Год: 2025

This year, AI has rapidly moved from tech labs into police stations, courtrooms and crowded streets across India. Several states embraced AI to address long-standing problems, including slow investigations, missing offenders, procedural lapses and overstretched personnel.

From facial-recognition systems that claim to solve robberies in hours, to biometric databases mapping criminal histories, to chatbots explaining legal procedures, these tools promise efficiency and precision. But every deployment comes with trade-offs. Critics warn of mass surveillance, misidentification, uninformed decision-making and weak safeguards.

Here’s a list of six instances in 2025 in which Indian states leveraged AI in everyday policing.

1. Delhi

Delhi Police updated their advanced facial recognition system (FRS) this year to address robbery and burglary cases. By late February, reports indicated its effective use, with notable successes including the solving of an ₹80-lakh robbery in Chandni Chowk within 24 hours, attributed to Israeli/Corsight technology. The system analyses CCTV footage and links it to existing databases, significantly expediting investigations and resulting in arrests and the recovery of stolen goods.

However, criticisms emerged regarding privacy concerns, wrongful arrests and the potential for bias, highlighted by a Pulitzer Center report from July.

2. Maharashtra

Maharashtra CID’s AI-based biometric data collection unit in Pune is part of the state’s larger Maharashtra Research and Vigilance for Enhanced Law Enforcement (MARVEL) initiative, which aims to modernise policing through analytics and digital evidence. Launched at Pune Rural Police headquarters, the unit captures multi-angle facial photographs, fingerprints and iris scans of accused individuals. The AI system standardises data and links it to the Crime and Criminal Tracking Network and Systems (CCTNS), enabling the police to track suspects across districts and recognise repeat offenders even if they alter their appearance.

MARVEL’s integration promise is ambitious, but its success depends on accuracy, governance, and transparency, not just technology. As of now, the system’s benefits are mostly projected, while debates over privacy and oversight remain unresolved.

3. West Bengal

West Bengal Police’s AI legal-assistant bot, introduced in December, is designed to reduce procedural errors in investigations and case files. Rolled out to roughly 400 investigators across eight police units, the tool was developed jointly by Birbhum Police and a Pune-based firm. It contains over 50,000 pages of legal material, including case law, NHRC and MHA guidelines, training manuals and victim-support procedures. Investigators enter a brief case description, and the bot recommends relevant IPC/BNS sections, correct procedures and documentation requirements.

The project’s main aim is to cut “legal slips” that can weaken charges or lead to acquittals, offering timely legal references to younger or overstretched officers. However, senior officials caution against over-reliance: AI cannot replace field judgement. Critics also highlight opacity, potential bias in the training data and unclear accountability if wrongful actions result from AI suggestions.

4. Uttar Pradesh

Uttar Pradesh Police and IIT-Kanpur’s RAG-based chatbot, unveiled in mid-2025, aims to make police procedures clearer for both officers and citizens. It indexes more than 1,000 Hindi police circulars, guidelines, and departmental instructions. Users can type everyday questions, such as how to report a vehicle theft, steps in passport verification or election-duty protocols, and the chatbot provides procedural answers in plain language.

For police personnel, this reduces dependence on senior officers or manual document searches, helping avoid procedural delays and misinformation at stations. For citizens, it counters the typical “come tomorrow” response by showing official rules directly. However, outdated or misinterpreted answers could mislead complainants, and query logs may expose sensitive personal information if data retention and privacy safeguards are not clearly defined.

5. Odisha

Odisha Police’s AI-enabled Integrated Command and Control Centre (CCC) in Puri was heavily deployed for the 2025 Rath Yatra to assist with crowd management using AI-processed CCTV feeds, drone footage and density analytics. The system was supposed to detect choke points, alert officials in real time and coordinate warnings via LED panels. However, during the June 29 stampede, which killed three people and injured several others, the technology failed to deliver actionable alerts.

Investigations found that only 123 of 275 cameras were functional, feeds were inconsistent and drones were under-utilised. Authorities recommended blacklisting the vendor and disciplinary action was initiated against seven senior officers for negligence.

