Рубрика: AI

Okta, a cloud-based Identity and Access Management (IAM) service platform, announced today a significant expansion of its operations in India, pledging a considerable investment to boost its research and development capabilities and its physical presence at its Bengaluru campus.

The updated facility will prioritise the development of the essential Identity Security Fabric necessary to safeguard this new ecosystem, expediting AI-driven identity advancements for both the region and the global stage. Since 2023, the team has expanded to 700 employees.

Furthermore, Okta intends to increase its workforce in India by 50% by 2026, focusing on advanced engineering and product development talent to further its goal of securing AI at scale.

According to the company, this expansion underscores India’s importance as a vital global tech talent centre and is closely linked to Okta’s international growth strategy to double its revenue from $5 billion to $10 billion.

“Our expansion in Bengaluru is about amplifying our ability to innovate at the speed of AI. India’s talent pool has the unique depth required to tackle the complex security challenges of securing AI agents and the expanded identity surface,” Shakeel Khan, regional vice president & country manager at Okta India, said in a statement.

“This new facility will be the engine that helps build the identity layer of the future, ensuring that Okta leads the charge in securing the Age of AI for customers across sectors,” he added.

Okta’s research shows that 91% of companies have AI agents, yet only 10% have security plans for them. This gap could result in a 40% failure rate for AI agent deployments by 2027 without proper authentication.

Stephanie Barnett, VP presales and interim GM for the APJ region, said, “Okta’s data shows that more than half of organisations now see modern identity and access management as their most critical defence in the AI era.”

As a result, organisations are prioritising modern identity as their key defence in the AI era. The rapid rise of Generative and Agentic AI has made IAM essential, as securing AI agents is crucial, the company said.

The post Okta to Invest in R&D to Expand AI Operations in its Bengaluru Campus appeared first on Analytics India Magazine.

India’s bioeconomy has expanded at breakneck speed over the past decade, rising from $10 billion in 2014 to $165.7 billion in 2024. The scale of manufacturing, the depth of clinical research talent, and the global reputation of Indian pharma have placed the country among the world’s top biotech hubs.

To replicate such success in biotechnology over the next decades, countries would require manufacturing muscle and speed at R&D to discover and develop new therapies. One source critical to this speed is Artificial Intelligence (AI).

Nowhere is that more evident than in China.

Its strategic use of AI to automate labs, model biological interactions, design molecules and optimise clinical trials has transformed it into one of the world’s fastest-rising biopharma powers. They say China has begun compressing drug discovery timelines at a pace India has not yet matched.

China in 2022 unveiled its first five-year bioeconomy plan that projected a target of a $3.28-trillion bioeconomy to be achieved by the end of 2025.

Western pharmaceutical giants are striking multibillion-dollar deals with Chinese biotech firms that use AI, signalling growing confidence in China’s ability to deliver faster and cheaper innovative drugs.

AstraZeneca, Pfizer, and Sanofi among the Big Pharma have struck major deals with Chinese AI-driven biotech firms, drawn by the country’s rapid development timelines, lower research costs, and a state-supported startup ecosystem.

While US companies continue to dominate the sector, China’s growing talent pipeline and strong government backing are beginning to shift global perceptions around where the next wave of biotech innovation may emerge.

At the Bengaluru Tech Summit in November this year, Kiran Mazumdar-Shaw, executive chairperson of Biocon, pointed out the rapid surge in China’s clinical R&D activity. “China has validated its use of AI to bring down pipeline time and costs, becoming one of the most efficient drug discovery ecosystems,” she said.

China’s share of global clinical trial starts has jumped from 3% in 2013 to 28% in 2023, a shift so dramatic that it is now considered the only country capable of challenging the US biotech machine across the full research-to-commercialisation pipeline.

Debjani Ghosh, president of NASSCOM, described this convergence as the defining feature of the new technological age. “The disruption is not in individual sciences. The disruption is at the convergence,” she said during her keynote at the Bengaluru Tech Summit.

For her, AI is no longer merely a powerful tool; it has become the centre of gravity in a planetary system of emerging technologies, with biology, physics, materials science and energy orbiting around it. “Everything else is anchored around intelligence,” she said. “This is where you get to see the maximum disruption and impact.”

Ghosh argued that India has spent too long in a “perpetual catch-up phase” in AI, in silicon, in quantum and the biotech race must not fall into the same pattern. “How do we move India out of this perpetual catch-up phase and leapfrog to a leadership position, where we are defining the evolution, the standards, and the alliances?” she asked.

