LTM Inaugurates its New Global Delivery & AI Competency Center

LTM, the Business Creativity partner to the world’s largest enterprises, today inaugurated its new Global Delivery & AI Competency Center in Kolkata. The move reinforces L&T Group’s long-term commitment to West Bengal as a strategic technology and innovation hub. The center was inaugurated by Shri Suvendu Adhikari, Honourable Chief Minister of West Bengal. The ceremony was also attended by Shri S. N. Subrahmanyan, Chairman & Managing Director, Larsen & Toubro and senior leadership of L&T and LTM, alongside other dignitaries.

The Global Delivery & AI Competency Center in Kolkata represents an important milestone in LTM’s growth journey in India and reflects the company’s continued investment in regional expansion, talent development and technology innovation. The facility will support LTM’s growing global delivery capabilities and AI ambition while creating new opportunities for skilled professionals across West Bengal and the neighbouring states.

Kolkata’s strong educational ecosystem, skilled talent pool and growing digital infrastructure make it an important destination in LTM’s India growth strategy. The center will serve as a hub for collaboration, innovation and delivery excellence, enabling the company to support clients globally while contributing to the state’s economic development.

“West Bengal is emerging as a preferred destination for technology and AI-driven investments. LTM’s decision to establish and expand its presence in Kolkata reinforces the state’s strengths in talent, innovation and digital infrastructure. We welcome this investment and the opportunities it will create for our youth, technology ecosystem and economic growth,” said Suvendu Adhikari, Honourable Chief Minister of West Bengal.

“The inauguration of LTM’s Global Delivery & AI Competency Center in Kolkata reflects L&T Group’s continued commitment to investing in India’s growth story. By expanding into high-potential regions and creating opportunities for local communities, we are helping build a stronger foundation for innovation, technology-led progress and inclusive economic development. We are delighted to partner with the Government of West Bengal in this important milestone,” said S. N. Subrahmanyan, Chairman & Managing Director, Larsen & Toubro.

“Kolkata’s deep talent pool, strong academic heritage and growing digital ecosystem make it a strategic location for LTM. As AI reshapes industries, our Global Delivery & AI Competency Center in Kolkata will help us harness local talent, build future-ready skills and deliver next-generation solutions for global clients while contributing to West Bengal’s technology-led growth,” said Venu Lambu, CEO and Managing Director, LTM.

The new Global Delivery & AI Competency Center further strengthens LTM’s nationwide delivery network and supports the company’s strategy of bringing opportunities closer to talent. Through continued investments in regional centers, digital infrastructure and employee experience, LTM remains focused on building future-ready workplaces that empower people, drive innovation and create long-term value for clients and communities alike.

Platform Neutrality is a Myth: How Design Choices Shape Market Outcomes

When the war around price, product and brand concludes, another war starts — one fought through load times, trust signals, catalogue quality, button placements and user journeys. In digital commerce, interface matters just as much as the products or services you are offering. A case study by Jared M Spool stated that customer purchases increased by 45%, just by replacing the register button with the continue button. This made customers feel like a part of the journey rather than being forced through a process, and thus, generated an additional $300 million in revenue within the first year itself.

Identifying the Problem

Users, today, compare every interaction against the speed of a food delivery app, the simplicity of a payments platform, or the speed of a quick commerce service. Expectations have become fluid across industries. But the businesses haven’t. A lot of businesses and companies still follow the approach where they not only underinvest in the technology, but also underestimate the commercial weight of a well-crafted design and website interface.

● Not a one-time thing: Design is still considered a one-time thing or an expensive revamp under marketing budgets in so many companies. That is a core issue because design is a constant, everyday job, evolving as per users’ needs. In the age of liquid expectations, the competition is no longer just the person selling the same product, but also the last best experience your customer had on any app.

● User-centric thinking: Airbnb founders once noticed that low-quality pictures were affecting the customers’ listings. So they invested in high-res, beautiful photos, leading to better financial growth immediately. Especially, in marketplace commerce, every little friction, such as unclear product specs, missing certifications, or confusing quotations are the reason for the buyer to switch to the next supplier. Thus, user-centric thinking remains at the core of a design development process, with constant feedback loops for evolution.

If not addressed appropriately, businesses can lose online presence (ultimately sales) and lower customer engagement (thus higher churn).

Why Does it Matter More in B2B?

In an online B2B environment, trust deficit is high. There are fragmented supply chains and varying levels of digital literacy that shape buying behavior. In such cases, interface quality is not about looking beautiful to the eyes, but about minimizing friction points. Unclear product specifications, incomplete business profiles, delayed responses, poor product imagery, confusing quotation flows, or a lack of verification indicators have a direct impact on the conversions.

With fragile levels of trust, buyers immediately move to the next supplier, thus costing you the business.

Addressing Digital Selling With 3S

Visual appeal, coupled with speed and trust are the true currency to win the game of interface.

● Simple: A well-structured, simple, and clean design makes the interface look reliable and adds a sense of credibility, while a poor design weakens consumer confidence. Especially in a B2B ecosystem where micro and small businesses are operating with limited digital literacy and limited manpower, simplicity becomes a competitive advantage.

● Secure: Similar to physical handshakes and offline meetings, digital handshakes are equally important to develop a sense of user security. Besides SSL certificates and firewalls, a design interface should work properly, not take a long time to load pages and should have a transparent way of showcasing the products. Added to it, the visual security elements such as verification badge, seller ratings and reviews, genuine product pictures and history of buyer and supplier bridge the trust gap and add a layer of security and safety with the platform. Basically, it should not be just secure, but also feel secure to the person using it.