6. Telangana

Hyderabad police expanded AI use in November across surveillance, crime analysis and cyber investigations. The city leverages AI-enhanced CCTV systems, facial recognition tools and video analytics to identify suspects, missing persons and risky behaviours in crowded spaces. Thousands of cameras feed data into command centres, where algorithms flag anomalies and help trace movement patterns.
Alongside this, police publicly discussed AI-driven tools for cybercrime, including blockchain analysis and social media monitoring to detect fraud networks and online threats. These systems could provide faster case turnaround and improved tracking in dense urban areas. As deployment scales, the city risks trading public safety for privacy, transparency, algorithmic bias and democratic accountability.

Country-Wide

India proposed the use of AI facial recognition at major railway stations, which emerged in mid-2025, when the Union government informed the Supreme Court that it planned to deploy FRS at seven high-traffic stations to monitor convicted and repeat sex offenders. The system would scan live CCTV feeds, compare faces against law-enforcement databases and alert authorities if a match appears.

However, the plan effectively creates mass surveillance zones that scan millions of passengers without their consent. It is worth noting that railway CCTV often has poor angles, lighting and image quality, which increases the risk of misidentification. It can also be argued that India lacks dedicated legislation governing facial recognition, including use, retention and appeal rights, raising questions about proportionality, privacy and accountability before deployment.

The post 6 Indian States Integrating AI Into Everyday Policing appeared first on Analytics India Magazine.

Despite AI transforming high-volume tech hiring, India’s senior IT roles remain sealed inside private networks and trust loops.

Sindhuja Maddela, founder of Plarty, a culture-tech platform that focuses on bringing people together through shared Indian experiences, realised how opaque senior hiring truly is when a ₹96-lakh CXO role disappeared before it ever went public.

According to Maddela, who said she was privy to the process, there was no LinkedIn post, no application pipeline, no screening, just a founder sending a casual message to someone he trusted: “Hey, I need a CXO. Any names?”

Within a day the conversations began; the following day the offer was issued.

“People like to believe senior hiring is structured. It isn’t,” she said. “At that level, nobody wants to take risks. Leaders pick people they already know, or someone a trusted voice vouches for.”

Maddela acknowledges that she has long benefitted from this quiet hiring circuit, but the ease came with a hidden downside.

“Over the years, getting roles through references became a disadvantage, because you stop preparing for interviews or updating yourself. When you finally have to apply without a referral, you feel stuck and the imposter syndrome hits,” she told AIM.

The comfort of referrals, she added, also narrowed her path. “I never went through rigorous FAANG-style interviews or tried for something bigger because I kept slipping into roles too easily.”

She believes companies need a deliberate counterbalance. “References give quick access, but others deserve a fair shot. Businesses should maintain a quota for open recruitment so new people and new ideas actually enter the system.”

Her experience reflects the broader patterns India’s largest talent platforms are now observing across the industry.

Academic research has long shown that referral-driven hiring is not just common but structurally influential in labour markets.

One study, The Role of Referrals in Immobility, Inequality, and Inefficiency in Labor Markets, argues that when companies rely heavily on referrals, it can limit mobility, reinforce inequality and create inefficiencies in how talent is matched to roles.

Another paper, Social Networks as a Mechanism for Discrimination, demonstrates how hiring through personal networks can inadvertently disadvantage certain groups, showing that network-based recruitment can shape access to opportunity as much as skill, or experience.
Clubbed together, these studies offer a useful lens for understanding India’s tech sector, where AI is reshaping hiring at the bottom even as networks continue to dominate the top.

LinkedIn’s global survey shows that nearly 80% of professionals believe networking is critical for career success, and 70% of people hired in 2016 landed roles at companies where they already had a connection.

In India’s IT sector, this pattern is as pronounced. AI now drives much of the junior and mid-level screening, but senior mandates, from CXO roles to practice heads, continue to move largely through private networks, trusted referrals and closed-door conversations.

