Palo Alto Networks Launches Frontier AI Critical Defense Program to Shield Infrastructure at AI Speed

A collaboration of leading technology providers to protect critical infrastructure against the rapid rise of AI-discovered vulnerabilities

Palo Alto Networks (NASDAQ: PANW) today announced the Frontier AI Critical Defense Program, a first-of-its-kind initiative to protect critical infrastructure from AI-driven exploits. Through the program, leaders across operational technology (OT), healthcare, commercial software and open-source communities coordinate with Palo Alto Networks to deploy proactive "virtual patches,” neutralizing vulnerabilities at the network-level before attackers can exploit them.

Palo Alto Networks recently used Frontier AI models to uncover more than 14,000 previously unknown vulnerabilities in open source software, underscoring how AI could enable threat actors to automate cyberattacks and shrink attack timelines. Yet, critical infrastructure operators, constrained by strict uptime and safety testing, cannot patch at AI speed. This mismatch creates a significant exposure gap, leaving essential systems vulnerable long before software fixes can be safely deployed.

​​True defense at AI speed requires joint action. This program builds on our existing collaborations with IBM and Red Hat (as part of Lightwell), Microsoft (as part of MAPP) and OT leaders like Siemens and the Idaho National Laboratory (as part of the OT Threat Research Lab).

Today, the collaboration is expanding to include Anthropic, OpenAI, OT leaders like Mitsubishi and Axis Communications, industry consortiums for sharing risk information like Analysis and Resilience Center for Systemic Risk and Health-ISAC, OT research organizations like the independent, non-profit Energy R&D Institute (EPRI) and OSS initiatives like Akrites (an initiative from the Linux Foundation).

Palo Alto Networks Frontier Virtual Patching puts these insights into action to deliver proactive protection for joint customers. By combining Frontier AI threat discovery with trusted vulnerability intelligence, it delivers rapid network-level patches while safeguarding sensitive vulnerability details from attackers.

Lee Klarich, Chief Product Officer, Palo Alto Networks, “In the age of Frontier AI, the traditional, reactive race to build and deploy software patches before adversaries exploit a flaw is a losing battle. Protecting critical infrastructure requires a structural shift from isolated patching to collective, proactive intelligence. Through initiatives like our Frontier AI Critical Defense Program, we can neutralize threats at the network layer before they are weaponized.”

Help safeguard critical infrastructure by joining the expanding Frontier AI Critical Defense Program, today. Visit the website to learn more on how to get involved, or explore Palo Alto Networks broader Frontier AI Defense Initiative.

Palo Alto Networks (NASDAQ: PANW), the global AI cybersecurity leader, protects our digital way of life with a comprehensive portfolio of cybersecurity solutions and platforms across Network, Cloud, Security Operations, AI and Identity. Trusted by 70,000+ customers and powered by Unit 42 threat intelligence, our AI-driven platforms eliminate complexity, empowering enterprises to modernize with confidence and securing the speed of innovation. Explore the future of security at www.paloaltonetworks.com.

Forward-Looking Statements

This release contains forward-looking statements with respect to Palo Alto Networks that involve risks, uncertainties and assumptions, including, without limitation, statements regarding the benefits, impact, or performance or potential benefits, impact or performance of Palo Alto Networks products, technologies, and integrations or future products, technologies, and integrations. These forward-looking statements are not guarantees of future performance, and there are a significant number of factors that could cause actual results to differ materially from statements made in this release. Palo Alto Networks identifies certain important risks and uncertainties that could affect its results and performance in its most recent Annual Report on Form 10-K, its most recent Quarterly Report on Form 10-Q, and its other filings with the Securities and Exchange Commission from time-to-time, each of which are available on Palo Alto Networks' website at investors.paloaltonetworks.com and on the SEC's website at www.sec.gov. All forward-looking statements in this release regarding Palo Alto Networks are based on information available to Palo Alto Networks as of the date hereof, and Palo Alto Networks does not assume any obligation to update the forward-looking statements provided to reflect events that occur or circumstances that exist after the date on which they were made.

India's AI Pilots Need a Frontline Failure Log Before They Scale

India is moving quickly from AI ambition to deployment. The IndiaAI Mission's Safe and Trusted AI work now spans 13 responsible-AI projects, 58 centres of excellence (CoEs) and 27 data and AI labs. A new innovation challenge is offering promising systems a path into MSME governance and AYUSH-enabled public health, including structured pilot support and the possibility of multi-year government contracts.

That is exactly the kind of momentum India needs. But the hard part begins after a demonstration succeeds.

A pilot can look impressive because the data is clean, the users are motivated and the exceptions are quietly handled by the people running the test. Production is different. Real users switch between languages. Records are incomplete. Policies change. A small error travels into a customer decision, a benefit application, a health recommendation or a business filing. The tool may save ten minutes at the front end while creating an hour of checking and correction somewhere else.

India's startups and public agencies therefore need a simple discipline before they scale an AI system: a frontline failure log.

The pilot-to-production gap

Most organisations already collect technical metrics such as response time, uptime and model accuracy. Those numbers matter, but they often miss the moment when a system fails in actual work.

Consider an AI assistant used to help a small business identify a government scheme. The model may retrieve the right programme but misunderstand the applicant's industry classification. A staff member catches the mistake, rewrites the query and gives the correct answer. The interaction may still be recorded as successful. Yet the correction reveals something important about the data, the prompt, the workflow and the training users need.

The same problem appears in health, finance, hiring and customer service. Human intervention makes the system appear more reliable than it is. Unless that intervention is recorded, leaders cannot see the true cost of adoption or the conditions under which the tool becomes unsafe.

India's AI Governance Guidelines rightly emphasise trust, people-first design, fairness, accountability, understandable systems and resilience. A frontline failure log turns those principles into operational evidence.

What the log should capture

The log does not need to become another compliance platform. A lightweight form can capture seven fields.

First, record the real task. "Drafted a reply" is too vague. Note whether the system interpreted an eligibility rule, summarised a medical history, classified a supplier, answered a customer or recommended an action.

Second, record the operating conditions. Was the source current? Was the user speaking Hindi, Tamil, Bengali or a mix of English and a regional language? Was a scanned document difficult to read? Did the system lack a crucial field?

Third, record what the system did. The point is not to save every word of every interaction. It is to capture the decision or output that mattered.

Fourth, record the human intervention. Did someone correct a fact, reject a recommendation, add missing context, change a category or stop the workflow entirely?

Fifth, record the downstream consequence. Did the error merely create awkward wording, or could it have delayed a payment, misdirected an applicant, exposed personal data or produced an unfair result?

Sixth, record the rework. Count the minutes spent checking, correcting, escalating and repairing the result. This is the difference between gross time saved and reliable work completed.

Seventh, name the owner and next action. Someone must decide whether the response calls for better training, fresher data, a changed workflow, a narrower use case or a stop rule.

A scaling asset for founders

For startups, the log is not an admission that the product is weak. It is evidence that the company understands the environment in which its product must operate.

A founder can use the data to distinguish a one-off user mistake from a recurring design problem. Product teams can see whether failures cluster around a language, a document type, a customer segment or a policy change. Sales teams can describe operating limits honestly. Investors and public-sector buyers can evaluate whether the system is becoming more dependable instead of relying on a polished demonstration.

The log also creates a better learning loop for employees. People are more likely to report a near miss when leaders treat it as useful evidence rather than proof that someone used the tool badly. That psychological safety matters because the most valuable information often comes from the employee who notices that the answer looks plausible but is wrong.

India's recent AI governance architecture, including the new inter-ministerial AI Governance and Economic Group, recognises that innovation, labour-market effects and public trust must be handled together. Frontline evidence is where those priorities meet.

Scale what survives reality

India does not need to slow its AI ambitions. It needs to make scaling more selective.

Before a pilot expands, leaders should be able to answer basic questions. What kinds of failures occurred? Who caught them? How much hidden work did correction require? Which users or communities faced the greatest risk? Did the failure rate fall after changes were made? Is there a clear human owner when the system is uncertain?

A pilot that cannot answer those questions is not ready for scale, no matter how impressive the demo appears.