● Seamless: Speed shapes perception. The customer needs an interface that quickly responds to its commands. Slow loading pages and late responses often lead to customer drop-off. Ensuring speed brings a seamless experience with it. In addition to being responsive, it has to be predictive and proactive, anticipating the next steps of the user journey. A seller struggling to improve visibility may need guided recommendations. A user dropping out right before checkout might need a nudge to complete the process, which can be done only if the system is seamless and agentic enough.

Design as a Continuous Discipline

Online platforms’ democratized access to markets, low entry barriers, and the option to scale further, all backed with seamless design and a smart interface can provide the edge you need to stand out. Experience is the key differentiator here, and the better experience your website provides, the longer the user will stay.

When interface and design are intentional, conscious and data-backed, it is no longer a cost center but a revenue multiplier. The moments of trust and clarity often shape the user’s decision, thus impacting the purchase funnel.

India’s Fraud Landscape Evolves as Organised Networks Grow More Sophisticated: Experian

Experian, a global data and technology company, launched its latest fraud insights report, “The New Frontier: Emerging Trends in Fraud Prevention.” The report offers insights into how fraud prevention is evolving alongside India’s rapidly expanding digital financial ecosystem, where greater connectivity, faster onboarding and increasing digital adoption are creating both new opportunities and new risk dynamics. The findings reveal a growing shift from isolated instances of fraud to more organised and financially impactful networks, reinforcing the importance of data-driven, intelligence-led risk management approaches.

The findings indicate that, while the volume of suspected fraud-linked applications has declined, the value associated with these cases has increased significantly over the last three years, pointing to an evolution in the nature of fraud risk across the financial services sector. Drawing on publicly available data from the Reserve Bank of India’s Annual Report 2025–26, along with Experian’s fraud prevention insights, the report highlights a shift in India’s fraud risk landscape. While the number of reported or suspected cases declined over the period analysed, the amount involved rose sharply from Rs.12,230 crore in FY24 to Rs.48,021 crore in FY26. These trends suggest that financial institutions are increasingly required to assess not only the frequency of suspicious activity but also its potential impact.

The report points to the growing role of organised and network-driven fraud patterns, including identity misuse, synthetic identities, mule accounts and misrepresentation of borrower information, driven by the increasing use of technology and artificial intelligence by fraudsters. These trends underline the need for lenders to strengthen fraud prevention across the customer lifecycle, from onboarding and application screening to ongoing monitoring.

A key trend emerging from the report is the persistence of application anomalies across lending products and geographies. Credit cards continue to record the highest anomaly rates among major retail lending products, while personal loans, auto loans and business loans also showed varying levels of vulnerability. Misrepresentation of income, employment, identity and contact information continues to fuel organised financial crime.

To address these evolving risks, the report highlights the growing importance of intelligence-led fraud prevention strategies that combine application-level analytics, behavioural intelligence and alternative data. Experian’s analysis demonstrates that advanced application-level risk attributes provide stronger discrimination of anomaly risk than traditional credit scores alone, enabling earlier and more accurate detection of suspicious applications.

Artificial Intelligence (AI) and Machine Learning (ML) are also playing an increasingly important role in strengthening fraud prevention. Among organisations already using ML-based fraud solutions, 58% reported an improved ability to identify emerging fraud types, 54% experienced higher fraud detection accuracy and 56% reduced friction for genuine customers through passive fraud checks, demonstrating the value of AI-driven decisioning in balancing security with customer experience.

Commenting on the emerging fraud prevention trends, Manish Jain, Country Managing Director, of Experian in India, said, “India’s financial ecosystem is becoming more digital, faster and increasingly connected, creating significant opportunities for consumers and lenders. At the same time, this transformation is changing the nature of fraud risk and reshaping how organisations approach trust, resilience and decision-making.

Our findings show that fraud prevention can no longer be viewed as a standalone control function. It must become an integral part of decision-making across the customer lifecycle. The ability to identify genuine opportunities while detecting emerging risks early will be a critical differentiator in an increasingly digital market.

Organisations that combine data, analytics and broader intelligence will be better positioned to protect customers, strengthen operational resilience and support sustainable growth.

The report concludes that fraud prevention is becoming a strategic business capability for financial institutions. As digital lending and onboarding continue to expand, organisations will need adaptive, data-led and intelligence-driven approaches to protect customers, strengthen decision-making and build long-term resilience.

Treat AI Accountability as a Design Decision, not a Post‑Incident Debate

For twenty years, the answer to who was responsible for the CIO’s mistakes was crystal clear. Technology teams built the tools, business teams made the decisions, and the business took accountability. However, AI’s advent has changed the rules of the game. If a machine-learned model makes an inappropriate decision (such as denying a valid loan application, shipping products to the wrong address, or fabricating factoids that breach compliance), this age-old separation between the people who built it and the people who are responsible no longer holds true.

CIONOW spoke to four senior technology leaders to seek their perspectives on accountability.

The starting point, according to Golok Kumar Simli, President-Technology & Innovation at BLS International, is that technology has stopped being something that happens downstream of business decisions.

“In the AI era, technology is no longer a back-office function. Business sits in the driving seat, and technology is welded into every process: marketing, operations, HR, finance, customer experience,” he says. “CIOs and CTOs are now at the centre of the boardroom conversation, not behind it. You cannot launch new business streams without technology; you cannot shut them down safely without it either.”