This widening divide between tech-driven hiring at the bottom and trust-driven hiring at the top is reshaping who gets seen, who gets considered and how leadership pipelines are formed.

Aditya Narayan Mishra, MD and CEO of CIEL HR, said AI has transformed hiring at the base and mid layers, where volumes are high and skill maps are predictable, but has barely moved the needle on senior recruitment.

“AI helps screen faster, match profiles better and maintain consistency. But leadership hiring is far more layered,” he said.

Strategic thinking, maturity, cultural alignment and the ability to lead complex teams are all qualities AI still cannot evaluate with the nuance that clients demand. As a result, senior mandates still rely on “experience, trust and established networks,” he said.

Interestingly, Mishra noted that AI is beginning to add value by surfacing senior candidates outside the usual inner circles, leaders who built practices in mid-sized firms, or scaled emerging roles.

He said that hiring for senior roles in Indian IT can be relationship driven. Sometimes that narrows the view. AI-enabled tools help widen it, he added.

Yet, he acknowledged that time pressure pushes organisations back toward low-risk, known profiles.

For AI to play a material role in senior hiring, companies must build trust in how insights are generated and adopt a mindset that blends data with human judgment.

Kartik Narayan, CEO–jobs marketplace at Apna, argued that the gap between AI’s actual capabilities and its adoption in senior hiring is larger than most realise.

“AI has moved faster than our intuition,” he said. Apna’s AI calling agent already conducts structured conversations in English and Hindi, produces transcripts and evaluates problem solving and clarity at a scale impossible for human panels. More than 3,000 employers now use it in early screening.

Narayan believes the barrier in senior hiring is not capability but comfort. “For seasoned developers, architects and technical leads, AI can already assess functional rigour and decision making with confidence. Companies simply want more human engagement later because conversations shift from skill to context.”

Leadership hiring, he said, involves assessing how someone carries culture, influences people and navigates ambiguity, qualities that require long, unstructured conversations.

Still, he sees AI as a powerful equaliser earlier in careers. “When the first look is an AI conversation, candidates are evaluated on skill, not pedigree or proximity.”

Over time, AI-driven skill graphs could help identify leadership potential earlier, reducing dependence on closed networks.

The dominance of networks at the top is something staffing firms openly acknowledge.

Neeti Sharma, CEO of TeamLease Digital, said the pattern is unmistakable. “Senior roles are rarely published in the open market. They move through known networks,” she said, adding that over two-thirds of roles such as delivery heads, practice leads and vertical heads are filled through internal or referral candidates.

Companies often post senior openings publicly “more for signalling,” she said, even when the real hire is made through internal candidates, references or search firms.

Sharma explained that business urgency reinforces this behaviour. “A known candidate ramps up faster, and for critical openings that matters.” Referrals confer an advantage because cultural fit and working style are already known.

But, she believes fairness is still possible if companies assess external candidates alongside internal ones, maintain transparent evaluation stages, and use unbiased panels.

The post Why AI Stops at the Door of Senior Hiring in Indian IT appeared first on Analytics India Magazine.

At re: Invent 2025,AWS announced the general availability of its new Amazon EC2 Trn3 UltraServers, powered by the Trainium3 chip built on 3nm technology, to help customers train and deploy AI models faster and at lower cost.

The company said the new servers deliver up to 4.4x more compute performance, 4x greater energy efficiency, and almost 4x more memory bandwidth compared to the previous Trainium2 generation. Each UltraServer can scale up to 144 Trainium3 chips, offering as much as 362 FP8 petaflops of compute.

Trainium3 follows AWS’s earlier deployment of 500,000 Trainium2 chips in Project Rainier, created with Anthropic and described as the world’s largest AI compute cluster.

AWS also revealed early details of Trainium4, expected to deliver at least 6x the processing performance in FP4, along with higher FP8 performance and memory bandwidth. The next-generation chip will support NVIDIA NVLink Fusion interconnects to operate alongside NVIDIA GPUs and AWS Graviton processors in MGX racks.

