Indian Companies Bullish on Long-Term AI Investments; 76% Surveyed Firms Achieved ROI-Driven Results: IBM Study - Related to investments;, ibm, says, companies, access
Indian Companies Bullish on Long-Term AI Investments; 76% Surveyed Firms Achieved ROI-Driven Results: IBM Study

A research study commissioned by computing giant IBM revealed on Wednesday that most Indian companies made significant progress in executing their AI strategies last year.
The study surveyed over 2,000 IT decision-makers worldwide, 224 of whom were from India. Among the ones surveyed in the country, 87% reported progress in their AI strategy, and 76% revealed that they had achieved results driven by return on investment (ROI).
About 89% of the Indian respondents expressed their companies have started more than 10 AI pilots in the last year. Furthermore, 93% of the Indian respondents expressed they will increase their AI investments this year.
The majority of them reported looking for open-source solutions, and 48% revealed that more than half of the AI solutions being used are based on open-source technologies.
Among the companies that are yet to achieve ROI-driven results from AI projects, 33% expect to see savings within the next 12 months, and all of them believe they will achieve a positive ROI within three years.
Only 1% of the surveyed Indian respondents revealed that their AI strategy had not made progress.
The surveyed Indian companies revealed that they are focusing their AI investments this year on IT operations, software coding, and data quality management.
The detailed study from IBM, outlining the findings from multiple countries worldwide, can be found here.
Indian Prime Minister Narendra Modi on Tuesday spoke at the AI Action Summit in Paris, where he expressed that India leads in AI adoption. , India has one of the world’s largest AI talent pools.
“India is building its own large language model. Considering our diversity, we also have a unique public-private partnership model for pooling resources like computing power,” he added. In this year’s Budget, the government allocated ₹2,000 crore for the IndiaAI mission – nearly a fifth of the scheme’s ₹10,370 crore presented last year.
Moreover, AIM lately reported that Indian companies and startups are increasingly using AI-enabled coding tools. Although companies were initially hesitant to adopt such tools, they are now leaning toward them due to their benefits.
Furthermore, a recent survey by GitHub revealed that 56% of Indian developers are using AI tools to help them boost their chances for employment owing to the skills they develop. Moreover, around 80% of them believe AI tools have improved code quality.
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iPhone users just got access to Gemini's Deep Research - how to try it

iPhone clients can now tap into Google's Deep Research agent to research a topic on their behalf. Added to the Gemini website in December and to the Android app last month, the tool is now making its way to the iPhone version. Be aware, though, that Deep Research is available only to Gemini Advanced clients who pay $20 a month for the subscription.
Also: Google Gemini's lock screen modification is a game-changer for my phone.
Deep Research is Gemini's first agent, a new breed of AI bots that can perform tasks on their own. Submit your request or question, and the agent browses the web independently without you having to direct or manage it each step of the way.
When you submit a prompt with a specific question or request, Gemini delivers the standard type of AI-generated information. Want more? Tell it to turn to Deep Research. After scouring the web for more data, Gemini provides you with a full and comprehensive investigation, listing all the information it consulted.
Next, type or speak your query. In my case, I asked it for advice on health insurance plans for freelance contractors.
In response, Gemini hints at a plan for tackling your topic. You can edit that plan and tell the AI what changes you'd like to see. Gemini then presents you with an updated plan. If you like the plan, tap the "Start Research" button to send the AI on its way around the web.
Depending on the complexity of the topic and how many websites are researched, you'll likely have to wait at least a few minutes for the full study. At that point, tap the "Open" button for the study to read and review it.
The reports I received were well-organized and formatted with text, tables, and bullet points to make them easy to read. A list of all the researched websites appears at the bottom.
From the study, you can share it with someone else, open it in Google Docs, or select all the text. The conversation is saved to your chat history, so you can access the study at any time in the future.
You're able to kick off another request while one is already running, though Google limits how many you can run at the same time. You're also limited in the number of requests you can submit per day. But you'll be told if you're bumping up against the quota.
If you're on the fence about shelling out the $20 a month for a Gemini Advanced subscription, Google offers other perks beyond Deep Research. With this plan, you also score 2TB of storage, Google Photos editing aspects, 10% back in Google Store rewards, Google Meet premium video calling aspects, and Gemini for Workspace.
