By Aditya Agarwal 
AI adoption in India is increasing, but many businesses are struggling to move from small experiments to large-scale deployment. Poor data quality, cybersecurity concerns and shortages of skilled professionals are among the barriers identified by industry experts.
An Indian Express report published on 19 October 2026 discussed these challenges with Sriram Raghavan, General Manager of IBM Software, India and Software Innovation Labs. The report highlights a growing divide between companies that have integrated AI into core operations and those still experimenting with individual tools. (The Indian Express)
Data Quality Remains a Major Challenge
AI systems depend on data that is sufficiently accurate, relevant and well organised for the task being performed.
Many businesses store information across disconnected systems, spreadsheets and legacy databases. Inconsistent formats, duplicated records and missing information can reduce the reliability of AI-generated results.
Connecting an AI model to a company’s data does not automatically solve these problems. Organisations need data governance, access controls, quality checks and clear ownership of important information.
Without these foundations, AI projects may produce unreliable results or fail to deliver the improvements businesses expect.
Cybersecurity and Privacy Risks
Enterprise AI systems may process customer records, financial information, internal documents and commercially sensitive data.
If access permissions are too broad or security controls are inadequate, AI tools can expose information that users should not be able to retrieve. Connecting AI systems to external services can introduce additional risks involving credentials, data transfers and third-party platforms.
Companies should therefore evaluate security before expanding AI deployment.
Important measures include restricting access to sensitive data, monitoring system activity, protecting credentials, testing for unintended disclosures and requiring human approval for high-impact actions.
Security should be part of the system’s design rather than a final step after deployment.
The Shortage of Practical AI Skills
Businesses also need employees who can integrate AI into existing workflows and evaluate its output.
Technical expertise is important, but successful deployment often requires collaboration between software engineers, data specialists, security teams and employees who understand the business process.
Workers must know when AI can help, how to recognise errors and when human judgement remains necessary.
Training should focus on practical tasks such as data preparation, workflow automation, model evaluation, privacy and responsible use—not merely on using AI chat interfaces.
Why Experiments Do Not Always Become Business Solutions
A demonstration can show that an AI system works under controlled conditions. A production deployment must also handle real users, incomplete data, system failures, security requirements and ongoing operating costs.
Companies need to define measurable objectives before scaling a project. These might include reduced processing time, improved customer response, fewer manual errors or lower operating costs.
They should then compare the results against a baseline and monitor performance over time.
A project that attracts attention but does not improve business outcomes may not justify the cost of wider deployment.
How Indian Businesses Can Scale AI More Effectively
Businesses can improve their chances of success through a structured approach.
Start with a defined problem: Select a workflow where improvement can be measured.
Prepare the data: Identify missing information, resolve inconsistencies and establish access controls.
Build security into deployment: Test permissions, monitor activity and protect confidential information.
Train employees: Ensure teams understand both the capabilities and limitations of AI.
Measure outcomes: Track productivity, reliability, cost and customer impact before expanding the system.
This approach can help organisations avoid investing in technology without a clear operational objective.
Outlook for India’s AI Industry
AI adoption in India will depend not only on access to advanced models but also on the quality of business data, workforce capabilities and the reliability of implementation.
Companies that address these foundations may be better positioned to turn experiments into practical systems. Those that overlook them could struggle to achieve consistent results, even when the underlying technology is capable.
The central challenge is no longer simply whether a business can use AI. It is whether the business can deploy it securely, reliably and at a scale that creates measurable value.
Tags: AI adoption in India, enterprise AI, artificial intelligence, AI cybersecurity, data quality, AI skills, Indian technology
Internal Link: FlairiusNews — Artificial Intelligence and Technology News
Outbound Source: The Indian Express — Why Indian Companies Are Struggling to Scale AI