As a veteran in the SaaS and software ecosystem, Vijay Raina has spent years architecting the digital frameworks that power global enterprises. With a deep focus on software design and enterprise tools, Raina has witnessed the cyclical “apocalypses” and “revolutions” of the tech world firsthand. Currently, he is closely monitoring the deployment of substantial capital into emerging markets, where the intersection of public policy, small-scale entrepreneurship, and agentic AI is creating a unique laboratory for the future of work. In this conversation, Raina breaks down the reality of digital transformation, moving beyond the hype of market valuations to explore the tangible human and operational shifts defining the next decade.
The current strategy involves a massive five-year, $500 million investment aimed at bridging the digital divide, but success isn’t just about spending; it’s about the balance between infrastructure and intellect. We are prioritizing funds across a “tripod” of people, processes, and technology, ensuring that training isn’t an afterthought to the software rollout. A traditional business has successfully “caught up” when AI transitions from a experimental prototype to a live, production-grade project that manages core operational tasks. We look for milestones where the technology enables a fundamental change in the business model, allowing a company to double its business volume—as we have seen in certain subsidiaries—without a linear increase in overhead.
A five-year, $500 million investment is currently being deployed to modernize SMEs and the public sector through artificial intelligence. How are you prioritizing these funds between infrastructure and human training, and what specific milestones indicate that a traditional business has successfully “caught up” to modern digital standards?
This investment is a long-term commitment where we are carefully balancing the technical architecture with the human element necessary to run it. We are focusing heavily on training because, while we provide the technology, the success of the digitalization of the public sector and SMEs depends on the people and processes behind it. A business has officially “caught up” when its AI applications are no longer just “nice-to-have” experiments but are live projects handling front-office customer service or back-office operational tasks. In Argentina, for instance, we’ve seen the subsidiary business double in just two years by moving past the “closed market” mindset and embracing these digital tools as core components of their growth strategy.
Local businesses, such as neighborhood flower shops, are now using autonomous agents to maintain 24/7 operations and expand their geographic reach. Beyond constant availability, what specific performance metrics should small business owners monitor, and what are the step-by-step requirements for an SME to launch its first AI agent?
Small business owners need to look at geographic expansion and conversion rates, much like the flower shop in Quilmes that managed to start selling in distant areas like Nordelta and Recoleta by being available 24/7. The metrics should focus on “service amplification”—tracking how many queries are resolved without human intervention and the subsequent growth in sales volume from new regions. To launch, an SME must first identify a repetitive operational task, such as customer inquiries or loyalty program management, and then integrate an “Agent Force” style tool that connects their inventory or services to a digital interface. The final step is ensuring the agent is “live” and interacting with real customers, moving away from the prototype phase into a functional, revenue-generating tool.
While some market analysts suggest a “SaaS Apocalypse” due to shifting tech valuations, others argue that AI represents a disruption larger than the birth of the internet. Why do you believe this shift fundamentally changes the human-to-human work model, and how should companies restructure their internal teams to prepare?
The idea of a “SaaS Apocalypse” ignores the reality that we are entering a transformation larger than the emergence of the internet itself. We are transitioning into the first era where humans no longer work exclusively with other humans, but instead collaborate directly with autonomous agents to handle the heavy lifting of data and repetitive tasks. Companies should restructure by moving their teams away from “repetitive or operational” back-office roles and refocusing them on high-level strategy and oversight. This shift means that instead of managing a workforce of individuals performing manual entries, managers will lead a hybrid team of humans and agents, requiring a total overhaul of traditional workflow hierarchies.
Projections indicate that while 92 million jobs may be displaced by automation, 172 million new roles will likely emerge. Which “entry-level” positions are most at risk in the current landscape, and how can the private sector collaborate with academic institutions to retrain workers for these new roles?
Entry-level positions that are purely operational or repetitive—think basic data entry, simple customer support, or routine administrative coordination—are the most vulnerable to displacement. However, the emergence of 172 million new roles means we must sit at a table with public, private, and academic stakeholders to design curriculum that focuses on “amplified” human work. The private sector can provide the technology and real-world use cases, like those seen in laboratories or loyalty programs, while academic institutions shift their focus toward training workers to manage and refine the AI agents that now handle those entry-level tasks. This is a massive “conversion” challenge that requires a unified strategy to ensure the 92 million displaced workers are the first in line for the new roles being created.
Public-private partnerships are currently focused on transforming national innovation and creating dedicated artificial intelligence districts in major cities. What specific administrative functions in legal, health, and education are most ready for digitalization, and how will these changes improve the daily experience of the average citizen?
We are seeing a massive appetite for digitalization in legal filings, justice systems, and healthcare record management, which are often bogged down by legacy paperwork. By moving these functions into an AI-driven innovation hub, citizens will experience faster processing times for legal matters and more personalized, efficient service in health and education. For the average person, this means interacting with government “secretariats” through intelligent interfaces that provide instant answers rather than waiting weeks for a manual response. These partnerships, like the one we recently signed with the Secretariat of Innovation and Technology, are designed to make the government feel less like a closed market and more like a responsive service provider.
There is a growing trend of returning to “human-centric” fields like sociology and history to provide the DNA for artificial intelligence processes. How do these humanities disciplines practically influence the development of AI agents, and what unique value does the human factor provide when working in an amplified, tech-driven environment?
We are seeing a “return to basics” because fields like sociology and history provide the ethical framework and cultural context that form the “DNA” of AI logic and decision-making. These disciplines help us program agents that understand human nuance, ensuring that when an AI interacts with a citizen or a customer, the process feels natural and culturally appropriate. The unique value of the human factor is its ability to provide “amplified” insight—taking the data-driven output of an agent and applying human empathy and historical context to make complex decisions. Technology provides the speed, but the humanities provide the direction and the soul of the interaction.
Digital transformation agreements are being signed to modernize national innovation and technology secretariats. What are the primary security and transparency hurdles when implementing AI at a federal level, and what does a successful rollout look like for a government transitioning away from a closed market model?
The biggest hurdles are moving away from decades of closed-market habits toward a transparent, open-data model that maintains citizen privacy while fostering innovation. A successful rollout is characterized by a memorandum of understanding that leads to concrete, live projects—not just prototypes—that are used daily by citizens to access public services. It looks like a government that acts as a “key player” in the regional tech scene, using AI to streamline administrative functions while donating a portion of its resources, much like our 1% program, to support non-profits and social causes. Transparency is achieved when the processes managed by AI are auditable and the benefits, such as increased efficiency in legal or health sectors, are clearly visible to the public.
What is your forecast for the evolution of agentic artificial intelligence over the next three years?
I forecast that within the next three years, the distinction between “software” and “agent” will disappear as AI becomes the primary interface for all business operations. We will see the penetration rate of AI in regions like Latin America skyrocket, transforming from one of the lowest to a global benchmark for dynamism as SMEs fully adopt 24/7 autonomous operations. The “SaaS Apocalypse” narrative will be forgotten, replaced by a reality where millions of small businesses operate with the same technological sophistication as global enterprises. Ultimately, the next three years will be defined by the successful “conversion” of the global workforce, where the 172 million new roles promised by the World Economic Forum become the new standard for a more efficient, human-centric economy.
