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Are Regulatory Delays Slowing Down the Indian Drone Revolution?

Are Regulatory Delays Slowing Down the Indian Drone Revolution?

India’s drone industry is awaiting a revolution with the potential to transform sectors like agriculture, infrastructure, and security. However, regulatory hurdles and slower approval processes continue to keep this industry grounded.

In an insightful conversation with AIM, Skylark Drones co-founder and CEO Mughilan Thiru Ramasamy gave first-hand insights into the impact of these delays on innovation and business growth.

Drones could revolutionise infrastructure monitoring, agriculture, law enforcement, and disaster response. However, he revealed that the enterprise still doesn’t have permission to fly drones in some regions of Bengaluru.

As of September last year, 10,208 type-certified commercial drones have been registered under the Digital Sky Platform, a digital system for managing drone operations in India, as per MoS civil aviation, Murlidhar Mohol, in a recent Rajya Sabha Q&A session.

The Directorate General of Civil Aviation (DGCA) has issued 96 type certificates for different drone models based on their purpose. Of these, 65 models are designed for agricultural applications, while 31 are focused on logistics and surveillance.

These figures highlight the growing adoption of drone technology, particularly in agriculture, where drones are increasingly used for crop spraying, monitoring, and yield assessment.

Despite a series of policy reforms aimed at streamlining drone operations, challenges in obtaining clearances and navigating airspace restrictions have created bottlenecks that are slowing down innovation and adoption.

“To fly a drone in a city, you need approval from multiple agencies – HAL airport, CISF, the Commissioner’s office, and so many others,” Ramasamy stated. Despite government initiatives, getting approvals for drone operations remains an uphill battle.

One of the primary regulatory challenges the industry faces is restricted airspace access. Under The Drone Rules 2021, India’s airspace is divided into three categories: red, yellow, and green zones. While 86% of the country’s airspace falls under the green zone, where drone operations do not require special permissions, the remaining areas are heavily regulated.

Red zones, totalling approximately 9,969, require special approvals from the civil aviation ministry and the concerned zone authorities before any operations can take place. Yellow zones, typically located around airports, require permission from air traffic control (ATC) before drone operations can commence.

This zoning system, while essential for safety, has created delays in obtaining necessary approvals, especially in urban areas where drone-based services like e-commerce deliveries, medical supply transport, and infrastructure monitoring could be transformative.

The biggest challenge is that there is no central platform where one can apply for permission. “Everything still runs on pen and paper, or at best, email, which is just digital paper,” Ramasamy explained.

Bodhisattwa Sanghapriya, founder and CEO of IG Drones, told AIM that while the government has made progress in easing compliance, faster clearances for trusted domestic players will further accelerate industry growth.

By prioritising reliable drone manufacturers and solution providers, India can strengthen national security while enhancing the country’s capabilities in surveillance, infrastructure monitoring, and disaster response.

The regulatory landscape for drones in India has improved significantly, with the government actively streamlining approval processes and promoting indigenous technology.

“Although some operational challenges remain, particularly in securing approvals for sensitive zones such as defence areas and no-drone zones, the regulatory mechanism is much more streamlined than before,” Sanghapriya added.

He further stated that compared to previous years, regulatory delays have been reduced, particularly for startups manufacturing 100% made-in-India drones with no Chinese components. This aligns with the government’s vision for Atmanirbhar Bharat and its push to make India a global drone hub by 2030.

The regulatory delays don’t just affect drone startups; they impact enterprises, government projects, and the broader ecosystem. Ramasamy pointed out that while India has focused on incentivising drone manufacturing, real growth will only happen when demand is created.

“More than subsidies, the government needs to create real use cases that push adoption,” he added. Until then, navigating the regulatory maze will remain one of the biggest roadblocks to India’s drone revolution.

Even though the Production Linked Incentive (PLI) Scheme for drone and drone components did not see any allocation in the recent Union Budget 2025, the government has prioritised funding for the space tech industry as a whole.

Notably, the government has allocated ₹[website] crore to the Namo Drone Didi program as part of its Central Sector Schemes. Regardless, the industry has yet to take centre stage in the Budget.

While China’s DJI dominates the global drone market with fully integrated unmanned aerial vehicle (UAV) systems, India continues to struggle with roadblocks despite both countries having started on similar grounds for innovation.

The government has taken several steps to ease the regulatory burden on drone operators. More in recent times, in August 2024, the government amended The Drone Rules to simplify the registration process by removing the requirement for a passport.

“Now, a government-issued proof of identity and address, [website] Voter ID, Ration Card or Driving License, can now be accepted for registration and de-registration or transfer of drones,” Mohol explained.

Despite these improvements, policy bottlenecks remain a concern. For instance, drone-based delivery services, which have the potential to improve healthcare access in remote areas, face operational delays due to lengthy bureaucratic approvals.

Similarly, drone surveying and mapping in the infrastructure sector require clearances from multiple authorities, leading to project slowdowns.