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‘We’d Like to Work With China,’ says OpenAI CEO Sam Altman

OpenAI CEO Sam Altman revealed in an interview with British media outlet Sky News on Tuesday that the business would like to work with China. Altman made the comment when he was asked if he was worried about the country’s progress.
However, responding to a question about whether the US would let him do that, Altman expressed, “I know that for sure, no. Should we try as hard as we can? Absolutely, yes.”.
Altman’s statement follows the shockwave created by DeepSeek in the AI ecosystem, which has sparked concerns among several industry leaders who fear China’s rise.
in recent times, Dario Amodei, CEO of Anthropic, stated, “If we can close [export control loopholes] fast enough, we may be able to prevent China from getting millions of chips, increasing the likelihood of a unipolar world with the US ahead.”.
Similarly, in an interview with CNBC at the Davos World Economic Forum 2025, Microsoft CEO Satya Nadella expressed, “We should take the development of China very seriously.”.
Venture capitalist Vinod Khosla echoed the industry’s strongest fear. In a blog post, he earlier expressed, “We may have to worry about sentient AI destroying humanity, but the risk of an asteroid hitting the Earth or a pandemic also exists. But the risk of China destroying our system is significantly larger, in my opinion.”.
Altman’s take on China seems refreshing, to say the least.
In a podcast with The Times on Monday, while speaking about China’s DeepSeek, Altman stated, “They did some nice work. I think there’s also some nice pieces of product work like showing the chain of thought.” He went on to praise the research behind DeepSeek’s model, although he feels “it isn’t a big upgrade” to the ecosystem.
OpenAI reportedly has evidence that DeepSeek trained on its models and is investigating this with Microsoft. However, Altman not long ago revealed that the corporation does not plan to sue DeepSeek right now.
In the same interview with Sky News, Altman added, “I think we should probably open source somewhat more.”.
In a Reddit ‘ask-me-anything’ (AMA) session, he noted, “I personally think we have been on the wrong side of history here and need to figure out a different open-source strategy.”.
This comes at a time when industry leaders are rooting for the development of more open-source models, owing to DeepSeek’s recent success.
Meanwhile, Chamath Palihapitiya, a venture capitalist, stated in an interview, “I think in the war of open versus closed, open has won.”.
Palihapitiya believes OpenAI is now considering to open source what they’re doing in some way.
That mentioned, OpenAI has released multiple open-source models and tools in the past. The organization’s second iteration of the GPT model was also made available for open-source use.
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Market Impact Analysis
Market Growth Trend
2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
---|---|---|---|---|---|---|
23.1% | 27.8% | 29.2% | 32.4% | 34.2% | 35.2% | 35.6% |
Quarterly Growth Rate
Q1 2024 | Q2 2024 | Q3 2024 | Q4 2024 |
---|---|---|---|
32.5% | 34.8% | 36.2% | 35.6% |
Market Segments and Growth Drivers
Segment | Market Share | Growth Rate |
---|---|---|
Machine Learning | 29% | 38.4% |
Computer Vision | 18% | 35.7% |
Natural Language Processing | 24% | 41.5% |
Robotics | 15% | 22.3% |
Other AI Technologies | 14% | 31.8% |
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity:
Competitive Landscape Analysis
Company | Market Share |
---|---|
Google AI | 18.3% |
Microsoft AI | 15.7% |
IBM Watson | 11.2% |
Amazon AI | 9.8% |
OpenAI | 8.4% |
Future Outlook and Predictions
The Indian Companies Bullish landscape is evolving rapidly, driven by technological advancements, changing threat vectors, and shifting business requirements. Based on current trends and expert analyses, we can anticipate several significant developments across different time horizons:
Year-by-Year Technology Evolution
Based on current trajectory and expert analyses, we can project the following development timeline:
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:
Innovation Trigger
- Generative AI for specialized domains
- Blockchain for supply chain verification
Peak of Inflated Expectations
- Digital twins for business processes
- Quantum-resistant cryptography
Trough of Disillusionment
- Consumer AR/VR applications
- General-purpose blockchain
Slope of Enlightenment
- AI-driven analytics
- Edge computing
Plateau of Productivity
- Cloud infrastructure
- Mobile applications
Technology Evolution Timeline
- Improved generative models
- specialized AI applications
- AI-human collaboration systems
- multimodal AI platforms
- General AI capabilities
- AI-driven scientific breakthroughs
Expert Perspectives
Leading experts in the ai tech sector provide diverse perspectives on how the landscape will evolve over the coming years:
"The next frontier is AI systems that can reason across modalities and domains with minimal human guidance."