India's advantage will not come only from building more models or funding more pilots. It will come from learning faster than others about how AI behaves in the messiness of real work. A frontline failure log gives founders, agencies and employees the evidence to do that—and turns responsible AI from an aspiration into a practical operating habit.

AUTHOR – Gleb Tsipursky, PhD, a behavioral scientist, CEO of Disaster Avoidance Experts, and author of The Psychology of AI Adoption at Work: From Resistance to Results (Georgetown University Press, 2026). https://disasteravoidanceexperts.com/aibook

From Bangkok to Delhi: OHM's Sathi Roy Brings AI and Consciousness Dialogue Worldwide

• OHM Founder Sathi Roy brings global AI and Consciousness tour to New Delhi following Bangkok launch with The AI Collective

India has one of the largest populations of AI users and builders in the world. As the technology becomes woven into everyday life, the questions surrounding it are beginning to shift. Less about what AI can do. More about what it is doing to us.

(L-R) Sathi Roy & Nathasha Kumar

In February, New Delhi hosted the India AI Impact Summit, the first of the global AI summits to be held in the Global South. Months later, the conversation has moved well beyond policy rooms and into boardrooms, startup communities and everyday life, where founders, investors and leaders are increasingly asking what rapid advances in artificial intelligence mean for human agency, identity and consciousness.

Against this backdrop, Our Highest Mantra (OHM) brought its global AI and Consciousness speaking tour to New Delhi. Following its opening event in Bangkok, presented in partnership with The AI Collective, OHM convened a private gathering of founders, investors, technologists, creators and operators to explore one of the defining questions of the AI era: how humanity evolves alongside increasingly intelligent machines.

The private gathering featured OHM Founder and CEO Sathi Roy alongside James Pimentel-Pinto, award-winning technologist, UK Regional Director of The AI Collective, Apple's first iPhone developer in Europe, and Chief Product Officer of OHM. Together, they led a discussion exploring how increasingly intelligent technologies are reshaping not only the way we work, but the way we think, relate, make decisions and experience being human.

Having followed Roy's work on AI, consciousness and modern spirituality, conscious entrepreneur Nathasha Kumar, Founder and Managing Partner of Maloka and Founder of NeoYug, invited her to continue the conversation through a filmed discussion for her platform exploring AI, consciousness and spirituality with founders, investors and modern India.

Roy comes to the subject from inside high-growth business rather than from outside it. She spent more than a decade scaling companies, including one that moved from 30 million dollars to 7 billion dollars in valuation, worked within the family office of a billionaire real estate principal, led teams across the United States and Asia, and has mentored over 1,000 people. She built OHM after watching what that pace does to the people sustaining it.

“It was a slow accumulation of evidence,” Roy says. “I watched people achieve the outcomes they were trained to pursue. But instead of arriving at steadiness, they were scattered and dysregulated.”

Roy is currently leading OHM's global AI and Consciousness speaking tour alongside James Pimentel-Pinto in partnership with The AI Collective, bringing conversations on artificial intelligence, consciousness and human agency to founders, investors and technology leaders across major cities worldwide. She believes AI and consciousness is one of the defining questions of the decade, as societies work out what increasingly capable technology means for human identity, agency and experience.

In New Delhi, she pushed back on a common assumption: that intelligence and consciousness are the same thing. AI may turn out to be one of the most powerful tools we have ever built, she said, but it is not the same as being alive.

“Artificial intelligence is becoming part of the operating system of modern life. It can organize information, speed up work, identify patterns and help us solve problems at a scale previously unimaginable,” said Roy. “But intelligence is not the same as consciousness. Consciousness is lived experience: memory, emotion, intuition, meaning, relationships and the ability to be transformed by life itself.”

The more urgent question, she argued, is not whether AI will replace people. It is what kind of people we become as we live with it.

"The deeper risk is not that machines become more capable,” Roy said. “It is that we begin surrendering our own agency without realizing it. We can outsource calculations and repetitive tasks, but we must be careful not to outsource curiosity, discernment and the discomfort of not knowing. Some of humanity’s greatest growth emerges from uncertainty, reflection and direct experience."

AI, in her reading, does not create that condition. It magnifies it.

"Technology amplifies whatever internal state it touches,” she says. If founders are scattered, the tools accelerate the scatter. If they are steady, the same tools extend their clarity. Which is why, she told the room, the next real advantage will not be speed. It will be internal stability.

She also expects the language around this to change as it goes mainstream.

“What will enter the mainstream will not look like spirituality,” Roy says. “It will look like emotional literacy, nervous system regulation and cognitive resilience. The future will call it good design.”

India, she said, sits in an unusual place in this. It has the demographic weight, the engineering talent and the pace. It also has a tradition that has been asking what consciousness actually is for several thousand years, and Roy reads that tradition as a set of internal technologies for regulation and inquiry rather than as belief or heritage.

“India carries a deep inheritance of universal spiritual wisdom,” she says. “Sharing that intelligently may be its most meaningful global contribution.”

Nathasha Kumar, Founder and Managing Partner of Maloka, said: “We need more humans like Sathi. An insider to the AI revolution, moving from dharma, not just bluntly building and optimizing. Consciousness and awareness are the core seeding of these actions, not just an afterthought or a good-to-have.”

The evening was one of a series of conversations Roy is having with founders, investors and researchers around the world about the responsibilities that come with building the next chapter of technology.

Our Highest Mantra (OHM)

Our Highest Mantra (OHM) is a human flourishing ecosystem that integrates frequency-based longevity support, Vedic-inspired spiritual wisdom, AI-guided reflection, and conscious community into a modern membership experience. Through non-invasive, clinically guided frequency protocols, educational content, and a global community, OHM supports greater vitality, awareness, and coherence while exploring the intersection of science, consciousness, and human potential.

The AI Collective

The AI Collective is a global platform that brings together builders, investors, researchers and thinkers to examine how artificial intelligence and new technologies are changing the world and what we must consider as we shape that change.

IBM and Sarvam Collaborate to Advance Full Stack Sovereignty for AI, Sovereignty for Government and Regulated Enterprises Across India

• IBM Sovereign Core and Sarvam's India stack to help organizations operationalize sovereign AI aligned with India's governance and sovereignty requirement

IBM (NYSE: IBM) and Sarvam, India's full-stack sovereign AI company, today announced a collaboration to advance the development and adoption of sovereign AI technologies in India. Through the collaboration, IBM and Sarvam will work together to demonstrate and pilot technologies and solutions targeting industry use cases relevant for central and state governments, public sector organisations, and regulated enterprises across India. 

This initiative aims to accelerate the adoption of trusted, governed, and sovereign AI capabilities across industry and public sector ecosystems through innovation pilots, solution accelerators, technical advisory, and knowledge-sharing initiatives. It brings together IBM Sovereign Core, IBM's AI-ready sovereign-by-design software that enables full control over data, operations and governance, with Sarvam's Sovereign AI stack, which includes reasoning models and India-first language and voice AI trained from scratch in India. The combined offering is purpose-built for the public sector and regulated enterprises aligned with the country’s regulatory, security, and operational requirements.

Together, IBM and Sarvam will power use cases like citizen services, grievance redressal, document processing, and administrative workflows on full stack sovereign AI. This ensures accelerated delivery of secure, multilingual, and voice-enabled Government-to-citizen services at scale.

The IBM GovTech AI Innovation Center in Lucknow will serve as a joint incubation and demonstration hub, where government departments, public sector organisations, enterprises and industry stakeholders can work through the technical, operational, and governance requirements of moving AI from pilots into production and explore practical applications of sovereign AI.

Sriram Raghavan, General Manager, IBM Software, India and Software Innovation Lab said, "Sovereign AI is not simply about where AI runs. It is about giving organizations control over how AI is governed, deployed, and operated. IBM Sovereign Core provides an open, enterprise-grade platform and middleware foundation designed to help governments and regulated enterprises scale AI within environments they trust while addressing governance, security, and compliance requirements. By combining these capabilities with Sarvam's India-first AI technologies, we aim to help organizations operationalize sovereign AI and accelerate the journey from experimentation to production-scale outcomes."

