Ramco System’s AI-Native Platform brings greater intelligence to payroll and enterprise operations

Ramco Systems is applying AI to help businesses reduce errors, simplify routine tasks and make better decisions across payroll, ERP and aviation

As enterprise technology enters the AI era, businesses are looking for more than software that simply records what has already happened. They want systems that can identify patterns, support decisions and make everyday processes easier to manage.

This is the direction Ramco Systems is taking with its AI-Native Platform, which is being developed to bring practical AI capabilities into payroll, enterprise resource planning (ERP), aviation and other enterprise operations.

Speaking with Hitech Century, Sandesh Bilagi, Chief Executive Officer of Ramco Systems, shared how the company is applying AI to address real business challenges, from detecting payroll anomalies to supporting data sovereignty across different regions.

As Chief Executive Officer of Ramco Systems, Sandesh is a veteran with more than two decades of experience across the enterprise technology space. In his extensive career, he expanded their Australia and New Zealand business, steered the organisation through two leadership transitions and has worked in key roles across India, China and Australia. Prior to Ramco, Sandesh has held leadership positions at Tech Mahindra and HCL Technologies with a proven track record of driving strategic growth and closing landmark engagements.

Ramco says AI can help payroll teams improve accuracy and save time

According to Bilagi, the fundamentals of payroll will remain the same: compliance, accuracy and speed.

What will change is how payroll teams use the data generated during the process. Rather than reviewing information only after payroll has been completed, Ramco’s AI-Native Platform can assist payroll managers while they are processing the data.

This can help them identify potential errors, make better decisions and respond to compliance changes earlier.

“AI will help them to be more accurate, more timely and probably give some time back to employees,” said Bilagi.

Payroll teams often work under significant time pressure, particularly when managing multiple employee records, changing regulations and last-minute adjustments. By supporting routine checks and providing assistance during processing, AI can help reduce some of the manual work involved.

Employees can also benefit from simpler and more intuitive processes. For example, if an employee regularly books travel between Kuala Lumpur and Penang, the system can recognise that pattern and suggest the same journey in the future.

The same approach can be applied to expense claims. If an employee has previously submitted the same types of expenses, the AI Native Platform can use that information to pre-populate the next report. Employees would then only need to review the details and complete any gaps.

AI-Native Platform can detect payroll anomalies and provide context

Another practical application of AI is anomaly detection.

Bilagi explained that Ramco’s system can identify activity that moves away from an established pattern. This could include a sudden increase in overtime, an allowance that is missing despite being paid regularly in previous quarters, or an unexpected change in payroll expenses for a particular employee group.

The system can then flag the issue to payroll managers.

However, Ramco’s AI-Native Platform is also designed to go beyond identifying an anomaly. Its reasoning capabilities can provide context around what may have caused the change.

For example, if a group of employees receives a significantly higher payment than usual, the system could identify whether the increase was linked to an additional bonus or salary increment.

“It will also try and reason it out based on the data that it has,” said Bilagi.

This can help payroll managers investigate unusual activity more efficiently and determine whether further action is needed.

Ramco is developing AI use cases based on customer needs

Ramco is taking a customer-led approach to developing its AI capabilities.

Rather than creating use cases in isolation, the company is speaking with customers to understand which business scenarios would make the greatest difference to their daily operations.

“We went back to our customers and asked them what will make your life easy,” said Bilagi.

In aviation, Ramco has identified around 62 potential use cases within maintenance and is working towards releasing capabilities in the Maintenance, Repair, and Overhaul (MRO) space.

In payroll, the company is exploring applications related to anomaly detection, error identification and forecasting. Within ERP, potential use cases span supply chain, production, planning, asset management and asset maintenance.

Many of these functions involve large volumes of data, repetitive processes and decisions that need to be made within a specific operational context. This makes them suitable for AI assistance, provided the technology remains relevant to the way businesses actually work.

Bilagi expects the use cases to become more advanced as the technology matures.

“There will be more advanced cases, more forecasting, more planning, more agenting,” he said. “This is a journey which is going to go on for a long time.”

Ramco’s AI-Native Platform keeps sensitive data within controlled environments

Data protection and sovereignty remain important considerations for businesses adopting AI, particularly when systems contain sensitive employee, payroll and business information.

Bilagi said Ramco does not push customer data out of the enterprise to external frontier models. Instead, it uses local large language models and hosts the data within the cloud environment created for the customer.

“The data will never leave a customer-controlled area, but it will still give the AI benefits,” he said.

Ramco’s AI-Native Platform also supports a multi-cloud approach, with cloud environments segregated by region. Where required, customers can hold and protect their data within local geographical cloud environments to support data sovereignty requirements.

For multinational organisations operating across markets such as Malaysia, the United Arab Emirates and Europe, data can be stored in separate regional environments, with access configured according to local regulations.

In some jurisdictions, particularly around employee data in the European Union, access may be heavily restricted. Regional segregation and access controls therefore remain an important part of the architecture.

Ramco sees AI as a long-term shift in enterprise software

From payroll and ERP to aviation and asset management, Ramco is applying AI to help businesses identify risks earlier, simplify repetitive processes and make better use of their data.

The company’s AI-Native Platform will continue to evolve as Ramco develops more forecasting, planning and agentic capabilities based on customer needs.

The longer-term aim is to make enterprise software easier to use and more useful, while ensuring that businesses can adopt AI without losing control of their data.