By Dr Subroto Kumar Panda, CIO – CTO, Anand and Anand
The strategic imperative for global enterprises has irrevocably shifted. For years, “digital transformation” was synonymous with cloud migration, but this view is now dangerously outdated. The contemporary challenge is not merely to become digital but to become intelligent and fast. This requires re-architecting the entire enterprise around the capabilities of Artificial Intelligence, using the cloud as a foundational platform, not a destination. However, enterprise AI initiatives fail most often when treated as isolated technology projects rather than fundamental business transformations. True success rests on two intertwined pillars that must be addressed before significant technology investment: radical Business Process Reengineering and the systematic purification of enterprise data.
The BPR Imperative: Redesigning for Intelligence
A new AI platform will not automatically fix broken processes. You cannot run a bullet train on the tracks of old rail tracks. Without a parallel effort to reengineer workflows, organisations simply create a more expensive way to execute the same inefficient operations. To avoid this “value inversion”—where the higher costs of a new platform are not offset by gains in efficiency—Business Process Reengineering (BPR) must be treated as a core discipline, not an ancillary task.
This involves a fundamental rethinking of how work gets done to achieve dramatic improvements in cost, quality, and speed. The essential approach is to meticulously diagnose the current “As-Is” processes and strategically redesign them for an AI-enhanced “To-Be” state. This proactive redesign ensures that AI tools are implemented to augment and streamline workflows, rather than automate flawed ones. expensive environment. True leaders will
architect a new vision for their business, using a cloud-native mindset as a platform for profound innovation. The rewards are tangible:
The Data Crucible: Forging the Fuel for AI
The success of any AI ambition is determined by the quality of its data. Most enterprises are burdened by a “legacy data leviathan”—a sprawling mass of historical information where up to 70% is often Redundant, Obsolete, or Trivial (ROT). This ROT data consumes 60-80% of IT maintenance budgets while creating significant security and compliance risks. Taming this leviathan is a prerequisite for building reliable AI systems. Not only for AI but also from the compliance prospect while considering the DPDP Act 2023.
A systematic and legally defensible disposition strategy is required. The process must begin with a comprehensive
Data Inventory to map the entire data landscape, classifying data based on its business value and regulatory requirements. This inventory informs a formal
Data Retention Policy that defines not just minimum retention periods but also maximums, after which data must be disposed of, aligning with privacy regulations like GDPR and CCPA. Finally, for data identified as ROT or exceeding its retention period, a
Secure Disposal protocol using irreversible methods like cryptographic erasure must be executed and meticulously documented.
This rigorous data purification is the most effective strategy for combating AI “hallucinations”. By purging ROT data, an organisation actively removes the primary sources of outdated and contradictory information that fuel unreliable AI outputs. This, followed by rigorous data sanitisation, transforms internal data into a trusted, high-verifiability corpus. Research confirms that Large Language Models trained on such high-quality datasets exhibit significantly lower hallucination rates.
Conclusion: Building the Future-Proof Enterprise
The journey to becoming an AI-first organisation is a holistic business transformation, not a technology project. It demands that business processes be reengineered in lockstep with technology and that data be treated as a strategic asset to be purified and protected. Once this foundation is laid, a modern, agile technology backbone—moving from monoliths to microservices connected via an API Gateway—can be built to bring these new processes and clean data to life. Ultimately, the goal is not simply to deploy AI, but to build an organisation that thinks with AI—a sentient enterprise that is perpetually adaptable and positioned to lead in an era of unprecedented change.