AWS has already deployed more than 1 million Trainium chips to date. The company says the latest performance improvements translate to faster training and lower inference latency. In internal tests using OpenAI’s GPT-OSS open-weight model, Trn3 UltraServers delivered three times higher throughput per chip and four times faster response times compared to Trn2 UltraServers.

Companies including Anthropic, Karakuri, Metagenomi, NetoAI, Ricoh and Splash Music are already reporting reduced training and inference costs up to 50% in some cases. AWS said its Bedrock service is already running production workloads on Trainium3.

Decart, which focuses on real-time generative video, said it has achieved 4x faster frame generation at half the cost of GPUs on Trainium3. AWS noted that such capabilities could support large-scale interactive applications.

The UltraServers are supported by an upgraded networking stack, including the new NeuronSwitch-v1, which provides twice the internal bandwidth, and a revised Neuron Fabric that brings inter-chip latency below 10 microseconds. The company said this reduces bottlenecks in distributed training and inference, especially for workloads such as agentic systems, mixture-of-experts architectures and reinforcement learning.

UltraClusters 3.0 can connect thousands of the new servers, scaling to as many as one million Trainium chips—10 times the previous generation. AWS said this level of scale enables training multimodal models on trillion-token datasets and serving millions of concurrent users.

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Amazon has added four new AI models to its Nova lineup, introduced a new way for companies to train their own custom versions, and launched a tool that helps build AI agents that can work inside web browsers, the company announced at re:Invent 2025 on Tuesday.

The company said tens of thousands of customers are already using Nova models for tasks including content generation, multi-step automation, and agent development. The new Nova 2 family is designed to balance speed, cost, and reasoning performance across text, image, video, and speech inputs.

New Nova 2 Models

Nova 2 Lite and Nova 2 Pro are built for reasoning-focused workloads with web grounding and code execution capabilities.

Nova 2 Lite is built for everyday applications such as customer support and document processing. Amazon said the model is “equal or better” across most benchmark comparisons with Claude Haiku 4.5, GPT-5 Mini, and Gemini Flash 2.5.

Nova 2 Pro, the company’s most capable reasoning model, is intended for complex tasks like agentic coding, long-range planning, and multi-document analysis. It outperformed or equalled Claude Sonnet 4.5, GPT-5.1, Gemini 2.5 Pro and Gemini 3 Pro Preview on a majority of benchmarks, according to Amazon.

Nova 2 Sonic is a speech-to-speech model built for real-time conversational AI with support for long context interactions and integration with telephony and voice frameworks.

Nova 2 Omni is a unified multimodal model that can process text, images, video, and audio while generating both text and images. Amazon said it can handle large-scale inputs such as product catalogues, long videos, and multi-format brand assets in a single workflow.

Organisations including Cisco, Siemens, Sumo Logic, and Trellix are using Nova 2 models for applications such as threat detection, video understanding, and voice assistants.

Nova Forge: Building Custom ‘Novella’ Models

Amazon also introduced Nova Forge, an open training capability that lets organisations build customised variants of Nova, called “Novellas,” by blending proprietary datasets with Nova’s training stages.

The service provides access to pre-trained, mid-trained, and post-trained checkpoints, allowing customers to integrate domain-specific knowledge throughout the training cycle. Amazon said the approach avoids the trade-offs of shallow fine-tuning or training from scratch.

Nova Forge includes reinforcement learning environments (“gyms”), support for synthetic data-driven distillation to create smaller models, and a responsible AI toolkit. Customers can deploy their custom models on Amazon Bedrock.

Early adopters include Booking.com, Reddit, Sony, Cosine AI, and Nomura Research Institute.

Nova Act: Automating Browser-Based Workflows

Amazon also launched Nova Act, a service for building and deploying AI agents that automate actions in web browsers. Powered by a Nova 2 Lite variant, Nova Act has reached 90% reliability in early customer workflows, the company said.

Nova Act uses reinforcement learning over thousands of simulated web tasks to improve its performance on UI-based actions such as CRM updates, website testing, and insurance form submissions.