“Sovereign AI has to run inside the systems governments and enterprises already depend on, and at the scale at which those systems operate,” said Pratyush Kumar, Co-Founder of Sarvam. "IBM's Sovereign Core gives governments an AI-ready technology foundation they control. Our stack puts models, voice, and language technologies on top of it, so a citizen can access a benefit or resolve a grievance in their own language, on a phone call."

About IBM

IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Thousands of government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM's long-standing commitment to trust, transparency, responsibility, inclusivity and service. Visit www.ibm.com for more information.

HCLTech Report Exposes Widening AI Divide With Only 18% of Enterprises Seeing Revenue Impact Despite Near-Universal Adoption

HCLTech, a leading global technology company, today released its global research report, The Blueprint for AI Leadership, revealing a widening gap between organizations adopting AI and those realizing meaningful business value.

The study, conducted with Raconteur among 500 enterprise decision-makers, shows that AI adoption is no longer the constraint. 90% of organizations report that GenAI and Agentic AI are transforming workflows, with 91% citing improved data access and 90% reporting productivity gains.

However, alarmingly, only 18% say AI is delivering significant revenue impact, exposing a critical gap between operational progress and business outcomes. These enterprises have emerged as AI Leaders by not just adopting AI, but by systematically converting it into growth, innovation and customer experience advantage. These enterprises are four times more likely to scale agentic and autonomous AI, with superior execution in defining measurable use cases (73% vs. 22%) and securing senior leadership sponsorship (63% vs. 36%).

The lagging enterprises that act as AI Followers remain trapped in incremental gains, evaluating AI through efficiency and cost lenses alone, limiting their ability to compete on differentiated outcomes or scale impact enterprise wide.

The divergence is sharpest in foundations. AI Leaders have integrated AI into business strategy, built data readiness and are far ahead in workforce transformation, with 93% having structured upskilling programs, compared to just 20% of AI Followers.

Critically, Leaders are embedding AI into core workflows and decision-making, enabling scaling, adaptability and continuous improvement, capabilities AI Followers are yet to institutionalize. This shift from deploying tools to orchestrating enterprise-wide transformation is the defining advantage in AI maturity.

“AI has entered a decisive phase, and success will come down to how well organizations bring people, data and technology together,” said Pawan Vadapalli, Corporate Vice President and Global Head, Digital Business Services at HCLTech. “The organizations pulling ahead are not just running more pilots; they are rethinking how the business works, embedding AI into everyday decisions and workflows. It is this coordinated shift across leadership, culture and foundations that turns AI from a tool into real, long-term advantage.”

To access the full report, please visit: The Blueprint for AI Leadership: AI ROI Report | HCLTech

TCS Launches Autonomous Engineering Lab with NVIDIA to Accelerate Industrial AI Deployment

Tata Consultancy Services (TCS), a global leader in IT services and consulting, has announced the launch of the TCS Autonomous Engineering Lab Powered by NVIDIA at its Global Axis campus in Bengaluru. This new facility is positioned as India’s first physical AI hub dedicated to accelerating industrial AI solutions, with a focus on mobility and manufacturing.

The lab represents a strategic expansion of TCS’ collaboration with NVIDIA, moving beyond technology integration into deeper capability building and enterprise-wide transformation. By combining TCS’ Industrial Autonomy & Engineering (IA&E) expertise with NVIDIA’s full-stack AI infrastructure, the lab provides enterprises with a scalable pathway to move from pilot projects into production-scale deployment.

Sreenivasa Chakravarti, Global Head & Vice President of Industrial Autonomy and Engineering at TCS, described Bengaluru as the “engine of India’s economy,” noting that the lab harnesses this energy to reimagine what’s possible with AI. He emphasized that the facility will enable rapid movement from concept to real-world impact, shaping the future of mobility and industrial systems. Alvin DaCosta, Vice President at NVIDIA, highlighted the importance of specialized infrastructure to bridge the gap between simulation and deployment, stressing that the lab offers enterprises a robust environment to validate and implement next-generation solutions.

The lab’s offerings span a wide spectrum of industrial AI applications. TCS DriveSphere™, an AI-led mobility platform, enables software-defined vehicles through digital twins, predictive analytics, and lifecycle management. Advanced driver assistance systems (ADAS), autonomous driving capabilities, and intelligent perception systems are supported by NVIDIA’s AI stack. In manufacturing, AI is embedded into machines and plants to enable predictive maintenance, automated quality inspection, and real-time process optimization. High-fidelity digital twin simulations further accelerate design validation and scenario testing, reducing risks and improving time-to-market.

Beyond technology, the lab is designed to help enterprises reimagine products, operations, and business models. By embedding intelligence across the engineering and manufacturing lifecycle, businesses can transition toward intelligent, software-defined, and autonomous enterprises. The controlled environment allows for rapid prototyping and validation, ensuring that solutions are tested thoroughly before rollout.

This initiative builds on the longstanding partnership between TCS and NVIDIA in AI and accelerated computing. With the Bengaluru lab, the collaboration enters a new phase—one that emphasizes scaled deployment, client engagement, and transformative impact across industries worldwide.

For a global audience, the launch underscores India’s growing role as a hub for industrial AI innovation, while also highlighting how enterprises worldwide can leverage this facility to accelerate their journey toward autonomy and intelligence.

TCS to Hire 8,900 AI Engineers, Eyes Big Tech Acquisitions

Tata Consultancy Services (TCS) is planning to deploy between 5,900 and 8,900 forward‑deployed AI engineers and is actively scouting acquisitions in AI, data security, and cybersecurity — marking one of its biggest strategic pivots in years.

Key Highlights

• Scale of Deployment: TCS aims for 1–1.5% of its workforce to serve as forward‑deployed engineers (FDEs), embedding directly with clients to accelerate AI adoption.
• Headcount Impact: With ~590,000 employees at end‑June 2026, this translates to 5,900–8,900 engineers.
• Acquisition Strategy: After years of organic growth, TCS is now evaluating AI, data security, and cybersecurity acquisitions to strengthen its positioning.
• Annual Investment: TCS spends about $1 billion annually on talent development and AI accessibility.

Strategic Context

Focus Area	Details	Competitive Angle
Forward‑deployed engineers	Embed with clients, tailor AI tools to business needs	Competes with OpenAI, Anthropic, Microsoft
AI acquisitions	Target firms in AI, data security, cybersecurity	First major inorganic push since late 2025
Talent development	$1B annual spend on training, niche recruitment	Builds internal AI‑native expertise
Client differentiation	Deep knowledge of customer environments	Counters fears of outsourcing disruption

⚔ Competitive Landscape

• OpenAI Deployment Company: Embeds engineers directly in enterprises, backed by $4B funding.
• Anthropic: Expanding Claude‑trained FDEs for enterprise workflows.
• Microsoft: Scaling Copilot deployments across industries.
• Indian peers: Wipro and LTM are also building large pools of FDEs, intensifying domestic competition.

📉 Risks & Challenges

• Investor Concerns: AI could reduce demand for large engineering teams, shorten project timelines, and pressure pricing.
• Revenue Growth: TCS’s AI revenue growth slowed to 13% in Q1 2026, down from 28% in the prior quarter.
• Execution Risk: Balancing retraining vs. external hiring for thousands of FDEs may strain resources.

🌍 Why It Matters for India

• Gurugram and Bengaluru — hubs for IT services — are likely to see early hiring waves of these AI deployment engineers.
• TCS’s pivot signals that India’s IT giants are betting on AI integration services as the next growth frontier, not just cost arbitrage.

NTT DATA and Hyster-Yale Materials Handling Announce Breakthrough Physical AI Solution in Manufacturing

Representative Image 

• Physical AI embeds intelligence into quality assurance for critical assembly operations
• When compared to legacy techniques, physical AI cuts deployment timelines from months to weeks
• Co-innovation improves product reliability and maintains quality standards at scale

NTT DATA, a global leader in AI, digital business and technology services, and Hyster-Yale Materials Handling, Inc. (HYMH), the manufacturer of Hyster® and Yale® lift trucks, today announced a breakthrough application of physical AI that embeds intelligence directly into manufacturing processes. This approach leverages sensor data to enable machines and systems to perceive, understand and act in real-time within real-world operations.

