Год: 2025

Wealthy, a wealth-tech startup based in Bengaluru, has raised ₹130 crore in a Series B funding round led by Bertelsmann India Investments. The round also included participation from existing investor Alphawave Global, new investor Shepherd’s Hill, and a group of prominent tech entrepreneurs.

According to the press release, this funding will support Wealthy’s goal of advancing India’s rapidly growing community of mutual fund distributors (MFDs) by providing advanced AI-powered tools and digital infrastructure.

Founded by IIT and IIM alumni Aditya Agarwal and Prashant Gupta, the startup processes over ₹300 crore in transactions monthly. It works with over 6,000 mutual fund distributors, serving more than 100,000 clients across 1,000 towns, and recruits over 350 distributors every month. The platform currently manages ₹5,000 crore in client assets.

In 2022, Wealthy secured Series A funding, also led by Alphawave Global. Over the past three years, the company’s assets under management (AUM) have surged from ₹200 crore to ₹5,000 crore.

The startup operates 20 offices across India, with a strong presence in major cities such as Bengaluru, Mumbai, Hyderabad, Ahmedabad, Surat, Jaipur, Gurugram, Delhi, Faridabad, Ghaziabad, Lucknow, Kanpur, and Kolkata, supported by a team of over 250 members.

“India has a fundamental advice gap that technology alone cannot solve. LIC serves over 40 crore customers, yet mutual funds have only five crore investors. This gap exists because we have too few advisors, and the ones we have lack the tools to scale,” Aditya Agarwal, co-founder of Wealthy, said.

Wealthy’s AI-powered platform offers a complete 360° solution that combines investments, mutual funds, stocks, PMS, FDs, and fixed-income securities, with protection products such as term and health insurance. Clients and distributors benefit from dedicated apps that provide access to over 200 financial institutions.

“Less than 15% of Indian households have any exposure to the Indian equities market, either directly or indirectly. As India marches on its way to being a developed country, we believe this number will move closer to 60% and catch up with developed markets,” Rohit Sood, partner at Bertelsmann India Investments, said.

Distributors are equipped with AI-powered workflows for real-time alerts and client engagement, quick KYC onboarding, and enterprise-grade tools to enhance their digital presence. The platform also provides advanced analytics to track portfolio performance and understand client behaviour.

The post Bengaluru-based AI Startup Wealthy Raises ₹130 Crore for Wealth Management appeared first on Analytics India Magazine.

It has been more than a year since the IndiaAI Mission was announced, yet the country awaits its homegrown LLM. While there have been efforts across compute, datasets and research collaborations, the Mission’s progress remains far behind global labs that are already rolling out next-generation models.

Google just launched its latest model, Gemini 3, which it claims outperforms OpenAI’s GPT-5.1 and Anthropic’s Claude Sonnet 4.5.

Speaking at the Bengaluru Tech Summit 2025, IndiaAI Mission CEO Abhishek Singh acknowledged that India continues to lag behind the US and China. While global players release new LLMs, the Mission remains focused on building its seven foundational pillars, which are compute capacity, IndiaAI Innovation Centre (IAIC), datasets platform AIKosh, application development initiative, startup financing, IndiaAI FutureSkills and safe & trusted AI.

On foundation models, Abhishek said the government is currently supporting 12 initiatives. One of them, a homegrown sovereign LLM being developed in Bengaluru by Sarvam AI, is slated for release in December and will be showcased at the IndiaAI Impact Summit in February, 2026. The model is a 120-billion-parameter foundation model trained on more than 17 trillion tokens, including 17–20% Indian data.

Frontier AI cannot be built on short-term funding cycles, according to Jacob Joseph, VP of data science at CleverTap, an all-in-one customer engagement platform. He told AIM that it needs “deep, patient capital,” especially when India’s R&D spending sits at roughly 0.6% of GDP, compared to the 2–3% committed by countries at the forefront of advanced AI.

Joseph added that training a world-class model is a years-long, multibillion-dollar journey, with substantial investment going into foundational research that may take time to show commercial payoff.

Meanwhile, Abhishek said the mission released its AI safety framework on November 5 and is trying to balance innovation with safeguards. “We are developing tools for detecting deepfakes or limiting AI-generated content,” he said, adding that these tools will be available on the IndiaAI platform.

With that structure in place, “we will be able to do much more to ensure that whatever AI we develop improves efficiency, productivity and the reach of services,” Abhishek said.