This precisely complicates the accountability question. When technology sat at the edges of the business, failure was easy to trace to a function head. When technology is welded into every process, failure has no single owner by default. Accountability must be assigned deliberately.

Shared Blame, But Not Equal Duty

Every technology leader we spoke to echoed the same sentiment: accountability for AI must be shared. None of them believe it is fair or functional to pin failure entirely on IT.

Dr Tejasvi Addagada, Head of AI Governance at HDFC Bank, says, “If something goes wrong, it’s usually the functional head or the business head who is held accountable. Technology is there to enable business and bring business agility. The business owns the balance sheet; technology doesn’t.” IT’s job, in his view, is to guarantee that internal controls are sufficient, and not to absorb blame for decisions made elsewhere.

Balaji Raghunathan, Data & AI Engineering Business Unit Leader at Sigmoid, says, “AI ownership must be shared, but the exact model depends on enterprise maturity. The technology organisation should own the platform, infrastructure, reliability and security. Business functions should own the quality of domain data, the use case, the operating process and the business outcome.”

In a federated data setup, he says, the function that generates the data — supply chain, sales, marketing — owns its quality, while the CIO’s team owns the horizontal platform it runs on. “This creates clearer accountability than asking IT to carry responsibility for data it does not create or use daily.”

But shared doesn’t mean equal. Simli points to a duty that sits uniquely with the technology leader. “The technology leader does carry a special obligation: risk assessment, risk scoring, continuous evaluation, and explainability. That ‘human layer’ is ours. We are often the only ones who truly understand how the model thinks, and therefore the only ones who can properly explain its behaviour and limitations,” he says. Explainability, in other words, isn’t a shared responsibility; it’s a CIO responsibility because no one else in the room is positioned to carry it.

Communication is the Real Control

If there’s a second theme running through these conversations, it’s that most AI failures aren’t really technology failures. They are communication failures.

Arun Goyal, CIO at Sir Ganga Ram Hospital, is blunt about where projects actually break down. He says, “A CIO alone cannot implement any digital perspective unless he communicates and discusses with the stakeholders. If you do this in silos, you create siloed projects destined to fail.”

His bigger concern isn’t model accuracy; it’s expectation management. Stakeholders, he argues, chronically overestimate what AI can do unsupervised. “They should not think that once AI is there, they will sit quietly and everything will be done by the AI,” he says.

The CIO’s job, as he sees it, is as much about educating business stakeholders on what a technology can and cannot do as it is about building the technology itself. Get that shared understanding right, he suggests, and “it will never fail” because everyone is accountable to a version of success they actually agreed on.

The Real Test isn’t Adoption, it’s Readiness

Balaji believes it is not whether to adopt AI. It’s whether the organization has done the unglamorous work first: “the data, architecture, governance and business accountability needed to make AI dependable at scale. Once AI moves out of pilot mode, it becomes a CIO-level concern by default, not because CIOs asked for the exposure, but because uptime, integration, data reliability, cybersecurity and scale all report to them regardless of who signed off on the use case,” he says.

The accountability conversation, then, isn’t really about assigning blame after the fact. It’s a governance decision that has to be made before the model goes live — who owns the data, who owns the outcome, who owns the explanation when a board member asks “why did it do that?”

Enterprises that wait until something breaks to answer that question will find, as these four leaders suggest, that the answer was never going to be simple and by then, it’s too late to design it properly.

Conclusion

Business heads own the outcome and the balance-sheet impact of AI use cases. CIOs own the platform, security, reliability, and critically the ability to explain how models work and where they can fail. That split must be documented before go‑live, with clear owners for data quality, monitoring, and incident response. Treat accountability as a design decision, not a post‑incident debate.

Colt Data Centre Services Appoints Quy Nguyen as Chief Executive Officer

Colt Data Centre Services (Colt DCS) announced the appointment of Quy Nguyen as Chief Executive Officer (CEO), effective immediately. Nguyen has served as Acting CEO since April 2026, following the retirement of Niclas Sanfridsson. After a comprehensive succession process, the Board of Directors appointed Nguyen as CEO, underscoring its confidence in his leadership and vision for the company’s next phase of growth.

Since joining the company in 2016, Nguyen has held a number of senior leadership positions, successfully leading teams across sales and marketing, customer experience, design, and delivery. Most recently, as Chief Sales Officer (CSO), he played a pivotal role in accelerating growth by strengthening the company’s commercial strategy and deepening customer relationships. Prior to joining Colt DCS, Nguyen held senior leadership roles spanning finance, strategy, and general management, providing him with broad experience in scaling businesses and delivering sustainable growth.

As CEO, Nguyen will lead Colt DCS through its next stage of growth, with a focus on scaling the company’s global platform and supporting customers’ rapidly evolving digital infrastructure needs. With nearly 800MW of capacity under development across key global markets, Colt DCS is well positioned to support growing demand for digital infrastructure driven by continued cloud adoption and the rapid expansion of AI workloads across Europe and Asia.

Tim Cohen, Chairman of Colt DCS, commented, “Quy has demonstrated exceptional leadership during a period of significant growth and transformation for Colt DCS. His deep understanding of our customers, our people, and our business, combined with his strategic vision and proven track record of execution, made him the outstanding choice to lead the company. As demand for digital infrastructure continues to accelerate globally, we are confident that Quy will build upon our strong foundations and position Colt DCS for continued long-term success.”