Bringing this capability into practice introduces AI-driven quality assurance directly into HYMH’s manufacturing operations. This co-developed approach represents a first-of-its-kind use case of how physical AI can be applied in an industrial assembly environment by embedding intelligence into production workflows, helping to safeguard that products are built to consistently high standards.

NTT DATA designed and developed the solution at HYMH’s manufacturing facility in Berea, KY, integrating vision sensors, edge AI that processes data on-site and advanced analytics into a critical assembly workflow.

Together with partner Archetype AI, NTT DATA in collaboration with HYMH, adapted a physical AI model that analyzes assembly activity against expected production steps, validating that all parts are installed and assembly stages are completed, flagging deviations before the product moves to the next stage. By validating quality throughout the assembly process, the solution helps identify and address potential issues before products leave the factory floor.

This initiative demonstrates a step-change in how AI can be applied in manufacturing environments. Combined with edge computing, the solution can run locally so all processing happens on-site, enabling faster rollout and quicker time-to-value. Early results showed that physical AI cuts deployment timelines from months to weeks when compared with legacy techniques, accelerating adoption and iteration across manufacturing operations.

“Our confidence in physical AI continues to grow, and we’re starting to see the countless benefits that AI can bring to our global manufacturing operations,” said Barbara Binda, Director of Global Manufacturing Innovation, Hyster-Yale Materials Handling. “Working with NTT DATA allows us to leverage how physical AI can help our production teams maintain high-quality standards and deliver the most reliable products to our clients.”

"This deployment shows what physical AI looks like in real production environments, not as a concept, but with tangible impact on the factory floor,” said Shahid Ahmed, Global Head of Edge Services, NTT DATA, Inc. "By combining real production data with physical AI models at the edge, we’re helping leading manufacturers like HYMH deliver high quality products, support frontline workers and apply AI in ways that deliver real-world outcomes.”

As manufacturers accelerate automation, demand is rising for physical AI that can operate safely in complex environments, driving efficiency, quality and resilience. NTT DATA is uniquely positioned to deliver this capability at scale, combining industry expertise with end-to-end services to integrate AI across IT and operational technology environments, enabling intelligent, data-driven operations.

Today’s news builds on a longstanding collaborative relationship between NTT DATA and HYMH. Together, the companies are advancing more adaptive and intelligent manufacturing processes and exploring how physical AI can be scaled to drive repeatable, high-quality production outcomes.

About NTT DATA

NTT DATA is a $30+ billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world’s leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. Our consulting and industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 70 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is part of NTT Group, which invests over $3 billion each year in R&D. Visit us at nttdata.com

About Hyster‑Yale Materials Handling

Hyster-Yale Materials Handling, Inc., designs, engineers, manufactures, sells and services a comprehensive line of lift trucks, parts and technology and energy solutions marketed globally primarily under the Hyster®, Yale®, Nuvera® and Maximal® brand names. The company’s subsidiary, Bolzoni S.p.A., is a leading worldwide producer of attachments, forks, masts and lift tables marketed under the Bolzoni®, Auramo® and Meyer® brand names.

Hyster-Yale Materials Handling is a wholly owned subsidiary of Hyster-Yale, Inc. (NYSE: HY).

Cisco’s AI Scans 1.8 Billion Lines of Code in Just Eight Weeks, Redefining Enterprise Cybersecurity

Cisco has compressed eight years of manual security research into just eight weeks by scanning 1.8 billion lines of code with AI, achieving unprecedented scale, speed, and accuracy. This breakthrough sets a new benchmark for enterprise cybersecurity and raises expectations across the industry.

Notably, several other companies have deployed AI to scan massive codebases, though Cisco’s 1.8 billion‑line achievement is among the largest. Firms like GitHub, Amazon, Checkmarx, and Snyk have rolled out AI‑native security scanning platforms that handle millions to billions of lines of code across enterprise portfolios.

Cisco’s AI Milestone in Cybersecurity

Cisco has compressed eight years of manual security research into just eight weeks by scanning 1.8 billion lines of code with AI, achieving unprecedented scale, speed, and accuracy. This breakthrough sets a new benchmark for enterprise cybersecurity and raises expectations across the industry.

The Problem Cisco Tackled

For decades, cybersecurity teams faced two major limitations:

• Selective scanning: Teams had to prioritize modules based on risk, leaving large portions of code unexamined.
• Noise overload: Static analysis tools often produced one useful finding for every 10,000 warnings, forcing teams into endless triage cycles.

This meant adversaries could exploit vulnerabilities in “unscanned” areas, while defenders struggled to separate signal from noise.

Cisco’s Approach

Cisco embedded years of domain knowledge from its Advanced Security Initiatives Group into a rigorous orchestration harness. The AI models acted as accelerants, but the harness was the engine ensuring consistency, quality, and independence from any single model. Tested across six frontier AI systems — including Claude Mythos Preview and GPT 5.5-Cyber — the framework achieved a false positive rate under 3%, a dramatic improvement over traditional tools.

Aspect	Traditional Approach	Cisco AI Approach
Timeframe	8 years	8 weeks
Scope	Selective modules	Entire codebase
Noise	High false positives	<3% false positives
Tools	Manual red teaming, static analysis	Frontier LLMs + orchestration harness
Impact	Limited visibility	Enterprise-wide vulnerability detection

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Strategic Implications

• Industry Benchmark: Cisco’s success establishes a new baseline for what large enterprises can achieve with AI-assisted code review.
• Regulatory Pressure: Auditors and insurers may now expect similar AI-driven reviews, raising the bar for defensible security postures.
• Security Debt Reduction: Enterprises with legacy codebases can accelerate vulnerability detection and remediation.
• Autonomous Defense: The achievement aligns with DARPA’s AI Cyber Challenge, moving closer to autonomous cyber defense systems.

Risks and Challenges

• Adoption Complexity: Integrating AI into legacy workflows requires cultural and technical adaptation.
• Model Dependence: Effectiveness depends on orchestration harnesses, not raw model power alone.
• Industry Scrutiny: Organizations delaying audits may face increased regulatory and reputational risk.

Conclusion

Cisco’s AI-powered code scan is more than a speed record — it is a structural shift in cybersecurity assurance. By proving that billions of lines of code can be scanned quickly and accurately, Cisco has set a precedent that regulators, insurers, and competitors will likely adopt as the new standard. 

Ambani & Mittal Join Global AI Commission to Shape Responsible Tech Future

Mukesh Ambani (Reliance Industries) and Sunil Bharti Mittal (Bharti Enterprises) have been named founding members of the ITU-backed AI for Good Global Commission, joining over 40 world leaders and tech executives to shape responsible, inclusive AI adoption worldwide. The inaugural meeting will take place in Geneva from July 7–10, 2026.

The International Telecommunication Union (ITU) is the United Nations’ specialized agency for digital technologies (ICTs), headquartered in Geneva, Switzerland. It coordinates global telecom networks, allocates radio spectrum, sets technical standards, and works to expand digital access worldwide. 

Mukesh Ambani & Sunil Mittal Join ITU AI Commission

India’s role in the ITU-backed AI for Good Global Commission is both strategic and symbolic — positioning the country as a key voice in shaping global AI governance. With Mukesh Ambani (Reliance Industries) and Sunil Bharti Mittal (Bharti Enterprises) as founding members, India’s presence reflects its ambition to influence how AI is deployed responsibly across sectors and geographies.

About the AI for Good Global Commission

• Launched by ITU – International Telecommunication Union, Rwanda’s President Paul Kagame, Salesforce CEO Marc Benioff, and ITU Secretary-General Doreen Bogdan-Martin.
• Founding members – Ambani, Mittal, Lakshmi Mittal (ArcelorMittal), Nvidia CEO Jensen Huang, Microsoft President Brad Smith, Amazon CEO Andy Jassy, Google’s James Manyika, Qualcomm CEO Cristiano Amon, Accenture CEO Julie Sweet, Vodafone CEO Margherita Della Valle.
• Mission – Strengthen trust in AI, expand access, and accelerate AI’s use to solve global challenges.

