into an affordable means for critical supplies, Pushp, at 20, has scaled his high-school project into Airbound, a drone-based delivery startup that has flown over 10,000 km, and raised $8.65 million. He is now focused on building drones so efficient that deliveries could one day cost just ₹1.

Drone delivery, especially in India, can feel like a far-off innovation, with the image of a sleek aircraft rising quietly over rooftops, bypassing traffic. This could extend to the most sensitive domains and deliver with more precision than any rider on a bike.

It was this driving thought that led the teenager Pushp to learn fluid mechanics through YouTube videos and MIT OpenCourseWare. And true to his ambition, Airbound has begun its flight by transporting medical supplies, before scaling up to everyday groceries and city logistics.

Going ahead, Pushp wants to make deliveries so cheap that moving blood samples between hospitals, sending medicines to remote villages, or even transporting organs could cost just ₹1.

Their drones can carry payloads of one kilogram, with the capacity set to expand to three kilograms soon. The company’s long-term aim is to redefine how goods move at scale.

“We genuinely want to create a world where roads are optional. As of today, we can offer deliveries at a lower price point than any other method that exists. We are globally the lowest cost delivery provider on a per-kilometre basis.” Pushp told AIM.

The Prime Time

By the 11th standard, Pushp had already built his first working prototypes. When he applied to gradCapital for funding, the firm didn’t have a category for teenagers designing unconventional aircraft.

gradCapital’s co-founder Abhishek Sethi still remembers the day they met the 16-year-old. “We had to call his parents before giving him the grant,” he laughed. “He’d be sanding drone parts during our interview. He went to school eight hours a day and worked another eight on his drone. That kind of obsession is rare.”

The firm gave him $25,000, which Pushp used to rent a small flat in Mumbai and build a working demo. This wasn’t a hobby. The work demonstrated technical depth, “like a PhD student,” as Sethi described during AIM’s recent visit to Airbound’s manufacturing facility.

The work was also dangerous.

“He almost burned down his flat, and one day, he fell from a roof while doing an experiment. I’m worried for his life. But he’s not joking around. He’s a serious person who has a serious project.”

gradCapital backed him before he had a company, before he finished school, and before he could legally sign the documents to register either. A few months later, that prototype caught the attention of Lightspeed India, who decided to invest after seeing the aircraft in action.

Pushp sees the timeline differently.

“This was more of a project than a company.” He said he has been working on his brand for the past five years, but the company has only really existed since 2023.

The early challenge was not convincing investors, but convincing engineers older than him to trust his intuition.

“A lot of the early days were me having to aggressively prove myself with the team by just staying at the office till like 2 a.m., building the carbon fibre part that nobody thought was possible.”

But as the aircraft designs evolved, so did the team’s respect. As Pushp said, “Engineers respect good engineers.” Airbound now has a team of 50 with drone production scaled to one drone a day and an improvement of 35x in reliability. “And this was only in the past three months. We’re so much more than a project now.”

“Each week he was learning so fast that it became clear he’d outpace most pedigreed teams,” said Sethi. “The mistake most investors make is waiting for people to go full-time before funding them. We took the opposite bet, and it paid off.”

The Carbon Cost

Most drones today either hover like quadcopters, using a lot of power to stay aloft or require runways like fixed-wing aircraft. Pushp wanted flight efficiency sans infrastructure. The result was a blended-wing-body tailsitter, a drone that can take off vertically, transition into efficient forward flight, and land anywhere.

The solution to his undertaking lay in rethinking the material from the ground up. “I never learned how to manufacture with metals.” Carbon fibre for him and his team wasn’t a replacement material; it was the starting point.

Airbound claims its carbon-fibre process is six times lighter and 2.2 times stiffer than anything comparable, a material breakthrough that directly reduces energy use. “We built from physics, not software,” Pushp said. “Every curve, every gram has a reason and we get to think about unique things that you could never, ever imagine with metals.”

Airbound is now able to manufacture many of its own components, which also shifts cost structures. Each drone now costs a fraction of what traditional manufacturing would, with prototype propellers alone coming in at five times cheaper than off-the-shelf parts, as per Airbound’s in-house carbon-fibre process. This efficiency makes every aircraft both lighter and dramatically less expensive to produce.