Quy Nguyen, CEO of Colt DCS, said, “I am honoured to be appointed CEO of Colt DCS at such an exciting time for our company and industry. We have built a strong track record for delivering world-class digital infrastructure, fostering trusted customer relationships, and executing ambitious growth plans across key markets.

As demand for sustainable cloud and AI infrastructure continues to accelerate, we are uniquely positioned to support our customers’ evolving needs through continued investment, operational excellence, and innovation. I look forward to working alongside our talented teams around the world to build on our strong foundations, expand our global platform, and deliver long-term value for our customers, partners, and stakeholders.”

$1M, $2M, $3M and Counting: The Cybersecurity Warranty Arms Race Heats Up

For years, the relationship between cybersecurity vendors and their enterprise clients was governed by standard disclaimers. If a hacker bypassed a firewall or endpoint agent, the vendor pointed to their End User License Agreement (EULA), which historically abdicated them of financial liability. But a structural shift is rewriting these rules. Driven by fierce competition and an urgent need to signal absolute efficacy, top-tier security providers are engaging in a high-stakes arms race, offering multi-million dollar ‘breach protection warranties’ directly to their buyers. What started a few years ago as a standard $1 million marketing guarantee has quickly escalated into a $3 million financial promise.

From Passive Defense to Multi-Million Dollar Guarantees

The concept behind these cybersecurity warranties is a simple one. If a customer deploys and utilizes a vendor’s product/solution and a breach or a ransomware event does happen, the customer receives a predetermined payout amount from the vendor in order to cover expenses of incident response.

The initial foundation of the boom was built from vendors that initially offered a million dollar warranty package, including Sophos with a million-dollar guarantee policy under its MDR tier, SonicWall, and SentinelOne, just to name a few.

Market share wars have since broken through that million dollar barrier. This has created immense hype when vendors boost their numbers significantly to grab market space from the competition.

CrowdStrike offers up to $2 million under its Falcon Complete Warranty for eligible customers using Falcon Complete with both endpoint detection and response and Identity Threat Protection.

Arctic Wolf has expanded its Security Operations Warranty to provide up to $3 million for eligible customers, with support for covered events including ransomware, business email compromise, legal liabilities, compliance events and business-income loss.

BreachRx has introduced a Cyber Incident Response Management Warranty offering up to $3 million in personal and corporate liability protection, including eligible regulatory defence costs, fines, penalties and negligence-related claims for incidents handled through its platform.

The Catch: Hype vs. Hard Realities

A $3 million warranty can sound like foolproof peace of mind. But the devil is in the details. These warranties are contingent contract commitments and don’t present a wholesale replacement for traditional cyber insurance.

Eligibility often depends on the customer maintaining defined security controls and deployment standards. Depending on the provider and programme, that can include timely patching, multi-factor authentication on critical systems, up-to-date endpoint protection, recoverable backups, and active deployment of the vendor’s service in the environment affected by an incident. If those obligations are not met, a claim may be excluded or the customer may not qualify for payment. Arctic Wolf’s published warranty terms, for instance, set out requirements around patching, MFA, endpoint protection, backups and active telemetry.

That does not make cybersecurity warranties meaningless. It changes how CIOs should view them: as an incentive to align operational accountability with the provider’s security commitment. One CIO I recently spoke with outsourced SOC operations to a managed security provider. The decision gave the provider direct operational responsibility for monitoring, detection and response, and created stronger commercial accountability if it failed to deliver the contracted service.

But the organisation did not transfer all of its cyber risk. The CIO still needed to ensure that internal teams met the shared responsibilities defined in the contract. The warranty is most valuable not as a replacement for cyber insurance, but as a mechanism that makes both the customer and provider accountable for maintaining the controls on which security outcomes depend.

Why This Trend Matters Now

Despite the heavy restrictions, this financial arms race highlights an important evolution: vendors can no longer hide behind passive software metrics. As reported by IBM, $ 4.99 Mn (million) has become the world’s average costs of a data breach, setting a record high by rising 12% over the year largely because of greater detection and identification expenses, escalation expenses and lost business expenses. By putting millions of dollars of their own balance sheets on the line, cybersecurity companies are forced to engineer products that do not just alert, but actively contain threats.

As automated, AI-driven attacks continue to collapse attack timelines down to minutes, these multi-million dollar performance guarantees will likely shift from a competitive marketing differentiator into a standard enterprise purchasing requirement. But how far cybersecurity warranties will go, and where the fine print will draw the line, remains anyone’s guess.

The Sale is Over Before the Farmer Walks in: TAFE’s Dilraj Singh Gandhi on AI’s Real Advantage

TAFE’s Dilraj Singh Gandhi, EVP Digital & AI, has been building the intelligence layer behind one of India’s largest tractor manufacturers, where AI now shapes farmer decisions long before they reach a showroom, and where legacy data and processes are being reworked for scale. In a discussion with CIONOW, he unpacks what it actually takes to move beyond basic digital transformation.

Manufacturing organizations are moving from basic automation toward Industry 4.0. How can an organization move beyond foundational digital transformation and build a fully intelligent, AI-enabled enterprise?

Organizations must work on four elements together. First, they must focus on people. Employees need to develop the right digital mindset, but mindset alone is not sufficient. Their capabilities must also be upgraded through investment in training, time and resources.

Second, organizations must demonstrate tangible benefits. The benefits may initially be small, but they must be visible. People should be able to see the tangible business result that a digital initiative has brought about. If the outcome we are looking for doesn’t materialise the organisation needs to go and work out what has gone wrong or else it will fail.

