“Firms must have the needed data foundations, data ecosystems, and data culture to embrace an AI-driven operating model,” says Akhilesh Ayer, executive vice chairman and global head of AI, analytics, data, and research practice at WNS Triange, a unit of business process management company WNS Global Services.

A unified data ecosystem

Embracing an AI-driven operating model requires firms to make data the inspiration of their business. Business leaders need to make sure “every decision-making process is data-driven, in order that individual judgment-based decisions are minimized,” says Ayer. This makes real-time data collection essential. “For instance, if I’m doing fraud analytics for a bank, I want real-time data of a transaction,” explains Ayer. “Due to this fact, the technology team could have to enable real-time data collection for that to occur.” 

Real-time data is only one element of a unified data ecosystem. Ayer says an all-round approach is needed. Firms need clear direction from senior management; well-defined control of knowledge assets; cultural and behavioral changes; and the power to discover the fitting business use cases and assess the impact they’ll create. 

Aligning business goals with data initiatives  

An AI-driven data strategy will only boost competitiveness if it underpins primary business goals. Ayer says firms must determine their business goals before deciding what to do with data. 

One option to start, Ayer explains, is a data-and-AI maturity audit or a planning exercise to find out whether an enterprise needs a knowledge product roadmap.This could determine if a business must “re-architect the best way data is organized or implement a knowledge modernization initiative,” hesays. 

The demand for personalization, convenience, and ease in the client experience is a central and differentiating factor. How businesses use customer data is especially vital for maintaining a competitive advantage, and might fundamentally transform business operations. 

Ayer cites WNS Triange’s work with a retail client for example of how evolving customer expectations drive businesses to make higher use of knowledge. The retailer wanted greater value from multiple data assets to enhance customer experience. In a knowledge triangulation exercise while modernizing the corporate’s data with cloud and AI, WNS Triange created a unified data store with personalization models to extend return on investment and reduce marketing spend. “Greater internal alignment of knowledge is just a technique firms can directly profit and offer an improved customer experience,” says Ayer. 

Removing silos 

No matter a company’s data ambitions, few manage to thrive without clear and effective communication. Modern data practices have process flows or application programming interfaces that enable reliable, consistent communication between departments to make sure secure and seamless data-sharing, says Ayer.