Modernising legacy systems: How traditional industries are rethinking business operations

A manufacturing plant may have decades of operational data, a bank may rely on core systems built around established processes, and a logistics company may manage information across multiple platforms. Each holds valuable business knowledge, but that information can remain difficult to access when systems operate in isolation.

For these organisations, introducing AI involves more than selecting a model or deploying a new application. The technology must work with existing infrastructure, interpret business-specific information, and fit into workflows where errors can have financial or operational consequences.

The challenge will be explored at The Economic Times’ Future of Knowledge Work Summit 2026 in Mumbai. The discussion will examine how established businesses can move from isolated technology deployments towards AI-enabled operations that deliver practical business value.

From pilots to enterprise-wide adoption

Indian enterprises are making progress in putting AI to work across business functions. Deloitte’s State of AI in the Enterprise 2026 report found that 40% of Indian respondents reported significant or full AI usage, compared with approximately 28% globally.

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At-scale deployment was strongest in product development, at 62%, followed by strategy and operations at 56%, marketing and sales at 55%, and supply chain at 48%. The figures indicate that AI is increasingly entering core business functions.

However, wider deployment does not automatically mean that systems are connected or that organisations have redesigned their processes around the technology.

Deloitte found that regulatory and compliance requirements were the leading AI integration challenge for Indian organisations, cited by 39% of respondents. Resistance to change followed at 34%. These barriers show why enterprise adoption involves governance and organisational decisions alongside technical implementation. Employees need to understand how AI recommendations fit into existing decision-making processes, while leaders must establish where human approval remains necessary.

Modernising without disrupting the business

For traditional industries, replacing established systems outright can be expensive and disruptive. A more practical route may involve connecting AI applications to selected workflows first, identifying where reliable data already exists, and expanding as the benefits become clearer.

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Manufacturing illustrates both the opportunity and the difficulty. Tata Consultancy Services’ Future-Ready Manufacturing Study 2025, based on a survey of more than 200 manufacturing respondents in North America, the UK and continental Europe, found that 54% identified integration with legacy systems as a major barrier to scaling AI.

Training time was another concern, cited by 50%. The findings are international rather than India-specific, but they highlight the operational issues manufacturers face when introducing AI into established environments.

The business case is strongest when AI addresses a defined operational problem. Predictive maintenance can help teams identify equipment issues before a breakdown occurs. In banking, AI can support fraud detection and risk assessment, while logistics operators can use it to improve demand forecasts and route planning. Each application requires appropriate data, clear performance measures and safeguards suited to the consequences of an incorrect recommendation.

Also Read: AI in Supply Chain Management: Building resilient, predictive and efficient operations

Building the intelligent enterprise

Successful implementation also depends on people and processes. Teams need the skills to interpret AI-generated insights, challenge unreliable outputs and act on recommendations. Leaders must define accountability, monitor performance and ensure that efficiency gains translate into measurable improvements.

As AI adoption expands across traditional industries, the priority will be to connect technology investment with operational change. The Future of Knowledge Work Summit 2026 will bring this question into focus: How can established businesses modernise the systems they already depend on while building the capabilities required for AI-enabled operations?

The answer will vary by industry, but the underlying task is consistent. AI creates value when it becomes part of how an organisation makes decisions and runs its business, supported by reliable data, integrated systems and people equipped to use it.

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