Third, digital transformation requires continuity. Organizations need a pipeline of use cases rather than waiting for one use case to be completed before beginning another. Momentum is critical because digital transformation involves inertia, change management and sustained adoption.

Finally, the use case must solve a genuine problem on the ground. Too often, use cases are created at the top for the convenience of a few individuals but do not address operational realities. Organizations must ask whether they are solving an actual problem, a perceived problem or merely a symptom. Digital succeeds when people adopt and use it even when nobody is monitoring them. That happens only when the underlying problem is genuinely solved.

A farmer’s relationship with an original equipment manufacturer now extends beyond the initial purchase across product discovery, equipment usage, maintenance and after-sales support. Where can AI create the greatest value for farmers across this journey?

Digital information has penetrated rural India deeply. Today, a farmer considering an equipment purchase may conduct extensive research through YouTube, Google, social media and other digital channels. In many cases, the farmer may have already made around 70% of the purchase decision before entering the showroom.

AI can aid farmers in making decisions at every step of the business, for example for selecting a tractor; an AI-enabled system could analyze horse power requirements, type of soil, crops growing, applications that need to be satisfied, tools that will be used, and the overall operational environment for the farmer to determine if he needs that particular tractor, a more horsepower enabled tractor or perhaps an implement suitable for the purpose. AI enables better financial access. Collaborating with financial institutions an AI could have wider parameters that are analyzed to calculate credit scores, and suggest available finance options.

Today, assessment of credibility usually is focused upon existing credit scores like CIBIL or Equifax; additional parameters could enable customers with no credit history to obtain loans.

AI could even help in taking the decision if the tractor needs to be purchased or rented. The nature of the challenge will mean that on some occasions (e.g. dictated by the season), an AI enabled app might recommend rental for the season and purchase for next season.

Besides farmer experience, where do you think AI would create the greatest operational impact?

I do not see there being one single ‘area’, but would instead choose whichever one had the greatest inefficiency, the greatest lag in decisions, the highest manual intervention rate and where the data quality was relatively poor.AI can potentially create value across every part of the manufacturing value chain, although the type of AI used may vary.

In the backend, organizations typically have structured and tabular data in ERP systems. Traditional or predictive AI can be useful for forecasting, scenario planning and what-if analysis. At the customer-facing end, generative AI and agentic AI can play a more significant role. Agentic AI could support finance processes, procure-to-pay activities and purchase-order workflows.

On the quality side, agentic AI combined with computer vision could help inspect products and recommend whether they should be accepted or rejected. The final judgment would remain with the relevant production or factory head, but AI could support faster and more informed decisions.

Organizations should invest across all three areas, even if investment levels differ. Otherwise, they risk creating a digital divide within the enterprise. One function may become highly intelligent through AI augmentation while another remains dependent on basic automation.

How difficult is it for CIOs to secure budgets for AI projects, especially when the business outcomes and ROI are not immediately clear?

A CIO should not try to secure an AI budget while working alone. The CIO’s role needs to be redefined. It is no longer sufficient to request a budget, implement a technology and report the outcome. AI is fundamentally about business. The CIO must act as a storyteller, evangelist and seller who helps business leaders understand the opportunity and encourages them to invest. The business should ultimately take the proposal to the board because AI must be embedded in the business and linked to business outcomes. Just sprinkling it over the application like the IT department would do is not an option.

CIO has to get engaged, find business problems (with business heads) along with a quick business relevant pilot and present the first value delivered and use relevant subject matter expert or an example from other enterprises and instill trust in business people.

Legacy organizations often have data scattered across silos, with problems relating to data quality, completeness and accessibility. How should a legacy enterprise begin its AI journey?

Data is important, but it should not become a reason to delay transformation. Legacy organizations do face data problems, including unverified data, incomplete data and the collection of irrelevant or outdated parameters. But those limitations needn’t be showstoppers. It’s easy for organizations to get stuck in a “chicken-and-egg” loop: they must have flawless data to start their AI journey, but they only will obtain high quality data if they begin work on viable AI use cases. Instead, the best approach is to choose a high value-use case and start with existing data, even if fragmented and unverified, possibly synthesizing missing data to develop an initial model.

Simultaneously, the organization can put in place the processes to create new data, clean existing data, vet accuracy, enhance completeness, and put data in transit.

Initial results might be noisy or imperfect and AI could make mistakes, err, or hallucinate. This they will know and must plan to manage. As the organization moves ahead, both data and models should improve. Transformation simply can’t wait. Indeed, it may be only through transformation that perfection becomes possible at all.

Looking ahead, what will define an intelligent enterprise in the agricultural and manufacturing sectors over the next five years?

The intelligent enterprise will be defined by its ability to break traditional linearity and place the customer at the centre of the operating model. At present, a customer complaint often follows a long chain: the customer contacts a call centre, dealer or salesperson; the dealer escalates the issue to sales or service; the service function approaches production; production involves quality; quality may involve R&D, procurement or another department; and the organization eventually responds to the customer.

What a smart enterprise could actually do, is to sever the line of complaint. When a customer reports an issue to someone, the details pertaining to the complaint are forwarded automatically to the person who needs to provide the resolution. Not only that, the customer needs to be informed immediately on what has already been done, whose complaint it is, by what method is being provided as an answer and how that specific complaint has led to enhancement of the process. This requires a connected mesh of information rather than a linear chain of departments.

AI will be one important component because it can process multiple parameters quickly, but other technologies will also need to work together. The intelligent enterprise will therefore be interconnected, customer-centric and capable of converting feedback directly into action.