As AI becomes woven into biology, computing and automation, it will reshape not just industries but national security, governance and global power. China has understood this, she said, and acted decisively to translate that understanding into policy and investment.

According to Stanford University’s AI Index Report 2024, China accounted for 61.1% of global AI patent filings in 2022, far ahead of the United States at 20.9%. Analysts point to a decade of investment in AI talent, biomedical research, and supportive policy as the foundation for this new phase of growth.

Mazumdar-Shaw expanded on why China’s momentum is particularly significant for India. China’s application of AI goes beyond prediction models or molecule screening. It has built automated laboratories capable of running experiments around the clock at speeds that would require dozens of human scientists to match.

These labs are beginning to merge with AI systems trained on enormous volumes of biological and chemical data, creating feedback loops that accelerate discovery with each cycle. “Biology is becoming programmable,” she said.

India may be the world’s largest supplier of generics and vaccines, but a future led by AI-driven biology will depend on rapid prototyping, automated validation, massive compute and the ability to shift from molecule development to clinical testing in record time. If China continues to compress its development cycles, it could redefine global supply chains and intellectual property flows in ways that blunt India’s current advantages.

However, Ghosh warned that this technological shift will not only shape industrial competitiveness but also global power structures. She pointed to DeepSeek, a Chinese AI breakthrough that triggered an immediate geopolitical recalibration.

“When DeepSeek came up, all of a sudden everybody was saying we have a new leader in AI,” she said. “What happens when you lead in some of these technologies? You’re not just a technology leader; you become an economic rule-maker.” AI, quantum computing and programmable biology, she said, have become core instruments of tech diplomacy—a domain India can no longer afford to treat as peripheral.

The panel stressed that India holds enormous untapped potential. Its talent base, cost advantages, biodiversity, clinical research capacity and growing entrepreneurial ecosystem create fertile ground for a leap in AI-led biotech innovation. But, such a leap requires India to embrace convergence, not just incremental improvement. It demands that biology be treated not as a lab science but as a computational discipline, one that requires high-performance computing, data interoperability, cross-sector collaboration and policy frameworks that enable bold experimentation.

Mazumdar-Shaw said she believes the answer lies in recognising that the next breakthroughs in healthcare, agriculture, materials, energy and climate will come from programmable systems.

Ghosh emphasised that the window for action is narrower than most policymakers realise. “It will happen sooner than we realise,” she said.

The post As AI Collapses Drug Discovery Timelines, Will India Follow China’s Lead? appeared first on Analytics India Magazine.

R Systems, a global IT services and digital solutions company, has set up a global capability centre (GCC) in Mangaluru after acquiring local firm Novigo in a ₹400-crore all-cash deal earlier this year. Since 2011, the end-to-end IT solutions provider has specialised in full-cycle IT Services and platform solutions.

While this acquisition may seem like another move by the US-headquartered firm to expand its India footprint, after taking over Pune-based product engineering firm Velotio Technologies in 2023, it hints at a larger trend of promoting balanced regional growth in tech across Karnataka.

Here, the Mangaluru cluster, encompassing Dakshina Kannada, Kodagu, Udupi, and Uttara Kannada districts, is an interesting piece of the puzzle.

With targeted innovation, the region has emerged as a key hub for port and logistics technology, fintech, IT, and deep tech industries.

Mangaluru offers plug-and-play infrastructure, driven by Karnataka’s Beyond Bengaluru initiative, with over 2 million sq ft of IT space currently under development, including the KEONICS IT Park.

The region already contributes over ₹4,200 crore in exports and aims to reach ₹40,000 crore by 2033. The cluster supports more than 200 startups and employs around 20,000 IT professionals, positioning it as a rapidly growing destination for technology and innovation.

In an exclusive conversation with AIM, Nitesh Bansal, CEO, R Systems, shared the rationale behind establishing a GCC in Mangaluru, the centre’s focus areas, and its vision for the future.

Why Mangaluru Over Other Cities?

On why R Systems chose Mangaluru instead of traditional tech hubs, Bansal explained, “R Systems has always had a presence in Noida, Pune, Chennai, and Bengaluru. But as we looked at expanding the talent pool, we were also exploring promising tier 2 centres offering both cost advantage and higher retention.”

