As AI evolves from experimentation to becoming a core business enabler, Indian enterprises are increasingly focused on scaling adoption while ensuring accountability, governance, and measurable outcomes. The conversation is no longer about whether AI works, but how organizations can deploy it responsibly across critical business functions. In this exclusive interaction, Rajeev Ranjan, Editor, Digital Terminal, speaks with Shoby Abdi, Senior Growth Partner – Digital Business & AI, Altimetrik, about India's AI transformation, decision accountability, governance frameworks, and the future of enterprise AI adoption.
Rajeev: How is the India AI conversation evolving from experimentation and pilots to business-critical deployment?
Shoby: India’s AI conversation has clearly moved beyond experimentation into real operational impact. Enterprises are no longer running isolated pilots to prove feasibility; they are embedding AI into core workflows across customer experience, operations, engineering, risk and compliance. The focus has shifted in 2 ways – (1) AI is no longer viewed as a discretionary innovation initiative, but as core operating infrastructure and (2) to measurable outcomes, revenue growth, cost optimization, speed and decision quality.
Organizations are prioritizing integration with enterprise data, scalability, and adoption within day-to-day business processes. The India AI Mission-providing public digital infrastructure has further amplified this momentum, has materially lowered the barrier to production grade AI adoption across sectors. The real shift is from “testing AI” to “running the business with AI” and positioning India as a global hub to build scalable, responsible, and industrialized AI deployment.
Rajeev: Why is the next challenge for India Inc not just scaling AI, but defining accountability around AI-driven decisions?
Shoby: As AI systems move from supporting decisions to influencing or automating them, the real challenge is no longer scale but accountability. Enterprises must clearly define who owns the outcome of an AI-driven decision, whether it sits with business leaders, data teams, technology, or risk and compliance functions. Without this clarity, scaling AI simply amplifies risk. This becomes even more critical in the context of India’s growing focus on data privacy, localization and responsible AI use, where enterprises must demonstrate control over how data is used and how decisions are made.
India’s regulatory frameworks and guidelines like DPDP Act, Digital India Act and FREE-AI follow a “graded-liability model” distributing accountability across the “AI value chain” based on the specific role entity plays. Accountability needs to be built into the operating model at every step, including decision ownership, auditability, explainability and escalation mechanisms. Organizations that treat governance as an afterthought will struggle, while those that design for accountability from the start will scale AI with confidence.
Rajeev: Which sectors in India are likely to face this first - BFSI, telecom, retail, healthcare, GCC-led enterprise operations?
Shoby: BFSI will likely face this first, given how deeply AI influences credit decisions, fraud detection, risk management and regulatory compliance, where accountability is critical. Telecom will follow due to large-scale customer operations and network optimization, while retail and healthcare will encounter it in personalization, pricing, diagnostics and patient engagement. India being the preferred hub for GCC for 500+ Fortune 2000 multinationals, GCC‑led enterprise operations will also face early pressure driven by innovation, AI-infused efficiency, global regulatory expectations and complex ownership models where AI is built in India but decisions affect overseas businesses.
Rajeev: Why is “human-in-the-loop” often treated as enough, even when the human may not have real authority or visibility?
Shoby: “Human-in-the-loop” is often seen as a sufficient safeguard because it is easy to implement conceptually, but in practice, it frequently falls short. In many cases, the human is only validating AI outputs without access to underlying data, model reasoning, confidence levels or alternative scenarios. This reduces the role to oversight without true control. Effective governance requires more than presence; it requires authority, transparency and the ability to intervene meaningfully. Organizations need to move toward a model where humans are not just in the loop, but are empowered decision-makers with clear accountability and visibility into how AI is influencing outcomes.
Rajeev: How does Altimetrik see enterprise AI adoption maturing in India?
Shoby: Indian organizations crossed a clear inflection point: 47% of enterprises now have multiple AI or Generative AI use cases live in production, while fewer than a quarter remain confined to pilots. Altimetrik sees enterprise AI adoption in India maturing across three critical dimensions: strong data foundations, workflow integration, and governance-led scale. Organizations are recognizing that AI success depends on trusted, well-governed data, combined with embedding intelligence directly into business processes rather than standalone tools.
At the same time, there is a growing emphasis on aligning AI adoption with India’s Sovereign AI priorities, including data residency, regulatory compliance and responsible AI frameworks, especially in regulated industries. The next phase of maturity will be defined by how effectively enterprises can industrialize AI, turning isolated success stories into repeatable, scalable capabilities tied to measurable business outcomes.
Rajeev: How is Altimetrik helping enterprises embed AI into operations while aligning speed with governance and business accountability?
Shoby: Altimetrik launched company-wide initiative, to build a 100% AI-trained workforce and enable software & operations delivery through AI-native engineering. Altimetrik helps enterprises move from experimentation to production by combining engineering rigor with governance-first thinking. The focus is on identifying high-value use cases, ensuring data readiness, defining decision ownership, and embedding AI into existing workflows where it can drive real impact.
This includes building secure AI architectures that respect data sovereignty requirements, regulatory compliance and enterprise-grade guardrails, integrating responsible AI practices, and enabling adoption through business-aligned KPIs and change management. By bringing together data, AI, and digital engineering, Altimetrik ensures that organizations can move quickly without compromising on control, enabling them to scale AI in a way that is both effective and accountable.
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