Gartner: AI can be catalyst for new business models

Artificial intelligence (AI) is no longer just a tool for boosting productivity and efficiency – it is now a driver of new markets and business model transformation, according to Gartner.

Speaking at the opening keynote of Tech Week Singapore, Nicholle Lindner, vice-president and managing executive partner of global technology services at Gartner Asia-Pacific and Japan, highlighted how companies like Swedish fintech firm Klarna are pioneering AI-driven innovations.

Klarna has integrated ChatGPT as a new channel for product recommendations, leveraging AI to enhance customer experience and boost revenue. Based on variables like customer profile, price and product set, it can generate recommendations and connect users with various services, like Expedia, through natural language search. When a product is selected, it directs the user to a Klarna-powered purchasing page, creating a seamless transaction process.

“This example of Klarna shows how AI can actually generate new revenue models,” said Lindner. 

Lindner emphasised that AI’s best applications currently lie in content generation, conversational interfaces, and knowledge discovery. While AI excels in these areas, its capabilities in prediction, forecasting, and autonomous systems are still behind those of human decision-makers. 

“AI is still in an ‘adolescent phase’ in some areas, such as intelligent automation and recommendation systems,” she noted.

However, Lindner cautioned that AI is not a one-size-fits-all solution. “There are circumstances where other technologies may be a better fit,” she said. “It’s like the old saying: when all you have is a hammer, everything looks like a nail.”

“So, where will you place your bets?” she asked. “You’ll want to invest in the area where AI currently shows the most natural capability.”

Lindner also advised organisations to carefully consider the costs involved in AI deployment. While AI-driven productivity enhancement is a natural step, businesses need to ensure the investment aligns with their broader strategy. Developing a customised generative AI (GenAI) model, for instance, can cost between $7m to $20m.

“Justifying such a large expenditure for a basic productivity boost isn’t enough,” she said. “But pushing beyond that to tap AI’s broader potential and create new revenue streams can justify the investment and convince skeptics.”

To maximise AI’s value, Lindner recommended organisations to start with defining their AI ambitions and objectives. 

“Are you looking for incremental AI improvements or aiming for breakthrough innovation? This depends on whether your organisation is customer-led or operations-driven, and how your financial model is structured,” she said. 

Businesses should also identify AI use cases specific to their industry and select an appropriate deployment approach, ranging from pre-built AI tools to more complex custom AI models. But the most important step, according to Lindner, is ensuring that AI adoption aligns with broader business goals.

“Don't get caught up in developing GenAI use case after use case,” said Lindner. “Focus on how AI can bring lasting business value.”