Рубрика: AI

Consulting firms rarely make headlines about AI. But when they do, it tends to be either about multibillion-dollar investments or a high-profile mishap linked to their use of AI. But looking at it as a whole, it is clear that the consulting industry’s approach to AI has plenty of cracks.

It is not a collapse, nor a downfall. It feels more like a pressure test. For three decades, the Big Four sat at the centre of every major transformation. Deloitte, PwC, EY and KPMG, along with Accenture, McKinsey or BCG, built vast engines that combined audit weight, global processes and armies of analysts. For years, that model felt unshakable.

Nishant Pahwa, connection and network manager at Cresent Core Consulting, with prior experience at EY and PwC, offered a telling example. He recalled that in early 2025, two CXOs walked out of a boardroom when someone remarked, “Deloitte gave a great deck… but I need someone who can solve this by next Friday, not next quarter.”

That sentiment is now widespread. Many companies would rather turn to smaller firms that adopt and deploy AI far more quickly, rather than spending thousands of dollars on big firms and waiting for quarterly results. IBM’s consulting head, Mohamad Ali, recently told The Times of India in an interview that consulting firms’ model is under threat.

“The future of consulting is going to be a hybrid of people plus software,” he said, adding that the companies that do not adopt this would fail.

The firms are not shrinking. Their numbers still climb. Deloitte stands at more than $70 billion in FY25, PwC at $57 billion, EY at $53 billion and KPMG at around $38 billion. But the market around them is changing faster than they can turn.

But, What is Actually Happening?

Clients want less analysis and more execution, less manpower and more speed. AI has pushed this shift forward at a pace that has surprised even insiders. Business Insider recently highlighted how smaller firms, which are basically consulting startups using AI, have become the biggest competition to the Big Four consulting firms.

Every large firm has pushed aggressively on AI. Deloitte built the Deloitte AI Institute, PwC announced a billion-dollar commitment, EY launched EY.ai, and KPMG tied up with Microsoft and AWS for large AI programs.

Accenture is a particularly interesting example. After spending more than $3 billion on AI, the firm described the returns as underwhelming. The company is now also rebranding its 8,00,000 workforce as ‘reinventors’ to adapt with AI and has announced a partnership with OpenAI to integrate ChatGPT Enterprise to all its employees.

In many ways, this mirrors the forward-deployed engineer model that Palantir has followed for a long time. These are essentially technology consultants who will advise companies while sitting within them, hinting strongly about where the industry is heading.

While the large firms continue to handle the multi-year projects, a different race is unfolding in the shorter cycles.

Boutique firms like Xavier AI, NextStrat, Consulting IQ, Perceptis, and a few others have stepped into high-speed, high-depth mandates. These firms win because a team of 12 specialists outpaces a team of 200 generalists inside a Big Four engine.

Ironically, these firms are built by several former McKinsey consultants. Meanwhile, McKinsey itself recently cut around 200 jobs to focus more on AI-related roles. This is also what went down with Accenture, which announced a slew of layoffs in its latest quarterly report.

Yet, the challenge is that the struggle continues. In Canada, The Independent found that a Deloitte report for Newfoundland and Labrador contained citations that did not exist or did not support the claims made. The report cost around $1.6 million Canadian.

This followed its first failure in Australia, where Deloitte refunded part of a A$459,000 contract because the report had fabricated references created by an Azure OpenAI system using GPT-4o. Australian senator Deborah O’Neill said, “Deloitte has a human intelligence problem.” She said government agencies might be “better off signing up for a ChatGPT subscription”.

Deloitte said the errors did not change the findings. But the episode shook trust across the sector.

The AI Hard Sell

This is what makes the current friction so interesting. The consulting world is selling AI harder than ever, and failing to do so.

KPMG went from zero to $650 million in revenue from generative AI in one year. Deloitte launched its $3 billion Zora AI with NVIDIA, focused on autonomous AI systems. BCG now earns about 20% of its revenue from AI projects. McKinsey offers more than 140 AI accelerators. The story they present to clients is clear. But clients are starting to notice something different.

Many of the tools these firms sell are now directly accessible to companies through OpenAI, Anthropic or even Google and Microsoft. They sit on cloud platforms and behind APIs. They come from the same model providers everyone uses, alongside forward-deployed engineers.

There is also a deeper problem. Enterprises are not ready for AI at the scale consultants promise. Mukesh Bansal, founder of Nurix AI, earlier said, “Everyone is building AI agents, and yet so few AI agents are in production doing real work.”

He said agentic AI companies need to operate like a mix of McKinsey and Infosys, pairing strategy with execution. Boutiques have stepped into this gap with AI native models. As Business Insider first reported, many of these firms are built by former MBB or Big Four consultants who wanted less bureaucracy and more speed.