As per Mohol, the government proposes to be working towards addressing these challenges. One significant safety measure implemented is the requirement for all certified drones to have a tamper-avoidance mechanism that protects both the firmware and hardware from unauthorised access. This ensures that drones used in critical sectors remain secure and resistant to hacking.

However, for India to fully harness the benefits of drone technology, further reforms are needed. The Digital Sky platform must be enhanced to enable real-time digital approvals for operations in restricted zones.

Additionally, expanding financial incentives and promoting drone adoption in sectors beyond agriculture will be key to unlocking new opportunities.

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How Daimler Truck is Using RAG Architecture for Smarter Solutions

How Daimler Truck is Using RAG Architecture for Smarter Solutions

Daimler Truck is one of the world’s largest manufacturers of commercial vehicles, including trucks and buses. The enterprise operates globally across four key regions—Europe, North America, Asia Pacific, and China.

Daimler Truck Innovation Centre India (DTICI) is a GCC based in Bengaluru, established three years ago.

It focuses on providing IT and engineering solutions for all segments, products, and regions of Daimler Truck. “Our goal is…to provide world-class engineering and IT solutions to our consumers and products,” Raghavendra Vaidya, managing director and CEO of DTICI, told AIM.

Vaidya also reaffirmed that retrieval-augmented generation (RAG) architecture remains a valuable approach. “I don’t think RAG architecture is dead. It’s working well for us,” the representative mentioned. Companies can either retrain a model with their data or use RAG to enhance a GPT model. Both methods have their own advantages.

Even major companies like Microsoft support RAG by offering models that help vectorise data efficiently, as per Vaidya.

However, the conversation around RAG is not new.

RAG has revolutionised how AI systems process and respond to user queries by using external knowledge data. However, as it doesn’t meet all the diverse needs of modern enterprises, everyone wants to replace RAG with something new.

This is where agentic RAG comes into play. Agentic RAG represents an advanced architecture that combines the foundational principles of RAG with the autonomy and flexibility of AI agents. It promises a future where AI systems are more adaptive, proactive, and intelligent.

Furthermore, last year, Google released its new Gemma model, DataGemma. While the world is experimenting with RAG to reduce hallucinations and increase accuracy, Google decided to use retrieval interleaved generation (RIG). This technique integrates LLMs with Data Commons, an open-source database of public data.

Vaidya highlighted that DTICI is not building large language models (LLMs) but is currently using OpenAI’s LLM for internal purposes.

Machine learning (ML) remains a core focus, even though the term is less commonly used today, he further expressed. For over a decade, the organization has been developing ML models from scratch, supported by a skilled team of data scientists and engineers who collaborate with business experts across different functions.

In the past year, the enterprise elevated its approach by assigning accountability for AI and data initiatives to its Bengaluru team.

Regarding GenAI, DTICI is currently running multiple pilot projects to assess its potential impact. The firm has already seen success in using Microsoft Copilot and GitHub Copilot to improve software development productivity, whether through code generation, test case creation, or code quality validation.

Beyond software engineering, DTICI is exploring GenAI in sales, procurement, and after-sales.

Rather than taking a technology-first approach, the business prioritises business needs. “We don’t bring in the technology, dabble and see what it can do. That’s not the approach we’re taking. We’re taking a business approach where we identify areas where it can produce business results and provide benefits, either to the top line or efficiency or the bottom line. And then we go and build a pilot around it,” Vaidya expressed.

DTICI also built a sandbox on Azure about a year ago, using an older version of OpenAI’s language model.

, DTICI has been using the model for some time and finds it effective. It has created internal chatbots and assistants that use OpenAI’s language model and its own secure data.

Vaidya acknowledged that training a model with its own data would be superior, but it would take excessive time and money. Instead, DTICI prefers its current approach and believes it to be a good balance.

Vaidya noted that Bengaluru remains the top choice for talent in India, with its unmatched depth and variety of skilled professionals. “The length, breadth, and depth of talent you have in Bengaluru is unmatched.”.

He believes that as global capability centres (GCCs) grow, they may expand to other cities where talent is available. Some of them have successfully established operations in multiple locations. However, Bengaluru remains the first choice for new GCCs, and DTICI has no recent plans to expand to tier-2 cities.

At DTICI Bengaluru, the focus is on engineering and IT. The team develops intelligent software for trucks and buses. Most of the innovation and investment are happening in IT, software, and electronics. Highlighting this trend, Vaidya expressed, “If you want to increase the frequency of innovation, or you want to innovate faster, then I think software and electronics is the place to be.”.

In IT, the organization is deeply focused on using data, ML, and artificial intelligence.

Vaidya revealed that a major project of predictive maintenance, directed from Bengaluru, aims to predict part failures using analytics and ML instead of traditional physics-based methods.

The system analyses real-time truck data to forecast when a part is likely to fail. However, accuracy is critical for this to be effective. “If the model is not 85% or more accurate, then nobody is going to buy it,” Vaidya unveiled.

Since clients rely on these predictions to replace parts before failure, achieving high accuracy is essential. DTICI has been deploying these solutions over the past few years, and they have proven extremely effective in terms of profitability and cutting warranty costs.