Sanchit Vir Gogia, CEO of Greyhound Research, was more direct. He told AIM, “India does not need more policy PDFs or GPU ribbon cuttings.” He argued that infrastructure must perform, not just exist, which means subsidised compute that is fast, reliable and tiered for real workloads.

According to him, India also needs to back national AI bets in sectors like healthcare, agri-tech, and financial inclusion with funding that runs from data curation all the way to deployment.

IndiaAI is a well-structured starting point, but its impact will hinge on “velocity, usability, and signal clarity,” Gogia said. While the mission lays out the right pillars across compute, data, fellowships and startup support, the real test will be whether these translate into tangible traction.

IndiaAI is trying its Best

“Our ecosystem is still too fragmented,” said Ashutosh Singh, co-founder and CEO of RevRag.AI, explaining why India still struggles with frontier AI research. “India has strong talent, but lacks the density, long-horizon R&D funding, and tightly integrated research groups needed for frontier breakthroughs. Our ecosystem is still too fragmented.”

At the Tech Summit, Abhishek said that the government is scaling up compute capacity, foundation model development and skilling initiatives under the national AI mission, as it prepares for larger investments in the coming months. Abhishek said the mission has acquired 38,000 GPUs, bringing down the effective cost to ₹65 per GPU/hour after subsidies.

He added that teams from BharatGen, IISc Bengaluru and IIIT-Hyderabad are also progressing on their respective models. “For foundation models, we provide 100% of the compute support that is required,” Abhishek said, adding that the goal is to build models trained on Indian datasets to reduce dependence on foreign models.

Notably, BharatGen has secured 13,640 H100 GPUs and close to ₹1,000 crore in funding, the single-largest allocation in the country. It already has a series of early releases under its belt — Param-1, a bilingual 2.9-billion-parameter model, Shrutam for speech recognition, and Patram, a vision-language model for document understanding. But scaling to a trillion parameters is a different order of challenge.

The Next Moves India Cannot Miss

Joseph said that for India to catch up would require more than hardware and infrastructure. India needs to give researchers the freedom to run ambitious, high-risk experiments “without friction,” and to build hubs where talent, compute, and capital come together with a shared purpose. “That’s how global labs operate,” he said, adding that building that kind of rhythm will take time.

Meanwhile, under the FutureSkills pillar, IndiaAI is offering fellowships to undergraduate, postgraduate and research students from all disciplines who take up AI projects.

“These fellowships are not limited to only engineering or science students,” Abhishek said, adding that they also extend to fields such as medicine, law, commerce and liberal arts. The mission is also exploring partnerships with industry to expand training programmes.

MeitY, under the IndiaAI Mission, has also launched ‘YUVA AI for ALL’, “a first-of-its-kind free course that introduces the world of AI to all Indians, especially the youth.”

Gogia said that India must become “the centre of gravity for AI researchers, not their backup plan.” He argued that this requires far more than fellowships. It demands world-class labs, stable infrastructure, academic freedom, and career paths that don’t push talent abroad.

If India can move these pieces together, Gogia said, it can run its own race; if not, it risks becoming merely a customer in someone else’s system.

The post ‘IndiaAI Does Not Need More Policy PDFs or GPU Ribbon Cuttings’ appeared first on Analytics India Magazine.

TCS has rolled out a new AI-powered upgrade to its TCS ADD Risk-Based Quality Management (RBQM) platform, designed to give drugmakers and research organisations sharper, real-time oversight of clinical trials. The platform aims to help teams spot risks earlier, improve data quality, and manage increasingly complex trial setups.

The new version introduces four AI-driven modules focused on risk assessment, quality tolerance limits, trial analytics, and subject-level data monitoring. Together, these tools allow researchers to detect problems much sooner than traditional monitoring methods.

TCS says these modules are among the few globally that are fully interoperable and can be customised to suit different trial designs. This feature can shorten deployment time for sponsors.

“In today’s rapidly evolving clinical research environment, traditional approaches to quality management are no longer sufficient,” said Rachna Malik, Global Head of TCS ADD. She said the upgraded platform supports faster, data-backed decisions and can help bring new therapies to patients more quickly.

Industry trends support TCS’s push toward AI-driven oversight. Adoption of AI in India’s healthcare GCCs has risen sharply from 65% in 2019 to 86% in 2024, Zinnov managing partner Karthik Padmanabhan told AIM, noting that AI tools are now central to improving patient recruitment, monitoring risks, and ensuring regulatory compliance.