They serve clients who could never have afforded a McKinsey team. Their growth is rapid. FT reported that Xavier AI says revenue is doubling month over month. Perceptis raised $3.6 million, SIB has identified more than $8 billion in savings, and Genpact cut $40 million in costs with AI through its Client Zero programme.

These firms show a version of consulting that feels more like a product—faster and easier to consume.

The consulting world is now a mix of giants, boutiques and AI native players. The cracks are not signs of collapse. They are signs of recalibration. The winners will be the ones who can combine real human judgment with AI in a way clients can trust. The old fortress is still massive, but the drawbridge is no longer one-way.

The post AI Was Meant to Save Consulting. It Exposed the Cracks Instead. appeared first on Analytics India Magazine.

Yoodli, a US-based AI-powered learning platform, has raised $40 million in its Series B funding round led by WestBridge Capital, with participation from Neotribe and Madrona.

Founded in 2021 by Varun Puri and Esha Joshi, Yoodli has secured Series B funding just a few months after announcing its Series A round in May. To date, the company has raised nearly $60 million.

The startup’s platform uses AI to simulate real-world scenarios, from sales calls and leadership coaching to interviews and feedback sessions, giving users instant, personalised feedback they can practice privately and repeatedly.

This funding will accelerate Yoodli’s investment in AI coaching, analytics, and personalisation while expanding its reach across enterprise learning, GTM enablement, and professional development. The company plans to grow its product, AI research, and customer success teams as it continues to scale globally.

“This round helps us scale our team and serve more enterprises on a true end-to-end experiential learning platform,” said Varun Puri, cofounder and CEO of Yoodli. “We’re reducing the time it takes to acquire real skills, ensuring employees are ready for game time, and saving organisations countless hours lost to passive coaching.

The Yoodli platform is used by major companies like Google, Snowflake, Databricks, RingCentral, and Sandler Sales. Adoption has surged due to a workplace shift towards experiential learning, allowing employees to improve through guided practice rather than just consuming training content.

“We see Yoodli defining a new category of AI-native learning tools for the enterprise, as companies today seek scalable, AI-driven solutions to train and upskill their workforces. The Yoodli team has built a platform that brings a high level of precision and scalability to skill development, and we’re excited to partner with them as they scale,” said Manthan Shah, principal at WestBridge Capital.

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KPIT Technologies has pursued a series of tightly scoped acquisitions and minority investments over the past few years to assemble a comprehensive software-defined vehicle (SDV) and mobility ecosystem.

While the moves appear varied at first glance, ranging from in-vehicle networking firms to digital experience platforms, the through line is deliberate.

The company has been aiming to deepen essential technology capabilities and expand the value it can deliver to global OEMs and partners navigating the transition to software-led mobility.

Elaborating on the approach, Mohit Kochar, chief marketing officer at KPIT, told AIM that the acquisitions “collectively strengthen its software-defined vehicle capabilities, deepen domain expertise, and enhance its long-term AI-enabled engineering roadmap.”

He pointed to investments in Caresoft Global Engineering’s Solutions business, N-Dream (AirConsole), FMS, PathPartner, Technica and a set of smaller minority stakes.
KPIT’s early additions like PathPartner and FMS strengthened embedded engineering and digital experience foundations.
The Technica acquisition expanded in-vehicle networking and test infrastructure depth; the stake in N-Dream (AirConsole) added next-generation in-car digital experience capabilities.
The purchase of Caresoft Global Engineering’s Solutions business broadened engineering scale and customer access; and the recent investment in helm.ai advanced the company’s AI-enabled mobility roadmap.

According to Kochar, these decisions follow a consistent two-fold intent: reinforcing capabilities critical to SDV programs and widening the scope of value KPIT brings to global OEMs and ecosystem partners.

In certain cases, Kochar said, the investments also create stronger strategic access to customers and open new areas of engagement.

Rather than approaching SDV as a monolithic shift, KPIT’s strategy distributes capability-building across the stack, aligning each acquisition with a specific engineering need that OEMs face, as vehicles become increasingly defined by software, data and AI-enabled functions.

Kochar underlined that autonomous driving is only one application within the wider SDV landscape.

He noted that KPIT has developed mature, organic capabilities in this space over many years as a strategic engineering partner to global OEMs.

Among the recent additions, Technica stands out for its deep expertise in automotive Ethernet, hardware, test infrastructure and reference architectures. These capabilities are essential for modern SDV programs in which reliable, high-speed data flow across ECU and zonal architectures becomes non-negotiable.

Other acquisitions similarly broaden KPIT’s ability to deliver differentiated software, systems engineering and next-generation digital experiences across mobility platforms.