Furthermore, Bansal’s previous experience at Infosys played a key role. He recalls overseeing the Mangaluru development centre during his tenure as senior vice president, impressed by the quality of talent and higher retention.

“Novigo is a recognised brand locally, consistently ranked among the top three preferred companies to work for. This combination of great talent, high retention, and strong brand recognition made Mangaluru very attractive for us,” he added.

Currently, R Systems’ Mangaluru office has about 350 employees, primarily from Novigo, with plans to hire another 35 through campus drives.

Bansal emphasised the importance of tier 2 talent while recognising the value of niche skills in tier 1 cities. “Some deeply skilled talent exists more in Bengaluru and Hyderabad because of experience with global enterprises. That talent is limited and expensive. But to scale, you need good, knowledgeable, hardworking people willing to learn.”

Mangaluru has seen gradual growth in its tech ecosystem over the years. Over time, Tech Mahindra, Cognizant, and others built their presence. Startups like RoboSoft and Novigo have contributed significantly. Recently, NTT Data and Bose created large GCCs.

When the size of the pie grows, everyone’s share grows too, and that’s exactly how the ecosystem benefits.

Building Innovation in Mangaluru

India’s GCCs have rapidly progressed from experimenting with AI to deploying it at enterprise scale.

According to the EY India GCC Pulse Survey 2025, 58% of GCCs are already investing in agentic AI, and another 29% plan to scale these initiatives in the coming year, signalling a decisive shift toward AI-driven value creation.

To achieve this, R Systems is leveraging Novigo’s expertise in low-code, no-code, and intelligent process automation to drive innovation.

“We are entering in a big manner in the agentic AI ecosystem. It is a combination of data, AI, and process skills. Some we bring from other centres, but some we will have to acquire and build. We believe Mangaluru will act as a great talent pool for us to build that ecosystem,” Bansal stated.

The company’s agentic AI portfolio spans three categories including internal agents that automate the software development lifecycle through the Optima AI suite already deployed across 70+ clients.

Vertical process agents deliver industry-specific AI solutions for sectors including healthcare, insurance, and travel. Telecom and horizontal corporate agents streamline enterprise functions, including accounts payable, legal document verification, and employee onboarding, supported by a library of nearly 125 pre-built agents.

“We use a variety of frameworks, including OpenAI, Anthropic, Llama 2, Llama 3, and even some Chinese models,” Bansal noted.

Looking ahead, R Systems aims for both quantitative and qualitative growth.

“We would like to scale the centre to around 500 people in the next two years,” he noted.

“Qualitatively, we want it to become the heartbeat of agentic innovation and a centre of excellence for verticals like insurance, travel, and other capabilities we develop,” Bansal added.

The post Why R Systems Turned to Mangaluru to Build Agentic Ecosystem appeared first on Analytics India Magazine.

IBM’s decision to buy real-time data streaming company Confluent for $11 billion signals a shift in the AI race. Tech giants believe that controlling real-time data is as important as building powerful models.

This acquisition comes in a year marked by similar moves. Salesforce purchased Informatica, while services firm Genpact acquired XponentL Data to expand its data and AI capabilities. These deals indicate that enterprise vendors want greater control over data feeding AI systems.

IBM said its acquisition will combine its AI and automation software with Confluent’s real-time data streaming capabilities, helping enterprises to connect, process, and govern data across environments. The companies said this will support AI agents and applications that require access to trusted, real-time data.

Confluent, built on the open source platform Apache Kafka, offers data streaming, connectors, governance, stream processing, and deployment options across Confluent Cloud, Confluent Platform, WarpStream, and Confluent Private Cloud. It claims to work with more than 6,500 clients, including over 40% of the Fortune 500.

IBM said the deal aligns with its hybrid cloud and open-source strategy, following earlier acquisitions such as Red Hat and HashiCorp.

Andrew Humphreys, senior director analyst at Gartner, told AIM that IBM wants to position itself to become the platform through which enterprises move, process and govern data for emerging AI workloads. He said Salesforce signalled a similar intent with its Informatica acquisition, placing IBM in direct competition with Salesforce and Oracle as companies seek to own the data infrastructure that feeds AI.

Humphreys said the acquisition gives IBM more than 50% share in the event broker market. He noted that alternatives such as Redpanda, AWS MSK, Google MSK and Azure Event Hub remain strong competitors as cloud providers bundle these services directly into their platforms.“Clients who do not have a multi-cloud strategy are more likely to choose IBM Confluent than they were likely to do so prior to the acquisition,” he said.