The update comes as the life sciences industry increasingly relies on AI and analytics to navigate stricter regulations and the challenges of decentralised, adaptive trials. TCS notes that the platform is aligned with international guidelines ICH E6(R2) and the upcoming E6(R3), and incorporates Quality by Design principles from the start of a study through execution.

TCS says the platform has been used in more than 1,300 studies across 32,000 sites, a sign that AI-driven oversight is fast becoming a standard part of modern clinical research.

The post TCS Launches AI Platform to Oversee Clinical Trial Oversight appeared first on Analytics India Magazine.

Sentient AI’s launch of Recursive Open Meta-Agent (ROMA), in September 2025 marks a pivotal moment in the evolution of open-source AI frameworks in India. Far from being “just another model,” ROMA represents a new way of thinking about how AI systems should reason, coordinate, and solve complex problems.

Built as a hierarchical meta-agent framework, ROMA abandons the monolithic design of traditional large models and instead orchestrates a structured tree of specialised agents, each responsible for solving part of a larger task.

The result? A transparent, auditable, high-performance system that challenges the dominance of closed AI architectures and sets a new direction for the future of decentralised intelligence.

What ROMA Actually Is, and Why It Matters

As Himanshu Tyagi, co-founder of Sentient, told AIM, “ROMA isn’t a model; it’s a reasoning architecture. That distinction is crucial as models generate outputs, while reasoning architectures determine how those models think, plan, and coordinate to solve problems.”

ROMA’s defining innovation lies in its recursive hierarchical task tree. At its foundation is an “Atomiser” that decides whether a task is atomic, directly executable, or requires further planning. If planning is required, control moves to the “Planner,” which decomposes the goal into subtasks, each of which is recursively fed back into the Atomiser. This allows ROMA to scale to long-horizon, multi-step reasoning tasks that typically confound single-model systems.

This architecture is not only algorithmically efficient but also involves minimal complexity structurally. As Tyagi said, “The algorithmic innovation is elegant yet simple (simple recursion), leading to phenomenal performance improvements.”

By treating reasoning as a planning problem rather than a generation problem, ROMA reduces compounding errors, improves long-term coherence, and enables parallel execution, allowing independent subtasks to be solved simultaneously, the founders said.

Tyagi argued that the narrative around openness and safety, often used by Big Tech to justify closed systems, is fundamentally flawed. “The argument that openness inherently risks safety is a form of semantic gymnastics used to justify building opaque, black-box models that monopolise control and knowledge,” he added.

Instead, transparency is woven directly into ROMA’s architecture. Because tasks propagate through an explicit recursive tree, every stage is traceable, auditable, and open to inspection. Builders can review how tasks were broken down, how context flowed, how tools were invoked, and where failures happened, something impossible in monolithic models.

ROMA is fully open-source and available for inspection and modification, reinforcing Sentient’s belief that the future of AI must be democratic, not centralised.

The Global, Not Local, Roots of ROMA

Although India features prominently in Sentient’s leadership and mission, ROMA’s design philosophy is not geographically bound. “Our team, researchers and vision are fundamentally global… AI should be borderless and not controlled by any single entity or sovereign state,” Tyagi clarified.

This borderless ethos directly shaped ROMA’s modular architecture. Contributors can plug in models, tools, datasets, or agents without relying on proprietary backends or closed APIs. More importantly, Sentient’s ecosystem is designed to reward contributors, not just extract value from them.

As Tyagi put it, ROMA’s architecture is meant to “provide the necessary architectural openness while also offering a transparent, traceable meta-agent framework that allows this global talent pool to build and control their own sovereign AI models.”

In a world where AI infrastructure threatens to be monopolised by a handful of technology giants, the open-source model offers not just an alternative, but a counterforce.

Why Developers Are Paying Attention

ROMA represents the next stage in a broader mission for Sentient co-founder Sandeep Nailwal, who previously helped with Polygon’s decentralisation of finance.

“With Sentient, we’re doing the same for intelligence. We want to stop AI from becoming a gated, private resource controlled by a handful of companies. Intelligence should be a public good,” he told AIM.

Just as Web3 democratised access to financial infrastructure, ROMA aims to democratise access to cognitive infrastructure. If Polygon built the rails for decentralised value, Sentient seeks to make the rails for decentralised intelligence.