Internally, each acquisition within the KPIT Group operates in a way that maximises customer outcomes rather than being forced into a uniform integration model.

Some maintain a high degree of independence, while others align closely with KPIT’s practices, delivery mechanisms and engineering processes.

Kochar emphasised that the common thread across them is customer value and execution excellence, not structural conformity.

AI Capabilities

This approach also extends to AI. KPIT positions AI and generative AI as central to its long-term roadmap, embedding them across the automotive software lifecycle to improve productivity, speed, quality and efficiency.

Acquired entities both benefit from and contribute to this ecosystem, but Kochar made clear that none function as isolated AI centres; instead, they operate within a shared technology framework.

The company’s differentiation, as it presents it, is grounded in deep domain expertise in mobility, long-standing OEM partnerships and its ability to translate advanced technologies, including AI, into real-world engineering impact.

KPIT’s strategy is not positioned around specific tools or platforms but around its capacity to scale complex automotive programs in markets where the shift to SDV architectures demands consistency, reliability and safety across multiple software layers.

In its Q2 FY26 results, KPIT reported revenues of $181 million, reflecting 4.4% year-on-year growth in dollar terms and 7.9% in rupee terms. The company also reported EBITDA margin expansion to 21.1% and a total contract value of new engagements worth $232 million.

Commenting on the performance, Kishor Patil, co-founder, CEO and MD, KPIT, described the quarter as one that strengthened the company’s foundation for the SDV transition.

He cited strategic investments including the closure of the Caresoft Engineering Solutions Business acquisition in Q2, the increase in stake in N-Dream and the investment in helm.ai in Q3 as building blocks aligned with industry direction.
However, the company did not address specific questions related to internal AI integration, data unification across acquisitions, AI safety workflows, generative AI usage in engineering, or the existence of a unified MLOps framework.

The post KPIT’s Big Bet: Buying the Brains of the Software-Defined Car appeared first on Analytics India Magazine.

Bangalore-based digital wellness platform Healthify has launched Ria Voice, a real-time AI health coach that uses OpenAI’s Realtime API to deliver natural, speech-to-speech coaching.

The global rollout makes Healthify one of the first companies to bring a fully multimodal, audio-native health agent into production, enabling users to speak to the coach, show it their meals or activity, and receive instant, context-aware guidance.

Ria Voice represents the 3.0 evolution of Healthify’s AI stack and moves beyond older voice assistants that depended on transcribing speech before generating a response. Operating natively in audio allows the system to detect emotions, handle diverse accents, support code-mixed languages, and respond with minimal latency.

The coach draws on nutrition, fitness, sleep, stress, glucose, heart rate and body composition data, enabling a level of personalisation that mimics human coaching. Users can log food by describing their meal or pointing their camera at a plate, eliminating the friction of manual entries.

“Our mission at Healthify has always been to put a personal coach in your pocket. With Ria Voice, we’re finally able to bring that vision to life at a global scale,” Tushar Vashisht, co-founder and CEO of Healthify, said. “By leveraging OpenAI’s Realtime API, Ria can understand your food, fitness, sleep, stress and metabolic data in one place and respond with human-like speed.”

Pragya Misra, head of strategy and global affairs, India at OpenAI, said the product reflects what OpenAI’s new real-time infrastructure was designed for. “Our Realtime API can transform voice AI from a static interface into a conversational experience. Healthify has used this capability to build a coach that is fast, responsive and capable of the nuance required for personal wellness,” she said.

Ria Voice supports more than 60 global languages, including over 14 Indian languages, and is trained on hundreds of millions of real-world conversations between Healthify customers and human coaches. It can also generate customised diet plans based on preferences, allergies and macro goals, expanding its role beyond logging and recommendations.

The company is making the experience available not just on its app but also through WhatsApp and wearable devices. Through integration with Ray-Ban Meta smart glasses, users can talk to Ria hands-free and track meals via photos captured directly on the device—making Healthify one of the earliest health apps built for Meta’s smart glasses ecosystem.

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After directing mobile phone makers on December 1 to pre-install the Sanchar Saathi app on new devices, the Department of Telecommunications has now reversed course.

The government has withdrawn the mandatory pre-installation requirement.
Industry groups and privacy experts had raised concerns about the implications of shipping the app by default.
The government, however, framed the rollback differently, stating that “Given Sanchar Saathi’s increasing acceptance, the Government has decided not to make the pre-installation mandatory for mobile manufacturers.”

Pre-installing an application operated by the government raised various concerns about surveillance or poor data safety management, even as several representatives of the ruling party clarified that this would not be the case.

Besides, there was also a misunderstanding about whether users could uninstall the pre-installed Sanchar Saathi app. However, telecommunications minister Jyotiraditya Scindia clarified that users will be able to remove it at their will.