“It is pretty simple; you get to work on the bleeding edge of the technology and the work that you do makes either a product more effective or consumers more profitable,” Vaidya concluded.

India’s first Drone Centres of Excellence (CoEs) have been launched in Odisha’s Kalahandi district under the Sansad Adarsh Gram Yojana (SAGY).

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Okta Appoints Shakeel Khan as Regional VP and Country Manager for India

Okta Appoints Shakeel Khan as Regional VP and Country Manager for India

Okta, the US based identity and access management corporation, has named Shakeel Khan as its regional vice president and country manager for India, reinforcing its commitment to securing digital identities and expanding its presence in the region.

With 27 years of experience in cybersecurity, Khan will focus on strengthening Okta’s position as a trusted identity security partner for Indian businesses. Based in Bangalore, he will oversee the corporation’s sales operations in India. His career spans pre-sales to enterprise sales leadership, giving him deep expertise across the cybersecurity ecosystem.

Ben Goodman, SVP & GM, Okta APJ, welcomed Khan’s appointment, stating, “We are delighted to have Shakeel Khan join Okta India at a stage where businesses are looking for robust and trusted identity solutions more than ever. Our commitment to India remains strong, with more than 19,000 customers worldwide leveraging Okta. Shakeel’s technical leadership and strategic vision will help us reach the next level of growth.”.

Khan expressed enthusiasm about his new role, highlighting the evolving cybersecurity landscape. “I look forward to contributing to Okta’s growth and success while working with some of the brightest minds in the industry,” he mentioned.

Since its India launch in 2023, Okta has seen rapid expansion, tripling its workforce to over 300 employees in just a year. The enterprise plans to surpass 500 employees by 2025 as part of its broader growth strategy.

India is a key market for Okta, with the country’s developer base expected to overtake the US by 2027 and over 75% of IT spending flowing through partner channels. The organization follows a partner-first approach, working with key players like Savex, ACPL, 22by7, and Valuepoint to drive further growth.

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Market Impact Analysis

Market Growth Trend

2018201920202021202220232024
23.1%27.8%29.2%32.4%34.2%35.2%35.6%
23.1%27.8%29.2%32.4%34.2%35.2%35.6% 2018201920202021202220232024

Quarterly Growth Rate

Q1 2024 Q2 2024 Q3 2024 Q4 2024
32.5% 34.8% 36.2% 35.6%
32.5% Q1 34.8% Q2 36.2% Q3 35.6% Q4

Market Segments and Growth Drivers

Segment Market Share Growth Rate
Machine Learning29%38.4%
Computer Vision18%35.7%
Natural Language Processing24%41.5%
Robotics15%22.3%
Other AI Technologies14%31.8%
Machine Learning29.0%Computer Vision18.0%Natural Language Processing24.0%Robotics15.0%Other AI Technologies14.0%

Technology Maturity Curve

Different technologies within the ecosystem are at varying stages of maturity:

Innovation Trigger Peak of Inflated Expectations Trough of Disillusionment Slope of Enlightenment Plateau of Productivity AI/ML Blockchain VR/AR Cloud Mobile

Competitive Landscape Analysis

Company Market Share
Google AI18.3%
Microsoft AI15.7%
IBM Watson11.2%
Amazon AI9.8%
OpenAI8.4%

Future Outlook and Predictions

The Regulatory Delays Slowing 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:

2024Early adopters begin implementing specialized solutions with measurable results
2025Industry standards emerging to facilitate broader adoption and integration
2026Mainstream adoption begins as technical barriers are addressed
2027Integration with adjacent technologies creates new capabilities
2028Business models transform as capabilities mature
2029Technology becomes embedded in core infrastructure and processes
2030New paradigms emerge as the technology reaches full maturity

Technology Maturity Curve

Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:

Time / Development Stage Adoption / Maturity Innovation Early Adoption Growth Maturity Decline/Legacy Emerging Tech Current Focus Established Tech Mature Solutions (Interactive diagram available in full report)

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

1-2 Years
  • Improved generative models
  • specialized AI applications
3-5 Years
  • AI-human collaboration systems
  • multimodal AI platforms
5+ Years
  • 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:

Ethical concerns about AI decision-making
Data privacy regulations
Algorithm bias

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

FactorOptimisticBase CaseConservative
Implementation TimelineAcceleratedSteadyDelayed
Market AdoptionWidespreadSelectiveLimited
Technology EvolutionRapidProgressiveIncremental
Regulatory EnvironmentSupportiveBalancedRestrictive
Business ImpactTransformativeSignificantModest

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.

Filter by difficulty:

machine learning intermediate

algorithm

platform intermediate

interface Platforms provide standardized environments that reduce development complexity and enable ecosystem growth through shared functionality and integration capabilities.

API beginner

platform APIs serve as the connective tissue in modern software architectures, enabling different applications and services to communicate and share data according to defined protocols and data formats.
API concept visualizationHow APIs enable communication between different software systems
Example: Cloud service providers like AWS, Google Cloud, and Azure offer extensive APIs that allow organizations to programmatically provision and manage infrastructure and services.

large language model intermediate

encryption