Nailwal views this as critical for global equity. “If it’s locked away, it kills participation… From startups to sovereigns, everyone needs access to intelligence as a public utility,” he said.

The company’s $1.2 billion valuation, as Nailwal explained, is anchored in investor confidence that open-source innovation scales faster and more efficiently than closed systems.

“No single company, no matter how large, can out-innovate a global network of open builders moving in parallel,” he clarified.

A key component of this ecosystem is GRID, Sentient’s open intelligence marketplace, where developers can contribute tools, models, and agents and earn based on their usage. This solves one of the fundamental challenges of open source: sustainability.

More importantly, the open source framework offers a philosophical and structural alternative to the consolidation of AI power. “Sentient represents Web3 for intelligence… We’re decentralising AI itself,” Nailwal summarised.

The startup said in its blog that ROMA’s prototype search agent, dubbed “ROMA Search”, achieved 45.6% accuracy on the SEALQA subset known as “Seal-0,” which tests complex multi-source reasoning, outperforming the previous best system, Kimi Researcher, at 36% and the proprietary Gemini 2.5 Pro at 19.8%.

The post Sentient Pushes Open AI Infrastructure into the Global Spotlight appeared first on Analytics India Magazine.

Google has been relatively quiet when it comes to making agentic coding or vibe coding announcements. The furthest it went was with Jules, Vertex AI, or Gemini CLI, obviously apart from acquiring Windsurf. But now, the company has decided to enter the field formally with Antigrativy.

The company is trying to land its agentic IDE into a space packed with hype, skepticism, forking drama and an audience tired of experimenting with half-finished tools. Antigravity allows agents to “autonomously plan and execute complex, end-to-end software tasks” with direct access to an editor, terminal and browser.

It comes with Gemini 3, which is already ruling a ton of hearts amongst creators and developers in Cursor, GitHub and Replit.

Out in the open, opinion on Antigravity remained split. Some are cancelling their $6o Cursor subscriptions, some claim “Google is changing the VIBE CODING game.” Others say that it seems like it’s still in Beta and is worse than Cursor and Copilot.

But What’s Really Happening?

Coming to the positives first, Antigravity does things that others cannot. It can do full screen recordings to verify your app actually works in real time. “Screen recording + live debugging gives AI the kind of context developers used to dream about,” a software engineer demoed on X.

I’m bullish on Google Antigravity for 1 simple reason.
It can now do full screen recordings to verify your app actually works in real time.
Screen recording + live debugging gives AI the kind of context developers used to dream about. pic.twitter.com/q64kVVyNjO

— corbin (@corbin_braun) November 20, 2025

Some are obsessed with the UI, while others with the agentic capabilities, where developers were able to fix errors that other AI editors struggle with. Some are in love with how it integrated Nano Banana Pro for multimodal reasoning.

But for many long time AI coding tool users, Antigravity arrived with a shape most developers recognised in the first second. A Hacker News thread on launch day saw more than a thousand comments, with users repeating a familiar line.

While one person wrote: “Oh no. Not another VSCode fork…,” another said, “Oh cool, another IDE for programming… aaaand it’s a vscode fork. I don’t know what I expected tbh.” This captured the fatigue that has grown around the rise of agentic editors that look the same, act the same, and often stumble the same.

Yet, this launch was not only about VS Code. In an attempt to push out Cursor, Antigravity pushed an older story back into view: Google’s decision in July 2025 to hire Varun Mohan from Windsurf and license its technology for roughly $2.4 billion. The Windsurf acquisition by Cognition Labs came later, though many developers now believe Antigravity carries Windsurf’s fingerprints, with Mohan in the lead.

This is reflected in its references to Cascade, the proprietary agent system inside Windsurf, which were spotted inside Antigravity’s code. Visual Studio Magazine had already hinted at the same direction in its piece titled “Google Joins AI IDE Race to Compete with VS Code, Apparently Forking VS Code.”

And yes, the discussion continues.

Rate Limits the Real Issue, At Least for Now

Adarsh Shirawalmath, founder of Tensoic, a company that provides custom LLMs, fine tuning, and inference, told AIM Antigravity provides good quality edits and debugging/testing tools in the browser, and brought new ideas that stood out. “It’s the first good agentic IDE I’ve used that can actually connect to my remote SSH machine through the CLI, run commands there, manage dependencies, and navigate the entire remote filesystem properly.”