Leading mobile phone manufacturer Apple reportedly declined to the directive that mandated pre-installation.

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

Since the app offers a wide range of functionalities, it seeks permission for the camera, file storage, phone call logs, messages, and more.

Concerns were voiced about sections of society who would not be aware of how to disable these permissions when not necessary, or who would leave the app installed on their devices, creating room for potential data misuse.

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

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OpenAI marked the third anniversary of ChatGPT at a moment when the AI industry has become highly competitive. Google’s release of Gemini 3 and Nano Banana Pro has prompted the company to adjust its focus, with CEO Sam Altman on Monday directing employees to accelerate product development and look at releasing a new reasoning model next week.

In a Slack memo, as reported by The Information, Altman declared ‘Code Red’, an internal alert signalling that a competitor’s development potentially affects its market position, and hence the teams will have to reprioritise their resources.

It is not solely Google whose activities are a source of concern for OpenAI. Chinese AI lab DeepSeek recently launched two new reasoning models, DeepSeek-V3.2 and DeepSeek-V3.2-Speciale, which rival Gemini 3.0 Pro and equal GPT-5.1 in performance. Amazon, too, launched its new Nova models at AWS re:Invent 2025.

In the memo, Altman said that projects tied to advertising, AI health and shopping agents, and the personal assistant known as Pulse will be pushed back. He also encouraged teams to consider temporarily relocating talented team members to priority areas, and mentioned that a daily check-in will be organised for those working on ChatGPT improvements.

It’s interesting to see OpenAI shift its focus. Usually juggling many big research projects, this change highlights how the company is now prioritising practical, reliable, and profitable products.

What is Garlic?

OpenAI is reportedly working on a new model called Garlic. The model is meant to compete with Google’s Gemini 3 and Anthropic’s Opus 4.5, particularly in coding and complex reasoning.

However, the company did not respond to AIM’s request for comments on the development.

The Information reported that initial internal results indicate strong performance, with a possible release as GPT-5.2 or GPT-5.5 in early 2026.

According to a report by SemiAnalysis, OpenAI hasn’t completed a full-scale pretraining run for any next-generation model since GPT-4o launched in May 2024. It says GPT-5 is essentially a heavily fine-tuned version of the old 4o foundation rather than a true new generation.

A report by Epoch AI notes that GPT-5 used less training compute than GPT-4.5 because OpenAI shifted its approach to scaling post-training rather than running larger pre-training cycles.

Users on social platforms have pointed towards the company for not addressing technical challenges, including the copyright issues over its video generation tool Sora.

Despite these challenges, OpenAI continues to lead in user adoption. According to Similarweb, Gemini saw 1.182 billion visits in October 2025, while ChatGPT reached 6.165 billion. However, Gemini users spend more time per visit than ChatGPT users.

“Today, ChatGPT is the #1 AI assistant worldwide, with around 70% of assistant usage,” OpenAI’s head of ChatGPT, Nick Turley, wrote in a post on X.

Turley emphasised the growing role of ChatGPT in search, dominated by Google, calling it one of the company’s biggest opportunities. “ChatGPT now accounts for roughly 10% of search activity, and it’s growing quickly.”

Scaling is Not Dead

OpenAI researcher Mark Chen, in a recent podcast, said that the company remains confident about the performance of its upcoming model despite stiff competition from Google’s Gemini 3. He acknowledged Google’s progress but added that internal OpenAI models are already performing at a similar level.

Chen mentioned that the company has dedicated the past two years to significantly enhancing reasoning capabilities. But in doing so, it “lost a little bit of muscle” in other crucial areas such as pre-training and post-training. Now, he mentioned, the company is rebuilding that strength and seeing significant gains as a result.

Addressing growing speculation that the limits of large-scale training have been reached, he pushed back firmly. “A lot of people say scaling is dead. We don’t think so at all,” he said, adding that OpenAI still sees “a lot of room” in large-scale pre-training. The company, he said, has already begun training “much stronger models” as a result of this renewed focus.

OpenAI should have enough compute resources, and it recently struck compute deals, including a multi-billion-dollar partnership with AWS.

Its annual revenue is set to hit $20 billion by year-end. According to an HSBC update cited by the Financial Times, ChatGPT is projected to reach 3 billion weekly users by 2030, with more than 220 million of them paying subscribers.

OpenAI may be growing fast, but HSBC warns it could still remain unprofitable by 2030 and will need over $200 billion in compute to sustain its plans.

As Pedro Domingos, professor emeritus of computer science and engineering at the University of Washington, joked, “just as it was about to go bankrupt – OpenAI stumbled on AGI.”

With new models and recent compute deals, the company is hoping for a comeback, much like Google.

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