Bijuraj Sarangi

The AI conversation today is taking place in a highly polarized environment. On one side are the advocates who believe organizations should invest aggressively and move fast before they are left behind. On the other side are those who prefer to wait, allowing the technology, regulations, and market dynamics to mature before making significant commitments.

The reality is that both positions have merit. Both carry risks.

What is interesting, however, is that many organizations are searching for definitive answers when the question itself is still evolving. We are trying to predict the destination while the road is still being built.

Two Possible Futures: When we look at AI’s impact on business, two broad visions of the future are beginning to emerge.

View One: AI Becomes the Primary Workforce

In this scenario, AI evolves beyond being an assistant and becomes the primary driver of work. Autonomous agents orchestrate processes, make decisions, manage operations, and execute tasks that today require large human workforces.

Many technology leaders already predict a future where a significant proportion of today’s workforce is replaced by AI agents. If that vision materializes, organizations could unlock extraordinary gains in efficiency, speed, and cost reduction.

Yet this view raises a broader economic question – If large numbers of people are displaced from work, where does the next cycle of earning, spending, investing, and consumption come from?

Economies function because people participate in them. People earn, spend, invest, and create demand. The assumption is often that new jobs and industries will emerge as older ones disappear. Historically, that has happened through previous industrial and technological revolutions.

But the speed and scale of AI-driven disruption may be unlike anything we have experienced before. The replacement cycle is not guaranteed to happen quickly enough, nor at the volume required. Today, there is little evidence that entirely new economic opportunities are emerging at the same pace as the disruption many anticipate.

This is not an argument against AI. It is simply a recognition that productivity gains alone do not automatically translate into economic sustainability.

View Two: Humans and AI Work Side by Side

The second vision is one where humans remain central to business while AI acts as a force multiplier. Organizations invest in AI to make employees more productive, more informed, and more effective.

This model preserves human participation while improving business performance. However, it introduces a different challenge – Organizations must simultaneously invest in AI platforms, data infrastructure, cloud capacity, governance, security, and workforce development while continuing to support existing employee structures.

The result is a business environment carrying both transformation costs and operating costs. The question then becomes: where does the funding come from?

Different Futures. Same Economic Question.

While these two visions appear very different, they share a common dependency. Both require a sustainable economic model. Whether AI replaces a significant proportion of work or augments human capability, the broader economy must continue to generate growth, create opportunities, and sustain demand.

Neither efficiency nor cost reduction alone cannot achieve that. At some point, value creation becomes more important than productivity creation. This is where I believe the AI conversation needs a fundamental shift.

For the past two years, much of the discussion has focused on how AI can reduce costs, automate work, and improve productivity. These outcomes are important, but they do not by themselves create a sustainable economic cycle.

The next phase of AI adoption must focus on revenue/value creation. Organizations need to ask a different question:

How can AI and Agentic AI help us create entirely new sources of value and revenue?

Not simply by doing existing work faster, but by expanding existing businesses, reaching markets that were previously uneconomical to serve, creating new products and services, and opening opportunities that did not previously exist.

If AI is to become a long-term positive force for business and society, it cannot merely replace effort. It must create new value.

The Hard Pivot Question

The real test for AI is whether it can help businesses expand the economic pie

• Can it help organizations generate revenue from customer segments that were previously too costly to serve?
• Can it help businesses create entirely new products and markets?
• Can it unlock new forms of value that were previously impossible to deliver at scale?

That is where the next wave of AI transformation will be won or lost.

Here are five areas where this becomes real.

Banking: Unlocking the Long-Tail SME Market

Banks have historically struggled to profitably serve small and micro SMEs at scale. The cost of acquisition, onboarding, credit assessment, servicing, and collections often outweighs the revenue potential.

AI changes that equation – agentic AI can provide automated financial advisory, real-time cash-flow forecasting, dynamic working capital offers, invoice financing, tax support, and business planning services to thousands of SMEs simultaneously.

This creates a new revenue pool from the long-tail SME segment, not by selling another loan product, but by becoming the financial operating partner for businesses that were previously too small to serve deeply.

Telecommunications: Turning Data into Real-Time Intelligence

For years, telecom operators have talked about data monetization. Yet most initiatives have remained limited to reports, dashboards, and analytics products.

AI allows telcos to move beyond data monetization into decision monetization – by combining network intelligence, location insights, behavioral patterns, and AI agents, telecom providers can offer real-time intelligence services to retailers, banks, insurers, city planners, advertisers, and logistics companies.

The opportunity shifts from selling connectivity or static insights to selling intelligence-as-a-service, where customers pay based on usage, outcomes, and business impact.

Retail: Monetizing Consumer Intent

Retailers have traditionally competed on product, price, convenience, and loyalty.

AI introduces an entirely different opportunity – agentic AI can act as a personal shopping manager, household consumption planner, wardrobe advisor, health-led meal planner, or lifestyle concierge. Instead of reacting to purchases, retailers can proactively anticipate needs and influence demand before purchase decisions are made.

This creates new revenue opportunities through subscriptions, premium AI services, supplier-funded recommendations, automated replenishment programs, and significantly higher customer lifetime value.

Healthcare: Creating Revenue Between Hospital Visits

Healthcare revenue today is largely tied to consultations, procedures, diagnostics, and treatment events.

AI enables continuous engagement – health agents can provide ongoing wellness coaching, chronic disease management, medication adherence support, post-discharge monitoring, elderly care assistance, and preventative health interventions.

The opportunity is not operational efficiency alone. It is the creation of entirely new subscription-based healthcare models built around prevention and continuous engagement rather than episodic treatment.

The Real AI Race

Perhaps the biggest misconception today is that the AI race is about who can automate the most. whereas the real race is about who can create the most value.

The first wave of AI has been dominated by productivity, efficiency, and cost reduction. Those gains are real and they matter. But cost reduction does not create a sustainable economic future on its own.

Whether one believes in a future dominated by AI agents or one where humans and AI work side by side, both futures ultimately depend on the same thing: the creation of new economic value.

The organizations that lead the next decade will not necessarily be those that automate the most work. They will be those that discover how AI can create entirely new revenue pools, unlock previously inaccessible markets, and generate growth that did not exist before.

Because in the end, the future of AI will not be determined by how much work it eliminates, it will be determined by how much new value it creates.