How a CIO is Rewriting Hospital Pillars with Data‑Lite AI

Sir Ganga Ram Hospital, is a leading multi‑specialty institution based in New Delhi. As the hospital’s scale and complexity have grown, so have the operational and clinical pressures: rising call volumes, continuous tendering and procurement, and increasingly demanding diagnostic workloads in the laboratory. Against this backdrop, Arun Kumar Goyal, CIO, at Sir Ganga Ram Hospital, has set out to redefine three key pillars of the hospital, patients, clinicians and back office, using a deliberately data‑lite AI strategy that focuses on small, high‑impact deployments rather than massive data overhauls.

The Challenge: Volume, Complexity and Fragmented Decisions

The hospital faces three intertwined challenges. On the patient side, thousands of people contact the institution every day, many unsure which specialist to consult when they experience multiple symptoms such as weakness, breathlessness, knee pain or arthritis‑like conditions. The call center, with about 25 people, receives “somewhere around 3,000 to 4,000 calls per day,” and, as Goyal admits, “we are missing a huge number of calls” because of sheer volume, leading to dropped calls.

In the back office, the hospital runs a “365 days, 24X7 tendering process” covering approximately 70,000 SKUs and 5,600 tenders. Hundreds of vendors quote for the same items in different units and packaging formats.

“One will give you the price of a single tablet, one in a strip, another guy gives you the price of a box, another guy gives you the liquid,” he says. “We have to normalize the whole data.” Comparing these bids and making decisions about margins, vendor performance and delivery reliability is complex and heavily manual, consuming an entire team’s time throughout the year.

In the laboratory, the hospital needs better visibility into how the lab is performing, which tests are most in demand, what trends are emerging, how many tests are rejected versus received, and how much reagent is consumed per test compared to standard norms. These insights are vital to streamlining operations but are not easily available in real time.

Goyal decided to “choose small use cases so that we are aware of how it will impact our operations, whether these are really AI cases or not.” The idea is to start with data‑lite applications, build awareness and capability, and then move towards more complex clinical AI such as decision support systems.

Pillar 1: Patient: AI Appointment and Chatbot

In the patient pillar, the flagship project is an AI‑driven appointment and chatbot system. Here, patients can speak with AI agents to seek doctor’s appointments, reach out to the appropriate doctor and, crucially, be guided to the right consultant based on symptoms. Goyal describes it as “patient appointment and chatbot, where patients can speak to the agents and seek doctor’s appointments, and reach out to the appropriate doctor.”

The key problem is simple but common: a patient may not know whether to consult orthopedics, cardiology or another specialty when faced with multiple conditions. “Suppose a patient is having weakness, breathlessness, knee pain or some arthritis type of problems, you don’t know whom to consult.” With the new system, “you just have to feed your symptoms and it will guide you to the right consultants.” The same system allows patients to fix appointments directly, and there will be an app for both iOS and Android, along with access through the hospital website and the call center.

In the call center, AI agents sit as the front layer. “These agents will be filtering more and more patients,” Goyal notes. The AI agent provides basic information and triage calls; if it is not able to resolve a query or has never handled a similar case, “then it will pass on to the human in real time so that the patient is not getting frustrated.” The human agent receives the information already captured by the AI and continues the interaction. Because the hospital is currently missing a large number of calls out of the 3,000–4,000 handled daily, Goyal expects that “AI agents will take care of those dropping calls, right, and they will not drop the patient calls.”

The same agents will sit on the website and in the mobile application, ensuring consistency across channels. Vendors, including global technology providers and specialized AI developers, have already presented demos, and the hospital is in the process of evaluation.

“I am hoping in the next three, four months” for implementation, says Goyal. He is clear that this patient‑facing AI project is intentionally data‑lite: “The appointment you completed with the doctor’s database and then time slots, that’s it. It’s not huge data.”

Pillar 2: Back Office: AI‑Enabled Tendering and Procurement

The second pillar, back office, focuses on the tendering process. Sir Ganga Ram Hospital’s tendering runs all year round, “24 by 7,” covering tens of thousands of SKUs and thousands of tenders. Hundreds of vendors participate, quoting for the same medicine or item in different units and packaging formats: single tablets, strips, boxes, liquids and more. As a result, the hospital has to normalize the whole data before any meaningful comparison or decision‑making.

Here, the AI agent’s first role is normalization: standardizing units of measurement, item descriptions and packaging types so that vendor quotes become comparable. Once this is done, the AI supports decision‑making. It helps answer questions such as “Where is the highest margin? Which is the best medicine? Which is the best performing vendor? Who offers services fast?”

“These decisions are today being taken manually, manually or semi‑automatic. With AI, the time and effort involved in the technical tender process will be reduced. There is a specific team which is always engaged all through the year. AI agents will not only provide the regular application, right, but the decision‑making as well,” Goyal says.

Again, he stresses that this is a learning phase: by implementing this application, the hospital will understand whether AI is truly the right solution, and “we will come to know what is the infrastructure required for this—how many GPUs consumption, how many token consumption, what will be the cost of these running through.”

Pillar 3: Laboratory – AI for Lab Performance

The third pillar is laboratory performance. “We are exploring the performance of the laboratory in terms of what is the return of the patient, which are the most performing tests, what is the trend, how many tests are rejected, how many received, what could be done, what is the standard agent consumption per test, how much are you consuming,” says Goyal.