Customers can prototype agents using natural language in a no-code playground, refine them in tools like VS Code, and deploy them through AWS. Hertz, Sola Systems, 1Password, and Amazon’s Project Kuiper team are among early users.

Amazon’s Leo satellite internet team also used Nova Act for test automation. The company said the system reduced test case creation from weeks of engineering effort to minutes.

The post Amazon Fires Back at OpenAI and Google with New Nova Models and Nova Forge appeared first on Analytics India Magazine.

The Department of Telecommunications (DoT) has directed that every new smartphone sold in India must come with the government controlled Sanchar Saathi app preinstalled, visible on first boot, and manufacturers need to ensure that features shall not be disabled or restricted.

The directive mandates completing the implementation in 90 days and submit a report in 120 days. For existing devices, it mandates the app to be pushed via software updates.

The government describes the application as a tool for verifying IMEI numbers of mobile devices to detect spoofing, block stolen devices, report fraudulent messages or calls, and check all the numbers registered under a name, among other functions.

It also claims that the app enabled the recovery of over 50,000 lost and stolen mobile handsets across India in October 2025, and that overall recoveries crossed 7 lakh devices.

Following uproar from the tech community and fellow political leaders, communications minister Jyotiraditya Scindia has said in a media interview that the app can be uninstalled, but the same is not explicitly mentioned by the government in the directive.

Even as several government representatives have clarified that the app will not misuse data — its pre-installation on devices continues to raise data privacy concerns.

Why The Concerns?

A report from Reuters stated that iPhone maker Apple does not intend to comply with the directive.

Sources familiar with the matter told the media outlet that the company is going to tell the government that it does not follow such mandates anywhere in the world “as they raise a host of privacy and security issues for the company’s iOS ecosystem.”

Pranesh Prakash, an independent tech, legal and policy consultant, told AIM that mandating the app doesn’t fix the problems highlighted in the press release. “People can report IMEI numbers even otherwise.”

Prakash highlighted how GSMA, the global industry body that maintains international IMEI standards and device-identity registries, and similar industry systems already support IMEI blocking. He said that failures originate in operator enforcement and registry maintenance, not in the absence of a reporting interface.

Joel Latto, threat advisor at F-Secure, a cybersecurity company, told AIM that whenever a government forces digital oversight, especially to such an intrusive level, it is a cause for concern.
The app asks for permissions to handle calls, send SMS, read call logs, access photos and files, and use the camera for IMEI scans.

Technically inclined users can dig into settings, disable permissions — pre-installed apps on Android and iOS allow doing so.

But, others are likely to leave the app untouched with every permission active, simply because they don’t know how to manage these controls.

Instances of awareness drives have occurred, such as travellers being nudged at airports into installing and using DigiYatra while sharing their personal details, biometrics, and other data. Once people opt in, they may rarely return to trim permissions.

Similarly, several people, including AIM staff, received an SMS encouraging them to install and use the Sanchar Saathi app. Users who download it without fully understanding its implications may place themselves at risk if the app or any sensitive data it handles is not managed with strict safeguards.

This is where the concern usually surfaces. An app that remains on a device with broad, continuous access creates more surface area for something to go wrong, whether through weak engineering, accidental exposure, or exploitation by those looking to take advantage.

“Even if the app would do nothing but what’s advertised at the moment…it opens up the possibility for future abuse by the powers that be,” said Latto.

“Rest assured if that happens, it will be also done all in the name of citizen safety. It’s a slippery slope that leads to the China-model.”

While Latto hasn’t used the application, he stated even if permissions are disabled, “I’d imagine that at least anything related to IMEI, or other device identifiers will always be broadcasted, as those are not part of typical app permissions.”

‘This Needs to Be Shelved’

Nikhil Pahwa, a digital rights activist and founder of MediaNama, in an interaction with AIM said that besides risks like opening the doors to your personal information for state entities, “A government app on your device can also be used to implant files.”
He pointed towards the 2018 Bhima Koregaon violence case, where independent forensic investigations alleged that activists’ computers had been compromised and incriminating documents were planted remotely.
He cited this to illustrate how a single privileged channel on a device can be misused, and said that mandating such an app “creates that channel” and raises the risk that similar compromises become easier in the future.