— AI Researcher
"Organizations that develop effective AI governance frameworks will gain competitive advantage."
— Industry Analyst
"The AI talent gap remains a critical barrier to implementation for most enterprises."
— Chief AI Officer
Areas of Expert Consensus
- Acceleration of Innovation: The pace of technological evolution will continue to increase
- Practical Integration: Focus will shift from proof-of-concept to operational deployment
- Human-Technology Partnership: Most effective implementations will optimize human-machine collaboration
- Regulatory Influence: Regulatory frameworks will increasingly shape technology development
Short-Term Outlook (1-2 Years)
In the immediate future, organizations will focus on implementing and optimizing currently available technologies to address pressing ai tech challenges:
- Improved generative models
- specialized AI applications
- enhanced AI ethics frameworks
These developments will be characterized by incremental improvements to existing frameworks rather than revolutionary changes, with emphasis on practical deployment and measurable outcomes.
Mid-Term Outlook (3-5 Years)
As technologies mature and organizations adapt, more substantial transformations will emerge in how security is approached and implemented:
- AI-human collaboration systems
- multimodal AI platforms
- democratized AI development
This period will see significant changes in security architecture and operational models, with increasing automation and integration between previously siloed security functions. Organizations will shift from reactive to proactive security postures.
Long-Term Outlook (5+ Years)
Looking further ahead, more fundamental shifts will reshape how cybersecurity is conceptualized and implemented across digital ecosystems:
- General AI capabilities
- AI-driven scientific breakthroughs
- new computing paradigms
These long-term developments will likely require significant technical breakthroughs, new regulatory frameworks, and evolution in how organizations approach security as a fundamental business function rather than a technical discipline.
Key Risk Factors and Uncertainties
Several critical factors could significantly impact the trajectory of ai tech evolution:
Organizations should monitor these factors closely and develop contingency strategies to mitigate potential negative impacts on technology implementation timelines.
Alternative Future Scenarios
The evolution of technology can follow different paths depending on various factors including regulatory developments, investment trends, technological breakthroughs, and market adoption. We analyze three potential scenarios:
Optimistic Scenario
Responsible AI driving innovation while minimizing societal disruption
Key Drivers: Supportive regulatory environment, significant research breakthroughs, strong market incentives, and rapid user adoption.
Probability: 25-30%
Base Case Scenario
Incremental adoption with mixed societal impacts and ongoing ethical challenges
Key Drivers: Balanced regulatory approach, steady technological progress, and selective implementation based on clear ROI.
Probability: 50-60%
Conservative Scenario
Technical and ethical barriers creating significant implementation challenges
Key Drivers: Restrictive regulations, technical limitations, implementation challenges, and risk-averse organizational cultures.
Probability: 15-20%
Scenario Comparison Matrix
Factor | Optimistic | Base Case | Conservative |
---|---|---|---|
Implementation Timeline | Accelerated | Steady | Delayed |
Market Adoption | Widespread | Selective | Limited |
Technology Evolution | Rapid | Progressive | Incremental |
Regulatory Environment | Supportive | Balanced | Restrictive |
Business Impact | Transformative | Significant | Modest |
Transformational Impact
Redefinition of knowledge work, automation of creative processes. This evolution will necessitate significant changes in organizational structures, talent development, and strategic planning processes.
The convergence of multiple technological trends—including artificial intelligence, quantum computing, and ubiquitous connectivity—will create both unprecedented security challenges and innovative defensive capabilities.
Implementation Challenges
Ethical concerns, computing resource limitations, talent shortages. Organizations will need to develop comprehensive change management strategies to successfully navigate these transitions.
Regulatory uncertainty, particularly around emerging technologies like AI in security applications, will require flexible security architectures that can adapt to evolving compliance requirements.
Key Innovations to Watch
Multimodal learning, resource-efficient AI, transparent decision systems. Organizations should monitor these developments closely to maintain competitive advantages and effective security postures.
Strategic investments in research partnerships, technology pilots, and talent development will position forward-thinking organizations to leverage these innovations early in their development cycle.
Technical Glossary
Key technical terms and definitions to help understand the technologies discussed in this article.
Understanding the following technical concepts is essential for grasping the full implications of the security threats and defensive measures discussed in this article. These definitions provide context for both technical and non-technical readers.