Pushp argues that the economics of last-mile delivery have remained largely unchanged for decades. Roads, fuel, vehicles, and delivery labour create cost floors that cannot fall much further. But air doesn’t have the same structural limits, if the aircraft is optimised.

How Automated Is It?

Drones move faster than ambulances. They bypass traffic. They are cheaper to operate continuously. This makes them suited to transporting blood samples, vaccines, or critical medicines between hospitals, something that Airbound is already piloting.

However, Pushp does not want organ delivery to be treated as an expensive, specialised service.

“I want to create a service that is so reliable that organ delivery also happens for ₹1.”

Airbound’s flights today are fully autonomous, with pilots only stepping in if something goes wrong. Their goal is not to remove humans, but rather to reduce the frequency of intervention to scale operations.

Airbound’s RUDRA control system now allows a single operator to oversee more than 100 autonomous flights (intervene for 1 in 1000 flights) at once. This automation, paired with the aircraft’s efficiency, brings down costs dramatically, from around ₹90 per km to ₹1 per km.

“It physically hurts to know what optimal looks like and go for something suboptimal,” Pushp added.

Scaling Comes with Demand

India’s major cities are dense, regulated airspaces. Pushp intends to start where drones can fly, not where they can’t. The long-term vision isn’t just to make deliveries cheaper, it’s to make location irrelevant.

“If we can give tier-2 and tier-3 cities access to the best possible logistics, the metros will follow. Everyone in Bangalore, Mumbai, Delhi is going to be banging at the government’s door saying, ‘we need this’.” he said.

The startup has now launched a pilot programme with Narayana Health to run 10 medical deliveries a day, transporting vaccines, samples, and emergency supplies across hospital networks.

Airbound’s $8.65 million in seed funding was led by Lachy Groom, Physical Intelligence, Humba Ventures, and Lightspeed, along with team members from Tesla, Anduril Industries, Ather Energy, and others.

“We’ve built the best drone in the world. Millions of deliveries is a great point where you can prove that this works,” Pushp said, adding that Airbound’s drone doesn’t even look like one, it’s more a plane that takes off and lands like a drone.

If they reach that stage, Pushp believes drones will no longer be a futuristic idea, but basic infrastructure, as invisible and inevitable as the internet.

The post This 20-Year-Old is Building Drones to Bring Delivery Costs Down to Just ₹1 appeared first on Analytics India Magazine.

For a senior software professional in Pune, a nightmare began three years after he left a company he had worked with diligently for over two decades. A new role at a leading multinational was within reach, until his background verification (BGV) report returned flagged as “Amber.”

The reason: a “pending full-and-final settlement” remark. He had since worked for three years elsewhere, but the same note resurfaced, this time through a client-side verification that reopened an inquiry long settled in practice.

His new job hung in the balance, his reputation questioned by an automated flag and an unresponsive HR chain.

It took weeks of mediation, repeated follow-ups, and an official intervention by the Forum for IT Employees (FITE) before the issue was resolved.

The full and final dues were cleared, the client closed its inquiry, and the BGV finally turned “green”. What stayed with him though was not relief, but disbelief.

“Imagine losing an opportunity because of a three-year-old remark,” says Pawanjit Mane, president of FITE’s Maharashtra chapter. “And all because there are no clear, fair, or time-bound rules for background checks in India’s IT sector.”

A System Meant for Integrity

In India’s multi-billion-dollar technology industry, where compliance and reputation are sacrosanct, background verification was once a simple administrative process. Employers confirmed service tenure through relieving and experience letters, sometimes cross-checking provident fund (PF) records.

Over time, as moonlighting scandals and fake experience certificates drew headlines, the system became stricter, and far more intrusive.

Today, verification agencies routinely reach out to former managers for feedback on work ethics, project performance, and even interpersonal behaviour.

Smaller and mid-sized firms, Mane says, sometimes use the opportunity to settle scores. “We’ve seen cases where employees are punished for leaving at short notice or for raising workplace complaints,” he says.

In one instance, six women from a Pune-based IT company were denied their relieving letters after they accused a senior executive of sexual harassment.

“The employer refused to give documents unless they apologised and withdrew their complaints,” recalls Mane.

“We had to go to the Labour Commissioner’s office multiple times over three months. Only then did they get what was rightfully theirs.”