Key Priorities

• AI Trust – Promote responsible AI to build public confidence.
• AI Access – Expand digital connectivity; with 2.2 billion people still offline, bridging the digital divide is central.
• AI Impact – Accelerate AI deployment for healthcare, education, and sustainability.
• Developing countries’ voice – Ensure equitable participation in shaping AI governance.

Upcoming Events

• Inaugural Meeting – July 7–10, 2026, Geneva, during ITU’s AI for Good Global Summit.
• Digital Week – July 6–10, 2026, includes the first UN-mandated Global Dialogue on AI Governance and WSIS Forum 2026.

Why This Matters for India

• Strategic positioning – Ambani and Mittal’s inclusion highlights India’s growing influence in global AI governance.
• Industry impact – Reliance and Bharti’s participation could accelerate AI adoption in telecom, cloud, and digital services.
• Policy influence – India gains a stronger voice in shaping international AI standards and equitable access frameworks.

Challenges Ahead

• Digital divide – With billions offline, equitable AI adoption remains a pressing issue.
• Governance gaps – Balancing innovation with safety, ethics, and regulation will be complex.
• Global coordination – Aligning governments, tech firms, and multilateral bodies requires sustained collaboration.

ITU is central to initiatives like the AI for Good Global Summit and the World Summit on the Information Society (WSIS). It plays a major role in global digital cooperation, ensuring equitable access and responsible governance of emerging technologies.

The ITU plays a central role in global AI governance by providing a neutral UN platform where governments, industry, academia, and civil society co‑create frameworks to ensure AI is safe, inclusive, and beneficial for all. It convenes dialogues, publishes governance reports, and develops standards to bridge the gap between principles and practical regulation.

AI Boom Is Creating a ‘Human Skills Economy’ as Indian Firms Accelerate Adoption: IWG Study

• 90% of HR leaders warn innovation will slow without human traits and qualities
• 65% say AI can’t replace empathy; 53% say leadership remains uniquely human
• India has the world’s highest rate of AI adoption and one of its youngest workforces, making human skills the sharpest edge in a tightening jobs market

As Indian companies accelerate AI adoption and focus on technical upskilling, new research from International Workplace Group (IWG), the world’s leading platform for work, suggests that the next workforce challenge is building the human capabilities needed to use AI creatively and collaboratively.

According to IWG’s survey, the vast majority (90%) of HR leaders believe that failing to prioritize human capabilities is a risk to innovation. This finding reflects the emergence of a new “Human Skills Economy,” in which empathy, judgment, creativity, and leadership are core to business performance.

Rise of AI: A New Operating Reality for Work

AI is now deeply embedded in everyday workflows across organizations. IWG’s survey of hundreds of HR and recruitment leaders revealed that 73% of hybrid teams are already using tools like ChatGPT and 82% of organisations offer AI training. However, HR leaders say their readiness must accelerate to keep up, with fewer than half (45%) saying they are effectively closing the skills gap, suggesting a significant number of organisations are still lagging in effective AI use.

The findings are particularly relevant for India, where organisations are rapidly moving towards AI-enabled ways of working. India leads the world in workplace AI adoption, with 73% of workers using AI tools regularly, well ahead of the United States (45%) and the United Kingdom (29%).

According to Microsoft’s 2025 Work Trend Index India findings, 93% of Indian business leaders intend to use AI agents to extend workforce capabilities within the next 12–18 months. This acceleration makes the development of human skills such as empathy, judgment, leadership and collaboration even more critical as companies redesign work around human-AI collaboration.

Humans + AI: The New Performance Model

As the labour market tightens - particularly at the entry level - employers are being forced to rethink what truly drives performance. Research from Randstad and the Institute of Student Employers shows that entry-level vacancies fell by 29% globally between January 2024 and the end of 2025, raising the bar for what differentiates candidates.

In India, that squeeze is already visible in the sector that defined its rise, with entry-level IT roles down an estimated 20 to 25% as automation absorbs routine work, even as the country fields one of the world’s youngest workforces.

While Gen Z brings a clear advantage in technological fluency, skills alone are no longer enough. The real differentiator is AI literacy: the ability to meaningfully apply AI tools in day-to-day work to unlock productivity and new ways of thinking.

In fact, research from International Workplace Group shows that nearly two-thirds of younger employees are already helping older colleagues adopt AI, from hands-on coaching to embedding tools into everyday workflows.

Against this backdrop, a new performance model is emerging, in which AI handles technical and repeatable tasks, and human capabilities define impact, leadership, and long-term value. HR leaders are clear about where humans remain essential:

• 65% say AI will never replicate human empathy
• 64% say it falls short in complex decision-making
• 53% say leadership will remain uniquely human

At the same time, boundaries are still evolving. Only 40% believe creativity will remain beyond AI’s reach, signalling a continued shift in how organisations define the line between human and machine capability.

Elements That Can’t Be Replicated: The Enduring Value of Human Skills

Even as automation expands, human skills are becoming the most durable source of competitive advantage. While 40% say missing AI or technology skills can disqualify candidates, two-thirds (66%) of HR leaders now say applicants’ ability to demonstrate human skills matters most in hiring, ranking above experience, technical skills, and education.

This shift is also reflected in evolving hiring signals: 45% of employers say they look for context around career moves and gaps to better understand a candidate’s overall experience and trajectory.

In India, industry body NASSCOM has noted that India has the capacity to reskill and develop 8 -10 million professionals in AI-related services by 2030. Deloitte and NASSCOM have also estimated that India’s AI talent demand could grow from 600,000–650,000 to more than 1.25 million between 2022 and 2027, while noting that a shortage of qualified professionals could slow innovation and growth. This makes the combination of AI fluency and human capabilities central to India’s future workforce readiness.

Further, more than half (55%) of HR leaders say hybrid workplaces are among the most effective settings for building empathy, judgment, and leadership skills, underscoring how hybrid work environments are seen as spaces where essential human traits like trust, mentorship, collaboration, and decision-making are actively developed and reinforced.

The Bottom Line

As AI reshapes work, organisations face a clear mandate. Success now depends not just on adopting new technologies, but also on strengthening human capabilities and helping technology and humans work as true teammates.

The future belongs to companies that integrate AI while intentionally building environments where human skills thrive.

Mark Dixon, CEO & Founder of International Workplace Group, commented: “Every major technological shift has redefined how we work — from the rise of the internet to email to smartphones. AI is no different, but what sets this moment apart is the speed and scale of change. Some roles will evolve or disappear, while entirely new ones will emerge.

As always, the organisations that resist transformation will fall behind. A key advantage will belong to those that combine AI’s efficiency with the uniquely human skills that drive innovation, leadership, and growth.”

Harsh Lambah, India Country Manager and VP Sales of South Asia at International Workplace Group, commented: “India is uniquely well positioned to lead the new 'Human skills economy' given that it has the world’s youngest workforce, and the fastest rate of AI adoption. The workplaces that will win in this new economy are the ones where people and AI grow together, and that is exactly what we are building across the country.”

Methodology:

The IWG Human Skills Economy Report was conducted in April 2026 by Mortar Research and targeted 510 U.S.-based HR, recruitment and hiring managers.

About International Workplace Group PLC

International Workplace Group (IWG) is the world’s leading platform for work enabling companies of all sizes to work more productively and profitably. We create personal, financial, and strategic value for the most exciting companies and well-known organizations on the planet as well as individuals and the next generation of industry leaders. All of them harness the power of IWG’s platform to increase their productivity, efficiency, agility, and market proximity.

International Workplace Group’s unrivalled network coverage includes more than 5,000 locations across 120 countries and 83% of Fortune 500 companies are amongst our growing customer base.

Our brands including Regus, Spaces, HQ and Signature serve millions of people, providing professional, inspiring and collaborative workspaces and all our digital services are available via the IWG app.