The AI agent will provide answers to these operational queries and help streamline the laboratory: understanding rejection patterns, test performance, and reagent usage, and pointing to areas where processes can be improved.

Data‑Lite AI and the Road to Clinical Decision Support

A distinctive element of Goyal’s approach is the focus on data‑lite AI applications in the first phase. “Before you implement AI, I think the most important part is to have your data,” he observes, which is why he has selected use cases that depend on relatively simple datasets.

The doctor’s appointments doesn’t need too much data. Similarly, in laboratory performance, the hospital will expose test performance metrics and reagent consumption rather than deep clinical histories.

More data‑heavy applications, such as clinical decision support systems (drug interaction checks and patient history‑based decisions), are reserved for a second phase.

“We are planning to implement a clinical decision support system where the AI agent will help clinicians with the help of previous or current data of the patient and aid in decision‑making. This requires richer data including a drug interaction database and patient history,” says Goyal.

Hybrid Architecture

Architecturally, Goyal is planning a hybrid approach. “AI will be trained on our assets,” he says, referring to the hospital’s own data. Initially, he is strategizing to keep patient data in a closed environment. “There may be situations where we need to go to the public,” he acknowledges. In that case, he envisions “creating a separate environment for the agents where they will be and then within the data.”

For instance, “patient data would be in a closed environment, but if I need to do global components comparison, or seek some suggestions from the AI to give me a global or a national perspective, I need to put my data on the public and then get it from them, or maybe get the perspective from the national or global perspective, bring that data into the closed environment and then compare with the environment. That would be a hybrid approach.” He adds, “I would say it’s a two different approach, one closed and one public, with careful separation.”

Ultimately, he sees these initial projects as defining the hospital’s future AI roadmap. “The outcome of this project would actually define the future AI strategy,” he notes. “It is a learning case, I would say, not only for us but for the whole management,” he adds.

JWIL’s Command-Centre Strategy Cuts Losses, Raises Service Levels

For water-infrastructure companies, the digital challenge does not end when a pipeline is laid or a treatment plant is commissioned. It begins when the asset enters operations and maintenance. Water networks span plants, pumping stations, reservoirs, pipelines, meters, field teams and customer interactions, often across difficult terrain and inconsistent connectivity.

That is the challenge JWIL Infra is addressing through an intelligent Integrated Command-and-Control Centre, or ICCC. The company is building a central operational platform designed to unify data from its water projects, improve real-time visibility and eventually create a technology-led services model for utilities.

“We have stopped describing ourselves only as an EPC water-infrastructure company. We are increasingly positioning ourselves as a technology company in the water sector,” says Prasenjit Mukherjee, CIO & CDO, JWIL Infra.

Intelligence Begins at the Endpoint

Mukherjee’s strategy begins with the physical asset. In older O&M (Operation and Maintenance) projects, which may have entered operations seven or eight years ago, automation was limited and many endpoint devices were not smart or connected. That makes it difficult to build intelligence at the backend because critical information remains manual, fragmented or unavailable in real time.

“Unless my endpoint device is smart, I will not be able to build intelligence at the back end. If everything is manual, it does not make sense,” Mukherjee says.

For new projects, JWIL is therefore specifying smart devices during the execution stage itself. The intent is that when projects move into O&M, they come with a digital operating foundation rather than needing to be retrofitted later.

This includes smart meters, SCADA-connected plants, IoT-enabled field operations, GIS-based asset mapping, quality-monitoring sensors and connected maintenance systems. JWIL has previously identified digital twins, IoT and AI as pillars of its technology roadmap.

The Operational Brain

The center is intended to become the operational brain for JWIL’s water infrastructure. The command center sits at the company’s office and includes a large video wall, operator workstations, high-end conferencing capability and a dedicated data-center setup. The software platform is being built on AWS, with the application development supported by a Singapore-based team.

The infrastructure was completed in around six weeks. JWIL has created a separate OT network, independent of its IT network, with dedicated connectivity from Tata Communications and Airtel. It has also deployed a smart data-center rack, firewalls, servers and systems supporting live project monitoring and video collaboration.

“All projects will be managed and monitored centrally from this intelligent command-and-control center,” Mukherjee says.

The platform will consolidate data from multiple operational and customer-facing systems. It will bring together customer master information, billing records, complaint and grievance data, asset-management data, SCADA feeds, GIS maps, field-service and fleet data, smart-meter readings, pipeline-health information and water-quality parameters.

In a Delhi project, for example, the platform is being designed to correlate a customer’s meter details and service requests with the assets serving that customer, the GIS location of pipelines and facilities, the health of associated equipment, and the availability of field teams. A customer complaint will not remain an isolated ticket; it can be assessed against the operating condition of the connected network.

“You can drill down to an asset and see its health condition, the number of complaints linked to it and the condition of the connected consumer network. Everything is correlated,” Mukherjee says.

The command center’s interface is designed to move from a global view to India, then to individual states, projects, packages, districts, distribution areas, pipelines, pumps and assets. JWIL currently has roughly 40 projects across India and one project in Tanzania, enabling the company to monitor a dispersed operating footprint from a single location.

Smart Meters Solve a Bigger Problem

Smart metering is central to the strategy. JWIL is piloting consumer smart meters in a zone near Pandav Nagar, Delhi, and has tied up with Tata Communications for the connectivity layer.

The company is using LoRaWAN gateways because conventional mobile connectivity may not reliably reach meters placed in basements, enclosed compounds or other difficult locations. Meter readings will move through the LoRaWAN network to a head-end system, then into JWIL’s metering-and-billing platform and finally into the ICCC environment.