Such incidents are not isolated. FITE receives a steady flow of complaints every month, around 15 on average, from professionals whose job offers were revoked, projects withdrawn, or contracts frozen because of ambiguous or unresolved BGV remarks.

Many of these cases arise from simple clerical mismatches in PF dates or from employers who have since shut down and cannot be reached.

How the Problem Began

The roots of the current BGV tangle trace back to a small section of employees who, during India’s IT hiring boom, provided fake experience credentials to secure better roles.

Despite being illegal, fake certificate services still persist online, exploiting job competition, verification gaps, and India’s fragmented employment records.

While seemingly low-risk, using such documents is a criminal offence that can lead to termination, blacklisting, and prosecution.

In response, major IT firms tightened their checks, introducing PF and UAN cross-verifications, digital records, and multi-source background reviews.

Fraud became easier to detect, but the crackdown also brought a new wave of collateral damage for genuine employees caught in procedural or administrative lapses.

A Process With Loopholes

Harpreet Singh Saluja, president of the National Information Technology Employees Senate (NITES), confirms that BGV-related grievances are now a regular occurrence.

“We keep getting complaints from employees whose verifications get delayed or fail because of miscommunication or arbitrary remarks,” he says.

A former employee of an Indian IT giant, who requested anonymity, described how flawed some third-party checks can be.

“Some vendors don’t even visit the candidate’s address. They just make a phone call, tick boxes, and submit a report,” the person told AIM.

Online forums echo these frustrations. In one widely shared Reddit post last month, a professional recounted being terminated barely a month after joining a product firm over an alleged BGV failure, despite having submitted valid documents. Their profile was deleted immediately, they were pressured to sign confidentiality papers, and their provident fund account wasn’t linked. “It was traumatic,” the user wrote.

Another user recently said their background check at a global IT services firm failed because they couldn’t provide PF details or Form 16, as their salary had been below the TDS threshold.

Even after producing bank statements, the verification team refused to accept them. Commenters explained that PF or UAN data has become the default verification source, and lacking it can trigger an automatic failure, an indication of how opaque and rigid the process has become.

When Red and Amber Decide Futures

In BGV parlance, a “green” means everything checks out. “Amber” indicates a pending query, while “red” signals non-clearance, a verdict that can end a career overnight.

“Employees leave secure jobs after getting an offer, only to be told that their BGV has failed,” says Mane, who is himself an IT employee.

“The previous company won’t take them back; the new one won’t hire them. They’re left jobless overnight, all because the verification process lacks transparency and regulation.”

He believes background checks should be limited to factual elements, service tenure, PF records, police verification, and relieving letters.

“Everything else, like assessing performance or behaviour, should be done during the interview,” he says.

“A background check cannot become a backdoor performance appraisal.”

More Awareness

From an industry standpoint, background checks have always been part of IT hiring. Neeti Sharma, CEO of TeamLease Digital, says the process has become more visible, but not necessarily more punitive.

“There hasn’t been a major rise in disputes,” she notes. “What’s changed is awareness, both employers and employees now realise how critical BGVs are for job security and future opportunities.”

She acknowledges, however, that reputational risk is real.

“A negative or incomplete BGV can follow someone across jobs. Transparency is key, employees must be informed of any negative findings and given a fair chance to respond,” Sharma says.

She believes the solution lies in standardisation, not centralisation.

“A shared, digital industry platform managed by verified BGV partners could make checks faster and fairer,” she adds.

“It would ensure data privacy while giving employees the right to clarify or correct information.”

The Need for Clear Rules

Legally, India’s Digital Personal Data Protection (DPDP) Act, 2023 sets a strong framework for handling personal data during background checks, emphasising privacy, consent, and data security.

Employers must obtain informed consent, collect only relevant information, and protect it with adequate safeguards.

However, laws stop short of defining a universal verification process or timelines, leaving companies wide discretion in how they implement checks.

That gap, experts say, is where most of the current problems arise.

FITE has urged the Ministries of Labour and IT to introduce clear, uniform standards, restricting BGVs to factual employment records, and requiring them to be completed before an employee resigns from their current job.

Mane insists this is not a call for more regulation but for consistency. “We’re not asking for money or compensation,” he says. “Just fair, time-bound rules. If companies already follow good HR practices, they should have no objection to formalising them.”

The post When a Background Check Becomes a Career Roadblock appeared first on Analytics India Magazine.