As AI adoption scales, so does its risks. India witnessed a significant rise in digital fraud cases in recent years, with AI-driven fraud, deep fakes, and identity manipulation emerging as key threats. According to a Cybersecurity Ventures report, global cybercrime damages have grown from $3 trillion in 2015 to $10.5 trillion annually in 2025.
For over a decade, India’s digital narrative has been driven by scale — around 958 million active internet users and 22+ billion transactions in a month, powered by UPI. But scale, by itself, is no longer a differentiator. The next phase of India’s digital economy will be defined by decision intelligence—and Artificial Intelligence is at its core.
AI is No Longer a Feature. It is the Operating System.
AI adoption has moved beyond experimentation. Today, over 70% of financial institutions in India have deployed AI/ML in some form, across underwriting, fraud detection, and customer engagement. But the real shift is this: AI is moving from supporting decisions to making them.
Across financial services, AI systems are now determining:
- Who should be engaged, when, and with what proposition?
- Which customer qualifies for credit, at what price, and under what risk thresholds?
- How should collections be approached—based on predicted intent and behaviour?
Historically, customers were grouped into broad segments, such as salaried vs. self-employed, or prime vs. near-prime, and assigned standardised pricing based on these buckets. AI fundamentally changed this paradigm. Instead of relying on static segments, machine learning models evaluate hundreds of variables simultaneously, including transaction patterns, repayment behaviour, device signals, and even interaction history, to arrive at a unique risk score for each individual customer.
This allows institutions to move from pricing for segments to pricing for individuals —improving risk accuracy, expanding credit access, and optimising yields.
The implication is clear: business logic is being rewritten as machine logic.
From Process Digitisation to Automated Decision-making
The first wave of digital transformation digitised processes — taking manual workflows and making them faster and more efficient through technology. The current wave goes a step further. It is reducing dependence on pre-defined processes altogether.
Traditionally, organisations operated on fixed workflows: if a customer behaves in a certain way, trigger a predefined action. These workflows were rule-based, linear, and designed in advance. AI disrupts this model by enabling dynamic, real-time decisioning. Instead of following a fixed process, systems continuously evaluate context and determine the next-best action on the fly.
For example, rather than following a standard collections script, an AI system can decide —based on real-time signals — whether to delay a call, change the tone of communication, offer restructuring, or escalate the case. We are, therefore, moving towards autonomous enterprises, where systems don’t just execute tasks—they decide, adapt, and optimise continuously.
Organisations deploying such systems are already seeing:
- 30–50% reduction in manual intervention
- 40% faster turnaround times in decision-making
- More consistent and personalised customer experiences
The Illusion of Parity
There is a growing belief that AI will level the playing field. In reality, it will do the opposite. While AI tools and models are becoming widely accessible, data advantages are not. Organisations that can combine proprietary data, strong feedback loops, and domain context will create self-reinforcing intelligence systems. These systems will improve automatically over time: every customer interaction generates new data, which refines models, which in turn improves future decisions.
This creates a compounding advantage:
- Better data leads to better predictions
- Better predictions lead to better customer outcomes
- Better outcomes generate more engagement and data
Where AI Collides with Reality: Trust, Risk, and Regulation
As AI adoption scales, so do its risks. India has seen a sharp rise in digital fraud, with AI-driven threats such as deepfakes and synthetic identities becoming more prevalent. According to a Cybersecurity Ventures report, global cybercrime damages have grown from $3 Trillion in 2015 to $10.5 Trillion annually in 2025. At the same time, regulatory expectations around explainability, fairness, and data privacy are intensifying.
This creates a critical inflection point: As AI becomes the core decision-making layer, trust becomes the foundation on which its adoption depends. Organisations that can build transparent, auditable, and accountable AI systems will not just mitigate regulatory and operational risks; they will earn a trust premium from customers, regulators, and partners. And it is this intersection of intelligence, trust, and scale that sets the stage for the broader convergence we are now witnessing across the digital ecosystem.
Convergence: One Force, Multiple Industries
What appears as separate industry trends is, in reality, a convergence driven by AI:
- Fintech: Evolving from access-led growth to AI-driven engagement and lifetime value
- Digital Advertising: Transitioning toward AI-led personalisation within privacy-first frameworks
- Cybersecurity: Becoming an AI vs. AI battleground
AI is not another layer in the stack—it is the force reshaping the entire stack.
The Real Question
The question is no longer whether organisations will adopt AI. It is whether they are prepared to re-architect themselves around it. Because AI will not just change how decisions are made; it will change who makes them, how fast they are made, and how consistently they scale.
And in that shift lies the next phase of India’s digital leadership.
The author is Group Chief Digital Officer, Shriram Capital Ltd. Views expressed are personal.