Besides, several experts in the industry are concerned about the government’s management of sensitive user data.

“Almost every [government] infra is infested with vulnerabilities routinely being exploited by malware and data thieves. You will often find [government] websites distributing malware,” said Shanthanu Goel, an engineer, on X. “But yes, [government] plans to save us from malware by installing their own malware.”

Over the years, there have been numerous reports of data breaches and leaks across various government-linked and public department sites. For example, the Aadhaar system in 2018, the SPARSH portal in 2023, and the Indian Council of Medical Research (ICMR) registry in 2023

In October 2025, a serious vulnerability was discovered and fixed in the Income Tax e‑filing portal. The flaw would have allowed a logged-in user to view sensitive data (names, addresses, bank details, Aadhaar numbers, and more.) of other taxpayers.

When AIM asked Pahwa what transparency measures users could realistically expect, he said there is nothing transparent about the directive. “This [the directive] needs to be shelved,” he said, arguing that the range of privacy and surveillance risks makes any attempt at mitigation insufficient.

The Legal Clarity

Having said that, Prakash also questioned the legal clarity. “It’s unclear to me whether the powers provided under the Telecom Act actually go so far as to enable the DoT to mandate that specific apps be installed on phones without the ability of people to remove them,” he said.

Besides, the Data Personal Data Protection Act widens the scope of concern. While Prakash explained that the DPDP Act “does not exempt the government sector” entirely — it allows specific exemptions through notifications.

The Act permits the government to exempt entire departments from core obligations such as consent, purpose limitation and notice. It also allows the State to process personal data without consent when performing functions under any law, or during situations framed as public safety or emergency.

While Apple, as per media reports, will not be complying with the mandate, the company has demonstrated an example of the limits it is willing to accept when governments attempt to compel architectural changes to personal devices.

Prakash pointed to the FBI–Apple dispute to illustrate how operating-system modifications raise constitutional questions.

Apple had argued that the FBI’s demand for a customised version of iOS during the San Bernardino shooting investigation to access the locked phone of the perpetrator, “was compelled speech — that was a violation of its freedom of speech rights”.

“Now a similar argument would obviously also work in [this] case,” said Prakash. “There are free speech rights associated with software, associated with companies like Google and Apple and so on.”

The Political Opposition

Political leaders have framed this mandate as a constitutional threat rather than a governance measure.
Congress leader K C Venugopal said on X, “Big Brother cannot watch us. This DoT Direction is beyond unconstitutional”. He described the directive as “a dystopian tool to monitor every Indian” and said it enables oversight of “every movement, interaction and decision of each citizen”.

Shiv Sena (UBT) MP Priyanka Chaturvedi said in a post on X, “Such shady ways to get into individual phones will be protested and opposed & if the IT Ministry thinks that instead of creating robust redressal systems it will create surveillance systems then it should be ready for a pushback!.”

Their concerns mirror those raised by experts.

Further clarity from the government is awaited over whether the app can be uninstalled. If the widespread backlash leads to any major changes in the directive remains to be seen.

“It’s [Sanchar Saathi] more about future-proofing one’s privacy and security hygiene. The point is that I see Sanchar Saathi as the first step,” said Latto.
“The road to hell is paved with good intentions.”

The post Sanchar Saathi Shouldn’t Turn into a Tool for State Surveillance appeared first on Analytics India Magazine.

For nearly 30 years, Virtusa operated as a product and platform engineering services company. But today, it is aggressively repositioning itself for the age of generative and agentic AI.

The company’s new offering, Helio, avoids the heavy, one-size-fits-all platform model. Instead, it delivers modular, configurable AI solutions that plug directly into whatever tech stack an enterprise already uses.

In a conversation with AIM, Nitesh Banga, CEO of Virtusa, explained how the company sees AI adoption evolving across industries, why organisations struggle to build these capabilities in-house, and what makes ROI so complex in this space.