He said it “may follow some design aspects of Windsurf but it is a form of VS code.” Shirawalmath was clear about the flaws too. “There are quite a lot of bugs such as agents being stuck in a loop, other models not working, quicker rate limits consumption.”

This is a recurring theme on discussion forums as well. “There were some UI glitches,” the top commenter on Hacker News said. Cursor, he argued, had “real annoying usability issues” and he found Antigravity “more polished.” He imported his old settings, got working on a project, enjoyed the Gemini 3 model for a while and then hit a wall.

But then, the dreaded rate limits kicked in. “After about 20 mins – oh, no. Out of credits,” he said, while adding that he looked for a purchase button, but found none. “If you release a product, let those who actually want to use it have a path to do so,” he wrote.

He switched back to Cursor soon after and discovered that Cursor itself already had Gemini 3 Pro. His conclusion was brutal: “Real developers want to pay real money for real useful things.” This reminded of a similar scenario when developers were cancelling Cursor subscriptions.

Adithya S Kolavi, founder of CognitiveLabs, told AIM that he has been trying to use Antigravity, but the rate limits don’t allow him to. “Not an overall good experience so far,” he said, while adding that integrating into the browser is unique. “Like most IDEs just focus on text basically code, they [Google] have taken a more multimodal approach natively, which is nice,” he said.

On the company side, Google tried to calm the situation. Varun Mohan said, “We’re also aware of the capacity constraints everyone is facing given our growth and working to address them as quickly as we can,” he said, which is fair since it is still in beta.

But, the forking allegations continue as some developers call it Windsurf 2.0 with a new UI.

wow google antigravity is a vscode fork with a seperate agent view ui
this is hilarious

— dax (@thdxr) November 18, 2025

This is where the story rests today. Antigravity has people excited, irritated, curious, hopeful, and tired — all at once. Google has built something that feels half Windsurf, half VS Code and fully trapped inside the expectations of a community that has seen too many promises from too many AI IDEs.

The post Why Developers are Fighting Over Google’s Cursor Killer Antigravity appeared first on Analytics India Magazine.

As enterprise IT evolves, the traditional model of managing IT services is being rewritten. What was once a service-heavy, project-led function is now morphing into a platform-driven, software-led ecosystem. This shift towards service-as-a-software, is redefining how Indian IT companies deliver, consume, and also compete in IT Service Management (ITSM).

The global IT Service Management (ITSM) market is consolidating into a platform-led ecosystem powered by AI, automation, and cloud-native operations. Valued at nearly US $12 billion in 2024, it is projected to exceed US $36.78 billion by 2032, growing at over 15% annually.

ServiceNow dominated with about 51.1% share in 2023, setting the standard for workflow automation, while Salesforce, BMC, and Atlassian are expanding their footprints through AI-integrated service layers.

The ITSM space is witnessing strategic partnerships that blur the line between software vendors and IT service firms. Salesforce is emerging as a challenger with its Agentforce IT Service, backed by an HCLTech alliance for AI-led enterprise solutions. BMC has teamed up with Infosys and Tech Mahindra on hybrid-cloud and automation, while ServiceNow has collaborated with Wipro, Tech Mahindra, and NTT DATA.

Service-as-a-software

Achyuta Ghosh, executive research leader HFS Research, says this is the Services-as-Software shift in motion. “As ITSM becomes productised, IT vendors are reinventing themselves from service integrators to platform providers.” They’re moving from time-and-material models to subscription-led outcomes, monetising uptime, resolution speed, and productivity rather than manpower,” he adds.

This evolution turns IT services into software-powered assets — repeatable, scalable, and designed for measurable business value.

The future of ITSM lies in consuming intelligent service platforms as a product, not as a project, according to Shelton Rego, VP of India business at Freshworks, a company that provides enterprise-grade service software solutions.

He says that modern businesses are looking for faster results, instead of slow rollouts, and uncomplicated AI-powered solutions, instead of consulting contracts. “Software-led servitisation delivers that promise, transforming ITSM into an engine of growth and efficiency. Vendors who build for speed, simplicity, and real-time adaptability will define the next era of service management,” Rego adds.

Therefore, companies providing ITSM remove complexity, so that organisations can focus their energy on customers and business outcomes rather than wrestling with IT processes.

How are IT companies adopting?

Across IT functions, adoption is accelerating as AI and automation embed themselves into daily workflows. Platforms like ServiceNow have redefined how IT teams handle incidents, changes, and problems.