Google and Tech Giants Unite to Launch Agentic Resource Discovery for AI Agents

Google has announced the Agentic Resource Discovery Specification (ARDS), an open standard designed to help AI agents autonomously discover, interpret, and interact with resources across the web. This initiative is backed by leading technology partners including Microsoft, GitHub, Hugging Face, NVIDIA, Amazon, Cisco, Salesforce, and Snowflake, underscoring its importance as a foundational step toward interoperable agentic AI ecosystems.

The Agentic Resource Discovery Specification is a framework that defines how AI agents can locate and understand resources in a standardized way.

This marks a significant step in Google’s broader push toward agentic AI ecosystems, where autonomous agents can perform tasks like booking services, retrieving data, or integrating apps without constant user input.

ARDS in Detail 

The Agentic Resource Discovery Specification (ARDS) is Google’s new open standard that allows AI agents to automatically find, interpret, and use resources across the web. It’s part of Google’s broader push into agentic AI ecosystems, where agents act autonomously to perform tasks like booking services, retrieving data, or integrating apps.

The ADRS provides a structured framework for AI agents to:

• Identify and locate resources across diverse platforms.
• Interpret metadata to understand capabilities and constraints.
• Interact programmatically with services, APIs, and applications.
• Enable interoperability between agents and external systems.

Why It Matters

Traditional resource discovery is fragmented, requiring manual integration. ARDS addresses this by offering:

• Consistency: A common language for agents to interpret resources.
• Scalability: Agents can autonomously expand their capabilities.
• Efficiency: Reduces developer overhead in connecting services.
• Future‑proofing: Aligns with the rise of multimodal, autonomous AI systems.

Google’s Partners in ARDS Development

ARDS is not just a Google initiative — it is co‑developed and supported by major industry players:

Partner	Contribution	Impact
Microsoft	Co‑author of the specification	Standardizes discovery across ecosystems
GitHub	Integrated ARDS into Copilot Agent Finder	Real‑time tool discovery for developers
Hugging Face	Connected ARDS to its AI model hub	Expands agent access to thousands of models
NVIDIA	Hardware and cloud support	Scales agent workloads efficiently
Amazon	Infrastructure backing	Broadens adoption across cloud services
Cisco	Networking and security expertise	Ensures secure agent connections
Salesforce	Enterprise workflow integration	Embeds ARDS in business AI systems
Snowflake	Data trust and metadata layer	Enhances verification and reliability

The Agentic Resource Discovery specification is licensed under Apache 2.0 and is built upon the foundational AI Catalog data model. 

Google’s Vision

ARDS is part of Google’s broader agentic AI strategy, which includes Gemini‑powered agents and multimodal interfaces. By standardizing resource discovery, Google aims to make agents more reliable, transparent, and interoperable. This reduces friction between intent and execution, allowing users to move from “wanting” to “doing” instantly.

Conclusion

The Agentic Resource Discovery Specification represents a collaborative effort between Google and its partners to build the backbone of the agentic AI era. By enabling agents to autonomously discover and interact with resources, ARDS lays the groundwork for a future where AI systems act as proactive digital companions, seamlessly bridging users and services across industries.

37% of Entry-Level Tasks in India Already Done by AI, Finds Cognizant and Pearson Study

• Employers to focus on interdisciplinary skills; expect new hires to supervise AI

Cognizant (NASDAQ: CTSH) and Pearson (FTSE: PSON.L) today released findings from their joint study, The AI Workforce Pulse: The Adaptability Imperative, highlighting how artificial intelligence (AI) is transforming India’s entry-level workforce at a faster pace than the global average, while simultaneously creating new career pathways and urgent skilling challenges.

Based on a survey of 750 HR leaders across the US, UK and India, the study finds that 37% of entry-level tasks in India are already performed by AI, compared to a 33% global average, with 18% of HR leaders reporting that AI now handles half or more of entry-level work, signalling accelerated disruption in one of the country’s largest workforce segments.

The findings point to four interconnected shifts shaping the AI workforce ahead:

Roles are Being Reinvented

• Nearly all (96%) HR leaders expect entry-level roles to evolve into positions where employees supervise or manage AI systems within the next five years.
• Nearly all HR professionals (94%) expect AI will generate new entry-level roles in the next five years that didn't exist before.
• More than 90% of respondents say middle managers are instrumental to redefining job roles as AI changes the day-to-day work of team members.
• Nearly all HR professionals (98%) are increasing focus on AI skills even for non-technical roles.

Employees in these roles are increasingly expected to manage AI outputs, validate decisions, interpret results and apply human judgment.

In India, 80% of organisations report that AI is enabling employees to focus on higher-value work, compared to 77% globally.

Human and Interdisciplinary Skills Are Increasingly Important

• Nearly all (97%) report soft skills matter more than ever, reflecting a need for adaptability, problem-solving, and human judgment.
• Two in three HR professionals (67%) report they value liberal arts degrees more than they used to in light of AI advancements.
• Nearly 7 in 10 (69%) HR professionals say broad, interdisciplinary backgrounds are more important for early-career talent than deep, specialized skillsets, with 65% of HR professionals in India reflecting this shift.

In addition, 91% of organisations in India place greater value on AI skills for non-technical roles, signalling a broader redefinition of what “job-ready” talent looks like.

Demand for AI Skills Is Rising, but Readiness Is Uneven

• 91% of HR professionals report increased employee demand for AI training over the past 12 months.
• 60% say their L&D programmes cannot keep pace with how quickly AI is transforming jobs, with India reporting a similar challenge at 63%.
• 54% of HR professionals say their organizations proactively arrange AI upskilling in anticipation of future roles evolving, while 46% say their organizations are not proactively arranging this training.

At the same time, India shows relative strength in how organisations are approaching learning:

• 63% of organisations in India have allotted time for AI training, higher than the U.S. (49%).

However, 61% of organisations in India report challenges finding the right talent, reflecting the pace at which skill requirements are evolving.

Middle Managers Are Critical to AI Adoption

• 95% of HR leaders say middle managers are critical to ensuring employees use AI effectively.
• 92% say middle managers play a crucial role in redefining job roles as AI reshapes day-to-day work.

Leadership Statements

Rajesh Varrier, President – Global Operations and Chairman & Managing Director, Cognizant India, said, India is at the forefront of how AI is transforming entry-level work, with organizations already embedding AI into day-to-day operations at scale. We are seeing a fundamental redesign of roles, where early-career talent is expected to work alongside AI and focus on higher-value outcomes.

AI is reshaping the talent landscape and exposing the limits of traditional talent and learning models,” said Kathy Diaz, Chief People Officer, Cognizant.

The new findings build on Cognizant’s earlier New Work, New World 2026 study, which found that AI is already impacting 93% of jobs, underscoring the urgency for employers to prepare for changing role expectations. Cognizant sees early-career talent as increasingly important in an AI-enabled workforce. After hiring 20,000 fresh graduates in 2025, the company expects to exceed that number in 2026, reflecting its continued investment in early-career talent and skill development as work evolves.

As work evolves, the most successful organizations will focus less on replacing tasks and more on building the capabilities that help humans and AI work together. That starts with early-career talent, said Ali Bebo, Chief Human Resources Officer, Pearson.

Through their partnership, Cognizant and Pearson are working together to help recent graduates, apprentices and mid-career professionals build skills in AI, cloud and digital technologies. Pearson supports Cognizant’s existing workforce development programs, including Synapse and its Immersive Learning Center in Chennai, to help create stronger development paths for the workforce.

Methodology

Cognizant and Pearson commissioned independent market research conducted by Wakefield Research in three markets: the US, UK and India, between March 23 and April 3, 2026, using an email invitation and an online survey among 750 HR professionals.

About Cognizant

Cognizant (NASDAQ: CTSH) is an AI builder and technology services provider, building full-stack AI solutions for clients. See how at www.cognizant.ai or @cognizant.

About Pearson

At Pearson, our purpose is simple: to help people realize the life they imagine through learning. Visit us at plc.pearson.com.

Forward-Looking Statements

This press release includes statements that may constitute forward-looking statements made pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995.