The company has also acquired a water-meter manufacturing business. This gives JWIL greater control over a critical part of the technology stack.

“The meter will also be ours. The device layer, the data layer and the operations layer have to work together,” Mukherjee says.

The smart-meter program is about more than automating billing. It gives JWIL an opportunity to measure the difference between water entering the system and the water recorded at the last mile. This can help identify both physical leakage and commercial loss.

Mukherjee identifies three outcomes that matter most in water operations: availability, quality and loss reduction.

“In water, three things matter: uptime or availability, quality, and reduction of losses,” he says.

In Delhi, it has the scope of slashing water losses from about 65% to 15%. Each pilot has unique KPIs, but the aim of using the command center is to increase the visibility in all the three metrics of availability, meeting quality expectations and cutting down on non-revenue water.

From Response to Prediction

JWIL is building digital twins for its projects so that operating teams can go beyond monitoring to simulation. The platform can model what may happen if pressure changes in a pipeline, an asset fails, a new device is added, or a section of the network has to be shut down for repair.

“If I have to repair a network, I should be able to see which customers will be affected and how I can reroute water through another loop. That is the value of the digital twin,” Mukherjee says.

The company expects to employ the digital-twin layer on existing water-treatment facilities, sewage plants and seawater-desalination facilities. Water-treatment plants, desalination facilities and sewage facilities operate under unique conditions, yet share the same goal: The creation of a virtual replica of the actual situation, which is then applied to better support business process-management decisions.

JWIL is also building an AI layer above the digital twin. The aim is not just descriptive reporting or alerts, but recommended actions. The system should help operators determine how to respond to a leakage risk, reroute supply, prioritize maintenance or resolve an emerging operational issue.

For pipeline management, JWIL is evaluating a proactive health-monitoring approach that includes robotic-camera-based inspection. Rather than wait for a pipe to burst or leak, the company wants to identify potential issues before they become service disruptions.

“Instead of waiting for a leak or a burst, we want to scan the infrastructure and act before the failure occurs,” says Mukherjee.

This also changes the way customer complaints are handled. If a consumer raises an issue, the ICCC platform can identify the nearest technician team, track the route of the field vehicle, understand the local asset condition, assess whether material or labor will be required and simulate whether supply to a zone needs to be isolated or rerouted.

The objective is faster resolution, stronger SLA adherence and fewer customer disruptions.

The Last-Mile Reality Check

The most difficult problem is not building a dashboard or deploying a digital twin. It is ensuring that data from remote, distributed infrastructure can reach the platform reliably and in real time.

Many water projects extend well beyond urban centers. The collection and storage systems (treatment plants, storage tanks and reservoirs) and distribution pipes can often be situated in isolated, out of the way locations lacking even consistent mobile signals, The data from a sensor providing indication of a low tank level are of no value if they are not transmitted promptly to enable automatic action at the control center.

“The real challenge is the data that must come from the last mile to the platform. In many remote locations, even mobile signals do not work. If I do not receive the signal in real time, I cannot act on it. Connectivity economics are strongest in metropolitan areas, but water systems need to function reliably in villages and remote locations,” Mukherjee says.

Turning O&M Into a Platform Business

The centre program is not only an internal digital transformation initiative. It supports a new commercial proposition for JWIL.

The company wants to offer its technology stack to state water utilities as a managed water-services platform. The model could include the command center, smart metering, connected assets, digital-twin capabilities and operational analytics, priced on a per-consumer, per-month basis.

“We can build the command center, digital twin and operational platform for a city, then offer water services as a platform. The model can be priced per consumer, per month,” Mukherjee says.

This would mark a significant evolution from an EPC-led business to a recurring, technology-enabled services model. Rather than simply execute a project and manage assets, JWIL could become a long-term digital operations partner to utilities.

The Next Layer: 5D Project Execution

The current ICQ rollout focuses on operations and maintenance. But Mukherjee’s longer-term objective is to extend the same command-center view to project execution.

The company is exploring how engineering drawings can be converted into 3D models and combined with schedule, cost and risk data to create a 5D Building Information Modelling (BIM) environment.

“Once I select a project in the command center, I should be able to see the scope, completion status, cost, risk and progress through a 5D BIM model,” Mukherjee says.

If successful, the approach could establish a continuous digital thread, from engineering and construction through commissioning, O&M, customer service and performance management.

For a sector built on complex physical infrastructure, that continuity is where digital transformation moves beyond technology deployment. It becomes a way to deliver more reliable water, respond faster to citizens and build a more commercially sustainable utility model.

Atul Nigam Moves On From Kajaria Ceramics

Atul Nigam has moved on from his role as Vice President-Information Technology at Kajaria Ceramics, where he led the tile manufacturer’s digital-transformation agenda since 2023. During his tenure, Nigam’s mandate included spearheading SAP S/4HANA, CRM and dealer-management capabilities, loyalty platforms, CXO dashboards, infrastructure modernization and cybersecurity governance.

He reported to the Managing Director and Board while driving a multi-year roadmap across manufacturing, sales and supply-chain operations.

Nigam brings more than three decades of technology leadership experience across manufacturing, consumer electronics, mobility and telecom. Before Kajaria Ceramics, he was Senior Vice President and CIO at Revolt Motors and previously served as CIO and Head of VAS at Micromax Informatics. Prior to that, Nigam spent 17 years at Samsung India Electronics & Samsung Data Systems.

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