Decoding Tech Evolution: Key Milestones and Insights

By Vikrant Gandhi, Vice President and Global Sector Leader, Information & Communications Technologies at Frost & Sullivan

Reflecting on the past two decades of technological evolution, it is remarkable to see how interconnected and interdependent the global technology ecosystem has become. From my early days as an analyst in mobile and wireless communications to my current role overseeing global research and consulting initiatives, I have witnessed the rapid shifts that have shaped the telecom, internet, and fintech industries.

The Evolution of Wireless and Telecommunications

The journey began in the GSM (2G) era, with the introduction of GPRS marking the first step toward mobile data services. As 3G networks emerged, the industry experienced a surge in mobile data consumption, laying the foundation for a more interconnected world. The convergence of mobile, wireline, and internet services became increasingly evident, leading to advancements in cybersecurity, early virtualization discussions, and the birth of machine-to-machine (M2M) communications—precursors to today’s Internet of Things (IoT). This period also saw the early movement towards integrated (mobile and web), programmatic, and omnichannel advertising, highlighting the importance of data and the use of advanced data processing techniques for customer segmentation and targeting, media buying, attribution, and real-time campaign optimization.

The introduction of 4G was a defining moment, driving the growth of the shared economy and over-the-top (OTT) services. The device protection and reverse logistics industries experienced significant growth, driven by emerging consumer purchase trends and supported by CSP marketing initiatives, especially in North America. This era also saw strong growth in cloud computing, with both public and private cloud markets becoming critical foundations for communication, collaboration, and the media and entertainment industries. Streaming platforms, digital payments, and mobile commerce flourished, reinforcing the need for advanced fraud prevention mechanisms. My involvement in digital payments, eCommerce fraud prevention and IoT security research highlighted the growing concerns around privacy and data security—issues that remain relevant today.

With 5G, the technology adoption cycle accelerated, bringing programmable communications, network slicing, and hyper-personalization into the mainstream. The rise of AI-driven network automation, API ecosystems, and the ongoing evolution of BSS/OSS platforms underscore the dynamic nature of telecom and enterprise wireless services.

Around 2017-2018, I expanded my focus to include OSS/BSS, gaining deeper insights into orchestration, automation, revenue management, customer experience, observability, and SaaS-driven opportunities. Within our team, we often emphasize that the success of 5G innovation is contingent upon the readiness and advancement of Telecom IT infrastructure.

The Expanding Frontiers: Blockchain, AI, and FinTech

Blockchain first piqued my interest in 2015, particularly in cross-border remittances. The decentralized nature of blockchain presented a compelling case for financial innovation, leading me to explore consensus protocols, cryptocurrencies, and decentralized finance (DeFi). While regulatory uncertainty hindered the development of certain blockchain applications, the principles of decentralized computation have found new relevance in today’s AI evolution.

During this period, I also explored launching a prediction market business on Ethereum, where vetted experts could predict stock prices or events and earn rewards in a deflationary ecosystem token. The firm, which I considered naming "Jyotish" (a nod to Vedic Astrology), was inspired by a conversation with a fellow passenger on a flight to India. However, unclear regulations, the evolving blockchain landscape, and concerns about online gambling laws meant the project never moved beyond the pilot phase. Despite this, the experience provided valuable insights into blockchain’s potential applications beyond finance.

AI is now shifting from centralized processing to distributed models, resembling early blockchain architectures. Edge AI and federated learning are addressing prior challenges related to trust, observability, and connectivity, making AI-driven networks more feasible and efficient. Interestingly, when I hear about networks evolving to support distributed AI today, I recall the early days of blockchain, when distributed nodes and specialized hardware enabled decentralized computation. The lack of trust, observability, and connectivity limited its feasibility then, but now, as AI shifts from centralized models to edge AI and beyond, the vision of AI anywhere is becoming a reality.

In the fintech space, the rise of embedded finance, B2B payments, and as-a-service models continues to reshape the industry. The evolution of digital payments, coupled with the emergence of non-traditional financial players, is driving increased competition and innovation.

Looking Ahead: The Next Wave of Disruption

As technology continues to evolve, new frontiers are emerging. Research into 6G, AI-driven radio access networks (AI RAN), and satellite non-terrestrial networks (NTN) is already underway. The expansion of third party-managed fulfillment services in eCommerce and the evolution of digital marketplaces are redefining supply chain strategies. I do think we are vastly underestimating the impact of AI/GenAI across industries. An “AI-everywhere” World will have significant ramifications on how consumers and businesses go about their daily tasks. The replacement, refinement, recreation, and reimagining of processes, approaches, monetization models, distribution strategies, and communication modalities will occur at speeds that are difficult to comprehend.

Meanwhile, the need for automation in decentralized finance remains an open challenge—particularly in areas such as tax reporting across multiple blockchain networks. Cloud-native solutions like Kubernetes are increasingly delivering on their promise by enabling scalability, resilience, and automation in modern IT infrastructure. As organizations adopt these technologies, the need for continuous learning and adaptation remains crucial due to the rapid evolution of cloud-native ecosystems, best practices, and tooling.

The journey of technological innovation is far from over. As modern technologies emerge, the need for strategic foresight and analytical rigor will be more critical than ever. The key to staying ahead in this rapidly evolving landscape is a relentless curiosity—a trait that will continue to shape the future of technology research and analysis.