IndiaAI backed Avataar Launches Varya, India’s Distilled Video AI Model, Delivering Frontier‑quality Video Generation at 10x Lower Cost

India AI mission backed AI-native transformation company, Avataar, has launched Varya, India’s first distilled video AI model under the IndiaAI Mission, promising frontier‑quality video generation at up to 10x lower cost.

Launch Overview

• Event: Press launch in New Delhi, June 12, 2026
• Key presence: Shri S. Krishnan, Secretary, MeitY, alongside Avataar leadership
• Backed by: IndiaAI Mission and Peak XV Partners
• Objective: Deliver efficient, culturally aware video AI for India’s billion‑plus users

What Makes Varya Unique

• Distilled architecture: Compresses video generation from 50 steps to 4, maintaining comparable quality
• Efficiency: Generates video at ₹0.48 per second (~$0.005), 20x cheaper than global rivals
• Scale: 14‑billion parameter model optimized for India’s contexts
• Speed: On NVIDIA H200 GPUs, a 5‑second 720p clip takes 45 seconds vs. 1,230 seconds for Alibaba’s Wan 2.2
• Accessibility: Available via IndiaAI Kosh for developers to self‑host or adapt

Cultural Context & Applications

• Education: Teachers creating visual lessons in rural classrooms
• MSMEs: Affordable product ads and digital storytelling
• Citizen services: Public information through video
• Commerce: E‑commerce and marketing campaigns

Leadership Statements

• S. Krishnan, MeitY: “The launch of one of the foundational models supported under the IndiaAI Mission marks a significant milestone in India’s AI journey. Varya reflects our commitment to building indigenous AI capabilities and fostering a vibrant deep‑tech ecosystem.”
• Sravanth Aluru, CEO, Avataar: “India’s AI opportunity will not be defined only by the largest models. It will also be defined by the most efficient models. For a country of 1.4 billion people, affordability is not a feature, it is a prerequisite.”

Global Comparison

Model	Generation Steps	Cost per Second	Cultural Context
Varya	4	₹0.48 ($0.005)	India‑specific, culturally rich
Wan 2.2	50	Higher (benchmark)	Generic global
Veo / Runway	50+	$0.10+	Global, less India‑specific

Risks & Opportunities

• Opportunities: Democratizes video AI for education, MSMEs, and governance; positions India as a global leader in frugal AI innovation
• Risks: Global competition may push rapid iterations; infrastructure dependency on subsidized compute; cultural bias risks if training data isn’t updated

In essence, Varya is not just a technical breakthrough but a strategic milestone — redefining India’s AI ambition by proving that efficiency, affordability, and cultural relevance can rival global frontier models.

NVIDIA's AI Push is Reaching Aircraft, and an Indian eVTOL is Part of the Story

A central theme of Jensen Huang's COMPUTEX keynote this week was Physical AI — the convergence of AI, simulation, robotics and autonomous systems. While much of the AI boom has focused on software, NVIDIA's vision is increasingly centered on intelligent machines operating in the real world. From industrial robots and autonomous systems to digital twins and edge computing, the company is building the technology stack that will power the next generation of physical machines. Increasingly, that vision is extending beyond factories and robotics into sectors such as aerospace.

One of the companies living inside that vision is an Indian aerospace startup called The ePlane Company. Featured during Jensen Huang's keynote, ePlane represents an emerging class of companies using AI, simulation and digital-twin technologies not to build software, but to engineer systems that operate in the physical world.

With this recognition, ePlane becomes only the third eVTOL company in the world to be supported by NVIDIA, and the only one from Asia (for their certification journey).

What distinguishes this partnership from a typical technology endorsement is its depth. ePlane was the first eVTOL company globally to publicly commit to NVIDIA hardware within its onboard avionics architecture, and is now actively pursuing aircraft certification with that hardware integrated into the system. This is a foundational hardware-software co-development programme designed from the ground up to meet aviation-grade certification requirements.

"Being on Jensen's stage at GTC Taipei is not a moment we take lightly," said Prof. Satya Chakraavarthy, Founder and CEO, ePlane. "It is a signal to the world that India is not a follower in this technology cycle. We are co-developing safety-critical systems with the most consequential embedded infrastructure company on the planet, at the standards required for certified flight. That is a different conversation that speaks to the growing maturity and global relevance of India’s aerospace ecosystem.”

For ePlane, the Omniverse integration goes well beyond simulation as a tool. By building physics-accurate digital twin environments that model aircraft behaviour across a comprehensive range of flight conditions, failure modes, and edge cases, the company is able to generate simulation evidence that directly feeds into its DGCA certification pathway. The rigour matches what global aerospace leaders apply, and is now being built indigenously, in Chennai.

"Partnerships like this one do not happen by accident," said Vishnu Ramakrishnan, SVP Customer Strategy & Business Partnerships, ePlane. "They happen when a company's technology is genuinely credible at the global level. Being selected by NVIDIA as a reference for how their platform is applied in certified aviation tells our partners and investors something no pitch deck can: that the world's leading embedded infrastructure company has looked at what we are building and decided it belongs on their stage."

ePlane is currently in the Ground Test Vehicle phase of its first full-scale aircraft, with a Series C fundraise underway.

About ePlane

The ePlane Company (Ubifly Technologies Pvt. Ltd.) is an IIT Madras-incubated eVTOL company building electric air mobility solutions for India and global markets. The company is developing a compact, safety-certified electric aircraft for passenger, cargo, and emergency medical services applications. ePlane holds Design Organisation Approval from the DGCA, with its Type Certification application officially accepted. The company is backed by [key investors] and is currently in advanced stages of its Series C fundraise.

Rajasthan Education Summit 2026 Highlights AI‑Driven Vision for Learning & Talent

Positioning Rajasthan as a future hub for AI-driven learning, innovation, and skilled talent emerged as the central focus of the Rajasthan Education Summit 2026 held in Jaipur on Friday. Organised by ASSOCHAM Rajasthan State Council in collaboration with IIHMR University the summit deliberated on the theme “Empowering Rajasthan through AI-Driven Education & Vision 2030”.

Dr. Prem Chand Bairwa, Deputy Chief Minister and Minister of Technical and Higher Education, Government of Rajasthan, along with more than 150 delegates, focused on strengthening Rajasthan’s education ecosystem through responsible integration of AI, industry-academia collaboration, digital transformation, skill development, innovation-led learning, and future-ready curricula.

Speakers including Vice Chancellors, Directors, policymakers, researchers, principals, entrepreneurs, and industry representatives highlighted the importance of ensuring that technology-driven education remains accessible, inclusive, and employment-oriented, particularly for students from emerging and underserved regions.

Dr. PR Sodani, President, IIHMR University, shared, “The future of education will be defined by how effectively we integrate technology with human-centric learning. AI-driven education is not only about digital transformation but also about creating equitable opportunities, strengthening critical thinking, and preparing youth for emerging global challenges. Platforms like these are important for fostering collaboration between academia, government, and industry.”

Discussions explored how AI can support personalised learning, improve educational governance, enhance research capabilities, and contribute to building a globally competitive talent pool from Rajasthan.

56% of Online Sellers are Already Using AI Tools to Grow Their Business: Snapdeal Bharat Seller Report 2026

• Report highlights rapid adoption of AI among sellers, rising strength of Bharat markets, and continued growth of value-driven shopping in India’s digital commerce economy

India’s online seller ecosystem is becoming increasingly technology-driven, manufacturing-led, and deeply aligned with the country’s value-conscious consumers, according to the latest “Snapdeal Bharat Seller Report 2026.”

The report, based on a survey of sellers across Bharat who sell on Snapdeal, reveals that artificial intelligence is no longer confined to large enterprises or digital-first brands. Instead, AI adoption is steadily moving into the mainstream among Indian online sellers, with 56% of surveyed sellers already using AI-powered tools in some form to improve their business operations.

The report captures emerging trends shaping India’s digital commerce economy, including AI adoption, online business dependence, marketplace diversification, consumer behaviour, and seller profitability pressures.