Michael Ni, VP and principal analyst at Constellation Research, told AIM that Confluent fills a structural gap for IBM by providing the “nervous system” needed for its AI and hybrid-cloud strategy. He said IBM already had strong capabilities in metadata, governance and AI lifecycle management, but lacked a unified streaming backbone.

“Confluent fills that gap with portable, governed data-in-motion infrastructure that complements IBM’s hybrid-first strategy, making it an alternative to hyperscalers for enterprises seeking vendor-neutral streaming and enterprise AI governance,” Ni said.

Humphreys added that IBM has long competed with Confluent through its IBM MQ product, which is central to many event-driven architectures. He said, “IBM MQ and Kafka solve different problems,” giving IBM a chance to offer a broader portfolio.

How does it help IBM?

In a blog post, IBM CEO Arvind Krishna wrote that companies increasingly want to run operations in real time as transactions, supply chains and fraud detection happen within milliseconds. He said AI models are “only as strong as the signals feeding them,” while digital workers and autonomous workflows require continuous, reliable data streams.

Ni said the deal helps IBM compete more directly with leading cloud companies. “You can’t compete with AWS, Google, or Databricks without a real-time nervous system that provides the streaming context that AI, agents, and automated decisions require to support autonomous workflows and agentic architectures,” he said.

Humphreys said both companies are positioning the deal as a step towards enabling more real-time data access for AI agents. But he warned that claims that everything will become event-driven should be treated cautiously, noting that “most AI agents still rely on static data snapshots” and that adoption will take time.

Salesforce is making a similar push

Salesforce recently expanded its Data 360 portfolio by adding Informatica to address one of the main challenges in enterprise AI adoption, which is unreliable results stemming from inconsistent, siloed, or low-quality data.

The company said the combination of Data 360, MuleSoft, and now Informatica creates a unified data foundation that provides AI systems with the context they need to operate accurately across enterprise applications.

Informatica brings deeper metadata intelligence, data lineage, governance, and quality controls into Salesforce’s data stack. The latter said the platform helps establish shared meaning for business entities across ERP, finance, supply chain, HR, and commerce systems, thereby reducing ambiguity for AI models.

“AI is only as good as the understanding behind it, and that understanding comes from context,” said Rahul Auradkar, EVP and general manager for Data 360 and AI Foundations at Salesforce. He said integrating Informatica with MuleSoft’s real-time connectivity gives customers a foundation of trusted enterprise context that strengthens every AI, automation, and analytics outcome.

Salesforce said these capabilities will help reduce hallucinations, support safer execution, and strengthen applications across Agentforce, Tableau, and the broader Salesforce platform.

The post IBM Bought a ‘Nervous System’ for $11 Billion appeared first on Analytics India Magazine.

In 2025, the enterprise technology landscape has been reshaped by the rise of agentic AI. Hyperscalers like Oracle, AWS, Microsoft, and Google Cloud have all made decisive moves to pivot from simple LLM-powered features to full-fledged autonomous software agents capable of handling complex business tasks.

Agent marketplaces, agent builders, orchestration layers, and “digital teammates” have become the new battleground. If every major cloud vendor now offers similar tooling, the obvious question is where the real moat lies.

Oracle believes it holds an answer and an advantage that others will find difficult to replicate. The company notes that where an agent lives matters as much as what it can do.

This became clear at its AI World 2025 conference in Las Vegas in October, where Natalia Rachelson, senior vice president of Fusion Applications Product Management division, articulated the company’s strategic shift.

While competitors race to build open agent ecosystems, Oracle has quietly turned its Fusion Applications suite into an engine of 600 embedded agents, treated as enterprise software rather than just automation tools.

At the event, Oracle expanded its AI Agent Studio for Fusion Applications and launched the Fusion Applications AI Agent Marketplace, a catalogue of pre-built, enterprise-grade agents that live inside Fusion ERP, SCM, HR, CX and other Oracle applications.

Unlike the horizontal agents marketplaces emerging across the industry, Oracle’s is purpose-built for the workflows and business objects of Fusion Applications. It stands in stark contrast to the infrastructural strategies of AWS, Microsoft and Google Cloud.