This shift in enterprise AI maturity comes as global investors are split on the trajectory of the AI boom. While organisations are experimenting aggressively, market commentators are questioning whether the sector’s rapid acceleration is built on solid fundamentals.

Responding to broader industry debates, including recent commentary from figures like Michael Burry about the AI bubble, Banga believes that the AI cycle resembles early cloud adoption, not the blockchain hype curve. “AI is probably the next cloud, not the next blockchain,” he said.

Banga stated that fluctuations are inevitable and expressed his strong belief that AI and agentic AI are here to stay. He highlighted that stability will only be achieved once enterprises modernise their data infrastructure and complete the foundational work that underpins AI.

Besides, Burry, Mark Cuban, investor and entrepreneur, recently warned that many AI investments are overspending, drawing parallels to the 1990s search-engine/tech race and saying that the current AI frenzy could end like a bubble.

On the contrary, IBM CEO Arvind Krishna said in a recent podcast that there is no AI bubble. He noted that while some of this capital will inevitably be wasted, the broader economics of AI still justify the aggressive investment, particularly in the consumer AI space.

Krishna said that a company building a highly attractive AI model with gains of half a billion users can generate significant value. “If you build a slightly better model by spending another $50 billion and that can attract another 200 million users, it seems to make economic sense,” he explained.

Meanwhile, Virtusa, supported by Helio, aims to become an AI-first services company by 2030.

“Helio will be at the tip of the spear, and all our other service lines will attach to it,” Banga said. The company reported that about 17 % of its current revenue is already AI-first and that this portion is growing at a higher profitability than traditional digital services.

What About ROI

One of the biggest misconceptions in enterprise AI is how to measure ROI. According to Banga, a distinction must be made between gross ROI and net ROI.

Productivity improvements may reduce headcount requirements, but once the costs of compute and models are added, the real benefit often shrinks or disappears. He believes the industry is obsessing over the wrong metric.

“There is a gross ROI, and there is a net ROI. You may reduce the work from 100 people to 40, but you must add the cost of compute…to know the real ROI,” Banga said.

He said that the world of automation has undergone a dramatic shift over the past decade. Enterprises first embraced deterministic automation through RPA, then moved into predictive analytics and later into cognitive automation. But the arrival of generative AI has completely changed the equation.

“The flip has happened from automation to creation,” he said, describing how AI systems are now capable of generating artefacts, decisions and experiences that never existed before.

Within the product development lifecycle, Virtusa sees an enormous opportunity beyond simple coding assistance. While the industry talks endlessly about AI coding tools, Banga noted that coding accounts for only a fraction of the lifecycle. “Coding efficiency is only 30% of the PDLC. The real impact lies in the rest of the lifecycle,” he said.

The IT services company is investing heavily in agentic testing, architecture generation, reverse engineering for forward engineering and AI-based CI/CD. Their testing suite, for instance, can reduce testing time by up to 60% by automatically generating test cases, test data, and execution flows.

Why Enterprises Can’t Build AI Platforms Alone

Despite the excitement around AI, most organisations face a serious reality check when they try building their own AI platforms. The challenge begins with data readiness.

As Banga put it, many organisations still have fragmented data, no single source of truth, and incomplete cloud journeys. Even before modelling begins, enterprises must realign infrastructure, prepare data, set up compute resources and modernise legacy systems.

While enthusiasm is high everywhere, as the CEO, he believes no sector can yet be called truly mature. He shared that tech ISVs (Independent Software Vendor) such as Google and AWS are long-time Virtusa customers.

Banga mentioned that among traditional enterprises, some sectors are progressing faster than others. Communications firms are moving ahead as network optimisation and software-defined networks become increasingly AI-driven.

Life sciences companies, especially in early-stage drug discovery and clinical research, are rapidly incorporating AI into lab and R&D workflows. Financial services players are using AI to streamline intensive operations like onboarding and KYC. Banga added that even manufacturing is exploring computer vision for warehouse optimisation and automation.

Still, he believes that the journey is just beginning: “I wouldn’t say any sector is mature, to be very honest. The industry is still early in the journey.”

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