“India’s online seller ecosystem is evolving rapidly. Sellers today are far more digitally mature, operationally agile, and increasingly technology-enabled. What is particularly interesting is that AI adoption is now becoming visible even among MSMEs and traditional sellers. At the same time, the continued growth of Bharat markets on the back of rising internet penetration and growing comfort in online transactions among consumers continue to define the next phase of Indian e-commerce,” said Achint Setia, CEO, Snapdeal.

The report highlights that among sellers already using AI, the most common use case was product listings and content creation, cited by 43% of respondents. Sellers are increasingly leveraging AI tools to generate product descriptions, improve cataloguing efficiency, optimise listings, and enhance discoverability on marketplaces.

Online commerce is now central to seller businesses

The report also highlights how deeply online commerce is now embedded within seller business models across India.

Nearly 46% of respondents said more than three-fourths of their overall business sales now come from online channels, underlining the extent to which digital commerce has become a primary route to market for a large segment of sellers.

Importantly, India’s online seller ecosystem continues to remain strongly manufacturing-led. About 66% of surveyed sellers identified themselves as manufacturers selling directly online, showcasing how marketplaces are increasingly enabling small manufacturers, entrepreneurs, and MSMEs to access customers across the country without relying heavily on traditional distribution networks.

Bharat markets continue to emerge as the key growth engine

The report reinforces a trend that has steadily gathered momentum over the past few years: India’s smaller cities and towns are becoming the biggest growth drivers for digital commerce.

About 51% of sellers said customers from Tier-2, Tier-3, and smaller markets are growing faster for their business compared to metro and Tier-1 consumers.

The findings reflect the widening reach of e-commerce across Bharat, aided by affordable smartphones, improving logistics infrastructure, greater internet penetration, digital payment adoption, and rising comfort with online shopping among consumers in non-metro markets.

Value continues to dominate Indian online shopping behaviour

The report findings also underline the enduring strength of value commerce in India.

While, nearly 49% of sellers said consumers primarily prioritise the best prices and discounts while shopping online, product quality ranked second at 38%, indicating that quality remains a key consideration even for value-conscious consumers.

This trend is also reflected in seller price points. About 66% of respondents operate in categories where the average order value is below ₹500, highlighting the scale and importance of India’s value-conscious consumption economy.

BIT Mesra Women’s Team Builds AI to Map Lunar Craters

A three-woman team at BIT Mesra in Ranchi — Dr. Sanchita Paul, Dr. Mili Ghosh, and Mimansa Sinha — has developed an ISRO-backed AI tool called CraterMorpho that can automatically detect and analyse lunar craters as small as 200 meters, revolutionizing lunar mapping and mission planning, reported regional news daily Jagran.com.

The Jagran article (May 17, 2026) reports that BIT Mesra researchers in Ranchi have developed an AI‑based technology to identify and analyse lunar craters using Chandrayaan‑2 data, aiming to support safer landings and geological studies.

Key Highlights

Photo of Chandrayaan taken with the help of a device developed by scientists of BIT Mesra. [Image - Jagran.com] 

• Team & Leadership: Led by Dr. Sanchita Paul with collaborators Dr. Mili Ghosh and Mimansa Sinha
• Institution: Birla Institute of Technology (BIT) Mesra, Ranchi
• Support: Funded and backed by the Indian Space Research Organisation (ISRO)
• Tool: CraterMorpho — AI system using Digital Elevation Models (DEM)
• Capabilities: Detects craters ≥ 200m, measures depth, roughness, morphology. 

Dr. Sanchita Pal, Dr. Mili Ghosh, Mimansa Sinha [Image - Jagran.com]

In the coming days, there are plans to develop this AI-based system as a fully automated real-time crater analysis pipeline, so that the technology can be fully utilized. Through this, the process of data collection can also be made easier.

Why It Matters

• Mission Safety: Identifies safer lunar landing sites
• Scientific Value: Supports crater dating and geological analysis
• Automation: Replaces manual crater identification
• Global Impact: Strengthens India’s role in AI planetary exploration

Technical Insights

Feature	Details
Data Source	Satellite-derived Digital Elevation Models (DEM)
AI Method	Deep learning neural networks adapted for elevation data
Detection Scale	Craters ≥ 200m diameter
Outputs	Depth, roughness, morphology
Validation	Tested on Aristarchus Plateau, peer-reviewed publications

Challenges Ahead

• Domain Shift: Adapting AI models across orbital sensors
• Extreme Terrain: Handling shadowed regions and overlaps
• Verification: Requires ground truth data from future missions

Context for India

This breakthrough aligns with India’s Space Vision 2047, where AI-driven exploration will play a central role in Moon landings, resource utilization, and habitat planning.

TCS Becomes First Global Integrator Partner for Mistral Forge

Tata Consultancy Services (TCS) has announced a landmark partnership with French AI firm Mistral, becoming the first global systems integrator for Mistral Forge — a frontier-grade enterprise AI platform. This collaboration positions TCS at the forefront of trusted, scalable AI adoption across industries like BFSI, healthcare, manufacturing, and the public sector.

Mistral is special because it is one of the few AI companies building frontier-grade, open-source models that rival proprietary systems, while giving enterprises full control over customization, deployment, and data privacy. TCS boasts of being the first global systems integrator for Mistral Forge because it positions them uniquely to deliver enterprise-ready AI solutions at scale, ahead of competitors.

Unlike closed black-box providers, Mistral releases high-performance models under permissive licenses (Apache 2.0), enabling enterprises to audit, customize, and deploy securely.

Partnership Overview

• TCS–Mistral Collaboration: TCS will leverage Mistral Forge to build custom AI models grounded in enterprise data and domain-specific knowledge.
• Strategic Role: TCS is the first global systems integrator partner for Mistral Forge, enabling enterprises to move from experimentation to production-ready AI systems.
• Global Reach: TCS will deploy solutions across North America, UK, Europe, and Asia-Pacific, tailoring them to industry-specific regulatory and operational needs.

Key Features of the Collaboration

• Custom AI Models: Enterprises can fine-tune models using proprietary data to improve decision-making.
• Centre of Excellence: TCS will establish a dedicated hub for joint innovation, industry-specific solutions, advanced training, and early access to Mistral’s beta models.
• Sectoral Focus: BFSI, manufacturing, healthcare, and public sector — industries where trusted AI adoption is critical — will be the initial focus.
• Leadership Statements:
  Arthur Mensch (CEO, Mistral): “TCS’ global scale and contextual industry knowledge make them an ideal partner.”
  K Krithivasan (CEO, TCS): “This partnership reinforces TCS’ commitment to scaling enterprise AI with trust, control, and measurable outcomes.”

Strategic Importance

• Infrastructure to Intelligence Strategy: This partnership aligns with TCS’ broader AI roadmap, which spans infrastructure, models, data, applications, and intelligence.
• AI Ecosystem Expansion: TCS aims to become the world’s largest AI-led technology services company, embedding AI across enterprise workflows.
• Differentiated Proposition: By combining Mistral’s frontier AI with TCS’ contextual expertise, enterprises gain industry-specific, sovereign-compliant AI solutions.

Headquartered in France, Mistral is known for open-source generative AI models and its decentralized, transparent approach to technology. It has a presence in the US, UK, and Singapore. 

Why This Matters

• Enterprise AI Adoption: The partnership accelerates the shift from pilot projects to large-scale AI deployment.
• Trusted AI: Focus on governance, regulatory compliance, and sovereign requirements ensures responsible scaling.
• Global Impact: With TCS’ reach and Mistral’s frontier models, enterprises worldwide gain access to production-ready AI systems.

Comparison Table: Mistral vs Other AI Providers

Feature	Mistral AI	Closed Providers (e.g., OpenAI, Anthropic)
Model Access	Open-source weights, Apache 2.0 license	Proprietary APIs only
Customization	Full fine-tuning with enterprise data	Limited or no customization
Data Privacy	Deploy on-premises or private cloud	Data flows through provider APIs
Innovation Speed	Rapid releases (Mistral 7B, Mistral 3, Forge)	Controlled, slower rollout
Enterprise Fit	Tailored industry-specific solutions	General-purpose, less contextual