Rachelson, in an exclusive interaction with AIM, explained the rationale and said, “We’re treating every AI agent as a piece of enterprise software. It has to follow the same security rules, the same access control, the same auditing as anything else in Fusion.”

Behind that sits a network of over 32,000 certified agent builders, steeped in the specifics of Fusion’s data structures and workflows.

The Hyperscaler Race

In the weeks following Oracle’s announcement, AWS, Microsoft and Google all intensified their agentic AI narratives. At AWS re:Invent this month, CEO Matt Garman outlined a future driven not by LLM outputs but by “billions of agents” performing real operational tasks inside enterprises.

AWS Marketplace, which had originally targeted 50 agent listings at launch, quietly crossed 800 and surged beyond 2,100 before re:Invent even began.

AWS is now talking in terms of “frontier agents,” anchored by Amazon Bedrock AgentCore, a platform designed to construct, constrain, evaluate, and provide long-term memory for production-grade agents.
AWS also introduced three flagship frontier agents: Kiro Autonomous Agent, a virtual developer that maintains persistent context across sessions, learns your coding style, repositories, and review patterns, and can work for hours or days before surfacing changes; AWS Security Agent, an embedded security consultant; and AWS DevOps Agent, effectively an autonomous SRE already achieving nearly 86% root-cause identification inside AWS and collapsing mean time to repair (MTTR) from hours to minutes.
Check out AWS re:Invent coverage on Front Page by AIM Network:

At Ignite 2025, Microsoft reinforced this momentum by declaring that agents are “the apps of the AI era.” Its new Agent 365 control plane sits atop the Microsoft 365 and Dynamics ecosystems, designed to manage more than a billion enterprise agents by the end of the decade.

Microsoft, once dominant in enterprise software via Windows and Office, now aims to do so with managed, policy-aware digital workers.

Google Cloud, meanwhile, has taken a more interoperability-first approach. At the Cloud Next event, it introduced the Agent2Agent protocol, enabling agents from different vendors to collaborate across platforms such as SAP, ServiceNow, Box, and Vertex. Google’s marketplace is being built not merely to host agents but to allow them to discover and talk to one another through open standards.

Every hyperscaler now has some version of the same pitch: an agent marketplace, a builder studio, and an orchestration layer that promises to turn LLMs into “digital employees” inside your business.

Each major cloud provider now offers a similar comprehensive suite: a marketplace for agents, a dedicated builder studio, and a robust orchestration layer. This stack is designed to transform large language models (LLMs) into functioning “digital employees” within an enterprise.
The Real Deal

Rather than competing to host the most extensive inventory of general-purpose agents, Oracle is optimising for depth, governance, and domain specificity.

The company states that it is focusing on a more specific, yet more profound goal: developing application-embedded agents for mission-critical enterprise workflows.

AWS is developing a large-scale partner-led agent ecosystem on Amazon Bedrock AgentCore and AWS Marketplace, allowing users to subscribe to agents in the same manner as SaaS services. Its “AI Factories” and AgentCore runtimes focus on infra maturity, long-running workflows, and global procurement.

Microsoft is turning agents into the default way work happens across Microsoft 365 and Dynamics. With Work IQ and Agent 365, its focus is on managing agents as “digital teammates” across productivity apps, security tools, and business systems, all tied to a single control panel.

Google Cloud is betting on orchestration and interoperability. Vertex AI Agent Builder, AgentSpace, and the Agent2Agent protocol aim to make it easy to build multi-agent systems, let agents from different vendors work together over an open protocol, and then surface those agents through an AI Agent Marketplace.

All three are building broad, horizontal ecosystems.

Agents as Enterprise Software

Gartner recently warned that over 40% of agentic AI projects will be scrapped by 2027 because many are simply rebranded chatbots without robust governance, security, or clear RoI.

Rachelson’s answer to that risk is to treat agents less like fancy prompts and more like SAP-era enterprise software.

In Oracle AI Agent Studio, the company claimed that every agent is fully traceable, monitored in production, evaluated systematically, and bound to Fusion’s security and role-based access control.

The company said it is adding support for the Model Context Protocol (MCP) and Agent-to-Agent (A2A) cards, so Fusion agents can securely interact with third-party tools and even other vendors’ agents, while still preserving governance and guardrails.

Where some marketplaces offer agents as APIs, Oracle is pushing agents as governed enterprise systems.

The post Inside Oracle’s Plan to Win Agentic AI Race appeared first on Analytics India Magazine.