Vineet, an IT service delivery professional at a large IT company, who wishes to go by his first name, explains: “Earlier, teams used multiple tools like Outlook for notifications, separate dashboards, and manual reporting. With ServiceNow, all of that happens in one place.” Built-in dashboards now visualise real-time ticket data, track change progress, and automate stakeholder alerts.

ServiceNow’s GenAI capabilities have further simplified operations. Based on historical incidents, the platform can generate proactive recommendations and summaries reducing reliance on separate knowledge databases. “Earlier, we had to use different tools for root cause analysis, but now GenAI does it automatically inside ServiceNow,” Vineet adds.

However, not all functions are fully automated. Post Implementation Reviews (PIRs) and final change reviews still require manual effort. “ServiceNow hasn’t yet matured in that area,” he says.

This mix of automation and manual intervention defines the current state of IT adoption rapidly evolving, but still balancing between machine intelligence and human oversight.

Business model

The platformisation of ITSM is not only a technology shift but also a business transformation. As automation and AI integrate deeper into delivery, IT firms are moving from manpower-based contracts to software and subscription-led engagement.

Ghosh explains, “Vendors are developing proprietary IP, automation frameworks, and cloud-native platforms that can be licensed or co-delivered with hyperscalers. Instead of billing for effort, they’re monetising outcomes—like uptime and productivity gains.”

This transformation is also changing workforce composition. Rego emphasises a “people-first AI strategy” where repetitive tasks are automated, allowing talent to focus on creative and strategic roles. “Traditional headcount-driven models are being replaced by AI strategies which focus on automating the mundane tasks and upskilling talent to do the things that only humans can do,” he says.

For IT service majors, this means balancing two operating models: one focused on scalable software products and another on managed services that integrate those products into enterprise ecosystems.

Integration Challenges

One of the primary challenges in transforming ITSM is integrating legacy systems that were not built for agility.

Regosays that these legacy systems often lack the flexibility to support modern day DevOps practices, leading to compatibility issues and slower deployment cycles. “Cloud and AI-native solutions are built with platform capabilities in mind. Unified platforms like Freshworks which have ITSM, ITAM and ITOM capabilities make it easier for companies to not just onboard quickly but also to start realising return on investment much faster,” he says.

Ghosh adds, “Transforming ITSM from process to product isn’t easy. Vendors face challenges such as standardising highly customised processes, integrating with legacy backends, and building unified data models for automation.”

Codifying ITSM requires profound architectural change—APIs, low-code frameworks, and AI-driven decisioning, not just process digitisation, he says, adding that IT service teams must re-skill from process managers to software engineers, adopting agile release cycles and product thinking.

Convergence creates competition

The ITSM market’s evolution is creating new competitive pressures. As SaaS companies encroach on areas once dominated by IT service firms, both are converging around the same goal—automation-led, software-powered service delivery.

Ghosh observes that the rise of ITSM-as-software blurs traditional boundaries between IT service providers and SaaS players. “While today they collaborate with services firms acting as implementation partners, over time, both will compete for the same automation and operations budgets.”

In India, this is reshaping customer behaviour, as they seek cost-effective digital solutions with speed and simplicity. “Our clients typically go live in under 90 days, and AI solutions in under 30 minutes. This rapid time-to-value is putting pressure on traditional IT service providers to adapt or risk obsolescence,” says Rego.

Ghosh says that the decision to go for an IT company or SaaS vendor depends on enterprise maturity. Digital-native organisations increasingly adopt ITSM platforms directly, valuing agility, control, and transparency. In contrast, large traditional enterprises still rely on IT service providers to manage ITSM due to integration complexity, legacy systems, and hybrid operations, he says.

However, competition is not only between vendors—it’s also driven by enterprise preferences. As Vineet explains, clients dictate tool choices. “Even if we have our own AI-enabled ITSM tools, customers often insist on ServiceNow or Salesforce. They know these are market leaders and prefer not to take risks with alternatives,” he says.

This customer-driven consolidation means newer or niche vendors must differentiate through speed, AI integration, and ROI visibility rather than tool variety.

ITSM as a platform marks a shift from manpower-driven services to software-led, AI-powered platforms. As IT firms and SaaS players converge, success will depend on delivering speed, simplicity, and measurable outcomes. The future of ITSM lies in turning service delivery into a strategic, software-defined advantage for enterprises.

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